A Thesis Submitted for the Partial Fulfilment of the ......I wish to thank Dr. Vipul Patel, Dr....
Transcript of A Thesis Submitted for the Partial Fulfilment of the ......I wish to thank Dr. Vipul Patel, Dr....
“TO STUDY THE LEVEL OF FINANCIAL
LITERACY AND ITS IMPACT ON INVESTMENT
DECISION – AN IN-DEPTH ANALYSIS OF
INVESTORS IN GUJARAT STATE”
A Thesis
Submitted for the Partial Fulfilment of the Requirements
For the Degree of
Doctor of Philosophy
IN
MANAGEMENT
Submitted to
GANPAT UNIVERSITY
RESEARCH GUIDE:
Dr. Mahendra S. Sharma
Ph.D., M.B.A. (Marketing)
Pro-Vice Chancellor
Ganpat University
RESEARCH SCHOLAR:
Ms. Harsha Vijaykumar Jariwala
M.B.A. (Finance), B.B.A. (Finance)
Reg. No: MM/001/011/2009
V. M. Patel Institute of Management
Ganpat University, Ganpat Vidyanagar
Mehsana-Gozaria Highway, Mehsana-384012
Gujarat, India.
SEPTEMBER - 2013
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PREFACE
Since last decade, the Indian economy has witnessed a number of structural and
fundamental changes in the financial markets. While Indian economy is on growth
trajectory, there is a wide spread realization amongst all in the financial spectrum that for
such growth to be sustainable, a corresponding deepening of financial sector must
precede. And, such deepening is possible, only when individuals and households are
financially literate.
The economies around the world have increasingly considered financial literacy as a key
pillar for the development of a sound financial system. In current times, financial literacy
has gained the attention of policymakers, regulators, governments and several other
organizations. In this area, substantial efforts have been made and resources have been
developed by the financial education providers to promote financial literacy through a
multitude of financial education programmes.
In India, policymakers have recognized financial literacy as an essential life skill.
Developing and promoting financial literacy through financial education has become an
important policy priority that complements financial consumer protection, inclusion and
prudential regulation. In India, the government has set up the Investors Education and
Protection Fund (IEPF) with the objective to support activities relating to investor
education, awareness and protection. The role of IEPF is to educate, empower and protect
investors by equipping them with information, fundamental knowledge and skills to
evaluate their saving/investment/credit options and enabling them to understand the
implications of alternative financial decisions and make the citizens more empowered on
the subject of personal finance by promoting financial literacy, that is crucial in today‟s
financial markets.
“People spend hours in comparing and studying mobile phone
models, before they buy. This is however never replicated when
they buy financial products that have far reaching consequences”.
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Due to developments in the financial markets and demographic, economic and policy
changes, financial markets are becoming more sophisticated. Today‟s investors across the
nation, have more options to spend, save and invest their money for a shorter and/or
longer period of time, as compared to their previous generations. These investors have
greater access to a number of credit, saving and investment instruments provided by the
large range of entities through on-line as well as off-line. Under the new pension plan, i.e.
Defined Contribution Plan (DCP) has shifted the financial burden from employer to
employee/ workers in terms of their financially secured retirement.
Overall, due to changing scenario of financial markets, financial products and services
innovations through financial engineering, developments in information and
communication technology, multifaceted features involved in the myriad of financial
products, the speed with which financial markets and new financial instruments have
emerged and/or number of institutions enter into the financial market with the more
complex products, changes in the pension arrangement, increase in the life expectancy and
the role of technology advances in marketing and delivering the financial products and/or
services in the financial services industry, do not only provide more choices to consumers,
but also challenges to understand the benefits and costs associated with the innovations,
and more specifically, the risk-return matrix inherent to each innovation and hence, leave
many individuals ill-equipped to cope up with the sophisticated choices that they need to
make, for wise saving and investment decisions.
This degree of preference towards informed investments decisions requires the investors
to be equip themselves with the fundamental knowledge and skills to evaluate the complex
saving and investment options among the myriad of products and identify those that best
suit their needs and circumstance, as a market with financially illiterate or ill-informed
consumers benefits no one.
Education may play an important role in equipping the individuals with fundamental
knowledge and skills required to evaluate and choose the best alternatives in terms of
financial products and/or services and providers too and to identify those that best suit
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their needs and circumstances. This may be especially true for the population which has
been traditionally underserved by the financial system.
On the part of consumers, the financial literacy is essential as it helps consumers
understand how to avoid becoming involved in transactions those are financially
destructive. Financial literacy may help the consumers/ investors to make a more realistic
assessment of given opportunity for saving or investment, enhance the skill and bargaining
power in financial matter, bring financial efficiency in terms of life time utility and
financial well being, active debt management, evaluating and choosing the right financial
product with confidence, control the spending, encourage the saving and investment
behavior and thus, make them prepared for retirement planning.
For financial systems and economy, financial literacy may result into increase in the
demand for financial products and services, greater competition, innovations and
financially engineered products which may satisfy the specific financial need of investors,
self-funding for retirement especially where government would no longer be there to
provide the social security after retirement and may overcome the “procyclicality” in
lending. At community level too, financial literacy may have considerable benefits, in
particular it may be helpful in enhancing the financial inclusion, avoid voluntary exclusion
in the financial markets and increasing the awareness among the investment community
on financial issues, thereby creating an informed citizenry which can evaluate the
appropriateness of government financial policies in an effective and efficient manner.
The absence of financial literacy or low level of financial literacy may result into lack of
healthy financial ways of thinking, lack of necessary financial knowledge and difficulties
in applying financial knowledge, which may lead to poor financial judgments and hence
poor personal financial management. Financially illiterate individual either voluntarily do
financial exclusion or may prefer to get the financial information from unreliable sources,
the analysis of which may result into misallocation of private wealth, can mire the
household into debt and lead to much lower living standards. At a macro level, it can
cause social decline and increase public expenditure in the form of social security.
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Absence of this knowledge and skill thus may pose a variety of risk to individual, societal
and economy as a whole
Thus, financial literacy is a hugely significant issue for financial systems of any country,
as it both drives and distorts investment behavior, the composition and direction of
demand for products of competing financial sector interests. Thus financial literacy means
empowering the investors to make correct choices when it comes to taking financial
decisions, be it investing/leveraging/ protecting. It is an important element for promoting
financial inclusion and ultimately financial stability. This may help individuals to
prioritize financial goals, make them aware of opportunities and risks associated with the
financial products and help them to invest with specific time horizons to meet their
financial goals and objectives. Thus, the developments in the domestic financial markets
suggest the importance of investigating the level of financial literacy, and its role in
investors‟ investment decision making.
The present study attempts to make significant contribution in the field of financial
literacy in India, where there is no baseline survey/data available and even the concept has
just caught the fancy of policy makers and others. The in-depth review of literature
suggests that the investment decision can be enhanced by developing financial literacy, by
providing the financial education. The study explores financial literacy level of investors
in the state of Gujarat, and describes its relationship with investment decision. Present
study also highlights the association of demographic and socio-economic profile of
investors with their financial literacy level.
This study may help policy makers, authorities, N.G.O., financial planners and institutions
those who are engaged in promotion of financial literacy through financial education with
the objective of converting the country from country of savers to country of informed
investors.
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ACKNOWLEDGEMENT
Every mature person in professional life is keenly aware of his/her sense of indebtedness
to many people, who have motivated and influenced his/her intellectual development
ordinarily. This feeling is formally expressed in gestures of acknowledgement. Therefore,
it seems right to acknowledge my gratitude with the sense of veneration to Almighty God
and various people who helped me during this course of investigation. This valuable
guidance and wise direction has enabled me to complete my investigation in a systematic
and smooth manner, obeying the norms of scholastic research.
In an ecstasy of delight, I express my profound sense of gratitude to my esteemed guide
and academic mentor Dr. Mahendra S. Sharma, Professor & Head, V. M. Patel Institute of
Management, Ganpat University, Kherva, Mehsana, for this schololarly, inspiring
supervision, incessant, painstaking efforts and stimulating guidance patronage. In spite of
his multifarious responsibilities, he helped me to accomplish this research successfully.
His consistently affectionate behavior has been much beyond my imagination.
I owe a debt of gratitude to Prof. P. I. Patel, Director, Mehsana District Education
Foundation and Dr. L. N. Patel, Vice- Chancellor, Ganpat University, Dr. D. V. Patel,
Founder, V. M. Patel Institute of Management, Ganpat Univerisity and the management of
Ganpat University for their moral support and motivation. I am also thankful to V. M.
Patel Institute of Management for all the library, infrastructure facility and other
supporting resources provided to me to complete my investigation.
At this juncture, I am thankful to S. K. School of Business Management,
Hemchandracharya North Gujarat University, Patan and B. J. Vainjya Mahavidyalaya,
Sardar Patel University, Vallabh Vidyanagar, from where I did my post graduation and
graduation. I am also thankful to my school Alembic Vidyalaya.
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I owe a debt of gratitude to Mr. Andele Antikson, Policy Maker, OECD, Dr. Jatin Pacholi,
Professor, Middlesex University, U.K., Dr. B. A. Prajapati, Head, S. K. School of
Business Management, Patan, Dr. D. M. Pestonjee, Ex-Professor, IIM- A, Dr. Shailesh
Gandhi, Professor, IIM-A, Dr. V. K. Sappovadia, Dr. Akash Patel , Associate Professor,
PDPU, Dr. S. O. Junare, Head, SJPIM, Gandhinagar, Dr. Narayan Baser, Associate
Professor, SJPIM, Gandhinagar, Dr. K. M. Chudasama, Principal, V.M. Patel College of
Management, Ganpat University for their time to time support to prepare the research
instrument.
I am grateful to Dr. Nitin Tike, Senior Vice President, School for Investor Education and
Financial Literacy, National Institute of Securities Market(An Educational initiative of
SEBI), Shri Sudeep Mishra, Deputy General Manager, Office of Investors Assistance and
Education, Western Region Office, SEBI, Shri Hariharan, Chief General Manager, Office
of Investors Assistance and Education, SEBI to motivate me, to review the research
instrument, to provide me the timely suggestion and to broaden my knowledge in the field
of financial literacy by providing their resources and continuous guidance.
I am also thankful to Shri Ramjibhai Mavani, Ex-Member of Parliament and President,
Rajkot Grahak Suraksha Mandal, Rajkot and Shri Malav Choksi, Deputy General
Manager, The Bombay Stock Exchange Ltd.; for helping me to fill up the research
instrument from the different regions of the state of Gujarat.
I am thankful to Dr. Ashwin G. Modi, Associate Professor, S. K. School of Business
Management, Patan; Dr. Nishith Bhatt, Reader, S. K. School of Business Management,
Patan; Dr. P. K. Priyan, Associate Professor, G. H. Patel Institute of Business
Management, Sardar Patel University, Vallabh Vidyanagar for their moral support.
I wish to thank Dr. Vipul Patel, Dr. Hiren J. Patel, Dr. Amit Patel, and Prof. Jayesh Patel,
Faculty members, V.M. Patel Institute of Management for their continuous support to
carry out the analysis of data. I am also thankful to whole teaching staff and non-teaching
staff of V. M. Patel Institute of Management for their continuous support.
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A thank is a small word to use for parents. My mother Smt. Mangalaben Jariwala and my
father Shri Vijaykumar Jariwala are a beacon of inspiration and encouragement
throughout my career and allowing me to carry out this study being away from my home. I
am also grateful to my sisters, Neela and Varsha and a brother, Yogen for their continuous
support in various stages of this study.
I am also thankful to all the respondents who have filled the questionnaires with their
sincere efforts and also for sparing their valuable time to fill up the questionnaires, without
whose contribution it was impossible to come out with findings.
The incredible help offered by my colleagues, friends, and past students, whose names are
not mentioned here, but is incomparable and deserve the appreciation. At last, I am highly
obliged to all the persons who helped me directly or indirectly.
Jariwala Harsha V.
I dedicate this thesis to SUDEEP who is the
light in my life and without whose constant
support & endless encouragement, this work
would not have seen the light of the day.
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EXECUTIVE SUMMARY
This thesis contains five chapters and a bibliography. The thesis is mainly divided into two
sections. The first section deals with an in-depth literature review on financial literacy,
financial behavior and investment decision. The second section covers an in-depth
research analysis to identify the financial literacy level of investors in the state of Gujarat
and its impact on investment decision.
Chapter 1 describes the background of the research and need for the current study. This
chapter also explains the meaning of literacy, historical developments that have been taken
place in the context of conceptualizing and defining the term “financial literacy”, financial
education as an important tool to promote the financial literacy, need for financial
literacy, consequences of financial illiteracy, scope of the term “financial literacy” and
scope of term "financial literacy” for present study. This chapter also gives the outline of
the whole thesis.
Chapter 2 begins with the relevance of financial systems to economic development
through the savings-investment process. To get an in-depth idea for the topic under study
and to support the academic research base to a research topic, the review of literature
presented in this chapter is divided into two sections. The first section of this chapter
incorporates the theoretical framework for this study by including behavioural finance
models to explain investor behaviour, when rational economic models fail to provide
sufficient explanation, followed by in-depth review of literature on studies related to
saving/investment motives and factors that influence the investment decision of investors.
This section also includes review of studies those had measured the association of
demographic factors of investors with investment decisions. It also incorporates the
importance of risk tolerance ability and need for/ sources of information search in the
investment decision making. The second section of this chapter includes analysis of the
studies conducted in various countries for measuring the financial literacy of their citizens.
It also discusses theoretical framework for financial behavior. In this section, review of
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literature is presented on various studies those have attempted to establish the relationship
between financial education, financial literacy and various financial behaviour.
The methodological approach adopted for this research is presented in detail in Chapter 3.
The initial part of this chapter talks about the research gap, followed by the research
objectives under study and scope for present research. The exploratory research design is
used. The research design includes the explanation about population for which the
research is conducted followed by the sampling techniques, sampling unit, sample and
sample size, construction of data collection instrument, data collection method, source of
data collection, description of variables used for this study. The second part of this chapter
contains the methodology used by various researchers to measure the financial literacy of
individuals in their studies, pitfalls of the various methods of measuring financial literacy,
difficulties faced by researchers to measure financial literacy and the appropriate method
selected by a researcher to measure the financial literacy level of investors in present
study, after providing sufficient justifications on demerits of other methods available to
measure financial literacy. The discussion on data analysis techniques and hypotheses
conclude this chapter.
Chapter 4 reports the data analysis and interpretation of data, following the objectives
under study. Different tools – frequency and percentage analysis, cross tabulation, chi-
square test, paired t-test, factor analysis, binary logistic regression and simple linear
regression is used for data analysis in details to draw a conclusion. The analysis is divided
into three major sections. The first section of this chapter deals with an in-depth analysis
of survey responses collected from each respondent towards performance test that is used
to measure financial literacy of investors. Financial literacy score, achieved by each
respondent is further divided into two categories to classify the respondents (investors)
into two categories, 1) investors with relatively low level of financial literacy and 2)
investors with relatively higher level of financial literacy. The second part includes the
analysis of investment objectives, existing investment pattern of investors and investors‟
preference towards various investment alternatives. This part also deals with the analysis
of informative variables on the basis of their preference, and the influence of selected
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informative variables on investment decision. The analysis is done by applying factor
analysis, identifying the mean score of each factor, and later applying paired t-test. The
factors analysis is also conducted to identify the factors that may influence the investment
decisions of investors. The categories of financial literacy levels, found in the first section
of analysis, are further used to check the association between each demographic and socio-
economic variable of investors and their financial literacy level. For this, purpose chi-
square test is used. To assess the combine impact of demographic and socio-economic
variables of investors on their financial literacy level, a binary logistic regression is
performed. The chi-square test is used to check the association between financial literacy
level of investors and their monthly spending to monthly income ratio and similarly, to
check the relationship between financial literacy level of investors and their monthly
saving to monthly income ratio. Finally, the correlation and simple regression is used to
check the impact of financial literacy level of investment decision of investors.
At the end of this thesis, Chapter 5, concludes the whole research work carried out by the
researcher. This chapter begins with the summary of the whole work. The discussion on
overall findings is presented in a systematic way following the objectives under study.
Based on the present work, the recommendations are given to policy makers, investors and
financial education providers who are engaged in the financial systems. A discussion on
opportunities for future research and the limitations of the present research work are also
noted at the end of this work.
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CERTIFICATE BY GUIDE
This is to certify that this thesis titled “TO STUDY THE LEVEL OF FINANCIAL
LITERACY AND ITS IMPACT ON INVESTMENT DECISION – AN IN-DEPTH
ANALYSIS OF INVESTORS IN GUJARAT STATE” submitted by Harsha Vijakumar
Jariwala, at Faculty of Management Studies, Ganpat University, Mehsana is the bonafide
work completed under my supervision and guidance for the fulfillment of the requirement
for the award of the degree of Doctor of Philosophy (Ph.D.) in management.
Research Guide:
Dr. Mahendra S. Sharma
Ph.D., M.B.A. (Marketing)
Professor & Head,
V. M. Patel Institute of Management,
Ganpat University,
Ganpat Vidyanagar, Gujarat, India
Forwarded through:
Dr. Mahendra S. Sharma
Ph.D., M.B.A. (Marketing)
Dean - Faculty of Management Studies,
Ganpat University,
Ganpat Vidyanagar, Gujarat, India
Date:
Place: Ganpat University, Ganpat Vidyanagar.
xiv
DECLARATION BY CANDIDATE
This thesis titled, “TO STUDY THE LEVEL OF FINANCIAL LITERACY AND ITS
IMPACT ON INVESTMENT DECISION – AN IN-DEPTH ANALYSIS OF
INVESTORS IN GUJARAT STATE” is submitted in fulfillment of the requirements for
the award of the degree of Doctor of Philosophy (Ph.D.) in Management to Ganpat
University, Mehsana. I hereby declare that this thesis is based on my original work except
for quotations and citations which have been duly acknowledged. I also declare that this
thesis has not been previously or concurrently submitted to either in whole or in part, for
any other qualification to Ganpat University or other institutions.
Research Scholar:
Jariwala Harsha V.
UGC – NET (Management), M.B.A. (Finance)
Reg. No: MM/001/011/2009
Date:
Place: Ganpat University, Ganpat Vidyanagar.
xv
CERTIFICATE
This is to certify that the thesis titled “To Study the Level of Financial Literacy and its
Impact on Investment Decision –An In-Depth Analysis of Investors in Gujarat State”
submitted by Ms. Harsha V. Jariwala fulfils the suggestions given by doctoral committee
during pre-doctoral seminar held on July 18, 2013, vide Ganpat University letter no. F.
No. 89/GNU/Ph.D./963/2013 dated 29/08/2013 are duly incorporated in this thesis.
Research Guide:
Dr. Mahendra S. Sharma
Ph.D., M.B.A. (Marketing)
Professor & Head,
V. M. Patel Institute of Management,
Ganpat University,
Ganpat Vidyanagar, Gujarat, India
Forwarded through:
Dr. Mahendra S. Sharma
Ph.D., M.B.A. (Marketing)
Dean - Faculty of Management Studies,
Ganpat University,
Ganpat Vidyanagar, Gujarat, India
Date:
Place: Ganpat University, Ganpat Vidyanagar.
xvi
THESIS APPROVAL SHEET
The Ph.D. thesis titled “To Study the Level of Financial Literacy and its Impact on
Investment Decision –An In-Depth Analysis of Investors in Gujarat State” submitted
by Ms. Harsha Jariwala has been approved for the award of the degree of Doctor of
Philosophy under the Faculty of Management, Ganpat University, Gujarat, India.
External Examiner:
Research Guide:
Dr. Mahendra S. Sharma
Date:
Place: Ganpat University, Ganpat Vidyanagar.
xvii
CONTENTS
Chapter
No.
Particulars Page
No.
Preface ii
Acknowledgement vi
Executive Summary x
Certificate by Guide xiii
Declaration by Candidate xiv
Certificate xv
Thesis Approval Sheet xvi
List of Tables xxi
List of Figures and Charts xxvii
List of abbreviations Xxviii
1 Introduction to research topic – Financial Literacy 1-34
1.1 Background of the research 2
1.2 Need for Study 4
1.3 Introduction to term „Literacy‟ 6
1.4 History of the term “Financial Literacy” 10
1.5 Need for Financial Literacy 20
1.6 Importance of Financial Literacy 22
1.7 Consequences of Financial Literacy 25
1.8 Scope of Financial Literacy 30
1.9 The scope of term “Financial Literacy” for present study 31
1.10 Outline of the Thesis 32
2 Review of Literature 35-109
Part I Investor’s saving and investment decision 36-59
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2.1 Introduction 36
2.2 Indian Financial Systems 36
2.3 Theoretical Framework for Present Study 37
2.4 Investors‟ Saving/Investment Motives 39
2.5 Investment Decision Making and Irrationality 46
2.6 Conclusion 59
Part – II Financial Literacy and Financial Behaviour 60-109
2.7 Introduction 61
2.8 Global Scenario 61
2.9 Domestic Scenario 80
2.10 Theoretical Framework for Financial Behaviour 82
2.11 Studies on Financial Literacy and Financial Behaviour 97
2.12 Conclusion 109
3 Research Methodology 110-151
3.1 Introduction 112
3.2 Research Gap 112
3.3 Objectives of the study 113
3.4 Scope of the study 114
3.5 Research Methodology 116
3.6 Sampling Design 116
3.7 Data collection 119
3.8 Research Instrument 120
3.9 Description of variables 124
3.10 Coding of Variables 136
3.11 Data analysis 139
3.12 Hypotheses of the study 148
3.13 Conclusion 150
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4 Data Analysis and Interpretation 152-298
4.1 Introduction 156
4.2 Frequency distribution showing profile of study‟s
respondents
156
4.3 Analysis of Financial Literacy Questions 161
4.4 Analysis of Existing investment pattern of investors 166
4.5 Cross Tabulation 167
4.6 Preference given to the variables as a Source of Information 174
4.7 Preference towards selected sources of information and their
influence on investment decisions
177
4.8 Reliability and Normality of data 183
4.9 Factor Analysis 188
4.10 Cross Tabulation and Statistical Tests (Explainable variables
and Financial Literacy Level)
228
4.11 Regression Analysis (Binary Logistic Regression) 246
4.12 Association between the financial literacy level of investors
and their monthly expenditure to monthly income ratio.
273
4.13 Association between financial literacy level of investors and
their monthly saving to monthly income ratio.
275
4.14 Regression Analysis: Impact of Financial Literacy Level on
Investment Decision
278
4.15 Conclusion 298
5 Findings and Conclusion 299-323
5.1 Introduction 300
5.2 Summary of the study 300
5.3 Findings of the study 302
5.4 Conclusion 316
5.5 Recommendations 318
5.6 Limitations of the study 321
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5.7 Scope for further research 322
6 Bibliography 324-346
Appendix 347-363
I Data collection instrument - English 347
II Data collection instrument - Gujarati 354
III List of Publications 363
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LIST OF TABLES
CHAPTER 1
Table 1.1 Literacy- Definition, characteristics, pre-requisites and outcomes 09
CHAPTER 2
Table 2.1 Stage of life cycle, corresponding objective- needs and requisite
financial behavior
87
CHAPTER 3
Table 3.1 Description of demographic and socio-economic variables used
under study
125
Table 3.2 Description of variables used as saving/investment alternatives 128
Table 3.3 Description of variables used as preferred source of information
used under study
129
Table 3.4 Description of variables influencing investment decision used
under study
131
Table 3.5 Codes assigned to variables used as preferred source of information
used under study
137
Table 3.6 Codes assigned to variables influencing investment decision used
under study
138
Table 3.7 Methodology adopted by various researchers for measuring
financial literacy
144
CHAPTER 4
Table 4.1 Respondents‟ profile 159
Table 4.2 Overall financial literacy of respondents 162
Table 4.3 Summary of answers given by the respondents under Basic
Financial Literacy Test
163
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Table 4.4 Summary of answers given by the respondents under advanced
financial literacy test
165
Table 4.5 Existing investment pattern of investors 167
Table 4.6 Cross Tabulation of ranks given to Investment Objectives 168
Table 4.7 Mean score of Investment objectives 170
Table 4.8 Cross Tabulation of Rank given for preferred Investment
Alternative
171
Table 4.9 Mean score of each investment alternatives on the basis of ranks
given by the respondents according to their preference
173
Table 4.10 Preference given by respondents towards factors as preferred
source of information
175
Table 4.11 Preference towards selected sources of information and their
influence on Investment Decisions
177
Table 4.12
Paired Sample Statistics 180
Table 4.13 Paired Samples t test values 182
Table 4.14 Cronbach‟s Alpha for the variables of preferred source of
information
184
Table 4.15 Cronbach‟s Alpha for variables influencing investment decision 184
Table 4.16 Data quality for variables of preferred sources of information 185
Table 4.17 Data quality for variables influencing investment decision 186
Table 4.18 KMO and Bartlett's Test 190
Table 4.19 Anti- Image Correlation Matrix 191
Table 4.20 Communalities 192
Table 4.21 Revised Anti- Image Correlation Matrix 193
Table 4.22 Revised Communalities 193
Table 4.23 Total Variance Explained 196
Table 4.24 Component Matrix 197
Table 4.25 Guidelines for identifying significant factor loadings based on
sample size
198
Table 4.26 Rotated Component Matrix 201
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Table 4.27 Composition of each factor identified in factor analysis 202
Table 4.28 Mean preference score of extracted factors 206
Table 4.29 KMO and Bartlett's Test 207
Table 4.30 Communalities 208
Table 4.31 Revised Communalities 210
Table 4.32 Anti Image Correlation Matrix (for 29 variables) 212
Table 4.33 Revised Communalities 213
Table 4.34 Total Variance Explained 215
Table 4.35 Component Matrix 216
Table 4.36 Rotated Component Matrix
218
Table 4.37 Composition of each factor identified in factor analysis 219
Table 4.38 Mean score of extracted factors 226
Table 4.39 Mean score of each variable within each factor 227
Table 4.40 Cross Tabulation of investors‟ age and their financial literacy level 229
Table 4.41 Chi-Square Tests 230
Table 4.42 Symmetric Measures 230
Table 4.43 Cross Tabulation of investors‟ gender and their financial literacy
level
231
Table 4.44 Chi-Square tests 231
Table 4.45 Cross Tabulation of investors‟ education and their financial literacy
level
232
Table 4.46 Chi-Square Tests 233
Table 4.47 Cross Tabulation of investors‟ monthly income and their financial
literacy level
234
Table 4.48 Chi-Square Tests 234
Table 4.49 Cross Tabulation of investors‟ stage in family life cycle their
financial literacy level
235
Table 4.50 Chi-Square Tests 236
Table 4.51 Cross Tabulation of investors‟ employment structure and financial
literacy level
236
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Table 4.52 Revised Cross Tabulation of investors‟ employment structure and
their financial literacy level
237
Table 4.53 Chi-Square Tests 238
Table 4.54 Cross Tabulation of investors‟ type of workplace activity and their
financial literacy level
239
Table 4.55 Chi-Square Tests 239
Table 4.56 Cross Tabulation of investors‟ years of work experience and their
financial literacy level
240
Table 4.57 Chi-Square Tests 241
Table 4.58 Cross Tabulation of investors‟ years of investment experience and
their financial literacy level
242
Table 4.59 Chi-Square Tests 242
Table 4.60 Cross Tabulation of numbers of times investors shop around and
their financial literacy level
243
Table 4.61 Chi-Square Tests 244
Table 4.62 Cross tabulation of risk tolerance level of investors and their
financial literacy level
245
Table 4.63 Chi-Square Tests 245
Table 4.64 Study Variables: Dependent and Independent Variables for Logistic
Regression
251
Table 4.65 Case Processing Summary 252
Table 4.66 Classification Table 253
Table 4.67 Variables not in the Equation 254
Table 4.68 Variables in the Equation 255
Table 4.69 Omnibus Tests of Model Coefficients 257
Table 4.70 Contingency Table for Hosmer and Lemeshow Test 258
Table 4.71 Model fitting information: Test of Significance: Hosmer-
Lemeshow Test
259
Table 4.72 Strength of the relationship model 261
Table 4.73 Significance of each individual predictor on dependent variable 262
xxv
Table 4.74 Classification Table 270
Table 4.75 Cross Tabulation of investors‟ financial literacy level and their
monthly expenditure to monthly income ratio
273
Table 4.76 Revised cross tabulation of investors‟ financial literacy level and
their monthly expenditure to monthly income ratio
274
Table 4.77 Chi-Square Tests 275
Table 4.78 Cross Tabulation of investors‟ financial literacy and their monthly
saving to monthly income ratio
275
Table 4.79 Revised cross tabulation of investors with various level of financial
literacy and their monthly saving to monthly income ratio
276
Table 4.80 Chi-Square Tests 277
Table 4.81 Correlations between FLL and Personal financial Need 281
Table 4.82 Model Summary 281
Table 4.83 ANOVA 281
Table 4.84 Coefficients 282
Table 4.85 Correlations between FLL and Accounting, Business and Financial
Information
283
Table 4.86 Correlations between FLL and Economic and Regulatory
Environment
284
Table 4.87 Correlations between FLL and Operational Feedback 285
Table 4.88 Correlations between FLL and Advocate recommendation 285
Table 4.89 Model Summary 286
Table 4.90 ANOVA 286
Table 4.91 Coefficients 286
Table 4.92 Correlations between FLL and Overall Group Performance 288
Table 4.93 Model Summary 288
Table 4.94 ANOVA 288
Table 4.95 Coefficients 289
Table 4.96 Correlations between FLL and Credit Features 290
Table 4.97 Correlation between FLL and Personal Inclination 291
xxvi
Table 4.98 Correlations between FLL and Monetary Expectation 291
Table 4.99 Model Summary 292
Table 4.100 ANOVA 292
Table 4.101 Coefficients 292
Table 4.102 Correlations between FLL and sum of Investment Decision Factors 294
Table 4.103 Model Summary 294
Table 4.104 ANOVA 294
Table 4.105 Coefficients 295
Table 4.106 Summary of Regression Analysis of Financial Literacy Level and
Investment Decision
297
xxvii
LIST OF FIGURES AND CHARTS
CHAPTER 1
Fig. 1.1 Conceptual model of financial literacy 17
CHAPTER 2
Fig. 2.1 An individual‟s financial life cycle and corresponding objectives 86
Fig. 2.2 Financial Need Hierarchy 89
CHAPTER 4
Fig. 4.1 Pie chart of classification of respondents 166
Fig. 4.2 Bar chart of ranks given by the Respondents to Investment
Objectives (in per cent)
169
Fig. 4.3 Bar chart of mean score of Investment objectives 170
Fig. 4.4 Bar chart of ranks given by the respondents to investment
alternatives
172
Fig. 4.5 Bar chart of mean score of each investment alternatives on the basis
of ranks given by the respondents according to their preference
174
Fig. 4.6 Bar chart of preference given by respondents towards factors as
preferred source of information
176
Fig. 4.7 Classification Plot 272
xxviii
LIST OF ABBREVIATIONS
AdFLAG : Adult Financial Literacy Advisory Group
ANOVA : Analysis of Variance
ANZ Bank : Australia and New Zealand Banking Group Ltd. (known as ANZ
Bank)
CBF : Commonwealth Bank Foundation
CRY : Child Rights and You
FESC : Financial Education Steering Committee
FINRA : Financial Industry Regulatory Authority
FLEC : Financial Literacy and Education Commission
FLC : Financial Literacy Centres
FLCCC : Financial Literacy and Credit Counseling Centre
FLL : Financial Literacy Level
HNIs : High Net Worth Investors
HRS : Health and Retirement Survey
IEPF : Investor Education Protection Fund
KVP : Kisan Vikas Patra
MNYL : Max New York Life Insurance Company Ltd.
NABARD : National Bank for Agriculture and Rural Development
NAV : Net Asset Value
NCAER : National Council of Applied Economic Research
NCEE : National Council on Economic Education
NEFE : National Endowment for Financial Education
NGO : Non Governmental Organization
NSC : National Savings Certificate
OECD : Organization for Economic Co-operation and Development
PACFL : President Advisory Council on Financial Literacy
PPF : Public Provident Fund
RMR : Roy Morgan Research
xxix
SCF : Survey of Consumer Finance
POMIS : Post Office Monthly Income Scheme
RBI : Reserve Bank of India
SEBI : Securities and Exchange Board of India
SEWA : Self Employed Women‟s Association
SPSS : Statistical Package for Social Sciences
TIAA-CERF : Teachers Insurance and Annuity Association – College Retirement
Equities Fund
UNESCO : United Nations Educational, Scientific and Cultural Organization
Introduction
to Research Topic:
Financial Literacy
Chapter 1
1
1.1 Background of the research
1.2 Need for study
1.3 Introduction to term „Literacy‟
1.4 History of the term “Financial Literacy”
1.5 Need for financial literacy
1.6 Importance of financial literacy
1.7 Consequences of financial illiteracy
1.8 Scope of the term “Financial Literacy”
1.9 The scope of term “Financial Literacy” for present study
1.10 Outline of the thesis
2
1.1 Background of the research
The growth and development of the Indian economy and the expansion of financial
markets through liberalization, privatization and globalization have given a way to a
plethora of financial products either as an investment alternative or a credit one. But, on
the other hand, financial illiteracy or low level of financial literacy prevents the
individuals from making a judicious choice with regard to his/her financial decisions. As
a result, the individuals are not able to choose the most suitable investment alternative
which can beat the rate of inflation prevailing in the economy and give them a net
positive return.
Over the last decade, the Indian economy has grown at a rapid pace. However, this
growth has faced increased pressure from both moderation in investment demand and an
uncertain global environment. Singh (20081) commented that ―….we must not forget that
growth is not the only measure of the development. Our ultimate objective is to achieve
broad based improvement in the living standards for all our people‖ (p.iii).
If growth is sufficiently inclusive, it would certainly provide an environment conducive
to bring about a broad-based improvement in living standards. This improvement in
living standards would seek the distribution of higher quality and quantity of goods and
services to the individuals and to the society, thereby contributing to material well-being.
Thus, for the Indian economy, the growth would need to be accompanied with a broad
based economic development. The basic tenet of ―process of development‖ is given in the
tenth five year plan, which stated that ―The process of development in any society should
ideally be viewed and assessed in terms of what it does for the average individual. The
decade of the 1990s saw a visible shift in the focus of development planning from the
mere expansion of the production of goods and services and the consequent growth in per
capita income to planning for enhancement of human well-being. Later, it was realized
that human development means much more than the rise or fall of national income. It is
1 Singh, M. (2008). ‗Forward‘, Eleventh Five Year Plan 2007-2012, Inclusive Growth, 1, planning
commission, government of India, New Delhi, India: Oxford University Press, p.iii.
3
about the quality of life, the level of human well-being and the access to basic social
service.‖ (Planning Commission, 20032, p.15)
The Planning Commission (20033) has stated that the three critical dimensions of human
well-being as mentioned in the tenth five years plan are (p. 15):
1. Longevity: The ability to lead a long and healthy life.
2. Education: The ability to read, write and acquire knowledge.
3. Command over the resources: The ability to enjoy a decent standard of living and
have a socially meaningful life.
As stated above, the third dimension of human well-being pertains to command over the
resources, which is in the crux of this research study. Money is one of the most
significant resources that form the backbone of any economic system. Oleson (20044)
stated that ―it is the most widely accepted medium of exchange and has a paramount
place in the ability of an individual to fulfill his/her social and material needs like
planning for children‘s education, marriage, own retirement, old age security, etc‖. Hira
et al. (19895) stated that ―the ability to manage effectively the monetary resources would
help individuals to achieve financial satisfaction and financial well-being which would
contribute to their life-satisfaction‖.
Ahuwalia (20086) said ―Indians are wise savers but the poor investors‖. The survey has
revealed that people in India do not plan for long-term future and keep themselves away
from investing in the long-term instruments though they save for long-term goals such as
emergencies, education and old age. The study also noted that Indians prefer to save
2 Planning Commission (2003). Tenth Five Year Plan 2002–2007, Sectoral Policies and Programmes, Vol.
2, New Delhi, India. 3 Ibid, 15. 4 Oleson, M. (2004). Exploring the relationship between money attitudes and Maslow‘s hierarchy of needs. International Journal of Consumer Studies, 28,.83–92, January. 5 Hira, T.H., Fanslow, A.M. & Titus, P.M. (1989). Changes in financial status influencing level of
satisfaction in households‘ Lifestyles. Family and Economic Issues, 10, 107–121. 6 Ahuvalia, M. S. (2006). Speech at launching of the book ―How India Earns, Spends and Saves‖, launched
by Deputy Chairman, Planning Commission, Government of India, Montek Singh Ahluwalia, February 6,
2006. New Delhi.
4
money in ―in-house savings‖ rather than ―in banks or investments‖. They save money for
―emergency and any mis-happening‖. The study concluded that this may pose serious
concern for India, where social security systems are virtually non-existent.
Hence, it is to be noted that, to cope up with the inflation and to navigate the financial
markets, an individual must invest his/her saving in a proper way. This plays a significant
role in the ability of the individual to enjoy a decent standard of living throughout his/her
lifespan. Thus, ability to manage money and take effective actions to plan the current and
future use of money i.e. financial literacy is of paramount importance in the achievement
of the developmental goals of our economic planning. Thus, this understanding of
personal financial management would help the individuals to have a command over the
monetary resources. Beal and Delpachitra (20037) and CBF (2004b
8) observed that
financial literacy skills enable individuals to navigate the financial world, make informed
decisions about their money and minimise their chances of being misled on financial
matters.
1.2 Need for study
Financial literacy has become increasingly important over the past decades. There is a
growing belief that the individual would need to become more self-reliant in the future.
Increased competition and more complex products in the financial services industry leave
many people ill- equipped to cope with the sophisticated choices they may need to make.
Compared to the previous generations, there is now a wide variety of ways individuals
generate and dispose their income. The changes in work life over the world are meant
that the income stream of individuals has become more inconsistent. The changing world
of work has made a inconsistent income over a long period. There are periods of high
income followed by low level of income or no income at all. At the same time, people are
living longer and they need to make a greater provision for retirement, health care and
7 Beal, D.J. & Delpachitra, S.B. (2003). Financial literacy among Australian university students, Economic
Papers, 22(1), 65-78. 8 Commonwealth Bank Foundation (CBF) (2004b). Improving Financial Literacy in Australia: Benefits for
the Individual and the Nation, Research Report, Commonwealth Bank Foundation, Sydney.
5
insurances to cover unpredictable eventualities, where government/state is no longer able
to provide the type of safety net that was available in the past. Given all this, individuals
must have necessary skills to make suitable financial decisions to enable them to be more
in control of their own circumstances and have a secure financial future.
Education can play a critical role in equipping consumers with the fundamental
knowledge required to choose among the myriad of products and providers in the
financial services industry. This is especially true for populations that have traditionally
been underserved by our financial system. In particular, financial literacy education may
help to prevent vulnerable sections of society from becoming entangled in financially
devastating credit arrangements.
In India, policymakers have recognized financial literacy as an essential life skill.
Developing and promoting financial literacy through financial education has become an
important policy priority to complement financial consumer protection, inclusion and
prudential regulation. The national financial education efforts vary according to set-up,
audience and subject matter. Several organizations jointly work to deliver financial
education including regulatory authorities like Reserve Bank of India, Ministry of
Corporate Affairs, Ministry of Human Resource Development, Securities and Exchange
Board of India, Insurance Regulatory and Development Authority, NABARD, Financial
Stability and Development Council, policy makers like OECD, educational institutions
like National Institute for Securities Market and Indian School of Microfinance for
Women, self regulatory organizations such as The National Stock Exchange Ltd. and The
Bombay Stock Exchange Limited, non-governmental organizations like Sanchayan,
SEWA, Meljol, CRY, City India and financial institutions such as ICICI Bank, India
Infoline Ltd., Bank of India, etc. The objectives of this initiative undertaken by these
organizations are to empower the people on the subject of personal finance by providing
them basic knowledge of planning their expenses against their incomes, inculcating the
saving habits so that they can live and enjoy dignified life after retirement.
6
The present study attempts to focus on the existing financial literacy level of retail
individual investors in the state of Gujarat. The researcher has examined the association
between demographic and socio-economic profile of investors and their financial literacy
level. The study also attempts to find whether financial literacy level of investors does
have any impact on their investment decision? Does financial literacy level have any
association with investors‘ monthly expenditure to monthly income ratio and monthly
saving to monthly income ratio? Researcher has also identified the most preferred source
of information preferred by investors while investing and whether these sources of
information do have any influence on investment decision of investors.
Given the lack of any clear approach for promoting financial literacy and that there is no
baseline data available in the Indian context as regards financial literacy, the present
study may provide useful insight to those, who are in the field of financial literacy.
1.3 Introduction to term „Literacy‟
Literacy has traditionally been described as the ability to read and write. It is a concept
claimed and defined by a range of different theoretical fields. The first internationally
agreed definition of literacy was provided in 1958 by The United Nations Educational,
Scientific and Cultural Organization (UNESCO). UNESCO (19589) stated ―a literate
person is one who can, with understanding, both read and write short simple statement
on his or her everyday life‖ (p. 12). This definition has evolved in 2003, when UNESCO
(200310
) proposed an operational definition that attempted to encompass the several
different dimensions of literacy. It had defined literacy as an ―ability to identify,
understand, interpret, create, communicate, compute and use printed and written
materials associated with varying contexts. Literacy involves a continuum of learning in
enabling individuals to achieve their goals, to develop their knowledge and potential and
to participate fully in their community and wider society‖ (p. 13).
9 UNESCO Educational Sector. The Plurality of Literacy and its implications for policies and programs:
Position Paper. [monograph on the Internet]. France: United Nations Educational Scientific and Cultural
Organization, 2004. Retrieved January 30, 2010 from:
http://unesdoc.unesco.org/images/0013/001362/136246e.pdf. 10 Ibid., p. 13.
7
The Workforce Investment Act (1998), National Institute for Literacy defined literacy as
―an individual's ability to read, write, speak in English, compute and solve problems at
the levels of proficiency necessary to function on the job, in the family of the individual
and in society‖11
. This is a broader view of literacy than just an individual's ability to
read, the more traditional concept of literacy.
Literacy is defined in the Collins dictionary as ‗the ability to read and write‘ or ―the
ability to use language effectively‖. The Oxford English Dictionary defines literacy as
―the quality or state of being literate; knowledge of letters; condition in respect to
education, ability to read and write‖. Literacy is a topic of interest to individuals from a
variety of backgrounds and is of interest for a variety of reasons. Literacy is examined,
researched, discussed and written about by individuals including educators,
psychologists, socio-anthropologists, civil servants, government ministers, researchers,
policy makers and those in the media. Literacy is subjected to a phenomenal level of
enquiry, however the perspectives of these interested parties are inevitably varied, each
drawing its own conclusions according to its area of specialization. Goody (198612
)
argued that ―literacy is ideological, which means that literacy always exists in a context,
in tandem with values associated with that context‖.
Burnet (196513
) explained that literacy is not simply about reading and writing (although
there is nothing simplistic about the acquisition of these skills), but it also includes:
Learning
Achieving Status
Achieving human rights
Knowing
Making Choices
Improving occupational status
11 The National Literacy Act of 1991, Public law 102-73, 105 Stat. 333 (JUL. 25. 1991). 12
Goody, Jack. 1986. The logic of writing and the organization of society. New York: Cambridge
University Press. 0521327458. Location: Dallas SIL Library 303.4 G658. 13 Burnet, M. (1965). ABC of Literacy, Paris: United Nations.
8
Improving leisure pursuits
Making comparisons
Creating and confirming conclusion
Margaret Jackson (199314
), in her book stated ―Literacy offers us access to information,
ideas, opinions and by creating the potential for reflecting, provides opportunities for
making and communicating meaning, and for learning‖ (p. 1). It enables individuals to
make meaning and to learn.
Literacy involves individuals being able to read and write. Jackson‘s definition above
also highlights that these skills on their own are not sufficient for an individual to become
literate; individuals must also be able to reflect in order to make and communicate
meaning and also to learn. This approach again highlights the importance of literacy.
The use of terms ‗offers‘, ‗potential‘ and ‗opportunities‘ indicates again that literacy is
important because of the benefits that arise for those who are literate. It is important to
note that these benefits are all possibilities rather than guaranteed consequences.
Gee (199015
) had added ―literacy as a socially constructed activity and suggests that
literacy contributes towards both, creating the reality in which it operates and is
simultaneously influenced by reality; ‗each has a part in the construction of the other‖
(p.5).
Reading and writing are skills necessary for the attainment of literacy but they do not
constitute literacy itself. Carolyne L.J.M. and Richard M.S.W (200016
) defined, literacy is
to be ―…a process whereby individuals use a combination of their skills and available
resources to make sense of or understand those resources in order to achieve objectives‖
(p. 22).
14 Jackson, M. (1993). Literacy. London: David Fulton Publishers. 15
Gee, J. (1990). Socio Linguistics and Literacies, Basingstoke: Falmer Press. 16 Carolyne L.J. M.,Richard M.S.W. (2000). Conceptualizing financial literacy. Business School Research,
7, Loughborough University, 1-40.
9
Table 1.1 Literacy- Definition, Characteristics, Pre-Requisites and Outcomes
Literacy is: Characteristics Is dependent on Leads to:
Meaning
making
Understanding
Highly valued,
High level of interest,
Concern over
standards,
Has various degrees,
Varies from culture to
culture,
Constantly evolving.
Acquiring
necessary skills/
Technology,
Availability of
resources.
Positive outcomes for both
individual and society
Learning, Achieving status ,
Achieving human rights,
Knowing, making Choices,
Improving occupational status
and wealth, Improving leisure
pursuits, making comparisons,
creating and confirming
conclusions
(Source: Conceptualizing Financial Literacy. Business School Research Paper Series: Paper 2000:7,
Loughborough University, By Carolyne L. J. Mason and Richard M.S. Wilson )
The term literacy is synonymous with understanding or meaning making and this
meaning-making is a prerequisite for the achievement of desired outcomes or objectives.
Mason L. J & Richard M.S.W. (200717
) explained while conceptualizing the term literacy
that ―literacy as a meaning making process which enables informed decisions to be made
in order to achieve desired outcomes‖ (p. 32).
The term literacy is one that has been adopted by practitioners from a variety of
backgrounds. A search of the literature identified a host of descriptions that included
literacy in their title. These included Statistical literacy (Haack, 197918
), Political literacy
(Institute of Education, 198319
), Computer literacy (Day, 198720
), Information literacy
17 Carolyne L.J. M.,Richard M.S.W. (2000). Conceptualizing financial literacy. Business School Research Research, 7, Loughborough University, 1-40. 18 Haack, D.G. (1979). Statistical Literacy – A Guide to Interpretation, North Scituate, Massachusetts:
Duxberry Press. 19 Institute of Education (1983). Teaching political literacy: implications for teacher training and
curriculum planning, University of London, Institute of Education, Paper 16. 20 Day, J.M. (1987). Computer literacy and library and information studies: A literature review, British
Library Research Paper 18, London: BL.
10
(Kulthau, 199121
), Visual literacy (Wilde, 199122
), Tele literacy (Bianculli, 199223
),
Scientific and technological literacy (Layton, 199424
), Multimedia literacy (Hofsteter,
199525
), Internet literacy (Martin, 199726
) and Electronic literacy (Craver, 199727
) and
Financial literacy (Noctor, Stoney and Stradling, 199228
).
The current chapter aims to define financial literacy as a concept by means of a literature
review of the existing definitions on financial literacy, conceptualize the term financial
literacy for current research and its need and importance in the present era and scope of
the term ―financial literacy‖ for present study.
1.4 History of the term “Financial Literacy”
Over the last several years, the issue of financial literacy and financial education has risen
on the agendas of educators, community groups, businesses, government agencies,
organizations, and policy makers. A number of researches have also been done in the
past, on the issue of financial education, either from a policy perspective or a pragmatic
perspective
21 Kulthau, C.C. (1991). Introduction in , Jana Varlejs (ed) (1991) Information Literacy: Learning How to
learn, Proceedings of the Twenty-eighth Annual Symposium of the Graduate Alumni and Faculty of the
Ritgers School of Communication, Information and Library Studies, 6 April 1990, Jefferson: McFarland &
Company. Layton. 22
Wilde, J. (1991). Visual Literacy: A conceptual approach to graphic problem solving, New York:
Watson-Guptill. 23 Bianculli, D. (1992). Teleliteracy, Continuum, New York. 24 Layton, D (1994). Scientific and technological literacy: meanings and rationales: An annotated
bibliography, Leeds: Centre for Studies in Science and Mathematics Education, University of Leeds in
association with UNESCO. 25 Hofsteter, F.T. (1995). Multi-media Literacy, New York; London: McGraw-Hill. 26 Martin, L.H.M. (Ed.) (1997). The Challenge of Internet Literacy: The instruction-Web Convergence.
New York; London: Haworth Press. 27 Craver, K.W. (1997). Teaching electronic literacy: a concepts-based approach for library media
specialists,Westport, Conn; London: Greenwood Press. 28 Noctor M., Stoney S., & Stradling S. (1992) Financial literacy; a discussion of concepts and competences
of financial literacy and opportunities for its introduction into young peoples‘ learning: National
Foundation for Educational Research commissioned by NatWest.
11
Jacob, Hudson, and Bush (200029
) in their report to the Woodstock Institute had stated
that ―financial knowledge has become not just a convenience but an essential survival
tool‖ (p.7). Coussens (200430
) has commented ―the financial product innovation and
marketing, technological advances, consolidation and restructuring of the financial
services industry, changes in retirement and pension plans, and shifts in consumer
attitudes are several trends that are significantly influencing financial attitudes and
decisions‖ and hence challenge the financial literacy.
Hogarth J. M. (200631
) has concluded in his study that ―financial education include: (1)
being knowledgeable, educated, and informed on the issues of managing money and
assets, banking, investments, credit, insurance, and taxes; (2) understanding the basic
concepts underlying the management of money and assets (e.g., the time value of money
in investments and the pooling of risks in insurance); and (3) using that knowledge and
understanding to plan, implement, and evaluate financial decisions‖ (p. 3).
Knapp (199132
) has explained ―modern consumer education is a lifelong process essential
to the economic well-being of society‖. To gather the views about the benefits of
consumer education, he surveyed consumer professionals and found that consumer
education offers the following benefits to individuals: (a) encourages critical thinking, (b)
imparts life skills that contribute to success in everyday living, (c) promotes self-
confidence and independence, (d) fosters broadly accepted values, and (e) improves the
quality of life.
29 Jacob, K., Hudson, S., & Bush, M. (2000). Tools for survival: An analysis of financial literacy programs
for lower-income families. Retrieved on June 27, 2006, from http://www.woodstockinst. 30 Coussens, M. (2005, October). Towards financial literacy: Program leaders comment on evaluation and
impact. Profitwise News and Views, 3-11. Retrieved June 30, 2009, from
http://www.chicagofed.org/community_development/files/10_2005_towards_financial_li teracy.pdf 31 Hogarth , J. M. (2006). Financial education and economic development. Paper presented at Improving
Financial Literacy International Conference hosted by the Russian G8 Presidency in Cooperation with the
OECD. November 29-30, 2006. 32 Knapp, J. P. (1991). The benefits of consumer education. A survey report. Yipsilanti, MI: Eastern
Michigan University,Consumer Education Center.
12
OECD (200533
) defines financial education is ―the process by which financial consumers/
investors improve their understanding of financial products and concepts and, through
information, instruction and/or objective advice, develop the skills and confidence to
become more aware of financial risks and opportunities, to make informed choices, to
know where to go for help, and to take other effective actions to improve their financial
well-being‖ (p. 26).
Where,
Information includes providing consumers the facts, data and specific knowledge to
make them aware of financial opportunities, choices and consequences.
Instruction involves ensuring that individual acquires the skills and ability to understand
the financial terms and concepts through the provisions of training and guidance.
Advice involves providing consumers with counsel about generic financial issues and
products so that they can make the best use of financial information and instruction they
have received.
More concisely, Vitt et al. (200534
) defined financial education as ―…helps the people to
develop the skills those are required to make informed choices and to take action that
improves their financial well-being‖ (p. 9). They also added that the process of helping
people in a financial context is known by various names, such as: investor or investment
education, economic education, financial education, savings education, pension
education, personal finance employee education, workplace financial education,
consumer education, consumer finance protection education, money management
education, retirement savings education and retirement education. They also concluded
that the bottom line of financial literacy is ―… to equip individuals and families with the
ability to negotiate the money management issues necessary for them to make self-
enhancing life choices‖ (p. 9). This focus is very similar to that of the consumer
protection.
33 OECD (2005). Improving Financial Literacy: Analysis of Issues and Policies. Organization for
Economic Co-operation and Development, Paris: OECD Publications. 34 Vitt, L. A., Reichbach, G. M., Kent, J. L., & Siegenthaler, J. K. (2005). Goodbye to Complacency:
Financial Literacy Education in the U.S. 2000-2005. Washington, DC: AARP.
13
Nieuwenhuyzen (200935
) had argued ―financial education is not a consumer protection
and although, it overlaps, the two should not be confused. Both concepts share the same
goals but have different approaches. Financial education provides information,
instruction and advice; consumer protection provides information by means of legislation
and regulation to point out minimum requirements, strengthen protection of consumers,
and provide avenues for redress. Financial education, however has the foremost aim of
improving financial literacy‖ (p. 93).
The President‘s Advisory Council on Financial Literacy (PACFL) (200836
) had defined
financial education as ―the process by which people improve their understanding of
financial products, services and concepts and hereby are empowered to make informed
choices, avoid pitfalls, know where to go for help and take other actions to improve their
present and long-term financial well-being‖. It also states, ―Financial education is a
process through which financial knowledge and skills are gained, rather than the
knowledge and skills themselves. Hence, financial education should be considered a
concept, that promotes financial literacy‖ (p. 4).
Through a number of studies, researchers and policy makers tried to offer a conceptual
definition of financial literacy. Here, breadth of conceptual definitions, drawn from a
number of studies and placed in chronological order. The most common basis for these
definitions is knowledge (or understanding), while some definitions merely requiring
familiarity (arguably a limited form of knowledge). Lusardi & Tufano (200837
) had
emphasized a judgment and decision-making are the aspects of financial literacy. They
have also focused on a specific form of financial literacy – debt literacy.
35 Bernard J. van Nieuwenhuyzen (2009). Financial literacy: As core competency of South African Military
Officers: A Measurement Instrument. (Doctoral dissertation, Stellenbosch University, 2009). Retrieved
May 15, 2010, from Stellenbosch University Digital Theses. 36 The Presidents‘ Advisory Council on Financial Literacy (PACFL) (2008). Annual Report to the President
of U.S. United States Department of The Treasury office of Financial Education. 37 Lusardi, A. & Tufano, P. (2008). Debt literacy, financial experiences, and over-indebtedness. Dartmouth
Working Paper.
14
Noctor, Stoney and Stradling (199238
) undertook a study on behalf of National
Westminster Bank in the U.K., where they have introduced, conceptualized and defined
the term ‗financial literacy‘ as ―the ability to make informed judgments and to take
effective decisions regarding the use and management of money‖(p. 4).
Anthes (200439
) stated that ―personal financial literacy is the ability to read, analyse,
manage and communicate about the personal financial conditions that affect material well
being‖. Mason and Wilson (200040
) have defined, financial literacy as a ―… ‗Meaning-
making process‘ in which individuals use a combination of skills and technologies,
resources and contextual knowledge to make sense of information in order to be
sufficiently informed to make decisions with an awareness of financial consequences‖
(p. 32). They interpreted, financial literacy as ―An individual‘s ability to obtain,
understand and evaluate the relevant information necessary to make decisions with an
awareness of the likely financial consequences‖ (p. 3141
).
Hogarth (200242
) explained financial literacy as ―the ability to read, analyze, manage, and
communicate about the personal financial conditions that affect material well-being. It
includes the ability to discern financial choices, discuss money and financial issues
without (or despite) discomfort, plan for the future, and respond competently to life
events that affect everyday financial decisions, including events in the general economy.
Hence, financial literacy includes knowledge and understanding of basic financial
concepts and the ability to use these to plan and implement financial decisions‖. Hilgert,
38 Noctor M., Stoney S., & Stradling S. (1992) Financial literacy: a discussion of concepts and competences
of financial literacy and opportunities for its introduction into young peoples‘ learning: National
Foundation for Educational Research commissioned by NatWest. 39Anthes, W.L. (2004). Frozen in the headlights: The dynamics of women and money, Journal of Financial
Planning, 13 (9), 130-142. 40 Mason, L. J. & Wilson, M.S. (2000). Conceptualizing financial literacy. Business School Research Paper
Series: Paper 2000:7, Loughborough University. 41 Ibid., p. 31 42 Hogarth, J. M., Hilgert, M. A., & Schuchardt, J. (2002). Money managers: The good, the bad, and the
lost. Proceedings of the Association for Financial Counseling and Planning Education, 12-23.
15
Hogarth & Beverley (200343
) argued that financial literacy is nothing but the financial
knowledge.
FINRA (200344
) stated, ―Financial literacy is the understanding that ordinary investors
have of market principles, instruments, organizations and regulations.‖ (p. 2).
Moore (200345
) argued that ―Individuals are considered financially literate if they are
competent and can demonstrate that they have used knowledge they have learned.
Financial literacy cannot be measured directly so proxies must be used. Literacy is
obtained through practical experience and active integration of knowledge. As people
become more literate they become increasingly more financially sophisticated and it is
conjectured that this may also mean that an individual may be more competent‖ (p. 29).
The U.S. Financial Literacy and Education Commission (200646
) defined financial
literacy as ―the ability to make informed judgments and to take effective actions
regarding the current and future use and management of money‖.
Lusardi and Mitchell (2007c47
) termed financial literacy as ―familiarity with the most
basic economic concepts needed to make sensible saving and investment decisions‖ (p.
36). Lusardi and Tufano (200848
) had focused on debt literacy, a component of financial
literacy, defining it as ―the ability to make simple decisions regarding debt contracts, in
43 Hilgert, M.A., Hogarth, J.M. & Beverly, S.G. (2003) Household financial management: The connection
between knowledge and behavior. Federal Reserve Bulletin, 89 (July): 309-322.Washinton, D.C. 44 FINRA (2003). NASD investor literacy research: Executive summary. Accessed March 30, 2010 at
http://www.finrafoundation.org/surveyexecsum.pdf. 45 Moore, D. (2003). Survey of financial literacy in Washington State: Knowledge, behavior, Attitudes, and Experiences. Technical Report # 03-39. Social and Economic Sciences Research
Center, Washington State Department of Financial Institutions, Olympia, WA . Puulman:
Washington State University. 46 Financial Literacy & Education Commission (2006). Financial Education and Taking Ownership of the
Future: The National Strategy for Financial Literacy. Financial Literacy & Education Commission. 47 Lusardi, A. & Mitchell, O.S. (2007c).Financial Literacy and Retirement Planning: New Evidence from
the Rand American Life Panel. Michigan Retirement Research Center Working Paper 2007-157. 48 Lusardi, A. & Tufano, P. (2008). Debt literacy, financial experiences, and overindebtedness. Dartmouth
Working Paper.
16
particular how one applies basic knowledge about interest compounding, measured in the
context of everyday financial choices‖ (p. 1).
ANZ Bank (200349
) defined, ―Financial literacy is about enabling people to make
informed and confident decisions regarding all aspects of their budgeting, spending and
saving and their use of financial products and services, from everyday banking through to
borrowing, investing and planning for the future‖ (p. 1). These reports also support the
definitions drawn from Schagen and Lines (199650
) who defined financial literacy as ―the
ability to make informed judgments and to take effective decisions regarding the use and
management of money‖ (p. 1).
However, it is important to note that financial literacy can only ensure individuals are
informed to make decisions, it cannot ensure the 'right' decision is actually made. This is
because individuals do not always make decisions based purely on economic rationality.
Towards composite definition of financial literacy
Financial knowledge is often considered central to financial literacy; it should be
distinguished from general knowledge. For example, Parker et al. (2008) found that
finance-specific knowledge outperformed general knowledge when predicting
performance on a hypothetical investment task.
Angela at el., (200951
) argued, ―Financial knowledge, skills, behavior, and their mutual
relationship should be considered while conceptualizing financial literacy‖ (p. 9). They
have added that, in particular, financial knowledge represents a particularly basic form of
financial literacy. Financial knowledge is reflected in perceived financial knowledge and
influences financial skills that depends on knowledge. Actual financial behavior, in turn
49 ANZ Bank (2003). ANZ Survey of Adult Financial Literacy in Australia. Roy Morgan Research. 50 Schagen, S. & Lines, A. (1996). Financial literacy in adult life: a report to the Natwest Group
Charitable Trust, Slough, Berkshire: National Foundation for Educational Research. 51 Angela, A. H., Parker, A. M., &Yoong K. J. (2009). Defining and measuring financial literacy. RAND
Labor and Population working paper series,708. Department of Labor and The National Institute on Aging
via the RAND Roybal Center for Financial Decision Making.
17
depends on actual knowledge, perceived knowledge, and skills. It is also found that these
relationships are likely to be imperfect, as each also depends on other factors internal and
external to the individual. Thus they have defined ―financial literacy is knowledge of
basic economic and financial concepts, as well as the ability to use that knowledge and
other financial skills to manage financial resources effectively for a life time well being‖
(p. 10). Hogarth, Marianne, & Beverly (200352
) explained that there is a correlation
between financial knowledge and behaviour, although the direction of the causality is
unclear. Figure 1 represents the logical relationships among of financial literacy
components.
(Source: Angella A. Hung, Andrewn M. Parker and Joanne K. (2009). Defining and measuring financial
literacy. Working Paper Series: WR 708. Department of Labor and the National Institute on Aging via the
RAND Roybal Center for Financial Decision Making)
Schagen and Lines (199653
) in a report to the National Foundation for Educational
Research, The United Kingdom, defined financial literacy as ―the ability to make
informed judgments and to take effective decisions regarding the use and management of
money‖. This definition has been later used by number of researchers, where they have
modified this definition. Royal Morgan Research (2003a, 2003b, 2003c) agreed on
financial literacy is about people being informed and confident decision makers in all
aspects of their budgeting, spending, and saving, but the measure of financial literacy
52 Hilgert, M.A., Hogarth, J.M. & Beverly, S.G. (2003). Household financial management: The connection
between knowledge and behavior. Federal Reserve Bulletin, 89 (July): 309-322.Washinton, D.C. 53 Schagen, S. (1997). The Evaluation of NatWest Face 2 Face with Finance. NFER.
18
should reflect individual circumstances and were therefore relative. Whereas knowledge
is ―…. only to be tested against an individual‘s needs and circumstances rather against
the entire array of financial products and services, some of which they neither use nor
need‖ (Roy Morgan Research, 2003c54
).
Alternatively, Beal and Delpachitra (200355
) argued, ―the financially literate should not
only possess the ability to understand the key concepts in money management, a working
knowledge of financial institutions, systems and services and a range of analytical skills,
but also possess a facilitating attitude to effective and responsible management of
financial affairs‖ (p. 65). This particular conceptualization of financial literacy, thus
comprises a skill base incorporating both cognitive (knowledge) and psychological
(willingness and confidence) concepts. In a survey article Hogarth (200256
) observed that
―a consistent theme running through most definitions of financial literacy included being:
1. Knowledgeable, educated and informed on issues of money and assets, banking,
investments, credit, insurance and taxes. 2. Understanding of basic concepts underlying
the management of money and assets and 3. Using the knowledge and understanding to
plan and implement the financial decisions‖. In other words, this study has concluded, the
most definitions of financial literacy include knowledge and understanding of basic
financial concepts and the ability to use these to plan and implement financial decisions.
Coussens (200557
) stated that ―financial literacy represents the culmination of financial
access, education, and understanding, as well as an individual‘s interest, attitude, and
practices that directly benefits the financial efficiency and effectiveness of the individual,
and directly and ultimately benefits that of society at large‖.
54 Roy Morgan Research. (2003c). ANZ Survey of Adult Financial Literacy in Australia: Stage 3: In-Depth
Interview Survey Report, Melbourne: ANZ Bank. 55 Beal, D.J. & Delpachtra, S.B. (2003). Financial literacy among Australian university students, Economic
Papers, 22(1), 65-78. 56 Hogarth, J. M. (2002). Financial literacy and family and consumer sciences. Journal of Family and
Consumer Sciences, 94(1), 14-28. 57 Coussens, M. (2005, October).Towards financial literacy: Program leaders comment on evaluation and
impact. Profitwise News and Views, 3-11. Retrieved June 30, 2009, from
http://www.chicagofed.org/community_development/files/10_2005_towards_financial_li teracy.pdf
19
Studies done by Lusardi & Mitchell (2007a58
, 2007b59
) and ANZ Bank (200860
)
explicitly noted numeracy as a component of financial literacy tests. Further, Peters et al.,
(200661
) explained, ―numeracy is considered as a basic number skill and to be a distinct
construct that is related to and supports financial literacy‖.
Britain‘s Financial Services Authority describes financial capability as encompassing the
outcomes of ―managing money‖, ―planning ahead‖ or retirement planning, ―choosing
products‖ or financial service usage, and product familiarity or financial literacy, which
they termed ―staying informed‖. Atkinson et al., (200662
) and Personal Finance Research
Centre (200563
) stated, ―Financial capability tends to refer to both financial knowledge
and financial management behavior‖. Thus, Financial Capability can be summarized as a
set of financial knowledge, skills and behaviors among individuals. Another group of
researchers Atkinson et al., (200664
), Alba and Hutchinson (200065
), argued that
―financial literacy is a component of financial numeracy‖.
Bruce et al. (200966
) applied the theories of consumer psychology of processing, learning
and knowledge to the ability to comprehend, acquire, and use financial information and
concepts. In their research, they developed a conceptual model of financial numeracy that
incorporates and is rooted in the broader theoretical constructs of cognitive capacity and
prior knowledge, where they defined financial numeracy as a construct of proficiency in
58 Lusardi, Annamaria & Olivia S. Mitchell (2007a). Financial literacy and retirement preparedness:
Evidence and implications for financial education. Business Economics, January, 2007, 35-44. 59 Lusardi, Annamaria & Olivia S. Mitchell (2007a). Financial literacy and retirement preparedness:
Evidence and implications for financial education. Business Economics, January, 2007, 35-44. 60 ANZ Bank (2008). ANZ survey of adult financial literacy in Australia. Retrieved on March 11, 2009
from
http://www.anz.com/Documents/AU/Aboutanz/AN_5654_Adult_Fin_Lit_Report_08_Web_Report_full.pdf 61 Peters, E., Västfjäll, D., Slovic, P., Mertz, C.K., Mazzocco, K., & Dickert, S. (2006). Numeracy and
decision making. Psychological Science, 17, 407-413. 62 Atkinson, A., McKay, S., Kempson, E. & Collard, S. (2006). Levels of Financial Capability in the UK:
Results of a baseline survey, Financial Services Authority, London. 63 Personal Finance Research Centre (2005), Measuring Financial Capability: An Exploratory Study,
available at: www.fsa.gov.uk/pubs/consumer-research/crpr37.pdf 64 Atkinson, A., McKay, S., Kempson, E. and Collard, S. (2006), Levels of Financial Capability in the UK: Results of a Baseline Survey, Financial Services Authority, London. 65 Alba, J.W. & Hutchinson, J.W. (2000). Knowledge calibration: what consumers know and what they
think they know. Journal of Consumer Research, 27 (2), 123-156. 66 Bruce, A. H. & McQuity, S. (2009). A model of consumer financial numeracy. International Journal of
Bank Marketing. 27 (4), 270-293.
20
processing, understanding, acquiring and using financial information and concepts,
numeracy based on a consumer‘s capacity (known as financial capacity) and prior
knowledge (i.e. financial literacy). According to them, financial numeracy has two
distinct components: financial capacity and financial literacy. Financial capacity is the
ability to process and comprehend information and statistics related to financial products,
whereas financial literacy involves adequate knowledge about financial concepts and how
financial products work. In other words, financial capacity is learning-based, whereas
financial literacy knowledge is memory-based.
Kintsch (198867
) explained that ―the financial knowledge can be used to filter,
comprehend, organize, and encode new information that passes through the constraints
imposed by one‘s financial capacity‖.
Operational Definition of Financial Literacy
Financial literacy is a set of skills and knowledge that allows an investor (individual) to
understand
- the financial principles that individual needs to know to make informed decisions
and
- the financial products that impact individual‘s financial well-being.
Here, financial literacy is concerned with the understanding of basic financial concepts,
principles, skills and ability to understand key financial products to make good financial
choices.
1.5 Need for financial literacy
Financial literacy is the process of acquiring knowledge about financial products,
understanding the concept of trade off between risk and return, utilizing the knowledge to
make informed choices and appreciating the available professional knowledge.
67 Kintsch, W. (1988). The role of knowledge discourse comprehension: a construction-integration model.
Psychological Review, 95 (2), 163-182.
21
Former Chairman of the Federal Reserve Bank, Alan Greenspan (200268
) had said,
“[financial education] … can help to inculcate individuals with the financial knowledge
necessary to create household budgets, initiate savings plans, and make strategic investment
decisions. Such financial planning can help families meet near term obligations and
maximize their longer term well being and is especially valuable for populations that have
traditionally been underserved by our financial system.”
Thus, financial literacy may be considered as the ultimate pillar of any financial system,
as it complements the important aspects like greater transparency, policies on consumer
protection and regulation of financial institutions.
Much of the interest shown in financial literacy stems from a concern over people‘s lack
of financial literacy. This is particularly true where individuals are viewed as consumers
of financial products. Schagen and Lines (199669
) expressed the concern about the ability
of these consumers to make effective decisions. The evidence available suggests that
financial information is used ineffectively to make decisions about financial products.
Hogarth (200270
) found that ―financial literacy is important because well-informed, well
educated consumers should make better decisions for their families; increase their
economic security and well-being; contribute to vital, thriving communities; and foster
community economic development‖.
Beal and Delpachitra (200371
) stated, ―the need for financial literacy has grown rapidly
over the last decade because financial markets have been deregulated and credit has
become easier to obtain, as financial institutions compete strongly with each other for
68 Remarks by Chairman Alan Greenspan at the 33rd Annual Legislative Conference of the Congressional
Black Caucus, Washington, D.C., September 26, 2003. Available on the Federal Reserve Board website at
http://www.federalreserve.gov/boarddocs/Speeches/ 2003/20030926/default.html 69 Schagen, S., & Lines, A. (1996). Financial literacy in adult life: a report to the Natwest Group
Charitable Trust. Slough, Berkshire: National Foundation for Educational Research. 70
Hogarth, J. M. (2002). Financial literacy and family and consumer sciences. Journal of Family and
Consumer Sciences, 94(1), 14-28. 71 Beal, D.J. & Delpachtra, S.B. (2003). Financial literacy among Australian university students, Economic
Papers, 22(1), 65-78
22
market share‖ (p. 65). For millions of households in India, to make independent decisions
regarding financial savings for their retirement calls for a change in the standard of
financial education. Moreover, this increased interest in financial education has been
prompted by the increasing complexity of financial products and the increasing
responsibility on the part of individuals for their own financial security (Mariame and
Hogarth, 200372
, p. 309).
1.6 Importance of financial literacy
The importance of financial literacy is realized when it is put in the perspective of the
following developments in the financial space:
Financial products and services innovation: Growing numbers of consumers are able
to access myriad of financial products and services provided by the variety of providers
through the various distribution channels. Deregulation and liberalization of financial
markets, and reduction of costs through financial engineering, developments in
information and telecommunication have brought many newer financial products and
services tailored to meet very specific market needs. These financial products and
services innovations enabled the consumers to gain access to greater variety of financial
products and provide them more choices to park their savings. The understanding of these
innovations is crucial on the part of consumers, as these innovations do not only provide
more choices to consumers, but also challenges to understand the benefits and costs
associated with the innovations, and more specific the risk-return matrix inherent to each
innovation.
Changing scenario of the domestic financial markets: Many developing countries
have undertaken structural changes in their financial markets and made it more
liberalized, deregulated and open for retail consumers and foreign investors for more
inflow of savings and investments to boost the growth rate of economy. In India, the LPG
72 Hilgert, M.A., Hogarth, J.M. & Beverly, S.G. (2003). Household financial management: The connection
between knowledge and behavior. Federal Reserve Bulletin, 89 (July): 309-322.Washinton, D.C.
23
policy along with the deregulation of financial markets has played an important role to
develop the domestic financial market. Post 1991, many important sectors of financial
services industry opened up for private players to gain wider access to consumers. As a
result, not only the giant domestic non-financial services companies have made their
entry, but also foreign companies entered into Indian financial services industry with the
introduction of newer financial products. To compete successfully and to gain market
share, these companies have started to provide generalized and customised financial
solutions and made it easier to obtain credit for consumers of this country, who had
limited prior experience with financial markets and hence, due to lack of experience and
knowledge about the working of financial markets and its products, consumers posses
low awareness about the financial products and services, distrust the modern financial
solutions and prefer traditional avenues for investing their money. As a result, they are
unable to derive optimum financial benefit of the opportunities which these companies‘
financial products/services provide.
Multifaceted features of financial products: The increased complexity of financial
products and services has meant that it is annoying for an average person to be asked to
take financial decision. Perhaps the confusion has arisen not only because of the speed at
which financial markets and new financial instruments have emerged or more number of
institutions providing more complex financial products, but also because of the inability
to understand basic financial concepts. The financial services are divided mainly into two
categories: Saving/ investment services which can be viewed as the instruments for
financing future consumption based on current earnings and credit services i.e. loans/
liabilities which are the instruments for financing current consumption based upon the
future earnings. Latter, is dependent on individual‘s financial needs (or objectives) and
abilities (resources) to acquire these financial assets and liabilities. The combination of
financial need priorities and resource availability at different stages of household‘s life
cycle influences the sequence in which financial services are acquired by the household.
But, nowadays consumers are faced with various financial instruments offering a wide
range of benefits and options with respect to fees, interest rates, length of contract,
exposure to risk etc. The quality of some of these financial products, such as pension,
24
insurance policies etc. and their financial implications on investors are difficult to
understand, as they require long term investment horizon and often they are purchased
infrequently and there is often a significant lapse of time between the investment made
and the return earned. The understanding of multifaceted features of financial products
and services is quite difficult for the average individual which result into greater
perceived risk, need to search greater volume of information to make comparison across a
number of factors which in turn makes decision making more complex, and subsequent
delay in making investment decision.
Increase in an individual‟s responsibility: Nuclear family structure requires an
individual to make number of financial decisions related to spending, saving,
investments, credit etc. not only for himself but also for his family. People also need to
assume more responsibility for funding personal or family healthcare needs. Moreover,
increasing education costs make it important for parents to plan and invest adequately for
their children‘s education.
Increase in the life expectancy, changes in pension agreement and transfer of risk:
Increase in the life expectancy means the possibility of more time spent in retirement and
thus, a greater need of financial planning, expanded insurance, and provisions of health
care related expenses to cover unpredictable eventualities. A case in point is the shift
from Defined Benefit Plan to Defined Contribution Plan, known as New Pension Scheme
(NPS). Since the last decade, there has been a widespread transfer of risk from both
governments and employers to individuals. The governments started to reduce the state-
supported pensions, and some are reducing healthcare benefits. Defined contribution
pension plans are quickly replacing defined benefit pension plans, shifting the
responsibility onto workers to save for their own financial security after retirement. Most
surveys show that a majority of workers are unaware of the risks they now have to face,
and do not have sufficient knowledge and skill to manage such risks adequately, even if
they are aware of them (OECD, 2008).
25
The implementation of New Pension Scheme requires the workers to make various
decisions regarding contribution to plan. As governments will no longer enough to
provide social security, increasing responsibility has come on the shoulders of an
individual. Thus, individuals need to consider not only investment risk and return trade
off, but also uncertainty regarding their life expectancy, attitudes towards risk, current
and future earning potential and likely changes in the personal and social circumstances.
Technological changes and market innovations: Technology advances have
transformed every aspect of processing, marketing and delivery of financial products and
services. The expansion of internet as a mean for communicating and delivering financial
services and/or products has also enabled financial services providers to market the
financial products and serve the customers more effectively and efficiently. These
communication and delivery innovations increase the amount of information available to
consumers enable them to select the best from the myriad of products and services
without geographic limitations. To benefit from innovations, however, consumers must
be financially knowledgeable and literate.
1.7 Consequences of financial illiteracy
As discussed above, to cope up with the financial products and services innovations,
changing scenario of the domestic financial markets, multifaceted features of financial
products, increase in an individual‘s responsibility, increase in the life expectancy,
changes in pension agreement and transfer of risk and technological changes and market
innovations, every individual needs to be financially literate. Chidambaram (200773
) said
that ―financial literacy needs to be embedded in our way of life. Everyone who earns an
income is a potential saver; every saver is a potential investor; every investor ought to be
financially literate‖.
73 Chidambaram, P. (2007) Sesquicentennial Lecture on Financial Literacy, Delivered at Rajiv Gandhi
Centre for Contemporary Studies, University of Mumbai, July 20th. Available at:
http://pib.nic.in/release/release.asp?relid=29306.
26
Lusardi and Mitchell (2007c74
) termed financial literacy as ―familiarity with the most
basic economic concepts needed to make sensible saving and investment decisions‖ (p.
36). Financial literacy also develops the understanding of market principles, instruments,
market participants and regulations. Reddy (200675
) pointed out ―the overriding purpose
of financial literacy is to equip individuals with the capacity to have familiarity with and
understanding of financial products, especially risks and rewards in order to make
informed decisions‖. Jariwala and Sharma (201176
) stated, ―Financial illiteracy or low
level of financial literacy is resulted into lack of healthy ways of financial thinking, lack
of necessary financial knowledge and difficulties in applying financial knowledge‖. They
have also added that ―Financially illiterate individuals either voluntarily do financial
exclusion or get the financial information from unreliable sources, the analysis of which
may be resultant into the misallocation of private wealth and can cause social decline and
increased public expenditure in the form of social security. Absence of this knowledge
and skill poses a variety of risk to individual, society and economy as a whole‖.
Lusardi and Tufano (200977
) found that low levels of financial literacy resulted into an
inability to understand basic financial concepts and poor judgment in borrowing
decisions and retirement planning and hence poor financial management. Poor financial
decisions can slush households in debt and lead to much lower living standards. Cole et
al. (200978
) said that there is a strong association between understanding of financial
concepts, better financial decisions, and household well-being. A compelling body of
evidence demonstrates a strong association between financial literacy and household
well-being. Surveys show that household which demonstrate low levels of financial
74 Lusardi, A. & Mitchell, O.S. (2007c). Financial Literacy and Retirement Planning: New Evidence from
the Rand American Life Panel. Michigan Retirement Research Center Working Paper, 2007-157. 75 Reddy, Y. V. (2006). The Role of Financial Education: The Indian Case. Available at:
http://www.rbi.org/review/r060921b.pdf, retrieved on July 20, 2011. 76
Jariwala, H. & Sharma, S. (2011). Financial literacy: A call for an attention. International Journal of
Academic Conference Proceedings, 1(1), Proceedings of Conference on Inclusive and Sustainable Growth. 77
Lusardi, A. & Tufano, P. (2008). Debt literacy, financial experiences, and overindebtedness. Dartmouth
Working Paper. 78 Cole, S., Thomas, S., & Zia, B. (2009). Financial literacy, financial decisions, and the demand for
financial services: Evidence from India and Indonesia.‖ Harvard Business School Working Paper, 09-117.
27
literacy are those who tend not to plan for retirement (Lusardi & Mitchell, 2007a79
),
borrow at high rates (Stango & Zinman, 200680
), and acquire fewer assets (Lusardi &
Mitchell, 2007b81
). Financial illiteracy or low level of financial literacy resulted into
financial stress. Bagwell (200282
) found that poor financial management leads to greater
absenteeism at work. Hendrix et al. (198783
) and Jacobson et al. (199684
) found that
financial stress was one of the stressors that affect absenteeism. Ullah (199085
) found that
financial stress influences psychological well-being and also mediates the effect of
income and mental health. Kinnenun and Pulkkinen, (199886
) found that these financial
problems are often the basis for divorce, mental illness and a variety of other unhappy
experiences. Having financial literacy skills is essential for avoiding and solving financial
problems, which in turn are vital to a prosperous, healthy and happy life (CBF, 2004a, p.
4). Drentea and Lavrakas (200087
) found that individual who reported higher level of
financial stress showed higher level of physical impairment and illness than lower level
of financial stress. Further, CBF (2004a88
) and Morton (200589
) and support, the high
number of people with low levels of financial literacy presents a serious problem for both
the economic well-being of nations and the personal well-being of such individuals and
79 Lusardi, Annamaria & Olivia S. Mitchell (2007a). Baby Boomer Retirement Security: The Role of
Planning, Financial Literacy, and Housing Wealth. Journal of Monetary Economics, 54, 205–224. 80 Stango, V. & Zinman, J. (2006). How a cognitive bias shapes competition: Evidence from consumer
credit markets. 81 Lusardi, Annamaria & Olivia S. Mitchell (2007b). Financial Literacy and Retirement Preparedness:
Evidence and Implications for Financial Education. Business Economics, January 2007, 35-44. 82 Bagwell, D.C. (2000). Work and Personal Financial Outcomes of Credit Counseling Clients. (Doctroal
Dissertation, Virginia Polytechnic Institute and State University, 2000) Blacksburg. 83 Hendrix, W.J., Steel, R.P. & Shultz, S.A. (1987). Job stress and life stress: Their causes and
consequences. Journal of Social Behavior and Personality, 2(3), 291-302. 84 Jacobson, B. H., Aldana, S. G., goetzel, R. Z., Vardell, K. D., Adams, T. B. & Pietras, R. J. (1996). The
relationship between perceived stress and self-reported illness-related absenteeism. American Journal of
HealthPromotion, 11(1), 54-61. 85 Ullah, P. (1990). The association between income, financial strain and psychological well-being among
unemployed youths. Journal of Occupational Psychology, 63, 317-330. 86 Kinnunen, U., & Pulkkinen, L. (1998). Linking economic stress to marital quality among Finnish marital
couples. Journal of Family Issues, 19, 705-724 87 Drentea, P. & Lavrakas, P.J. (2000). Over the limit: The association among health status, race and debt.
Social Science & Medicine, 50, 517-529. 88 Commonwealth Bank Foundation (CBF), 2004a, Australians and Financial Literacy, Commonwealth
Bank Foundation, Sydney. 89 Morton, J. S. (2005). The interdependence of economic and personal finance education. Social
Education, 69, 66-70.
28
their family. Perry and Morris (200590
) stated that ―there are substantial negative costs to
consumers and the society when consumers lack financial knowledge‖ (p. 310).
Surveys also demonstrate that the adults do not possess the basic knowledge needed to
make informed financial choices. Beal and Delpachitra (200391
) stated, ―Since
governments world-wide are moving down the path of encouraging their citizens to take
more responsibility for their retirement incomes and to move away from public pensions‖
(p. 65). Compared to previous generations, today there is a variety of ways individuals
generate and dispose of their income. The changes in the work life over the world have
meant that the income stream of individuals has become more inconsistent over a long
period. There are periods of high income followed by low level of income or no income
at all. At the same time, people are living longer and they need to make greater provision
for retirement, health care and insurances to cover unpredictable eventualities and the
state would not be longer enough to provide financial security which was available in the
past. So, today individuals must have necessary skills to make informed financial
decisions among the myriad of products and services, which may allow them to secure
their future in financial terms. Beal and Delpachitra (200392
) and CBF (2004b93
) said
that the financial literacy skills enable individuals to navigate the financial world, make
informed decisions about their money and minimise their chances of being misled on
financial matters.
Hogarth, Marianne & Beverly (200394
) stated ―there is a correlation between financial
knowledge and behaviour, although the direction of the causality is unclear. Those who
scored higher on the financial literacy tests, were more likely to follow the recommended
financial practice and recommended financial behaviour‖ (p. 311).
90 Perry, V.G. & Morris, M.D. (2005). Who is in control? The role of self-perception, knowledge, and
income in explaining consumer financial behavior. Journal of Consumer Affairs, 39, pp.299–313. 91
Beal, D.J. & Delpachtra, S.B. (2003). Financial literacy among Australian university students, Economic
Papers, 22(1), 65-78. 92 Beal, D.J. & Delpachtra, S.B. (2003). Financial literacy among Australian university students, Economic
Papers, 22(1), 65-78. 93 Commonwealth Bank Foundation (CBF) (2004b) Improving Financial Literacy in Australia: Benefits for
the Individual and the Nation, Research Report, Commonwealth Bank Foundation, Sydney. 94 Hilgert, M.A., Hogarth, J.M. & Beverly, S.G. (2003). Household financial management: The connection
between knowledge and behavior. Federal Reserve Bulletin, 89 (July): 309-322.
29
Financial education is important not only for the investor or his/her family, but also the
community mass as well. Again, there is a difference between providing information and
providing education, where education may require collection of information, skill
building and motivation on the right time to make the desired changes in behaviour.
Hilgert, Hogarth, & Beverly (200395
) also agree that financial knowledge appears to be
directly correlated with self-beneficial financial behaviour. Mandell (200996
) states,
―Financial behaviour seems to be positively affected by financial literacy, however the
long term financial education on financial behavior are less certain‖ (p. 10).
Personal financial literacy prepares an individual to manage money, credit, and debt,
effectively. Better informed consumers make more effective choices and more
appropriate decisions. They are less likely to be mis-sold or mis-buy products and
services. From a broader perspective, market operations and competitive forces are
compromised when consumers do not have the skills to manage their finances effectively.
As financially knowledgeable consumers demand products that meet their short term and
long term financial needs. Hence, it helps to create a more competitive and more efficient
financial market.
The findings of recent survey97
, found that; In India due to the lack of social security
system, over 80% of Indians save, where 36 % of Indian households keep their savings at
home. While 51 % household keep their saving in bank deposits, and stock and insurance
accounted for only 3 % of estimated household income (out of given alternatives). 74 %
Indians are aware about life insurance, but only 24 % households own life insurance
cover. And due to inability to manage the personal finance, in India, 25 % households are
financially vulnerable.
95 Hilgert, M.A., Hogarth, J.M. & Beverly, S.G. (2003). Household financial management: The connection
between knowledge and behavior. Federal Reserve Bulletin, 89 (July): 309-322.Washinton, D.C. 96 Mandell, L. (2009, January).The impact of financial education in high school and college on financial
literacy and subsequent financial decision making. Presented at the American Economic Association
Meetings San Francisco, CA. 97 Result drawn from a survey report ―How India Earns, Spends and Saves‖ done by National Council of
Applied Economic Research (NCAER) & Max New York Life Insurance Company Ltd. 2009.
30
Looking to the recent scenario of Indian financial market, the Indians are having a high
propensity to save, but they choose to put their money in low-yielding instruments and
one of the factors responsible for this is lack of financial plan for future. Furthermore,
‗misplaced financial optimism‘ is a direct fallout of the lack of financial literacy among
Indian households.
Thus, people are suffering from the financial disease like underinsurance, debt trap,
insufficient retirement fund, low return on investment etc., and the cause of all these is
one and the same i.e. ‗Financial Illiteracy‘.
1.8 Scope of Financial Literacy
Roy Morgan Research (200398
) explained the scope of the term ―financial literacy‖ is not
limited up to mathematical literacy and standard literacy, but it also includes knowledge
and understanding, behaviour, attitudes, perceptions and awareness towards financial
world. This includes:
1. Mathematical Literacy and Standard Literacy
- Essential mathematical, reading and comprehension skills
2. Financial Understanding
- Understanding of what money is and how it is exchanged
- Understanding of where money comes from and goes
3. Financial Competence
- Understanding the main features of basic financial services
- Understanding financial records and appreciating the importance of
reading and attitudes to spending money and saving
98 Roy Morgan Research . (2003). ANZ Survey of Adult Financial Literacy in Australia. ANZ Banking
Group.Melbourne: Roy Morgan Research.1-77.
31
- Awareness of risks associated with some financial products and
appreciation of the relationship between risk and return
4. Financial Responsibility
- Ability to make appropriate personal life choices about financial issues
- Understanding consumer rights and responsibilities
- Ability and confidence to access assistance when things go wrong
1.9 The scope of term “Financial Literacy” for present study
To explore the level of financial literacy, basic financial literacy and advanced financial
literacy will be used. The scope of term basic financial literacy is mentioned as follows.
A. Basic Financial Literacy (Financial Competence)
Knowledge and understanding of basic financial concepts, principles and numeracy,
that includes:
1. Scope of investment 2. Financial worth
3. Concept of disposable income 4. Types of Bank accounts
5. Numeracy 6. Compounding
7. Concept of inflation 8. Time value of money
9. Functioning of stock market 10. Diversification
11. Risk return trade off 12. Risk
13. Risk return trade off of two assets 14. Relationship between investment time
horizon and asset growth
15. Relationship between investment time
horizon and fluctuation
16. Asset allocation
17. Relationship between interest asset
prices
18. Consumer rights and responsibility
19. Regulatory body as a part of market
structure
20. KYC
32
B. Advanced Financial Literacy (Product Literacy)
Understanding the features of various financial services/investment instruments,
covering:
1. Fixed deposit 2. National Saving Certificates
3. Public Provident Funds 4. Employee Provident Funds
5. Equity Shares 6. Preference Shares
7. Mutual Funds 8. Debentures and bonds
9. Post Office Monthly Income Schemes 10. Insurance Policy
1.10 Outline of the thesis
This thesis contains five chapters and a bibliography. The thesis is mainly divided into
two sections. The first section deals with an in-depth literature review on financial
literacy, investment decision and financial behavior. The second section covers an in-
depth research analysis to identify the financial literacy level of investors in the state of
Gujarat and its impact on investment decision.
Chapter 1 describes the background of the research and need for the current study. This
chapter also explains the meaning of literacy, historical developments that have been
taken place in the context of conceptualizing and defining the term ―financial literacy‖,
financial education as an important tool to promote the financial literacy, need for
financial literacy, consequences of financial illiteracy, scope of the term ―financial
literacy‖ and scope of term "financial literacy‖ for present study.
Chapter 2 begins with the relevance of financial systems to economic development
through the savings-investment process. To get an in-depth idea for the topic under study
and to support the academic research base to a research topic, the review of literature
presented in this chapter is divided into two sections. The first section of this chapter
33
incorporates the theoretical framework for this study by including behavioural finance
models to explain investor behaviour, when rational economic models fail to provide
sufficient explanation, followed by in-depth review of literature on studies related to
investment motives and factors that influence the investment decision of investors. This
section also includes review of studies those had measured the association of
demographic factors of investors and investment decisions. It also incorporates the
importance of risk tolerance ability and need for/ sources of information search in
decision making. The second section of this chapter includes analysis of the studies
conducted in various countries for measuring the financial literacy of their citizens. It also
discusses theoretical framework for financial behavior. In this section, review of
literature is presented on various studies those have attempted to establish the relationship
between financial education, financial literacy and various financial behaviour.
The methodological approach adopted for this research is presented in detail in Chapter 3.
The initial part of this chapter talks about the research gap, followed by the research
objectives under study and scope for present research. The exploratory research design is
used. The research design includes the explanation about population for which the
research is conducted followed by the sampling techniques, sampling unit, sample and
sample size, construction of data collection instrument, data collection method, source of
data collection, description of variables used for this study. The second part of this
chapter contains the methodology used by various researchers to measure the financial
literacy of individuals in their studies, pitfalls of the various methods of measuring
financial literacy, difficulties faced by researchers to measure financial literacy and the
appropriate method selected by a researcher to measure the financial literacy level of
investors in present study, after providing sufficient justifications on demerits of other
methods available to measure financial literacy. The discussion on data analysis
techniques and hypotheses concludes this chapter.
Chapter 4 reports the data analysis and interpretation of data, following the objectives
under study. Different tools – frequency and percentage analysis, cross tabulation, chi-
square test, paired t-test, factor analysis, binary logistic regression and simple linear
34
regression is used for data analysis in details to draw a conclusion. The analysis is
divided into three major sections. The first section of this chapter deals with an in-depth
analysis of survey responses collected from each respondent towards performance test,
that is used to measure financial literacy of investors. Financial literacy score, achieved
by each respondent is further divided into two categories to classify the respondents
(investors) into two categories, 1) investors with relatively low level of financial literacy
and 2) investors with relatively higher level of financial literacy. The second part includes
the analysis of investment objectives, existing investment pattern of investors and
investors‘ preference towards various investment alternatives. This part also deals with
the analysis of informative variables on the basis of their preference, and the influence of
selected informative variables on investment decision. The analysis is done by applying
factor analysis, identifying the mean score of each factor, and later applying paired t-test.
The factors analysis is also conducted to identify the factors that may influence the
investment decisions of investors. The categories of financial literacy levels, found in the
first section of analysis, are further used to check the association between each
demographic and socio-economic variable of investors and their financial literacy level.
For this, purpose chi-square test is used. To assess the combine impact of demographic
and socio-economic variables of investors on their financial literacy level, a binary
logistic regression is performed. The chi-square test is used to check the association
between financial literacy level of investors and their monthly spending to monthly
income ratio and similarly, to check the relationship between financial literacy level of
investors and their monthly saving to monthly income ratio. Finally, the correlation and
simple regression is used to check the impact of financial literacy level of investment
decision of investors.
At the end of this thesis, Chapter 5, concludes the whole research work carried out by the
researcher and it discusses the overall findings following the objectives under study. A
discussion on opportunities for future research and the limitations of the present research
work are also noted.
Review of literature
Chapter 2
Investors’ Saving
and
Investment Decision
Part I
35
2.1 Introduction
2.2 Indian Financial Systems
2.3 Theoretical Framework for Present Study
2.4 Investors‟ Saving/ Investment Motives
2.5 Investment Decision Making and Irrationality
2.5.1 Risk Tolerance and Investment Decision
2.5.2 Financial Information and Investment Decision
2.6 Conclusion
36
2.1 Introduction
This chapter begins with the relevance of financial systems to economic development
through the savings-investment process. To get an in-depth idea for the topic under study
and to support the academic research base to a research topic, the review of literature
presented in this chapter is divided into two sections. The first section of this chapter
incorporates the theoretical framework for this study by including behavioural finance
models to explain investor behaviour, when rational economic models fail to provide
sufficient explanation, followed by in-depth review of literature on studies related to
investment motives and factors that influence the investment decision of investors. This
section also includes studies those had measured the association of demographic factors
of investors with investment decisions. It also incorporates the importance of risk
tolerance ability and need for/ sources of information search in decision making. The
second section of this chapter includes analysis of studies conducted in various countries
for measuring financial literacy of their citizens. It also discusses theoretical framework
for financial behavior. In this section, review of literature is presented on various studies
those have attempted to establish the relationship between financial education, financial
literacy and various financial behaviour.
2.2 Indian Financial Systems
Financial systems are of crucial significance of capital formation. It needs no reiteration
that adequate capital formation is indispensable to a speedy economic growth and
development. The main function of capital markets the collection of savings and their
distribution for industrial investment, thereby stimulating the capital formation and, to
that extent, accelerating the process of economic growth. The process this capital
formation involves three distinct, although inter-related activities.
1) Savings: The ability by which claims to resources are set aside and become
available for the other purpose.
37
2) Finance: The activity by which claims to resources are either assembled from
those released by domestic savings, obtained from abroad, or specially created
usually as bank deposits or notes and then placed in the hands of investors.
3) Investments: The activity by which resources are actually committed to
production.
The volume of capital formation depends upon the intensity and efficiency with which
these activities are carried on. The effective mobilization of savings, the efficiency of the
financial organization and the channelization of these savings into the most desirable and
productive forms of investment are all inter-connected and have a great bearing on the
contribution of capital formation to the economic development. Their relevance to the
savings-investment process is derived from what is called transfer process.
The efficient, articulate and developed financial system is indispensible for this speeder
transfer process, and hence for rapid economic growth and development of a country.
The broad based organization structure of Indian financial system comprises of three
interdependent components: 1) Financial Markets, 2) Financial Institutions/intermediaries
and 3) Financial assets/ instruments/ securities/ services available for saving/investment.
2.3 Theoretical Framework for Present Study
Economic theory on investment decision treats the investment decision of the individual
as a macroeconomic aggregate and the microeconomic foundations of it are drawn from
intertemporal utility theory. Under the standard assumption of this theory, decision
makers are perfectly rational and able to fully utilize all the information available.
Accordingly, individuals maximize their utility based on classic wealth criteria making a
choice between consumption and investment through time. It suggests that they make
optimum choices that maximize the expected value of their private utility, based on
preferences that are consistent across time and independent of context profile.
38
However, some empirical research studies appeared in 1970s focused on the individual
rather than aggregate investor. They found that the assumptions of this traditional
economic utility theory is far from approaching the reality of everyday human being, and
economists have long argued that they were never meant to do so- rather, they provide
mathematically tractable and empirical reasonable approximations for modeling and
analysis of actual behaviour.
A growing body of evidence across multiple domains of observed behaviour however
suggests that this rationale does not always hold true- it finds systematic biases and
anomalies as well as common decision making heuristics that contradict the predictions
of models populated soley by homo economicus. The emerging field of behavioural
economics and its related discipline i.e. behavioural finance, provide theoretical
framework for this study increasingly questioning the notion of absolute rationality.
Rationality itself is bound by context and directed to personal and community ends.
There is no one rational outcome for every financial decision. This discipline also draws
on insights from psychology and cognitive scientists to study aspect(s) of individual
behaviour in various market settings and deviated from this standard assumption. While
explaining behavioural economics DellaVigna (200999
) considers three broad categories
of anomalies or deviations from the standard model, namely, non-standard preference,
non-standard beliefs and non-standard decision making processes. Glaser (2004100
) added
behavioural finance theory incorporates findings from psychology and sociology into its
theory, and uses behavioural finance models to explain investor behaviour or market
anomalies when rational models fail to provide sufficient explanation. Researches in
behavioural finance produced three major theoretical streams, namely: Prospect Theory,
Regret Aversion and Self Control.
The psychological phenomenon explains the reasons for which why do investors behave
irrationally are: representativeness heuristic; conservatism; overconfidence and self-
99 DellaVigna, S. (2009). Psychology and Economics: Evidence from the field. Journal of Economic
Literature, 47 (2), 315-372. 100 Glaser, M., Noth, M. & Weber, M. (2004). Behavioural finance. In D. J. Koehler & N. Harvey (Eds.),
Blackwell Handbook of Judgment & Decision Making, Oxford: Blackwell, pp. 527-546.
39
attribution. Representativeness heuristic explains that after a series of positive earnings,
the investor is most likely started to believe that the next period earnings will again be
positive and fail to consider the probability of a decrease. Conservatism defined as the
process by which individuals modify their beliefs in light of new information. Not only
are beliefs based on short run trends, but the principle of conservatism suggests that
individuals are slow to modify their beliefs in light of new information. In experiments,
individuals are found to update their prior beliefs in the right direction but comparatively,
still not enough to the rational benchmark that assumes full weight is given to all new
information. Investors subject to conservatism might ignore the full information newly
came to the market and take the decision. The behaviour finance also suggests that
investors tend to overestimate the precision of their knowledge. Literature suggests that
people overestimate their ability to do well on tasks and these overestimates increase with
the personal importance of the task. People tend to be unrealistically optimistic about
future events. Each of these research disciplines captured and analyzed behavioural
attributes of individual investors related to his/her own finances.
With regard to financial behaviour, Xiao (2008101
) said that ―financial behaviour can be
defined as any human behaviour that is relevant to money management. The common
financial behaviour includes cash, credit, saving and investment‖ (p. 70).
2.4 Investors‟ Saving/ Investment Motives
Saving may be regarded as a process undertaken by consumers and may be calculated by
the balance from disposable income after accounting for current expenditure. Warneryed
(1999102
) said that ―from a psychology point of view, saving is considered as the result of
a deliberate decision making process and to save as the act of regularly keeping away
some resources for a goal.‖
101 Xiao, J. J. (2008) Handbook of Consumer Finance Research. 102 Warneryd, K.E. (1999). The psychology of saving. A study of economic psychology. Cheltenham:
Edward Elgar Publishing.
40
Goodfellow J. H. (2007103
) said that ―saving is a routine decision may occur where a
consumer‘s monthly consumption expenditure is consistently and significantly below the
level of their monthly income, albeit subject to fluctuations‖ (p. 46).
In economics, saving has been the object of intense theoretical and empirical concern.
(i.e. Keynes‘s, Modi-gliani‘s, Friedman‘s and Duesenberry‘s theories); in psychology it
has been treated from a variety of points of view. Since the early studies of Katona
(1975104
), some have focused on the influence of personality traits, such as the ability to
delay gratification, self-control, aversion to risk, locus of control, time-preference (Daniel
& Webley, 1998105
; Livingstone & Lunt, 1993106
; Lunt & Livingstone, 1991107
). Others
have analyzed the effect of socio-economic variables (age, education, income), habits,
and attitudes (Furnham, 1985108
, 1999109
) showing that the decision to save, although
influenced by economic factors, involves complex psychological and socio-psychological
processes.
People‘s decision regarding how much to save and invest for future depends upon the
trade-off between immediate and future consumption. Freidman (1957110
) modeled this
trade-off as a problem of optimizing utility or happiness over life span. Within this
framework, optimal saving and consumption path depends on how much people value the
consumption at different times in the future. For example, people put higher value on
immediate consumption because the effects of declining health result in less enjoyment
103 Goodfellow, J. H. (2007). Consumer perceptions and attitudes towards savings and investments.
International Journal of Bank Marketing , 32-48. 104 Katona, George (1975). Psychological Economics. New York: Elsevier. 105 Daniel, T., & Webley, P. (1998). Individual differences and research into saving. Talk given at a
workshop on Individual Differences in Economic Behaviour held at the International Centre for Economic
Research, Turin, March 1998. 106 Livingstone, S., & Lunt, P. (1993). Savers and borrowers: Strategies of personal financial management.
Human Relations, 46, 943–985. 107 Lunt, P., & Livingstone, S. (1991). Psychological, social and economic determinants of saving:
Comparing recurrent and total savings. Journal of Economic Psychology, 12, 621–641. 108 Furnham, A. (1985). Why do people save? Attitudes to, habits of, saving money in Britain. Journal of
Applied Social Psychology, 15, 354–373. 109 Furnham, A. (1999). The saving and spending habits of young people. Journal of Economic Psychology,
20(6), 677–697. 110
Friedman, M. (1957). A Theory of the Consumption Function. Princeton, NJ: Princeton University Press.
41
from consumption in on old age. Some people strongly prefer present consumption to
future consumption causing them rationally to choose not to save.
There are a variety of motives for household savings. In broad terms, these motives can
be grouped into four categories: to provide resources for retirement, to finance expected
large life time expenditure (including house purchase and education), to finance
unexpected losses of income (precautionary saving), and to smooth the availability of
financial resources over time to maintain a more stable consumption profile.
The theme of saving motives was first identifies by Keynes (1936111
). He found motives
to save are relatively consistent over time and the likelihood to consume is affected by
saving motives. He identified eight different saving motives: (1) ‗‗Precaution‘‘, which
implies building up a reserve against unforeseen contingencies; (2) ‗‗Foresight‘‘, which
includes providing for anticipated future differences between income and expenditure
(the life-cycle motive); (3) ‗‗Calculation‘‘, which refers to the wish to earn interest; (4)
‗‗Improvement‘‘, which means to enjoy a gradually improving standard of living over
time; (5) ‗‗Independence‘‘, which refers to the need to feel independent and to have the
power to do things; (6) ‗‗Enterprise‘‘, which means having the freedom to invest money
if and when it is favourable; (7) ‗‗Pride‘‘, which concerns leaving money to heirs (the
bequest motive); and (8) ‗‗Avarice‘‘ or pure miserliness. Browning and Lusardi (1996112
)
have added a further motivation to this list, the down-payment motive, which is the desire
to accumulate lump sums to use as down payments for expensive and durable goods such
as a house or a car.
Katona (1975113
) identified the following savings motives (in order of importance):
emergencies (ill-health or unemployment), reserve funds for necessities, retirement or old
age, children‘s needs, buying a house or durable goods, and holidays. Few individuals
were motivated to save for future income (in the form of interest or dividends) or to leave
money to heirs.
111 Keynes, J. M. (1936). The General Theory of Employment Interest and Money. London: Macmillan. 112 Browning, M., & Lusardi, A. (1996). Household saving: Micro theories and micro facts. Journal of
Economic Literature, XXXIV(4), 1797–1855 113
Katona, G. (1975). Psychological economics. New York: Elsevier.
42
Lindqvist (1981114
) had proposed hierarchical structures of saving motives and has
included reasons for savings, in which at the lowest level is the need to handle cash to
deal with short-term financial goals and at the second level, the need to have a reserve of
money as a precautionary measure. At the third level is the need to have a large amount
of money to buy something expensive and finally at the top level there is the need to
manage accumulated wealth. These various levels of reasons for saving correspond to
different types of savers.
Several studies used the human needs hierarchy of Maslow (1954115
). Maslow‘s
hierarchy suggests that all needs are not of equal importance and that people will pursue
higher level needs once lower level needs are achieved. Xiao and Anderson (1993116
) and
Xiao and Olson (1993117
) incorporated Maslow‘s need hierarchy theory and the
behavioural life cycle hypothesis into their approach. They found that families show
different saving motivations and save according to different categories of mental
accounting to provide for different needs. Xiao and Noring (1994118
) claimed that
motivations for current and future consumption may be defined as financial needs and
that these reflect the human needs described by Maslow. Lower level financial needs
concern current consumption whereas those of a higher level reflect future consumption.
They maintain that when a family has many financial resources available (income, assets,
and net worth) it is more probable that it satisfies financial needs of a higher level and
their empirical work provide some support for this claim. They proposed that the families
with few resources would save to provide for daily expenses; families in middle income
or net worth groups would save for emergencies; and, families in the highest income or
114 Lindqvist, A. (1981). A note on determinants of household saving behaviour. Journal of Economic Psychology, 1(1), 39-57. 115 Maslow, A. (1954). Motivation and personality. New York: Harper and Row. 116 Xiao, J. J., & Anderson, J. G. (1993). A hierarchy of financial needs reflected by household paper assets.
In T. Mauldin (Ed.). The proceedings of the American council on consumer interests 39th annual
conference, Columbia, MO: American Council on Consumer Interests, 207–214. 117 Xiao, J. J., & Olson, G. I. (1993). Mental accounting and saving behaviour. Home Economics Research
Journal, 22(1), 92–109. 118 Xiao, J. J., & Noring, F. E. (1994). Perceived saving motives and hierarchical financial needs. Financial
Counseling and Planning, 5, 25–44.
43
net worth groups would save for retirement, children, and advancing a standard of living.
The researchers found household saving motivations are hierarchical, with daily expenses
being the basic level, emergency being the intermediate level, and retirement, children,
and advancing being the highest level. These findings were consistent with their
expectations based on Maslow‘s hierarchy.
Warneryd (1995119
) distinguished four motives for saving and stressed that a person can
save for one or more motives at the same time. He labels the first ―Saving as a continuous
habit‘‘. This is a well established habit of saving which is not related to any specific goal.
The second, the so-called ‗‗Precautionary motive‘‘, is due to uncertainty about the future.
The third motive for saving is the ‗‗Bequest motive‘‘, which is saving for the well-being
of the family after the person‘s death. The fourth and last motive is called the ‗‗Profit
motive‘‘ and consists of the wish to make an income from money put aside. The results
of a multiple regression analysis indicated that the motivations ―Saving as a continuous
habit‖ and ―Precaution‖ contribute significantly to explaining the variance of the total
sum of money saved.
Canova et al. (2005120
) studied goals that motivate the individual‘s decision to save and
represented these goals in hierarchical structure of the linkages between the goals. Fifteen
salient goals were identified and found to function hierarchically. They found that at the
bottom of hierarchy are more concrete goals (e.g. purchase, holidays or money
availability), while at the top are more abstract goals (e.g. self esteem, self gratification).
In the intermediate position are goals which channel the more concrete towards the more
abstract (p. 21, 30). In their study they found three general orientations toward the focal
goal of saving can be discerned. One of these is concerned with ways of avoiding debt
and of achieving a certain security in life. A second orientation is reflected in the desire
for self-gratification, which can be reached by means of holidays, hobbies, purchases, and
intermediaries such as looking after the family. The last orientation focuses upon thinking
119 Warneryd, K.E. (1995). A study of saving behaviour towards the end of the life cycle. Center for
Economic Research, Tilburg University, Progress Report No. 28. 120 Canova L., Rattazzi M. A. A., & Webley P. (2005). The hierarchical structure of saving motives. The
Journal of Economic Psychology. 26, 21-34.
44
about old age. For these respondents, saving for retirement is important to guarantee
gratification in this period of life. Then there are linkages among the three super ordinate
goals: security (leading to autonomy), self-esteem and self-gratification are reciprocally
connected. (p. 31).
DeVaney, Anong, and Whirl (2007121
) analyzed the likelihood of progressing to a higher
level motive after fulfilling a goal associated with a lower level saving motive. Their
proposed saving motive hierarchy went from saving for physiological or basic needs, for
safety needs, for security, for love and social needs, for esteem and luxury needs, and for
self-actualization. They added an additional level reflecting the absence of any saving.
They also found younger households tended to move from no savings, to saving for basic
needs, to saving for security. Older households were more likely to move from saving for
love or social needs, to saving for luxuries.
Public pension benefits also influence the saving behaviour of individuals. Empirical
evidence on the impact of public pension schemes on household saving has generally
been inclusive. Feldstein (1980122
) argued that ―public pension schemes have a negative
impact on private saving. Ehrlich and Zhong (1998123
) studies with a sample of 49
countries over 29 years (1960-89) and found that ―pension benefits having significant
depressing effect on savings‖ (p. 155).
Callen and Thimann (1997124
) explained that ―the overall impact of the social security
and welfare systems on individual saving behaviour is likely to be dependent on a
number of features of the systems including the value of the benefit payments (the higher
the replacement ratio –defined as the entitlement as a percentage of previous earnings-
121 Devaney, S., Anong, S., & Whirl, S. (2007). Household savings motives. Journal of Consumer Affairs,
41(1), 174-186. 122 Feldstein, M. (1980). The effect of social security on private savings: The time series evidence. NBER
Working Paper 0314, National Bureau of Economic Research, Inc. 123 Ehrlich, I. & J-G. Zhong. (1998). Social security and the real Economy: An inquiry into some
neglected issues, American Economic Review, 88(2), 151-157. 124 Challen, T. & Thimann, C. (1997). Empirical determinants of household saving: Evidemce from OECD
countris. Asia and Pecific Department. International Monetary Fund.IMF Working Paper No. WP/97/181,
1-26.
45
the less incentive there is for private saving provision); the length of time over which
payments are available; the certainty with which people regard the future benefits; and
the general availability of such payments‖ (p. 8). They also found a standard
demographic impact on household saving. Result showed that with a higher old-age
dependency ratio being associated with lower household saving. The young-dependency
and overall-dependency ratios were also tested, but were not found to be significant (p.
13). NCAER (1961125
) concluded that desire for making provisions for emergencies were
a very important motive for saving for old age.
These saving motives, in turn suggest a large number of variables that may influence
household saving decisions. Among the most commonly used in empirical studies are:
growth in income, demographics, real interest rate, and inflation (Callen and Thimann,
1997126
), household wealth, unemployment (Skinner, 1988127
), terms of trade, tax
structure (Boadway and Wildasin, 1994128
) and proxies for financial deregulation
(Aghevli et al., 1990129
).
Goodfellow (2007130
) found that ―among all the determinants, stage in the life cycle is a
significant factor, influencing attitudes and behaviour of savings‖ (p. 38). He also found
that ―anticipated time horizon is the second most influencing factor for saving behaviour,
while level of saving amount the influenced by an individual‘s routine and non-routine
expenditures‖ (p. 45, 46). Belk (1974131
) found that ―differences are prevailing in the
125 National Council for Applied Economic Research & Max New York Life Insurance Company Ltd.
(2007). How India Earns, Spends and Saves: India Financial Protection Survey. Delhi: National Council
for Applied Economic Research and Max New York Life Insurance Company Ltd. Research Report. 126 Challen, T. and Thimann, C. (1997). Empirical determinants of household saving: Evidemce from
OECD countris. Asia and Pecific Department. International Monetary Fund.IMF Working Paper No.
WP/97/181, 1-26. 127 Skinner, J. (1988). Risky income, life cycle consumption and precautionary saving. Journal of Monetary
Economics, 22, 237–255. 128 Boadway, R. & Wildasin, D. (1994). Taxation and savings: A Survey. Fiscal Studies 15(3): 19-63. 129 Aghevli, B., Boughton, J., Montiel, P., Villanueva, D., & Woglom, G., (1990). The role of national
saving in the world economy. Occasional Paper, 67. Wasnigton, D.C. International Monetary Fund. 130 Goodfellow, J. H. (2007). Consumer perceptions and attitudes towards savings and investments.
International Journal of Bank Marketin, 5(3), 32-48. 131 Belk, R., (1974). An exploratory assessment of situational effects in buyer behaviour. Journal of
Marketing Research, 156-163.
46
preferences between the individuals is due to the fact that choices are situation specific
and that different individuals may give their preferences with varying context‖.
The development in financial system has increased the opportunities for, and returns to,
financial saving, but it may also enhance access to credit and ease liquidity constraints
faced by households and could, therefore, at least initially, lead to lower household
saving.
As a result of privatization, liberalization and deregulation, financial services industry is
characterized by an intense competition not only within traditional banking institutions,
but also with other non banking financial firms, this intensified competition brings the
critical importance of understanding consumer decision making to the financial services
industry‘s attention.
2.5 Investment Decision Making and Irrationality
As discussed in previous section, theories of behavioural finance and behavioural
economics assume that people make irrational decision. These theories are increasingly
questioning the notion of absolute rationality. Rationality itself is bound by context and
directed to personal and community ends. There is no one rational outcome for every
financial decision.
Decision making can be defined as the process of choosing a particular alternative from a
number of alternatives. It is an activity that follows after proper evaluation of all the
alternatives. Karlsson et al., (2004132
) said that ―Individuals make decisions with regard
to their personal finances on a daily basis, and even though these decisions are necessary
for day-to-day survival, it can be a daunting task‖ (p. 754). The value associated with
analysis of the consumer decision making process is widely recognized by various
researchers.
132 Karlsson, N., Dellgran, P., Klingander, B. and Gärling, T. (2004). Household consumption: Influences
of aspiration level, social comparison, and money management. Journal of Economic Psychology, 25(6),
753– 769.
47
Previous research in the area of consumer behaviour and financial services has revealed
that lifecycle stages, age, education, gender and income are important factors to consider
when seeking to understand the needs and wants of customers (Gerrans and Clark-
Murphy, 2004133
; Gough and Sozou, 2005134
) and the type of financial product or service
being purchased greatly influences the consumers‘ purchasing behaviour (Beckett et al.,
2000135
; Howcroft et al., 2003136
).
There is evident that individual‘s choice of decision is found to be influenced by the
following factors: decision making complexity (Bettman and Park, 1980137
; Johnson et
al., 1989138
) time pressure (Wright and Weitz, 1977139
), product knowledge and
experience (Lee and Geistfeld, 1998140
; Moore and Lehman, 1980141
), involvement and
need for cognition (Mantel and Kardes, 1999142
), socio economic status (Capon and
133 Clark-Murphy, M. & Gerrans, P. (2002). Women‘s ‗problems‘ with finance and investment: a result of
gender differences in information processing? Paper presented at the 11th International Women in
Leadership Conference, Perth. 134 Gough, O. & Sozou, P.D. (2005). Pensions and retirement savings: cluster analysis of consumer
behaviour and attitudes. International Journal of Bank Marketing, 23(7), 558-570. 135 Beckett, A., Hewer, P. & Howcroft, B. (2000). An exposition of consumer behaviour in the financial services industry. International Journal of Bank Marketing, 18(1), 15-26. 136
Howcroft, B., Hewer, P. & Hamilton, R. (2003). Consumer decision-making styles and the purchase of
financial services. The Service Industries Journal,(23)3, 63-81. 137 Bettman, J. R. & Park, C. W. (1980). Effects of prior knowledge and experience and phase of the choice
process on consumer decision processes: a protocol analysis. Journal of Consumer Research, 7(2), 234-
248. 138 Johnson, E. J., Meyer, R. J. & Ghose, S. (1989). When choice models fall: compensatory models in
negatively correlated environments. Journal of Marketing Research, 26, 255-270. 139 Wright, P. & Weitz, B. (1977). The relationship between Luce‘s choice axiom, Thurstone‘s theory of
comparative judgement and the double exponential distribution. Journal of Mathematical Psychology, 15,
109-144. 140 Lee, J. & Geistfeld, L. V. (1998). Enhancing consumer choice: are we making appropriate recommendations?. Jounal of Consumer Affairs, 32(2), 252-274. 141 Moore, W. L. & Lehmann, D. (1980). Individual differences in search behaviour for a non-durable.
Journal of Consumer Research, 7, 296-307. 142 Mantel, S. P. & Kardes, F. R. (1999). The role of direction of comparison, attribute-based processing,
and attitude-based processing in consumer preference. Journal of Consumer Research, 25(3), 335-352.
48
Burke, 1980143
; Lee and Geistfied, 1998144
), and demographics (Darley and Smith,
1995145
) and various sources of information (Chandra et al., 2011146
).
With regard to factors influencing individual investors‘ decision making, Baker and
Haslem (1974147
) contended that dividends, expected returns and firm‘s financial stability
are critical investment considerations for individual investors. Merikas et al. (2003148
)
studied 26 economic variables that affect the individual investors in Greece. They did not
rely on single integrated approach, but rather on many categories of factors such as
‗accounting information‘, ‗subjective/ personal‘, ‗neutral information‘, ‗advocate
recommendation‘, and ‗personal financial needs‘. This study found that Accounting
information that includes, condition of financial statements, expected corporate earnings,
expected dividends, firm‘s status in industry, affordable share price, past performance
firm are the most influencing factors on investment decision of investors. This study also
found that environmental criteria such as, ‗coverage in press‘, ‗statement from politicians
and government officials‘, ‗ease of obtaining borrowed funds‘ and ‗political party
affiliation‘ are the least important for individual investors.
Nagy and Obenberger (1994149
) examined factors influencing investors‘ behaviour. They
studied 34 variables such as expected corporate earnings, diversification needs, feeling
for firm‘s products and services, past performance of the firm, broker/ advisor/ analyst‘ s
recommendation to a name few. The findings suggest that the classical wealth
maximization criteria are important for investors. Contemporary concerns such as local
143 Capon, N. &Burke, M. (1980). Individual product class, and task related factors in consumer
information processing. Journal of Consumer Research, 7(3), 314-326. 144 Lee, J. & Geistfeld, L. V. (1998). Enhancing consumer choice: are we making appropriate
recommendations?. Jounal of Consumer Affairs, 32(2), 252-274. 145 Darley, W. K. & Smith, R. E. (1995). Gender differences in information-processing strategies: an
empirical test of the selectivity model in advertising response. Journal of Advertising, 24 (1), 44-56. 146 Chandra, A. & Kumar, R. (2011). Determinants of individual investor behaviour: An orthogonal linear
transformation approach. Munich Personal RePEc Archive No. 29722, accessed on April 15, 2011 at
http://mpra.ub.uni-muenchen.de/29722/ 147 Baker, H., & Haslem, J. (1974). Toward the development of client-specified valuation models. Journal
of Finance, 29 (4), 1255-1263. 148 Merikas, A. A., Merikas, A. G, Vozikis, G. S. & Prasad, D. (2003). Factors influencing Greek Investor
behaviour on the Athens stock exchange. Journal of Applied Business Research, 20 (4), 93-99. 149 Nagy, R. A. & Obenberger, R. W. (1994). Factors affecting investors‘ behaviour. Financial Analyst
Journal, 50, 63-68.
49
and international operations, environmental track record, and firm‘s ethical posture
appear to be given only cursory considerations.
Hodge (2003150
) analyzed the investors‘ perceptions of earning quality, auditor
independence, and the usefulness of audited financial information. This study concluded
that lower perceptions of earning quality are associated with greater reliance on a firm‘s
audited financial statements and fundamental analysis of those statements when making
investment decisions.
Benston et al. (2006151
) concluded that investors require a substantial amount of
information that goes beyond financial accounting numbers. They also require
information about ―current and expected changes in market conditions, competitors‘
products and performance, the potential value of new products and processes, prospective
changes in foreign exchange value and domestic inflation rates, government policies,
employee and customer relations, and the quality of management‖.
Draft Green Paper on Consumer Policy Framework (DTI) (2004152
) states that more and
more consumers are interested in the world behind the products, the production process
and the ethics of the company that produces goods and services. Blumberg et al.
(1997153
) added that the stakeholders are always interested to the extent to which the
company is investing in social and related issues. They always want to see company as a
going concern. Epstein (1994154
) examined the demand for social information by
individual investors. The results of this study indicate a strong demand for information
about the product safety and quality, and about the company‘s environmental activities.
150 Hodge, F. D. (2003). Investors‘ perceptions of earning quality, auditor independence, and usefulness of
audited financial information. Accounting, Horizons, 17, 37-48. 151
Benston, G. J., Bromwich, M. & Wagenhofer, A. (2006). Principles-versus rules-based accounting
standards: the FASB‘s standard setting strategy. ABACUS, A Journal of Accounting, Finance and Business
Studies, 42 (2), 165-188. 152
Government Gazette (2004). Draft Green Paper on Consumer Policy Framework. Republic of South
Africa, 471 (9), September 9, 2004, Pretoria. 153
Blumberg, J., Korswold, A. & Blum G. (1996). Environmental Performance and Shareholder Value
.World Business Council for Sustainable Development: Geneva. 154 Epstein, M. J. (1994). Social disclosure and the individual investor. Accounting, Auditing and
Accountability Journal, 4, 94-109.
50
Furthermore, a majority of the investors surveyed also want the company to report on
corporate ethics, employee relations and community involvement.
Chandra et al. (2011155
) explained that not only professional and contextual sources of
information, which include stock brokers, financial consultants and investment advisors,
but also contextual factors such as, market share and reputation of the firm, accounting
and financial information, publicly available information through various media,
advocate recommendation that of brokers, family and friends and personal financial need
influence the investment decision of investors. They also found that investors also follow
technical analysis, fundamentals (accounting and financial information) and market share
of a company while investing.
Hussein et al. (2009156
) also indentified 37 variables influencing investment decision of
UAE investors. This study categorized 37 variables into eight groups. Eight variables
corresponding to self-image/ firm image coincidence, eleven variables corresponding to
accounting information, six variables corresponding to neutral information, five variables
to advocate recommendation, and seven variables to personal financial needs. SEBI and
NCAER (2011157
) reported that safety and liquidity were the primary considerations
which determined the choice of asset to invest in.
Literature also establishes relation between individuals‘ demographic and socio-
economic variables and behavioural finance related to investment decision. There is
evidence that women are less confident (Clark-Murphy and Gerrens, 2002158
; Taylor,
2003159
) and less knowledgeable (Chen and Volpe, 1998160
) than men on the topics of
155
Chandra, A. & Kumar, R. (2011). Determinants of individual investor behaviour: An orthogonal linear
transformation approach. Munich Personal RePEc Archive No. 29722, accessed on April 15, 2011 at
http://mpra.ub.uni-muenchen.de/29722/ 156 Hussein A. Hassan Al-Tamimi & Al Anood Bin Kalli (2009). Financial literacy and investment
decisions of UAE investors. The Journal of Finance, 10(5), 500- 516. 157 Securities and Exchange Board of India and National Council of Applied Economic Research (2000).
How Households Save and Invest: Evidence from NCAER Household Survey. New Delhi. 158 Clark-Murphy, M. & Gerrans, P. (2002). Women‘s ‗problems‘ with finance and investment: A result of
gender differences in information processing Paper presented at the 11th International Women in
Leadership Conference, Perth. 159 Taylor, P. (2003). Gender gap creates niche in financial services. Financial News, 8 March, p. 24.
51
personal finance. Males generally exhibit more confidence in dealing with financial
affairs (Taylor, 2003161
), whereas ―women are more conservative in their investment
practices‖ (Bajtelsmit, V. and Bernasek, 1996162
). Age is another demographic factor that
affects investment decision of investors examined the older investors (Korniotis and
Kumar, 2011163
). Harrison (2003164
) suggested, the past investment experience and
expertise often influence the investors‘ decision with regard to purchase of financial
products. Lewellen et al. (1977165
) suggested, investors with lower age, young, higher
level of income, higher level of education, and less family members choose to invest their
money in risky assets than conservative instruments.
The behavioural finance suggests that investment decision is only influenced by the
demographics and socioeconomic variables, but also by risk tolerance capacity and
amount of financial information available with the investor, as well as his/her information
processing capacity to analyze the environmental set. In the next section, the discussion
on the association between 1) risk tolerance and decision making and 2) financial
information and investment decision making of investors is presented through in-depth
review of literature.
2.5.1 Risk Tolerance and Investment Decision Making
Risk tolerance is an important concept that has a direct and obvious link with the
investment decision-making process. The number of factors have been studied, proposed
and tested as determinants of risk tolerance.
160 Chen, H. & Volpe, R. P. (1998). An analysis of personal financial literacy among college students.
Financial Services Review, 7 (2), 107-128. 161 Taylor, P. (2003). Gender gap creates niche in financial services. Financial News, 8 March, p. 24. 162 Bajtelsmit, V. & Bernasek, A. (1996). Why do women invest differently than men? Journal of Financial
Counseling and Investing, 7, 1-10. 163 Kornitiotis, G. M. & Kumar, A. (2011). Do older investors make better investment decisions? Review of Economics and Statistics, 93 (1), 244-245. 164 Harrison. (2003). Understanding the behavior of financial services consumers: A research agenda.
Journal of Financial Services Marketing, 8 (1), pp. 6-9. 165 Lewellen, W., Lease, R. & Schlarbaum G. (1997). Pattern of investment strategy and behaviour among
individual investors. The Journal of Business, 50, 296-333.
52
With respect to age and risk-tolerance ability, previous studies found that the risk
tolerance decreases with the age (Wallech & Kogan, 1961166
; Palsson, 1996167
), although
the studies also concluded that this relationship may not be necessarily linear (Riley &
Chow, 1992168
; Bajelsmit & VanDerhei, 1997169
). In fact, the reality can be explained by
the fact that younger investors have a greater number of years to recover from the losses
that may be incurred with the risky investments.
However, the issue of gender and risk-taking ability is complex. Bajtelsmit and Bernasek
(1996170
) suggest that gender risk differences ―have their root in discrimination and/or
differences in individual preferences‖ (p. 5). Similarly, the Gerrans and Clark-Murphy
(2004171
) found ―the gender effect is not uniform and can be demonstrated as depending
on marital status, whether the member considered themselves informed and age‖ (p. 27).
The studies have shown that the single women are more risk averse (Bajtelsmit et al.,
1999172
; Gerrans and Clark-Murphy, 2004173
) than single men or married couples (Sung
and Hanna, 1996174
). While another group of researchers and financial practitioners have
suggested that women choose to invest their financial resources more conservatively and
166 Wallach, M. A. & Kogan, N. (1961.) Aspects of judgment and decision making: Interrelationships and
changes with age. Behavioural Science, 6, 23- 26. 167 Palsson, A.M. (1996). Does the Degree of Relative Risk Aversion Vary with Household Characteristics?
Journal of Economic Psychology, 17, 771 – 787. 168
Riley, W.B., & Chow, K.V. (1992). Asset al.location and Individual Risk Aversion, Financial Analysts
Journal, 48, 32 – 37. 169
Bajtelsmit, V.L. & VanDerhai, J.L. (1997). Risk Aversion and Pension Investment Choices. In O.S.
Mitchell, (ed), Positioning Pensions for the Year 2000. Philadelphia, PA: University of Pennsylvania Press. 170 Bajtelsmit, V.L. and Bernasek, A. (1996). Why do women invest differently than men? Journal of
Association for Financial Counseling and Planning Education, 7, 1-10. 171 Gerrans, P. & Clark-Murphy, M. (2004). Gender differences in retirement savings decisions. Research
paper, Edith Cowan University, Perth, p. 29. 172 Bajtelsmit, V.L., Bernasek, A. and Jianakoplos, N.A. (1999). Gender differences in defined contribution pension decisions. Financial Services Review, 8(1), 1-10. 173 Gerrans, P. & Clark-Murphy, M. (2004). Gender differences in retirement savings decisions. Research
paper, Edith Cowan University, Perth, p. 29. 174 Sung, J. & Hanna, S. (1996). Factors related to risk tolerance. Financial Counseling and Planning, 7,
11-20.
53
are generally more risk averse than men (Bajtelsmit & VanDerhei, 1997175
; Yuh &
Hanna, 1997176
).
Contrary to these findings, Grable and Joo (1999177
) did not find gender to be a
significant predictor of an individual‘s risk tolerance level. Furthermore, Embrey and Fox
(1997178
) examined gender differences in the investment decision making process and
found that women were more risk averse than men. Based on the Survey of Consumer
Finances (SCF, 1997179
) measure of risk tolerance but that gender did not influence
investment choice; more specifically, it was found that ―differences in purely financial
investment decisions between men and women appeared to be more a result of
differences in wealth as measured by net worth and the expectation of an inheritance‖ (p.
38). Sung and Hunna (1996180
) have found that education is also an important factor to
evaluate the risk- tolerance ability of an individual.
The amount and nature of perceived risk define consumers‘ information needs.
Accordingly, consumers search for the sources, types, and amounts of information that
seems most likely to satisfy their particular information needs. Thus, information search
as a strategy of risk reduction in the face of risk in the investment decision making
process. The following section explains the importance of financial information in
investment decision making.
2.5.2 Financial Information and Investment Decision Making
175 Bajtelsmit, V.L. & VanDerhei, J.A. (1997). Risk aversion and retirement income adequacy. Positioning
pensions for the twenty-first century. Michael S. Gordon, Olivia S. Mitchell, Marc M. Twinney, Eds.
Philadelphia: University of Pennsylvania Press. 176 Yuh, Y. & Hanna, S. (1997). The Demand for Risky Assets in Retirement Portfolios. Proceedings of the
Academy of Financial Services. 177 Grable, J. & Joo, S. (1999). Factors related to risk tolerance: a further examination.,
Consumer interests annual, 45, 53-58. 178 Embrey, L. C., & Fox, J. J. (1997). Gender differences in the investment decision-making process.
Financial Counseling and Planning, 8(2), 33-40. 179 Kennickell, A.B. (1997). Codebook for 1995 Survey of Consumer Finances. Federal Reserve System,
Washington, D.C. 180 Sung, J. & Hanna, S. (1996). Factors related to risk tolerance. Financial Counseling and Planning, 7,
11-20.
54
The decision-making for investment products can be described within the framework of
consumer purchase decision-making, which is depicted as a series of steps that include
problem recognition, information search, evaluation of alternatives, purchase decision,
and post-purchase behaviour (Schmidt & Spreng, 1996181
). Under this framework,
information search is one of the critical elements of consumer decision-making (Moore &
Lehmann, 1980182
).
Individuals make decisions with regard to their personal finances on a daily basis.
Investment generally involves substantial amount of money and risk, and information
search is therefore an important activity for many consumers before making investment
decisions. This is especially true for long-term financial decisions, such as retirement
planning (Bernheim and Garrett, 2003183
). However, they suggested that consumers not
only need information, they want information on relevant products and services for
retirement. Previous studies found that consumer expertise and knowledge are important
to the decision-making process for the purchase of financial products or services and
consumer knowledge also influences financial behaviour (Howcroft et al., 2003184
). Thus,
financial knowledge and money-management skills are crucial for making good financial
decisions. Lusardi and Mitchell (2006, 2007a, 2008) also found that the least literate are
also the least likely to plan and save for retirement. Kim (2007185
) found that
―excessively high debt levels, low saving rates, becoming targets of investment fraud,
delinquency on credit cards and bankruptcy have all been found to be related to financial
illiteracy in individuals‖ (p. 1). Kim (2007186
) and Joo (1998187
) found that ―adults lack
the financial knowledge to make competent and effective financial choices‖. Lusardi and
181 Schmidt, J. B. & Spreng, A. R. (1996). A proposed mode of external consumer information search.
Journal of theAcademy of Marketing Science, 23 (Winter), 57-65. 182
Moore, W., & Lehmann, D. (1980). Individual differences in search behavior of nondurable. Journal of
Consumer Reseaerch, 7 (December), 296-307. 183 Bernheim, B. D., & Garrett, D. M. (2003). The effects of financial education in the workplace: Evidence
from a survey of households. Journal of Public Economics, 87(7-8), 1487-1519. 184 Howcroft, B., Hewer, P. & Hamilton, R. (2003). Consumer decision-making styles and the purchase of
financial services. The Service Industries Journal, 23 (3), 63-81 185 Kim, J. (2007). Workplace financial education program: Does it have an impact on employees' personal
finances? Journal of Family and Consumer Science, 99 (1), 43-47. 186 Ibid 187 Joo, S. (1998). Personal financial wellness and worker job productivity. Unpublished Ph.D. thesis.
Blacksburg: Virginia Polytechnic and State University.
55
Mitchell (2007188
) found that financially unsophisticated households tend to avoid the
stock market investments‖ (p.19) On the subject of risk, there is clear evidence to show
that services have a higher degree of perceived risk when compared directly with goods
(Murray, 1991189
; Zeithaml et al., 1985190
) which directly and positively correlates to
information search and purchasing decision. That is, the greater the perceived risk, the
greater the information search (Turley and LeBlanc, 1993191
), and the subsequent delay in
making a purchasing decision. In the literature, there is a consensus, both conceptually
and empirically, that a higher level of perceived risk in a pre-purchase context increases
consumers‘ propensity to seek information about a product or service (Dowling and
Staelin, 1994192
). Abdelkarim, et al. (2009193
) said that the financial information is
supposed to facilitate the prediction of firm‘s future cash flows and help the investors to
assess the future securities risk and return; hence performance forecast should be crux of
the financial information (p. 47).
The importance of information in the pre-purchase decision-making process for the
consumers of financial product and/or services has also been established (Friedman and
Smith, 1993194
). However, Schmidt and Spreng‘s (1996195
) model suggests, while making
investment decisions, consumers‘ information search (behaviour) depends on their
motivation to search and their ability to search, none of these studies ranked personal
information sources. Lin (2002196
) suggests investors collect financial information from
188 Lusardi, Annamaria & Olivia S. Mitchell (2007a). Financial Literacy and Retirement Preparedness:
Evidence and Implications for Financial Education. Business Economics, January 2007, 35-44. 189 Murray, K. (1991). A test of services marketing theory: consumer information acquisition activities. Journal of Marketing, 55 (1), 10-25. 190 Zeithaml, V.A., Parasuraman, A. &Berry, L.L. (1985). Problems and strategies in services marketing.
Journal of Marketing, 49 (2), 33-46. 191 Turley, L.W. & LeBlanc, R.P. (1993). An exploratory investigation of consumer decision making in the
service sector. Journal of Services Marketing, 7(4), 11-18. 192 Dowling, G. R. &Staelin, R. (1994). A model of perceived risk and intended risk-handling activity,
Journal of Consumer Research, 21 (June), 119-134. 193 Abdelkarim, N., Shahin, Y., & Arqawi, B. (2009). Investor perception of information disclosed in
financial reports of Palestine securities exchange listed companies. Accounting and Taxation, 1 (1), p. 45-
61. 194 Friedman, M.L. & Smith, L.J. (1993). Consumer evaluation processes in a service setting. Journal of
Services Marketing, 7(2), 47-61. 195
Schmidt, J. B. &Spreng, A. R. (1996). A proposed mode of external consumer information search.
Journal of theAcademy of Marketing Science, 23 (Winter), 57-65. 196 Lin (2002). Consumer’ information search when making investment decisions. (Doctoral thesis,
University of Georgia, Athens, Georgea, 2002).
56
personal as well as impersonal sources. Personal sources include professional financial
services providers (brokers, financial planners, and other professionals), friends/relatives,
experienced investors, and third party agents. Impersonal sources include written material
(e.g. books, brochures, reports, magazines), media (e.g. TV, radio programs), and the
Internet. The literature evident that family is a particularly credible source considered to
be more trustworthy than friends or professional advisors such as accountants or financial
planners. In addition participants mentioned that seeing a relevant television segment, or
reading a relevant newspaper article or listening to a relevant radio program had
prompted them to give some thought to their long-term financial future.
Arlen et al. (2007197
) emphasized that due to lack of confidence, investors heavily rely on
financial advice. Sung and Sandager (1997198
) examined the characteristics of the
consumers, who need certified financial planners. This study found that potential
investors want financial advice particularly on retirement planning, investment planning,
and tax planning and they prefer a certified financial planner to be affiliated with an
independent financial firm. Krishnan and Brooker (2002199
) found that investors use
analyst‘s recommendation while investing. Nagy and Obenberger (1994200
) found that the
recommendation of brokerage houses, individual stock brokers, family members and co-
workers (colleagues) go largely unheeded.
Lee and Hogarth (2002b201
) and Capon and Lutz (1979202
) condensed consumer
information sources into three principal clusters that effectively espoused most of the
perspectives articulated in the other studies. These are: 1) Personal Sources, which
197 Arlen, C. Ponston, L. R. & Akbulut, A. Y. (2007). Advice availability and gender differences in risky
decision making: A study of online retirement planning. Proceedings of the 40th Hawai International
Conference on System Sciences. 198 Sung, B. C. & Sandager, J. P. (1997). What consumers look for in financial planners? Association for
Financial Counseling and Planning Education. 199 Krishnan, R. & Brooker, D. M. (2002). Investors use of analysts‘ recommendations. Behavioural
Research in Accounting, 14, 129-158. 200
Nagy, R. A. & Obenberger, R. W. (1994). Factors affecting investors behaviour. Financial Analyst
Journal, 50, 63-68. 201
Lee, J. & Hogarth, J. M. (2000b). Relationships among information search activities when shopping for
a credit card. The Journal of Consumer Affairs, 34 (2), 330-360. 202 Capon, N. & Lutz, R. (1979). A model and methodology for the development of consumer information
programs. Journal of Marketing, 43 (1), 58-67.
57
largely take form of the informal inquires with personal contacts, for instances friends,
relatives, neighbours, acquaintances, co-workers and peers. 2) Independent Sources,
which encompass various neutral agencies and levels of government, independent rating
agencies and organizations that certify the quality of products and firms, and 3)
Commercial Sources, which include all entities that have a direct economic interest in an
product and include manufacturers, retailers and trade associations.
Toussaint- Comeau (2002203
) using the data from a household survey conducted in
Chicago, found that socio-economic, demographic and life style characteristics affect
consumers‘ preferences as to receipt of financial information. They found, households
with lower income, less educated are less likely to select internet as means of finding
information about personal financial issues, but more likely to prefer formal courses
offered in the local community. Low-income and minority consumers are also more
likely to select radio programmes as a means of receiving information about financial
issues; while, older adults are more likely to choose seminars to receive financial
information. Hogarth and Hilgert et al. (2003204
) asked the respondents about preferred
sources of information on financial topics. They found that more financially sophisticated
consumers prefer internet. In general, however, households prefer to receive financial
information through media sources such as television, radio, magazines and newspapers
as well as through informational videos and brochures.
Nick et al. (2010205
) surveyed respondents to know consumer decision making in retail
services with regard to information search. This study found that various information
sources such as, websites, telephone representatives of a company, newspapers/
magazines, family members, friends, financial professionals, television, formal study,
consumer advice organizations are those who are employed in financial services industry
are more likely to be used for creating/ enhancing background financial knowledge than
203 Toussaint-Comeau, M. (2002). Delivery of financial literacy programs. The Journal of Consumer
Education, 19 (20). 204 Hilgert, M.A., Hogarth, J.M. and Beverly, S.G. (2003) Household financial management: The
connection between knowledge and behaviour. Federal Reserve Bulletin, 89 (July): 309-322.Washinton,
D.C. 205 Nick, C., Huck, S. & Inderest, R. (2010). Consumer Decision-Making in Retail Investment Services: A
Behavioural Economics Perspective. Decision Technology Ltd.
58
searching for available investment options. Bennett and Harrell (1975206
) and Howard
and Sheth (1969207
) also found that reduction of risk is a major benefit of information
search.
With reference to gender, a number of research studies support notion that women more
comprehensively process the information than do men in the same task context
(Banyamini et al., 2000208
; Graham, 1994209
). Taylor (2003210
) found that the males
generally exhibit more confidence when dealing with their financial affairs. Bajtelsmit
and Bernasek (1996211
) found that women to be more conservative in their investment
practices. Clark-Murphy and Gerrans (2002212
) found that men and women are simply
process information differently. Their risk profiles and confidence levels are different on
the subject of finances. The impact of information on investment decision making has
two separate dimensions to it. Women may differ in access to information and they may
also differ in their ability or inclination to use available information.
The lens model approach, also known as the Brunswik lens model, uses a set of explicit
cues from environment to assess the situations in which decision makers make
judgments. 1) The environmental criteria and the information set, which explains, the
environmental changes impact on the way in which information is processed. Hence,
Decision makers need to be aware of environmental changes and their effect on
information and 2) The information set and subject responses, which explains, the
decision makers‘ response to the available information is dependent on their cognitive
ability and knowledge of subject matter.
206 Benett, P. D. & Harrell, G. D. (1975). The role of confidence in understanding and predicting buyers‘
attitude and purchase intentions. Jourrnal of Consumer Research, 2, 110-117. 207
Howard, J. & Sheth, J. (1969). The Theory of Buyer Behaviour. John Wiley & Sons. New York. 208 Banyamini, Y., Leventhal, E. & Levanthal, H. (2000). Gender differences in processing information for
making self-assessments of health. Psychosomatic Medicine, 63(3), 354-394. 209 Graham, J. (1994), Marketing communication receivers‘ perception of source-self similarity: some new
findings. Journal of Marketing Theory and Practice, 2(4), 11-19. 210 Taylor, P. (2003). Gender gap creates niche in financial services. Financial News, 8 March, p. 24. 211 Bajtelsmit, V. & Bernasek, A. (1996). Why do women invest differently than men? Journal of Financial
Counseling and Investing, 7, 1-10. 212 Clark-Murphy, M. &Gerrans, P. (2002). Women‘s ‗problems‘ with finance and investment: a result of
gender differences in information processing?. paper presented at the 11th International Women in
Leadership Conference, Perth.
59
Accordingly, investors tend to engage in more extensive search activities while
purchasing products that are more expensive or carry more risk. By searching for
information, investors may find products with greater benefits that may enhance their
satisfaction with the products and/or the decisions and/or reduce the risk associated with
those.
2.6 Conclusion
From above discussion, it can be concluded that in the financial markets investors are
distinguished by extent of their activity, demographics and socio economic profiles,
saving motives, risk tolerance capacity, their motivation and ability to search the financial
information and to process the same for making investment decision, which in turn also
gets influenced by other variables. In this whole process, level of their sophistication (that
investors possess), among other things, also plays a crucial role. In the literature, this
level of sophistication also refers as ―level of financial literacy‖.
Financial Literacy
and
Financial Behaviour
Part II
60
2.7 Introduction
2.8 Global Scenario: International Studies on Financial Literacy
2.8.1 U.K. Studies
2.8.2 U.S. Studies
2.8.3 Australian Studies
2.8.4 Singapore Study
2.8.5 OECD Studies
2.9 Domestic Scenario
2.9.1 India Protection Survey - How India Earns, Spends and Saves
2.9.2 Financial Literacy and domestic Regulatory Authority
2.10 Theoretical Framework for Financial Behaviour
2.10.1 Goal Setting Theory
2.10.2 Human Ecological Model
2.10.3 Family Management Systems
2.10.4 Discounted Utility Model
2.10.5 Life Cycle Hypothesis of Saving
2.10.6 Behavioural Life Cycle Hypothesis
2.10.7 Maslow‟s Need Hierarchy Theory
2.10.8 Theory of Reasoned Action and Theory of Planned Behaviour
2.10.9 Transtheoretical Model of Behaviour Change
2.10.10 Myers brigg‟s Type Indicator
2.10.11 Temperament Theory
2.11 Studies on Financial Literacy and Financial Behaviour
2.12 Conclusion
61
2.7 Introduction
The definition of financial literacy began in the UK from National Foundation for
Education Research and was initially adopted across the UK with a view to achieve
international consistency (Noctor, Stoney and Strandling, 1992). Since then, financial
literacy has become a prominent research topic in other developed countries, where this
definition has been widely accepted and adopted by many studies. This part of the
chapter is divided into three sections. The first section presents the international studies
conducted for measuring the financial literacy of citizens of various countries such as,
The United States, The United Kingdom, Australia, Singapore and other OECD member
countries. This section also briefs the efforts made in India in the field of financial
literacy. The second section of this chapter discusses on theoretical framework for
financial behaviour. As personal finance and/or financial behaviour is based on the
theories from several disciplines such as family studies, economics, psychology and
sociology. In the third section, review of literature is presented on various studies those
have attempted to establish the relationship between financial education, financial
literacy and various financial behaviour.
2.8 Global Scenario: International Studies on Financial Literacy
2.8.1 U.K. Studies
2.8.1.1 Schagen and Lines (1996213)
Schagen and Lines (1996) conducted a financial literacy survey of the general population
on behalf of the NatWest Group Charitable Trust, with a particular focus on four groups:
young people in work or training (having age of 16 to 21 years ), students in higher
education living from home, single parents and families living in subsidized housing. The
survey questions focused on the respondents‘ attitudes to save and borrow, their use of
213 Schagen, S. & Lines, A. (1996). Financial literacy in adult life: a report to the Natwest Group
Charitable Trust, Slough, Berkshire: National Foundation for Educational Research.
62
financial information and institutions, money management in families and their
confidence in dealing with financial issues. The survey had also covered the questions
that tested respondents‘ understanding and knowledge of financial markets, financial
instruments, financial decision making, financial problem solving and financial planning.
The results had indicated that, most respondents were confident in their financial
dealings. The notable exceptions were single parents, who were less committed to
savings, and students, who were the least confident group while dealing with the financial
matters, with very few keeping any financial records.
2.8.1.2 The Adult Financial Literacy Advisory Group (AdFLAG) (2000214)
The Adult Financial Literacy Advisory Group had undertaken a study to determine ―how
to promote better access to financial education to young people and adults‖ (p. 10) by
conducting the relevant research, investigating areas of good practice, consulting with a
variety of organization across the public, private and voluntary sectors and visiting
organizations active in providing financial education programs to socially excluded
people.
The study concluded that the need for financial literacy would continue to grow because
individuals were expected to become more self-reliant, difficulties arising from changing
work patterns, an ageing population, less government involvement and increasingly
complex financial products. To this end, The Adult Financial Advisory Group
recommended that short term financial literacy education should be built around the
education, employment, housing, financial services and communication with particular
focus on needy population sectors such as older people, young people, single parents, and
people with disabilities and people living in social housing.
2.8.2 U.S. Studies
214 UK Adult Financial Literacy Advisory Group (2000), Report to the Secretary of State for Education and
Employment, December 2000
63
2.8.2.1 Chen and Volpe (1998215)
Chen and Volpe (1998) conducted a survey of 924 college students from thirteen colleges
to examine their personal financial literacy; the relationship between financial literacy
and students‘ characteristics and impact of financial literacy on students‘ opinions and
decisions. The survey attempted to examine the personal financial literacy of the U.S.
college students across four main areas: general knowledge, savings and borrowings,
insurance and investments. Chen and Volpe (1998) used Analysis of Variance (ANOVA)
technique to show the variations in the levels of financial literacy among subgroups of
students. In addition, logistic regression models were used to examine the financial
literacy levels of students across different demographic characteristics. The participants
were classified into two subgroups using the median percentage of correct answers.
Students with scores higher than the median were classified as having relatively more
knowledge and students with scores equal to or below the median were classified as
having relatively less knowledge. This dichotomous variable was used as the dependent
variable in the logistic model and the independent variable were represented by the
demographic characteristic variables.
This study found that personal finance skills and knowledge are inadequate; with the
overall median percentage of correct scores was 55.56 percent. Results of the survey
showed that the most poorly answered questions were those involving investments, while
the best answered questions were those on general knowledge. The demographic
variables that were used in the analysis of the study were academic discipline, class rank,
gender, race, nationality, years of work experience, age and income. It was found that
those students with a non-business majors, women, students in a lower class rank, under
the age of 30 and had little work experience have lower levels of knowledge. It was also
found that the participants with better financial knowledge identified more efficient
options, while in their decision making concerning personal financial issues, they reacted
more effectively. This study concluded that students with less knowledge were more
215 Chen, H. & Volpe, R. P., (1998). An analysis of personal financial literacy among college students.
Financial Services Review , 7 (2), 107-128.
64
likely to hold wrong opinions and make incorrect financial decisions and college students
are not knowledgeable about personal finance. The low level of knowledge will limit
their ability to make informed decisions. The study also concluded that the level of an
individual‘s financial knowledge tends to influence attitudes that in turn affect the
individual‘s financial behaviour.
2.8.2.2 Volpe et al. (2002216)
In extension of the previous study (Volpe and Chen, 1998), they surveyed 530 online
investors to examine their investment literacy (financial literacy) and the relationship
between the literacy and online investor characteristics (e.g. education, gender investing
experience and other factors). In this study, their respondents were online investors as the
advent of online investing has significantly impacted an investor‘s decision making
process by providing instant access to a vast amount of financial information, lower
transaction costs, and quick order execution. The survey included the questions based on
investment concepts like: effect of distribution from a mutual fund on its net asset value
(NAV), blue chip stock terminology, compounding of interest, beta as a volatility
measure, capital gain tax rate, portfolio diversification, stock splits, financial ratio
analysis, appropriate asset al.location strategies, and the relationship between interest
rates and bond prices. The survey was conducted online. The results of the study found
that online investors answered about 50 percent of the questions correctly. Investors with
50 years of age or older were more knowledgeable than those who were younger and it
was consistent with those found in previous research (Volpe and Chen, 1998). Women
had lower level of investment knowledge than men. Investors with graduate degrees were
more knowledgeable than those with some high school or college education and those
who have traded online were more knowledgeable than those who have not. The overall
study concluded that the online investors‘ knowledge of investments is insufficient and
needs to be improved in the future.
216 Volpe, R. P., Kotel, J. E. & Chen, H. (2002). A survey of investment literacy among online investors.
Financial Counseling and Planning, 13(1), 1-13.
65
2.8.2.3 Moore (2003217)
The Washington State Department of Financial Institute (DFI) established and
commissioned the Social and Economic Sciences Research Centre (SESRC) at the
Washington State University to conduct a financial literacy study to investigate financial
knowledge, behaviour, attitudes and experiences. The study focused basically on two
groups: consumers who had borrowed from a lender that recently settled in a large
predatory lending case (labeled the victim pool); and the general population (labeled the
general pool).
A telephonic survey was conducted on a 1,483 adults (891 from the victim pool and 592
from the general pool). In addition to the survey, 31 participants from the victim pool
were randomly chosen to take part in four focus group sessions. These participants had
actually filed complaints with DFI (or the office of Attorney General) regarding their
recent mortgage transactions and these sessions were formed to allow a more in-depth
understanding of these individuals‘ mortgage experiences, as well as to support the
survey findings.
The results showed statistically significant differences between two population pools and,
in general, the results were less positive for respondents in the victim pool compared to
those in the general pool. In particular, the victim pool had less understanding of specific
financial terms that were thought to have a critical impact on decisions regarding loans.
Respondents in the victim pool were less likely to invest in stocks, have long term
savings and financial plans, spread their investments, or invest in retirement plans. These
respondents also showed higher tendencies towards risky behaviour.
This study also recognized financial illiteracy as a concern amongst Washington State
residents and the main purpose of the study was to provide DFI with the information
needed for them to develop an effective financial literacy program to educate, inform and
217 Moore, D. (2003). Survey of financial literacy in Washington State: Knowledge, behaviour, Attitudes,
and Experiences. Technical Report # 03-39. Social and Economic Sciences Research Center, Washington
State Department of Financial Institutions, Olympia, WA . Puulman: Washington State University.
66
assist consumers in making financial decisions. Although the results indicated that
consumers would benefit from such a program, especially those in the victim pool, the
major challenge identified was to motivate participation.
2.8.2.4 Hilgert, Hogarth and Beverly (2003218)
Hilgert, Hogarth and Beverly (2003) used results from the University of Michigan‘s
monthly Survey of Consumers (conducted in November and December 2001) to explore
the connection between financial knowledge and behaviours. A financial practice index
was calculated for each of the four financial management activities: cash-flow
management, credit management, saving and retirement. Each index comprised three
categories: low, medium and high, and the survey respondents were placed into these
categories based on their participation in each activity.
In addition, a financial knowledge score was calculated across five sections: credit
management, saving, investment, mortgage, and other. The average financial knowledge
score was examined by the financial practice index and index level. Differences were
revealed between financial practice index and financial knowledge scores, indicating that
there is a relationship between behaviour (as measured by the index) and knowledge (as
measured by score). In general, medium to high index levels were associated with higher
scores under most sections of knowledge. Research confirmed a correlation between
financial knowledge and behaviour, although the direction of the causality is unclear. It is
also concluded that those who score higher on financial literacy tests are more likely to
follow recommended financial practices. Compared with those who have less financial
knowledge, those with more financial knowledge are also more likely to follow
recommended financial practices and engage in recommended financial behaviours.
2.8.2.5 Chen and Volpe (2005219)
218 Hilgert, M.A., Hogarth, J.M. & Beverly, S.G. (2003). Household financial management: The connection
between knowledge and behaviour. Federal Reserve Bulletin, 89 (July): 309-322.Washinton, D.C.
67
Chen and Volpe (2005) conducted a survey of 212 company human resource and benefits
administrators to find out the financial literacy of the U.S. workers. The survey
instrument was consisted of 68 questions focusing on importance of various personal
financial topics, the level of knowledge possessed by employees, whether inadequate
personal finance knowledge leads to a decline in productivity, the use of a financial
literacy test to screen new hires and the most effective approach to improve employee‘s
financial literacy in the workplace.
The results of the survey showed that participants ranked all of the surveyed personal
finance topics as important and that they believed that employees do not have adequate
knowledge about these topics. Retirement planning was ranked as being the most
important topic, followed by personal finance basics, insurance, company benefit plans,
taxes, investments and estate planning. Respondents identified that the least
knowledgeable areas among employees are financial planning basics and retirement
planning.
More than 55 % of respondents who answered a question about whether inadequate
personal finance knowledge leads to a decline in productivity believed that this is the
case, although a few respondents recommended using a financial literacy test to screen
new hires. Results of the survey also showed that respondents believed that outsourcing
to outside financial planners is the most effective approach to educating employees on
personal finance.
2.8.2.6 National Council on Economic Education (2007220)
219 Chen, H. &Volpe, R. P., (2005). Financial literacy, education, and services in the Workplace. B>Quest
(Business Quest). A Journal of Applied Topics in Business and Economics. Available at
http://www.westga.edu/~bquest/2005/workplace.pdf. retrieved on March 22, 2010. 220 National Council on Economic Education, (2007). Economic, Personal Finance & Entrepreneurhip
Education in Our Nation's Schools in 2007. New York: National Council on Economic Education.
68
The National Council on Economic Education attempted to investigate personal financial
education in schools across all states by running a biennial survey, the first survey was
conducted in 1998 (NCEE, 2005221) and the second survey in 2004. Results of the later
survey revealed that only 34 states had standards for personal finance and 20 of these
required that these standards be implemented. In addition, only six states required that
students complete a course that covers personal finance before graduating from high
school and only eight states actually tested students‘ personal financial knowledge. From
the results of this survey, it was concluded that the vast majority of young people in the
U.S. were not being taught about personal finance in school.
2.8.2.7 Lusardi and Mitchell (2006222)
Lusardi and Mitchell (2006) developed a module on retirement planning and financial
literacy as part of Health and Retirement Study conducted in 2004 in The United States.
The module aimed to explore the hypothesis that poor planning may be a primary result
of financial illiteracy. In order to do this, the module measured how workers make their
saving decisions, how they collect information for making these decisions, and whether
they possess required amount of financial literacy needed to make these decisions.
A total of 1,269 adults aged over 50 years responded to the module and results showed
that there is a strong relationship between financial knowledge and planning; in that those
with financial knowledge were more likely to plan and to succeed in their planning. They
also found that overall, financial literacy is poor among older Americans and certain
groups are particularly at risk. In addition to this, they also found that the least literate are
also the least likely to plan and save for retirement.
2.8.2.8 Lusardi and Mitchell (2009223)
221 National Council on Economic Research. (2005). What American teens and adults know about Economics. Washington, D. C. 222 Lusardi, A. &Mitchell, O.S. (2006). Financial literacy and planning: Implications for Retirement
Wellbeing. Michigan Retirement Centre. Working Paper 2005-108, Michigan United Sates, October 2006. 223 Lusardi, M.& Mitchell, O. (2009). How ordinary consumers make complex Economic decisions:
Financial literacy and retirement readiness. NBER working paper No. 15350.
69
Based upon the findings of previous research , the least literate are also likely to plan and
save for retirement (Lusardi and Mitchell, 2006; 2007a224; 2008225), Lusardi and Mitchell
developed a study to report on several self-assessed and objective measures of financial
literacy newly added to the American Life Panel (ALP), and identify the links between
financial literacy and retirement planning by exploiting information about respondents‘
financial knowledge acquired in school – before entering the labor market and certainly
before starting to plan for retirement. The Rand American Life Panel (ALP) was an
internet based survey of respondents age 18+ recruited by the University of Michigan‘s
Survey Research Center. They used two sets of questions to test economic knowledge of
population.
The first set followed HRS approach which captured people‘s capacity to handle basic
financial literacy concepts, which intended to measure simple concepts crucial for
everyday financial transactions and decision making. This set included the questions
based on numeracy, compound interest, inflation, time value of money and inflation/
money illusion. From 989 observations taken from ALP, it was found that 87.1%
respondents can do simple calculations regarding interest rates and they also understand
the effects of inflation. Yet al.most three quarters of respondents (69.0%) cannot give
correct answer about compound interest. Similarly, a sizable fraction of respondents
(78.4%) suffer from money illusion. Moreover, fewer than half (47%) of the respondents
can correctly answer all the five questions. Moving towards socioeconomic
characteristics, respondents aged 50+ were more consistently better informed, although
the age differences were not often statistically significant. Those who had not attended
the college were more likely to respond incorrectly. It was also found that women
exhibited much lower levels of financial literacy than men, where sex differences were
statistically significant for all except money illusion question. These findings were
similar to those in the older sample of Health and Retirement Survey (HRS).
224 Lusardi, Annamaria & Olivia S. Mitchell (2007a). Baby Boomer Retirement Security: The Role of
Planning, Financial Literacy, and Housing Wealth. Journal of Monetary Economics, 54, 205–224. 225
Lusardi, A. & Tufano, P. (2008). Debt literacy, financial experiences, and over-indebtness. Dartmouth
Working Paper.
70
The second set was used to capture sophisticated financial literacy, and measured
responses to more advanced application based financial knowledge questions. Both the
sets also included self assessment questions, which are intended to reflect people‘s
confidence about understanding of economics. With the same number of observations, it
was found that most respondents responded to most of the questions correctly. The
respondents were found to be knowledgeable about the functioning of stock markets and
diversification. They were also found to be more knowledgeable about the fluctuations in
assets than about patterns of asset returns. But the questions linked to bond prices and
interest rates proved very difficult. Only 16% respondents could answer the questions
correctly confirming that sophisticated financial literacy was not widespread. Under the
socio economic characteristics, it was found that younger respondents were less well
informed. In addition, it was found that better educated respondents were more
knowledgeable than less educated counterparts. Sex differences were also marked in that
women knew substantially less than men with regards to the stock market, risk, and bond
returns versus stocks, risk diversification, and basic asset pricing.
They used multivariate analysis to link retirement planning with financial literacy,
holding other socioeconomic factors constant. Under the ordinary linear multivariate
regression it was found that financial knowledge is influential in retirement planning. But
sophisticated financial knowledge is the most important factor, while basic literacy is not
that statistically significant. The overall findings of this study suggested that the impact
of financial literacy index in the retirement planning is positive, statistically significant
and it strongly influences retirement planning.
2.8.3 Australian Studies
2.8.3.1 Beal and Delpachitra (2003226)
226 Beal, D.J. & Delpachtra, S.B. (2003). Financial literacy among Australian university students, Economic
Papers, 22 (1), 65-78.
71
The first Australian financial literacy survey was conducted in 2002 by Beal and
Delpachitra (2003) on a sample of students from the University of Southern Queensland.
The survey was targeted a first year students across five disciplines: Arts, Business,
Education Engineering and Surveying and Sciences. In addition, students studying
psychology as a major and some post-graduate psychology students were targeted. It was
found that many psychological problems in the community are connected with financial
difficulties and so practicing psychologists should have an understanding of personal
financial issues, hence the interest in gaining an understanding of the levels of financial
literacy among psychology students.
Complete primary data was collected from 789 students, through which the following
five main skills were tested by asking technical multiple choice questions: basic concept,
markets and instruments of financial markets, planning, analysis and decision making,
and insurance.
The methodology used to analyze the survey responses was very similar to that of Chen
and Volpe (1998227). Results for the first main area of skill, ‗basic concepts‘, showed that
questions best answered was about how saving is achieved, where students correctly
answered 97.1 percent of cases. The question that was worst answered in this area
queried about the balance in the bank account where an initial $ 100 had been deposited
for one year at 12 percent simple interest versus 1 percent per month compound interest,
in which only 52.9 percent of respondents answered correctly. Another poorly answered
question in this area was reason for diversifying an investment portfolio, in which only
58.5 percent of students answered correctly. These results indicated that simple concepts
in finance, such as the effect of compounding interest and the relationship between risk
and return, are not well understood by university students.
Results for the second main area of skill, ‗markets and instruments of the financial
markets‘, showed that the question best answered was one that queried about the nature
227
Chen, H. &Volpe, R. P., (1998). An analysis of personal financial literacy among college students.
Financial Services Review , 7 (2), 107-128.
72
of the liability undertaken when guaranteeing a friend‘s loan. This question was correctly
answered by 87.5% of respondents. Majority of the remaining questions for this main
area of skill were poorly answered. Only 44.1 percent of students understood the role of
the cash rate in the economy and only 36.7 percent of students were able to correctly
identify which Australian asset class has given the best returns over the last two decades.
These results strongly confirmed that many students are not aware of common financial
information.
Results for the third main area of skill, ‗financial planning‘, showed that the best
answered questions queried about the advantages of keeping a daily track of expenditure
and about the awareness of bank statements allowing them to keep watch on interest rates
and bank charges. The worst answered question, to which only 27.9 percent of students
answered correctly, required respondents to indicate the correct method of subtracting
outstanding cheques from the apparent balance on a bank statement to achieve the actual
balance. These results indicate that the majority of participating students have a poor
understanding about the method of effecting bank reconciliation.
Results for the fourth main area of skill examined, ‗analysis and decision making‘,
showed that solving financial problems together with the knowledge of insurance matters
were the areas which students generally answered least well. The best answered question
where 64.5 percent of respondents answered correctly, involved mortgage-buster or
saving offset accounts. The best method to rectify a persistent credit card debt was able to
be identified correctly by 58 percent of respondents and only 43.1 percent of respondents
were able to correctly solve a simple present value problem. The worst two answered
questions involved an asset-rich, cash-poor person getting funds quickly for an urgent
medical procedure (36.3 percent correctly answered) and a calculation of the best deal on
a motor vehicle in which only 33 percent of respondents answered correctly.
The final main skill areas that were examined paying attention on insurance and results
showed that 77 percent of respondents were correctly able to identify the determinants of
vehicle insurance premiums. Approximately 57 percent understood the nature of
73
insurance excesses, but only 42 percent were able to identify the risks covered by
compulsory third party vehicle insurance. The worst answered questions involved flood
not normally being covered by householders‘ policies (31.6 percent answered correctly)
and the nature of term life insurance, in which only 21.5 percent of respondents were able
to answer correctly. These results indicated that while students appear to have a
reasonable knowledge about vehicle insurance, many are lacking knowledge about other
types of insurance, such as household and life insurance.
Analysis of the full model showed that five independent variables, major, sex,
occupation, experience and risk preference impact significantly on the dependent
variable. The significant demographic variables are sex, work experience, income and
risk preference. Students with higher financial literacy scores were more likely to be
male, have greater work experience, have a higher income and have a lower aggregate
risk preference. An analysis of the first of the five separate models showed that students
with higher general financial knowledge and skills were more likely to be studying
business, be male, working in more highly skilled occupation and have more work
experience. Overall, it was concluded that university students in Australia were not
skilled nor knowledgeable in financial matters and that this will tend to impact negatively
on their future lives through incompetent financial management.
2.8.3.2 ANZ Bank Study (2003228)
In 2003, Roy Morgan Research conducted Australia‘s first national survey on financial
literacy on behalf of the ANZ bank (RMR, 2003). There were two components of the
study: a telephonic survey of 3,548 adults and an in-depth survey of 202 people which
included a self component and an in-depth interview of around one to one and a half
hours each. The telephone survey consisted of 145 finance and 25 demographic
questions. The finance questions were split into four categories: mathematical and
standard literacy, financial understanding, financial competence and financial
228 Roy Morgan Research . (2003). ANZ Survey of Adult Financial Literacy in Australia. ANZ Banking
Group. Melbourne: Roy Morgan Research.1-77.
74
responsibility. The objective of this study was to test the knowledge of respondents
against an individual‘s needs and circumstances and hence not all the respondents were
asked all the questions. As a result, a bias in the results has been generated that
respondents were asked only questions about the financial products and services that they
currently use, then it could be expected that their knowledge of such products and/or
services would be higher than the respondents who does not use that particular product or
service. Use of self-rating questions in the survey also introduced a bias in the findings of
study.
Despite these limitations, the ANZ survey attempted to measure knowledge and
understanding, behaviour, attitude, perceptions and awareness as they related to the four
categories mentioned above, rather than simply measuring skills. In addition to this, the
chosen sample was highly representative of Australian population, with confidence
interval of less than + 2 at the 95 % confidence level. In the analysis, ten levels of
financial literacy were used which were combined to form financial literacy quintiles,
where quintile one was the lowest level of financial literacy and quintile five was the
highest. Correlations, averages and percentages were also used to summarize results. The
survey findings showed that Australians overall are a financially literate society, but that
certain groups have particular challenges that need to be addressed. Those groups were
identified as those having lower level of education, those not working or in unskilled
work, those with lower incomes (household income under $ 20,000), those with lower
saving levels (under $5,000), single people and people at both extremes of the age profile
(18-24 years old and those above 70 years). In contrast, respondents in the highest
financial quintile were males, people with tertiary degrees, professionals and business
owners, couples with no children and people aged between 45 and 59 years. Thus, despite
respondents only being tested on the issues relevant to their current circumstances, a
strong correlation was found between financial literacy levels and socio economic status.
2.8.3.3 Commonwealth Bank Study (2004229)
229 Commonwealth Bank Foundation. (2004). Improving Financial Literacy in Australia: Benefits for teh
individual and the Nation. Sydeny: Commonwealth Bank Foundation.
75
The CBF‘s survey on financial literacy was the first study that investigated the strength of
association between financial literacy and outcomes for both individuals and the
Australian economy. This was achieved in three phases. The first phase was a national
telephonic survey of 5,000 Australians aged between 16 to 65 years; the second phase
investigated the microeconomic effects of improving financial literacy, and the third
phase investigated the macroeconomic effects of improving financial literacy.
The national telephonic survey consisted of 20 multiple choice questions. The survey was
designed to test each respondent‘s ability to make financial decisions, rather than testing
knowledge of financial information. The survey also collected demographic information
such as personal finances, whether the individual had ever owned a business, personal
and health history, and sources of financial knowledge.
The results of the survey showed that those who were unemployed had poorer financial
literacy skills. Also among this group were younger people (aged 16-20 years), males,
students, people with lower levels of education, people with lower personal income
(under $ 10, 000), people with lower annual household income (under $ 50,000) and
people who had never worked in paid employment. Participants with these demographic
characteristics made up 10 percent of the respondents with the lowest financial literacy
scores. Results of the survey also showed that people in older age groups displayed lower
financial literacy, suggesting that financial literacy was not merely a function of age or
life experience.
Results also indicated that the higher an individual‘s financial literacy, the lower the
probability that they were unemployed. In addition, lower financial literacy was found to
have an impact on an individual‘s health. The survey also revealed that 85 percent of
respondents primarily learn about managing their finances through experience or ‗trial
and error‘, and a significant proportion of respondents (36 %) specified financial
institutions as a source of their financial knowledge.
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The second phase of the study revealed that improvements in financial literacy can result
in lifestyle gains for individuals of all ages, across the whole community (p.4). For those
respondents who had the lowest levels of financial literacy, the expected probability of
unemployment was 14.3 percent, compared to 1.7 percent for those who had the highest
levels of financial literacy. Further, there was evidence that a modest improvement for
the financial literacy of the 10 percent of respondents who had the lowest financial
literacy scores, would have resulted in these people having an average increase in annual
income of $3,204.
The third phase of the study revealed that an improvement in financial literacy has the
potential to create more than 16,000 new jobs boosting Australia‘s economy by $ 6
billion per annum (CBF, 2004, p. 3). Other anticipated macroeconomic effects of
improving financial literacy includes the strengthening of national savings, a boost to
both public and private consumption, and the creation of more successful small
businesses.
2.8.4 The Singapore Study
2.8.4.1 MoneySENSE (2005230)
The MoneySENSE Financial Education Steering Committee 231 (FESC) (2005)
commissioned a national survey of 2,023 Singapore citizens and Permanent Residents.
The basic objective of the study was to measure the current levels of financial literacy
among different segments of the Singapore population with reference to their financial
knowledge and understanding of common financial products and services and the actions
taken by Singaporeans in dealing with financial matters. The questionnaire was divided
230 MoneySENSE (2005). Quantitative Research on financial literacy levels in Singapore. THe
MoneySENSE Financial Education Steering Committee. Singapore: Media Research Consultants Pvt. Ltd. 231 TheMoneySENSE Financial Education Steering Committee was formed in February 2003 to provide
strategic direction and oversight of financial education programmes in Singapore. It comprises
representatives from the Monetary Authority of Singapore, Ministry of Community Development, Youth
and Sports, Ministry of Education, Ministry of Manpower, Central Provident Fund Board, and People‘s
Association.
77
into three tiers namely, basic money management, financial planning and investment
know-how. The findings of the study showed that the mean financial literacy score
including knowledge and action for tier I was 74 and for tier II, it was 62 from a total of
2023 respondents. Mean financial literacy score for tier III was 67 out of 662 who had
invested.
2.8.5 OECD Study (2005232)
With an objective to identify financial literacy skills and knowledge possessed by the
consumers and to establish the baseline measurement of financial literacy for policy
makers and financial institutions OECD has conducted financial literacy surveys in
selected five countries (Australia, Japan, Korea, The United States, and the United
Kingdom). These surveys exhibited two different approaches for measuring financial
literacy. One approach was to give respondents an objective test that measures their
knowledge and understanding of financial terms and their ability to apply financial
concepts to particular situations. Surveys of this type were undertaken in the United
States and Korea and were targeted at high school students. The other approach was to
ask respondents for a self assessment, or for their self perceptions, about their financial
understanding and knowledge, as well as for their attitudes towards financial instruments,
decisions, information and its receipt. This was the approach used by the surveys
undertaken in the United Kingdom, Japan, and Australia, although the Australian survey
has also included some of the objective measurement of financial literacy.
Although, the surveys differed in target audience, approach for measuring financial
literacy and survey methodology, there are a number of similarities in the results. Among
which,
1. All the surveys found low level of financial understanding among respondents.
232
Organization for Economic Co-operation and Development (2005). Improving Financial Literacy:
Analysis of Issues and Policies. Paris, France : OECD Publications.
78
In Australia, 67 per cent of respondents indicated that they understood the concept of
compound interest, yet when they were asked to solve a problem using the concept,
only 28 per cent had a good level of understanding.
A British survey found that consumers do not actively seek out financial
information. The information they do receive is acquired by chance, for example, by
picking up a pamphlet at a bank or having a chance talk with a bank employee.
A Canadian survey found that respondents considered choosing the right investments
to be more stressful than going to the dentist.
A survey of Korean and American high-school students showed that they had failing
scores - that is, they answered fewer than 60 per cent of the questions correctly - on
tests designed to measure their ability to choose and manage a credit card, their
knowledge about saving and investing for retirement, and their awareness of risk and
the importance of insuring against it.
A survey in The U.S. found that four out of ten American workers are not saving for
retirement.
The Japanese Consumer Survey on Finance found that 71 percent of adult
respondents had no knowledge about investment in equity and bonds, 57 percent had
no knowledge of financial products in general, and 29 percent had no knowledge
about insurance, pension and tax.
2. The surveys that included the questions about respondents‘ social characteristics found
that financial understanding is correlated with education and income level.
In Australia, the lowest level of financial literacy are associated with low levels of
education (year 10 or less), unemployment or low skilled work, low incomes
(household income under $ 20,000), low levels of savings (under $ 5,000), being
single, and being at either end of the age profile (18 to 24 years old and those
aged 70 years or older).
In the United Kingdom, individuals in the lower social grades and the lowest
income band, as well as young people aged 18 to 24, are likely to be the least
receptive consumers and also uninterested, unconfident and least active. By
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contrast, the higher social grades, those with higher income, young couples and
older respondents with no family are more likely to be sophisticated financial
consumers, knowing how to get the information they need and understanding the
advice they receive.
In the Korean and American surveys, scores broken down by demographic
characteristics indicate that students from families with less educated parents
and/or students who have low income and professional expectations score the
lowest.
3. The surveys found that respondents often feel they know more about financial matters
than is actually the case.
Respondents in United States, The United Kingdom, and Australia felt confident
in their knowledge of financial issues even though when given a test on basic
finance, it was clear that they had only limited understanding of these issues. It
was found that if consumers do not realize they need information, they will not be
in a position to seek it.
The survey in United Sates found that 65 percent of students said that they are
somewhat sure or very sure of their ability to manage their own finances.
However, the scores of these students were not much higher than those of their
less confident peers, which suggested that, the students are unable to judge
accurately how capable they are to manage their money.
When asked for their perceptions, most respondents to the Australian survey
stated that they are financially literate. However, when asked to apply their
financial knowledge to solve a particular problem, they demonstrated a lack of
financial understanding. Although 67 percent of respondents indicated that they
understand the concept of compound interest, only 28 percent correctly answered
a problem using this concept.
4. The surveys found that respondents often feel financial information is difficult to find
and understand.
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The Japanese Consumer Survey on personal finance found that respondents felt
frustrated about the difficulty in finding easy-to-understand information on
financial products. When asked about the financial information provided by
various organizations and companies, 39 percent of respondents said they had not
seen much information and 29 percent found the content of the information
difficult and hard to follow.
The British survey conducted in the United Kingdom found that consumers do not
actively seek out financial information. The information they do receive is
acquired by luck or chance or hazard, for example, picking up a pamphlet at a
bank or having a chance to talk with a bank employee. This survey also found that
consumers‘ perceived complexity of financial products is one reason given for not
going ahead for purchase.
2.9 Domestic Scenario
2.9.1 India Protection Survey - How India Earns, Spends and Saves (2007233)
National Council for Applied Economic Research and Max New York Life Insurance
Company Limited did a study titled ―India Protection Survey - How India earns, spends
and saves‖ and developed a data on attitudes and practices that have a bearing on the
financial security of Indian households. The survey collected the data from sample size of
over 63,000 households spread across the length and breadth of the country. The survey
found that out of 86 percent of Indian household that save, 36 percent keep their saving at
home as cash. The results also say that India saves primarily for emergencies (83
percent), wedding and social events (63 percent), children‘s education (81 percent) and
inheritance, old age (47 percent). The results also show that less than 3 percent buy bonds
233 Max New York Life Insurance Company Ltd. & National Council for Applied Economic Research
.(2007). How India Earns, Spends and Saves: India Financial Protection Survey. Delhi: Max New York
Life Insurance Company Ltd. and National Council for Applied Economic Research. Research Report.
81
and other financial instruments. The survey also found little correlation between saving
and long term gain. The survey concluded that the lack of long term financial planning
and poor levels of financial literacy comprise of the core of India‘s financial insecurity.
2.9.2 Financial Literacy and Domestic Regulatory Authorities
The Reserve Bank of India launched an initiative in 2007 to establish Financial Literacy
and Credit Counseling Centers throughout the country which would offer free financial
education and counseling to urban and rural populations. The broad objective of the
FLCCCs is to make the people in urban and rural areas educated and aware about various
financial products and services available from the formal financial sector and providing
them free financial literacy/education and credit counseling.
The Reserve Bank has undertaken a project titled 'Project Financial Literacy'. The
objective of the project is to disseminate information regarding the central bank and
general banking concepts to various target groups, such as, school and college going
children, women, rural and urban poor, defense personnel and senior citizens. The
information is disseminated to the target audience with the help of among others, banks,
local governments, NGOs, schools, and colleges through presentations, pamphlets,
brochures, films, as also through the website. It also disseminates the financial
information through its web site in English, Hindi and 12 other Indian regional languages
to provide ease for access to the public.
Securities and Exchange Board of India (SEBI) promotes financial literacy through
financial education with the help of investors associations and through the resource
persons empanelled by it. The first panel of resource persons was empanelled in June,
2010; under this initiative, six target groups are identified to promote financial literacy.
These are: school children, college going students, middle income groups, executives,
home makers and retirees. The financial literacy information is disseminated through
presentations, pamphlets, brochures etc. SEBI has also started to spread financial
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education to school children through ‗pocket money program‘ with National Institute of
Securities Market.
2.10 Theoretical Framework for Financial Behaviour
Consumer Economists have studied financial behaviour for the last three decades.
Fitzsimmons, Hira, Bauer and Hafstrom (1993234) did a study on financial behaviour from
1970s to early 1990s. The trend has continued through the years and recently, there have
been more studies on financial behaviour (Hilgert, Hogarth & Beverly, 2003235; Hogarth,
Hilgert, and Schuchardt, 2002236; O‘Neill & Xiao, 2003237; Xiao, 2006238).
Xiao (2008239) said that ―financial behaviour can be defined as any human behaviour that
is relevant to money management. The common financial behaviour includes cash, credit,
saving and investment behaviours‖ (p. 70).
As discovered by economic psychologists, there are certain human psychological
behaviour patterns that impact individuals‘ financial behaviours. They have identified the
contextual factors that influence decision making. Personal finance is an interdisciplinary
subject and derives its context from subjects such as family studies, economics,
psychology, and sociology.
Theory is a general framework of ideas. A pattern emerges, after collection of specific
types of data, and a theory is developed to provide explanation for this pattern. This topic
describes theoretical frameworks to explain individual financial behaviour.
234 Fitzsimmons, V. S., Hira, T. K., Bauer, J. W., & Hafstrom, J. L. (1993). Financial management:
Development of scales. Journal of Family and Economic Issues, 14, 257-273. 235 Hilgert, M.A., Hogarth, J.M. & Beverly, S.G. (2003) Household financial management: The connection
between knowledge and behaviour. Federal Reserve Bulletin, 89 (July): 309-322.Washinton, D.C. 236 Hogarth, J. M., Hilgert, M. A., & Schuchardt, J. (2002). Money managers: The good, the bad, and the
lost. Proceedings of the Association of Financial Counseling and Planning Education, 12-23. 237 O'Neill, B., & Xiao, J. (2003). Financial fitness quiz: A tool for analyzing financial behaviour.
Consumer Interests Annual, 49, American Council on Consumer Interests. 238 Xiao, J. J. (2006). Applying behaviour theories to financial behaviour. In J. J. Xiao (Ed.),
Handbook of consumer finance research (pp. 69–81). New York: Springer. 239 Xiao, J. J. (2008). Handbook of Consumer Finance Research. p. 70
83
2.10.1 Goal Setting Theory
Edwin Locke (1960s240) has given the Goal-setting theory of motivation. The theory
states that in any measure of task performance, goal setting is the essential element. In
other words, according to this theory, goal setting should be essentially linked to task
performance. It explains that not only the specific goals but also the goals which
challenge individuals to achieve something addition to the specific goals along with
appropriate feedback contribute to higher and better task performance. Work on the
theory of goal-setting suggests that it's an effective tool for making progress by ensuring
that participants in a group with a common goal are clearly aware of what is expected
from them if an objective is to be achieved. At an individual level, setting of goals is a
process that allows people to specify the task to be performed for achieving their own
objectives and/or goals. The goal setting theory is the most valid and useful motivation
theory not only in industrial and organizational psychology, human resource
management, and organizational behaviour but also in the field of financial behaviour. In
the context of personal finance, financial planning and investment decision, the goals
must be S.M.A.R.T. (Specific, Measurable, Achievable, Realistic, Time-Bound (SEBI,
2010241).
2.10.2 Human Ecological Model
The human ecological model was proposed by Bronfrenbrenner (1979242). It describes
individuals as dynamic factors that often influence and are influenced by interaction with
and within larger and interdependent systems. The four basic systems that make up the
ecological environment are the microsystem, the mesosystem, the exosystem, and the
macrosystem. The microsystem includes immediate family, friends, classmates or
employees, and members of one's faith community. The second level, the mesosystem,
240
Lucke. E. A. (1968). Toward a theory of task motivation and incentives. Organizational Behaviour and
Human Performance, 3, 157-189 241
Securities and Exchange Board of India. (2010, July). Financial Education for School Children, p. 5. 242 Bronfenbrenner, U. (1979). The Ecology of Human Development. Cambridge, MA: Harvard University
Press.
84
recognizes that parts of the micro system interact with each other and with the other
systems. The third level, the exosystem, includes groups, organizations, or entities that
influence the micro system. The fourth level, the macro system, surrounds and affects all
other systems. It includes cultural values, personal and social conditions, political
ideologies, and market and economic performance. Studies on financial behavior suggest
that the theory of human ecological model does have its impact on the decisions made by
families and the reciprocal interactions of families and environments.
2.10.3 Family Management Systems
Deacon and Firebaugh (1988)243 used the human ecological model and systems theory as
a base to provide a context for understanding the goal-directed behaviour of families. The
important components of systems theory are inputs, throughputs, and outputs. In the
family systems, the demands and resources enter the managerial subsystem as inputs. The
activities, through which families clarify their demands and assess their resources to
attain their goals, are known as throughputs. Then, a particular sequence of actions to
achieve the goal is developed, and a plan is implemented, known as an output. Feedback
provides information to various parts of the system for their use. This managerial process
outlined by Deacon and Firebaugh is similar to any investment decision that underlie a
financial planning process: setting goals, gathering data, analyzing information,
developing a plan, implementing the plan, and monitoring progress toward the goal.
2.10.4 Discounted Utility Model
The discounted utility framework was initially developed to incorporate inter-temporal
considerations into the formal models of decision making employed by economists and
other social scientists. Introduced by economist Paul Samuelson in 1938, the model states
that costs and benefits occurring at different times can be made comparable by
243 Deacon, R. E., & Firebaugh, F. M. (1988). Family resource management: Principles and applications
(2nd ed.). Boston: Allyn and Bacon.
85
discounting future utility244 by some constant factor. Economists assume that when a
person is faced with a choice from amongst a number of possible options, the person will
choose the one that yields the highest utility to him/her. However, people are constrained
by the amount of their income. The utility function assessed both in terms of current and
future consumption. According to this discounted utility model, what one gets in the
future is less valued now than it will be later.
2.10.5 Life Cycle Hypothesis of Savings
The life cycle hypothesis of savings developed by Ando and Modigliani (1963 245 )
assumes that a person consumes a constant percentage of their income over the life cycle
and that they are born without an inheritance and die without leaving a bequest.
According to this theory, many young investors would prefer to borrow to finance
consumption while they acquire education and skills. At middle stage of their lives, they
are expected to repay early debts and save for later life. In retirement, they are expected
to spend down their accumulated assets. However, many retired individuals continue to
save and also plan to transfer their remaining wealth to the next generation or to
philanthropic causes or organizations.
244 The utility of an option is a numerical value that refers to the option‘s consistency with the decision maker‘s preferences. Options that have higher utility are preferred to options with lower utility. The actual
numbers representing utility values are arbitrary; what matters are the relative utilities of the various
options. 245 Ando, A., & Modigliani, F. (1963). The life cycle hypothesis of saving: Aggregate implications and
tests. American Economic Review, 53, 55-84.
86
Fig. 2.1 – An individual‟s financial life cycle and corresponding objectives
Source: Life cycle of financial planning by Gail M. Gordon, University of Wyoming Cooperative
Extension Service, 2001
The stage of life in which an investor finds himself, also plays an important role in
selecting the components of a particular investment portfolio and demands a strategic
focus for disciplined financial planning. These demographic factors play an important
role while predicting the expected financial obligation such as child‘s education, marriage
and provision for retirement.
In the context of investment decision as a part of financial planning, each stage in the
human life cycle, has a unique financial objective which needs to be fulfilled, which in
turn plays an important role in credit, saving and investment decisions.
87
Table 2.1 Stage of Life Cycle, corresponding Objective- Needs and Requisite
Financial Behaviour
Objective Need Proposed Financial
Behaviour
Stage 1- To protect
yourself and your
family
Protection against risks of
unexpected circumstances and the
risk related to life, disability,
health etc.
Setting up of emergency
fund or to purchase
adequate amount of
insurance
Stage 2- To provide
for financial security
for yourself and your
family
To provide financial security to
extended family members,
fulfilling the needs for education,
social events, or fulfilling other
needs
Providing adequate
financial security without
placing undue stress on
your resources which may
result in financial crises,
emphasis on proper credit
& debt management
Stage 3- To enjoy a
comfortable standard
of living
To accumulate the wealth for
secured retirement
Budgeting financial
security for retirement,
State 4- To be
financially
independent,
Being financially independent
and have comfortable retirement,
with the same standard of living
Enjoying the return made
during the stage 1 and
stage 2
Stage 5- To
distribute the wealth
To distribute the wealth to the
beneficiaries or next generation
Following up the strategy
of estate planning
Source: Jariwala, H. (2010). Strategic approach to investment: A new paradigm in financial planning. In I.
E. Centre (Ed.), The Role of Financial Innovation:Corporate Sustenance and Growth. Mumbai: Excel India
Publishers.
88
2.10.6 Behavioural Life Cycle Hypothesis
Thaler and Shefrin (1981 246) developed a theory of self control which suggests that
individuals have personality traits to be either a planner who is focused on deriving utility
both in the present and in the future or a doer who is focused on the present. Later, they
proposed the behavioural life cycle hypothesis (Shefrin & Thaler, 1988247) suggesting that
individuals practice mental accounting, meaning that they have different propensities to
save in different categories of accounts. For example, they may think differently about
funds in a retirement account than those in a cash reserve for emergencies. Thus, the
main tenet of this theory suggests that individuals might be long- or short-term planners
and that they plan to use money in different accounts for different purposes.
2.10.7 Maslow‟s Need Hierarchy Theory and understanding the Financial Needs
In the context of financial behaviour, the financial need hierarchy is similar to Maslow‘s
need hierarchy theory (1943248), which defines that the lower, basic needs pertaining to
human survival must be met before the higher needs can be addressed, which are not
directly related to the survival but relate to the life enhancement and quality of life.
246 Thaler, R. H., & Shefrin, H. M. (1981). An economic theory of self-control. Journal of Political Economy, 89 (2), 392-406. 247 Shefrin, H. M., & Thalen, R. H. (1988). The behavioural life-cycle hypothesis. Economic Inquiry, 26,
609-643. 248 Maslow, A. (1943). A theory of human motivation. Psychological Review, 50, 370-396. Retrieved on
June 25, 2010, from http://psychclassics.yorku.ca/Maslow/motivation.html.
89
Fig. 2.2 Financial Need Hierarchy
The explanation of these financial needs hierarchy is given below.
Survival money: The money that an individual spends simply to survive.
What-if money: The money required to protect the life.
Freedom money: The money needed to do the things that bring joy and
fulfillment to the life.
Gift money: This is the replacement for ―love‖.
Dream money: This is the elusive ―self-actualization‖ level where an individual
finds true happiness and meaning.
The decisions regarding savings, ownership of investments and choice of credit
instruments also depend upon individual‘s financial needs (or objectives) and abilities
(resources) to acquire these financial assets and liabilities (Katona, 1960249). The financial
need priorities which a household has and the resource availability at each stage of a
household‘s life cycle determine the sequence in which financial services and/or
instruments are acquired by the household.
2.10.8 Theory of Reasoned Action and Theory of Planned Behaviour
249 Katona, G., (1960). The powerful consumer. New York: Mc-Graw Hill.
Survival
MoneyWhat-if Money
Freedom Money
Gift Money
Dream Money
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Ajzen and Fishbein (1980250
) formulated the theory of reasoned action (TRA). Theory of
Reasoned Action suggests that a person's behaviour is determined by his/her intention to
perform a specific behaviour in a particular situation and that this intention is, in turn,
depends on his/her attitude toward the behaviour and his/her subjective norm. The best
predictor of behaviour is intention. Intention is the cognitive representation of a person's
readiness to perform a given behaviour, and behaviour is immediately preceded by
intension. Intention is determined by three things: attitude towards the specific behaviour,
subjective norms and perceived behavioural control. The theory of planned behaviour
holds that only specific attitudes towards the behaviour in question can be expected to
predict that behaviour. In addition to attitudes toward the behaviour, measuring people‘s
subjective norms is also important. Subjective norms are people‘s beliefs about how
people they care about will view the behaviour in question. To predict someone‘s
intentions, knowing these beliefs can be as important as knowing the person‘s attitudes.
Finally, perceived behavioural control influences intentions. Perceived behavioural
control is how people perceive their ability to perform a given behaviour. These
predictors lead to intention. As a general rule, the more favorable the attitude and the
subjective norm, the greater the perceived control, the stronger would be the person‘s
intention to perform the behaviour in question.
2.10.9 Transtheoretical Model of Behaviour Change
Prochaska (1979 251 ) developed the transtheoretical model (TM) from a comparative
analysis of leading theories of psychotherapy and behaviour change. This model
underpins the different levels of readiness to change a problem behaviour or develop a
desirable behaviour. Prochaska and DiClemente (1983252) developed six steps under this
model, which are (a) precontemplation, (b) contemplation, (c) preparation, (d) action, (e)
250 Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting behaviour. Englewood Cliffs,
NJ: Prentice Hall. 251 Prochaska (1979) developed the transtheoretical model (TM) from a comparative analysis of leading
theories of psychotherapy and behaviour change. 252 Prochaska, J. & DiClemente, C. (1983). Stages and processes of self-change of smoking: toward an
integrative model of change. Journal Consulting Clinical Psychology 51(3), 390–395 retrieved on May 21,
2009
91
maintenance, and (f) termination. The stages of change are precontemplation (no
intention to change behaviour), contemplation (aware of problem but not committed to
changing behaviour), preparation (intending to change within a month), action (changing
the problem behaviour by employing a variety of strategies), and maintenance (working
to prevent relapse). Movement to a higher level of readiness to change behaviour is
influenced by the processes of change. The TTM involves ten processes of change.
Change processes include activities and experiences that individuals engage in as they
attempt to modify their behaviour. Each process is a broad category encompassing
multiple techniques, methods, and interventions. Research has shown that successful self-
changers employ a variety of strategies to achieve their goal. One of the important
concepts which has been tested by this theory is understanding how individuals can be
motivated to increase savings and reduce debt.
2.10.10 Myers-Briggs Type Indicator
This theory was developed by Carl Jung (1923 253) and later widely researched and
applied by various researchers, for better understanding of human behaviour. It says that
―essence of the theory is that much apparently random variation in behaviour, which is
actually quite orderly and consistent. If people differ systematically in what they perceive
and in how they reach to conclusions, then it is only reasonable for them to differ
correspondingly in their reactions, interests, values, motivations, skills and interests‖
(Myers & McCaulley, 1985254, p 1).
The MBTI was developed to help people understand and appreciate themselves and
others. These psychological type concepts concentrate on personal acceptance and
healthy differences among people. Since 1942, instrument items have been written,
validated, and subjected to tests for validity and reliability. The current form uses 94
items to identify four pairs of preference alternatives. Although every individual is able to
use all eight preferences, Jung‘s theory suggests that individuals are predisposed to use
253 Jung, C.G. (1923). Psychological Types. New York: Harcourt Brace. 254 Myers, I.B. & McCaulley, M.H. (1985). Manual: A guide to the development and use of the Myers-
Briggs Type Indicator. Palo Alto, CA: Consulting Psychologists Press, Inc.
92
one preference more than the other and that, through use, the preference is strengthened.
Validation studies indicate a high correlation between an individual‘s self-awareness and
the corresponding report generated from the answers to questions in the MBTI
instrument. The real value of this theory is that many people find exceptional personal
insight when introduced to the MBTI concepts.
Till date, there is limited attention on linkages between psychological types and financial
decision-making. Studies of general decision-making attempt to build a bridge of
understanding that can be used to connect specific type of personalities to specific
financial decisions. Huitt (1992255) describes decision-making differences among people
based on their psychological preferences. For example, some people naturally make
decisions based on logic and critical thinking. They employ analysis, backwards
planning, categorizing and classifying, challenging assumptions, judging, systematic
reasoning, and task analysis. Others make their best decisions when they use
brainstorming, visualizing, relaxing, taking another‘s perspective, and values
clarification.
The MBTI instrument measures four pairs of preferences. These concepts suggest that
individuals exhibit strengths based on their personality preferences selected from four
scales (Myres, 1980256
).
Extroversion/Introversion: The extroversion/intro-version set of preferences defines how
people are energized. Some people get their energy from the external world – extroverts.
They seek relationships with other people and often interpret their own thoughts in terms
of what other people think of their thoughts. They prefer generating ideas with a group
and want affirmation from friends and associates about their ideas. Extroverts are ready
and willing to talk about what they need and want. They do not give too much
importance to written material. They may hear what they want to hear, not what the
255 Huitt, W.G. (1992). Problem solving and decision making: Consideration of individual differences using
the Myers-Briggs Type Indicator. Journal of Psychological Type, 24, 33-44. 256
Myers, I.B. (1980). Gifts differing. Palo Alto, CA: Consulting Psychologists Press, Inc.
93
others are recommending. They may change their portfolio if convinced to do so by
others.
Introverts get their energy internally. They need time to think about all aspects of a
particular decision before speaking and don‘t like competing in fast-flowing
conversations. Introverts are often secretive about personal information and do not share
personal information with people they do not know well. They want written materials and
they want time to read them. They tend to concentrate, focus and reflect on information.
They are more likely to have conservative asset portfolios.
Sensing/Intuitive: Sensing and intuitive preferences describe the process that people rely
on to gather information. Sensing people trust their senses as they gather information and
want to turn the theory they have learnt into practical applications. They often ask, ―will
it work?‖ Sensing people are comfortable focusing on the present, have concrete ideas,
and are at their best dealing with specifics. These type of people like to deal with facts.
They look for practical application of financial concepts. They want information
presented in a sequential manner, and they like examples.
On the other hand, an intuitive person often rearranges information received into a new
idea based on hunches and inspiration. Intuitives enjoy brainstorming future possibilities,
and are good at describing the big picture. This type of personalities like planning to
address future needs but they may not be as interested when it comes to evaluating and
selecting a specific course of action.
Thinking/Feeling Thinking and feeling preferences describe how people make decisions
in terms of their thinking or feelings. People who prefer thinking work in a logical and
scientific manner. They take any decision based on numbers and figures. They would
prefer to be correct and do not pay too much attention to what other people their decision.
Logical implications underpin their decisions. Thinkers prefer to be objective. This
preference comes closest to the ideal ―rational man‖ theory. Individuals who prefer
94
thinking look at cost-benefit tradeoffs. Because, managing money is impersonal to them,
they enjoy the challenge. They can create systematic plan for their financial well-being.
Those with a feeling preference make decisions by taking the feelings of other people
into account. They prefer harmony over clarity and may give up their own comfort to
accommodate others. They make decisions based on the potential impact of these
decisions on people. They select products and choices with a personal emphasis. They try
to please by agreeing with everyone, and they may not ask if they don‘t understand
something.
Judging/Perceiving: Judging and perceiving preferences describe lifestyle orientations.
Judging persons are decisive, planful, and orderly, and find making decisions easy. They
work best when they can plan and then follow the plan. They are time driven, like making
lists, and are generally considered well organized. They love to make decisions and
―getting on with things.‖ People with judging preferences are willing to make decisions
and implement them quickly. They are good at long term planning including retirement
planning.
Those with a perceiving preference are flexible, adaptable, and spontaneous. They like to
keep their options open and find that bringing closure is difficult. They prefer gathering
information to see what a task demands rather than detailed planning. They work best in
last-minute spurts of energy to meet deadlines. Their important characteristics are
curiosity, spontaneity, and responsiveness. They delight in processing and considering
new ideas and information. These individuals are open to many different possibilities and
they look for a wide range of information and ideas. They don‘t like to make systematic
plans and implementing plans is difficult for them.
2.10.11 Temperament Theory
The temperaments describe four unique ways that people interact with the environment to
satisfy their needs. In essence, temperament is best understood as a pattern of consistently
95
observable behaviours. According to the father of modern temperament theory, Dr. David
Keirsey (Keirsey & Bates, 1984 257 ) ―temperament determines behaviour because
behaviour is the instrument for getting us what we must have…‖ (p. 30).
Temperament is a powerful tool for understanding how we are motivated to use money to
meet our psychological needs. The temperament model of human behaviour provides us
with great insight into financial behaviour. Because each temperament has a particular set
of natural skills they employ to be effective and satisfied in financial matters, an
understanding of temperament is essential to an understanding of financial behaviour.
Judy McKenna et al. (2003)258 said that ―the MBTI can be used to identify the four
temperaments Idealist, Guardian, Rational, and Artisan‖ (p. 22).
Temperaments and management of money
Guardians are of what might be called the preserving temperament. Guardians have a
sensible and judicious temperament (Montgomery, 2002 259 ). For them money
management is about preserving a comfortable, secure and organized home.
They have natural disposition for: measurement which allows them to gauge how much
time, money and material is necessary to properly maintain their standard of living;
sequential thinking, which allows them to manage their finances in an orderly fashion;
supervision, allows them to check that money is managed in the right way by following
the rules: frugal spending, saving money, minimizing debt, preparing for future; and
caretaking, which allows them protecting and providing for their families and loved ones
(Judy Mcenna et al., 2003260
, p. 24).
257
Keirsey, D. & Bates, M. (1984). Please understand me. Character & temperament types. Del Mar, CA:
Prometheus Nemesis Book Company. 258 McKenna, J., Hyllegard, K. & Linder, R. (2003). Linking psychological type to decision making.
Journal of Finanical Counseling and Planning. Association for Financial Counseling and Planning
Education 14 (1). 19-29. 259 Montgomery, S. (2002). People patterns: A modern guide to the four temperaments. Del Mar, CA:
Archer Publications. 260
McKenna, J., Hyllegard, K. & Linder, R. (2003). Linking psychological type to decision making.
Journal of Finanical Counseling and Planning. Association for Financial Counseling and Planning
Education 14 (1). 19-29.
96
Artisans are part of what might be called the ―doing‖ temperament. Artisans have a spur-
of-the moment and playful temperament (Montgomery, 2002). For them, management of
money is about having the freedom to do what feels good. They are skilled in tactics,
reading the immediate situation and making instant decisions to accomplish the purpose.
They have natural disposition for: adaption, which allows them to make right financial
adjustment to deal with unforeseen situation; contextual thinking, which allows them to
understand the urgency of the immediate moment as it relates to their finances;
promotion, which allows them how money can be used to advance and accomplish the
interest; and performance, which allows them getting things to be done as expediently as
possible (Judy Mcenna et al., 2003, p. 24).
Idealists are apart of what might be called the inspiring temperament. Idealists have an
insightful and fervent temperament (Montgomery, 2002). For them, money management
is about cultivating relationships, growing personality and helping others to achieve their
potential.
They have natural disposition for: interpretation, which allows them understanding the
meaning of other‘s behaviour; integrative thinking, which allows them seeing how things
come together; counseling, which allows them seeing how others can help themselves;
and revelation, which allows the insight into underlying motives and desires (Judy
Mcenna et al., 2003261
, p. 25).
Rationals apart of what might be called the achieving temperament. Rationals have an
ingenious and theoretical temperament (Montgomery, 2002). For them, money
management is acquiring necessary competence to understand, explain and predict, and
therefore controlling financial forces that affect their lives.
261 Ibid. p. 25
97
They have natural disposition for: analysis, which allows them to make calculations by
distinguishing components and discerning their interrelationships; differential thinking,
which allows them to see differences, categories, classifications, functions; designing,
allows them to think on all components for plans; and Marshalling, which allow them to
lead, to guide and to rearrange plan components in the necessary order (Judy Mcenna et
al., 2003262
, p. 25).
The above discussion shows that individuals are not rational with regard to any financial
behaviour displayed by them. After presenting the theoretical background for various
financial behaviours displayed by the individuals in this section, the researcher has
proceeded further with the review of various studies, which has attempted to establish the
relationship between financial literacy and financial behaviour. The review of these
studies is presented in the next section.
2.11 Studies on Financial Literacy and Financial Behaviour
This section discusses the findings of past studies; those have attempted to find out the
relationship between financial literacy of individuals and various financial behaviours
displayed by them. Empirical research studies indicate that both economic and
psychological factors are associated with various financial behaviours of individuals,
such as, cash flow management, saving, investment and credit behaviours. Researchers
from diverse fields have contributed to the literature on consumer financial behaviour.
Among them, consumer economists have conducted the research studies to identify
association of financial literacy/education with financial behaviours of individuals. The
in-depth discussion of these studies is presented below.
2.11.1 DeVaney, Gorham, Bechman and Haldeman (1996)
262 Ibid. p. 25
98
DeVaney, Gorham, Bechman and Haldeman (1996263) conducted an experimental study
to examine cash flow and credit use three months after participants completed a series of
women's financial information workshops. The results showed that 24% of the
participants started to set up bill payment system as a practice after attending the
workshop. This study found that age was positively related to the probability of using bill
paying system (p. 75). For creating and maintaining an emergency fund, 44% of the
respondents started the practice after getting financial knowledge. Regarding obtaining
credit, 22% of respondents reported making a change in obtaining credit on their own
name. The only variable that was significantly related to adoption of this financial
behaviour was marital status. Those not married were less likely to adopt this behaviour.
One third of the participants indicated that they limited the number or use of credit cards
after completion of workshop. While four out of ten participants began to save regularly
or increased regular saving (p. 76).
2.11.2 Berhiem, Garrett & Maki (1997)
Berhiem, Garrett & Maki (1997 264 ) examined the effects of mandated financial
curriculum in schools. They attempted to determine whether mandated financial
education across the states and over the time in high school had any long term behaviour
effect on adult decisions regarding savings. The data were collected in the month of
November 1995, from the nationally representative sample of respondents between the
ages of 30 and 49. By using regression analysis on variables related to consumer
education in high school and adult financial behaviour, their results have shown mandates
significantly increase the exposure to financial education, and has positively raised the
rates of saving (a flow variable), and hence accumulate more wealth (a stock variable)
during the adult lives (p. 29). The results indicated that while the effects of this mandate
have been significant, but the effect is gradual rather than immediate. They also found
that the self reported rates of saving rise significantly with education and earnings. The
263 DeVaney, S. A., Gorham, E. E., Bechman, J. C., & Haldeman, V. (1995). Saving and investing for
retirement. Family Economics and Resource Management Biennial, 1, 153-158. 264
Bernheim, D., Garret, M. & Maki, D. (1997). Education and saving: The long term effects of high
school financial curriculum mandates. NBER Working Paper Series: Working Papar No. 0685, National
Bureau of Economic Research, Cambridge, MA.
99
results also show that individuals who received personal finance instructions had a saving
rate 1.5 precent greater than individuals who received no instructions, and wealth is
increased by an amount equal to earnings for one year within 15 to 20 years after
graduation from high school. They also concluded that financial education can be used to
stimulate personal savings (p. 30).
2.11.3 Boyce and Danes (1998)
Boyce and Danes (1998 265 ) have introduced the curriculum on Personal Financial
Planning Program for school children in the NEFE High School Financial Planning
Program (HSFPP). The curriculum is a collaborative effort between the National
Endowment for Financial Education (NEFE), the USDA (United States Department of
Agriculture), and the Cooperative State Research, Education, and Extension Service
(CSREES)—the federal partner of the Cooperative Extension System. It includes an
extensive instructor‘s manual, student workbook, and student personal financial portfolio
which are provided free by NEFE. The curriculum acquainted the students with basic
financial planning concepts and illustrated how these concepts apply to everyday life.
The goal of the curriculum was to increase the financial planning literacy of teens. The
curriculum was divided into seven units: (1) providing an introduction to the financial
planning process, (2) explaining the relationship between career/work factors and earning
potential, (3) developing a personal spending/savings plan, (4) using and managing credit
effectively, (5) describing risk management techniques and explaining the importance of
protecting their assets, (6) explaining the importance of saving and investing and the
benefits of utilizing the time value of money into their savings plans, (7) completing the
personal financial plan. 434 schools were covered under this study. After three months,
the results of the program evaluation found that teaching financial literacy through the
NEFE curriculum has a positive impact on students‘ financial management knowledge,
behaviour and self-efficacy. For all questions, there was a statistically significant increase
in financial behaviour, knowledge, and self-efficacy questions immediately after
265 Boyce, L. & Danes, S. M. (1998) Evaluation of the NEFE high school financial planning program.
Denver CO: National Endowment for Financial Education.
100
participating in the program. The area where the most students increased in knowledge
was in understanding the cost of credit. When investigating the total gains in financial
knowledge, behaviours, and self-efficacy across the 13 questions, 86% of the students
showed an increase in financial knowledge or behaviours. Greater percentages of students
reported that they ―almost always‖: compare prices when shopping (44.5%), set money
aside for future needs or wants (40.5%), and repay the money they owe on time (60%). In
the follow-up, students reported ―almost always‖ knowing the cost of buying on credit
approximately 43 percent of the time. Finally, about 76 % of the students reported that
they ―almost always‖ felt as though the way they managed their money would affect their
future. 58% said they had changed spending habits and 56% said they had changed
saving habits. More than one-third of students (39%) reported that they had started saving
money 3 months after completing the HSFPP. 13.8% (1.0% of 418) reported that they
have now started saving. For determining the saving corpus, a large proportion of
students (38%) reported that they decide on a specific percent or amount of earnings to
save while many students (28%) reported to save only when they had a specific purchase
to make. Nearly one third of students (31%) reported that the most important financial
planning activity they had taken on since participating in the HSFPP was establishing a
savings account.
2.11.4 German and Kim (1999)
German and Kim (1999 266 ) conducted a study to investigate the effectiveness of
workplace financial education at one of plants owned by a Southeastern chemical
production company.
Differences and similarities between participants and nonparticipants in the financial
workshops were explored. The objectives of the workshop were to assess the workers‘
perception of financial wellness and personal financial behaviours, to measure changes in
workers‘ personal financial behaviour who participated in the workshops and to obtain
266 Garman, E. T., Kim, J., Kratzer, C. Y., Brunson, B. H., and Joo, S. (1999). Workplace financial
education improves personal financial wellness. Financial Counseling and Planning, 10 (1), 89- 94.
101
self-reported measures of health and job performance ratings. Workshop participants
reported a positive change number of personal financial behaviour as a result of the
education received. Three-quarters (75%) of the workshop participants reported that
―they have made better financial decisions since attending the workshops‖. Seventy-five
percent of the participants related that they were more confident in making investment
decisions. Seventy percent reported they changed their investment strategy by
appropriately diversifying or being more aggressive in their investment choices. Fifty-six
percent of participants agreed that ―their financial situation had improved because of the
financial education.‖ Almost half (45%) of the workshop participants increased the
amount of contribution to the employer‘s 401(k) plan. Thirty-four percent workers agreed
that they started for contributing towards the 401(k) plan as a result of the financial
education workshops (p. 85). It was also found that those who participated in the
workshops tended to have credit under control, a budget and a financial plan and had
improved their financial situation. The overall findings suggest that the most workshop
participants took positive actions to improve their financial well being after they receive
financial education.
2.11.5 Clark et al. (2003)
Clark et al. (2003 267 ) found that financial education seminars have an effect on
individuals‘ retirement goals and saving behaviour. They studied the responses to
questionnaires distributed and collected from 60 TIAA- CERF financial education
seminars held at educational institutions and non-profit organizations between March
2001 to May 2002. In these seminars, participants completed a survey on retirement goals
and after being asked to attain a one-hour financial education seminar, they filled up a
second questionnaire and checked whether their retirement goals have changed? Analysis
of the results showed that 34 percent of the participants changed either their retirement
income goal or their retirement age goal in response to the seminar. 90 percent of
respondents reported that they anticipated making changes to their retirement saving
267
Clark, R. et al., (2003, March). Financial education and retirement savings. Paper presented at the
conference on Substantial Community Development: What Works, What Does‘t, and Why, sponsored by
the Federal Reserve System, Washington, D.C.
102
plans. The researcher concluded that the provision of financial information has an
important effect on saving for retirement.
2.11.6 Hilgert, Hogarth and Beverly (2003)
The study of Hilgert, Hogarth and Beverly (2003268
) is published in Federal Reserve
Bulletin, utilized the discipline of behaviour economics as a framework for exploring the
differences in consumer knowledge and consumer behaviour, and also explained how
these two are connected (p. 320). They focused on four primary activities of financial
management: cash flow management, credit management, saving and investment. The
data was collected from University of Michigan‘s Monthly Surveys of Consumers and
the Survey of Consumer Finances conducted in November and December 2001 in order
to assess 1) financial literacy of adults and 2) effect of this financial literacy on their
financial behaviours. Eighteen financial management behaviours were reported on by
consumers, like basic skills while managing money i.e. cash-flow management, saving to
and more sophisticated skills such as investment and credit behaviours. Information about
their use of 13 different financial products was also provided. When using an index,
where behaviours were divided into classifications of low, medium, and high, they found
that financial behaviours are hierarchical. This means that individuals with limited
amounts of cash should engage in practices that manage their cash flows, but not in
saving or investing.
They found there is a direct correlation between financial knowledge and behaviour,
although the direction of the causality is unclear. Those who score higher on financial
literacy tests were more likely to follow recommended financial practices as well as more
likely to engage in recommended financial behaviour. They concluded that increased
index scores for investment, saving, and credit management practices are partially
contributable to understanding and being knowledgeable about investment, savings, and
credit respectively. According to this data, improvements in individuals‘ financial
268 Hilgert, M.A., Hogarth, J.M. & Beverly, S.G. (2003). Household financial management: The connection
between knowledge and behaviour. Federal Reserve Bulletin, 89 (July): 309-322.Washinton, D.C.
103
management practices are directly related to the amount of knowledge and experience
possessed (p. 321).
2.11.7 Grable and Joo (2004)
Grable and Joo (2004 269 ) conducted an exploratory study on understanding the
determinants of financial satisfaction. They have reviewed the various determinants of
the financial satisfaction and developed a framework of financial satisfaction. They found
numerous direct, indirect and total effects in their study. They determined that education,
financial knowledge, financial risk tolerance, financial solvency, financial behaviours and
financial stress levels had a direct effect on the financial satisfaction.
2.11.8 Grable and Joo (2004)
Grable and Joo (2004270
) analyzed effect of environmental and biopsychosocial factors
on financial risk tolerance of an individual. The objective of the research was to test the
effects of demographic, socioeconomic and psychological variables on financial risk
tolerance among the college faculty and staff members, using environmental (age,
gender, racial background, birth order, self-esteem, Type-A personality, and financial
satisfaction) and biopsychosocial (income, net worth, financial knowledge, home
ownership, education, marital status) factor classifications. The intent of test was to
determine whether environmental and biopsychosocial factors play a role in determining
the person‘s tolerance for financial risk, and if it confirmed, when and which factor
appeared to be the most important. The study was premised on Irvin‘s (1993271) risk
taking behavioural model. Financial risk tolerance is defined as the willingness to engage
in ―behaviours in which the outcomes remain uncertain with the possibility of an
identifiable negative outcome‖ (Irvin, 1993, p. 11). Findings derived after analyzing data
269 Grable, J.E., & Joo, S. (2004). Environmental and biopsychosocial factors associated with risk tolerance.
Financial Counseling and Planning 15(1), 73–82. 270
Grable, J. E. & Joo, S. (2004). Environmental and biopsychosocial factors associated with financial risk
tolerance. Financial Counseling and Planning 15(1), 73–82. 271 Irwin, C. E. (1993). Adolescence and risk taking: How are they related? In N. J. Bell & R.W. Bell
(Eds.), Adolescent risk taking. Newbury Park, CA: Sage.
104
collected from the sample (N= 406) of faculty and staff from two universities, indicated
that education, marital status, net worth, financial knowledge, household income, and
self-esteem were significantly related to financial risk tolerance. Among all of these
significant factors, except self-esteem, were environmental factors, while self-esteem
represented only one significant biopsychosocial factor. The study concluded that
respondents who have a higher net worth, a high level of education, high household
income and more financial knowledge tend to exhibit a greater level of preference for
financial risk. While, in terms of biopsychosocial factors, self esteem is related positively
to financial risk tolerance. Study also confirmed that environmental factors influence risk
tolerance more directly than biopsychosocial factors.
2.11.9 Mandell (2005)
Mandell (2005272) used the data collected by The Jumpstart Coalition and conducted a
survey with an objective to find out financial literacy of high school students. The survey
contained 31 questions and distributed to a sample of approximately 4,000 high school
students every two years. The results of the survey have proven that students are fairly
financially illiterate, which has continued in a downward trend since then. Mandell
evaluated the survey results to determine the correlation between financial literacy and
propensity to save. This study found that financial education does contribute, although
only slightly, to measures of financial literacy and financial education does positively
affect the savings. Saving also appears to have some impact, however small, on the
propensity to save. Although the study produced unusual results, the overall conclusion
of the study was that financial education affects propensity to save without necessarily
improving financial literacy. In other words, students who take courses dealing with
personal finance may emerge with little improvement in financial literacy, but they may
end up more savings oriented than students who have not had a course. Mandell also
concludes that even if financial literacy does matter, educators must first determine which
272
Mandell, L. (2005) Financial Literacy – Does it Matter? The Jumpstart Coalition for Personal Financial
Literacy. Washington D.C.: Retrieved on December 2008 from: http://www.jumpstart.org/ download.cfm.
105
areas are of the most important to teach and the appropriate time tables for educating
students.
2.11.10 Kim (2007)
Kim (2007 273) examined the impact of a Cooperative Extension workplace financial
education program on a selected group of university employees. A pre- and post- survey
design was used. The University of Maryland Cooperative Extension offered four two-
hour workshops for 97 University employees from the summer and fall of 2002 to the
spring and fall of 2003. The workshop curriculum was consisted of eight modules, such
as financial decision making/goal setting, financial record organization, cash
management, credit management, risk management, investment, retirement planning, and
estate planning. The study examined the influences of the program on university
employees‘ self-assessed financial knowledge, financial behaviours, and perceived
financial well-being. The data collected during pre- and post-assessment found that
participants made significant improvements in financial knowledge, financial behaviours,
and perceived financial well-being. The present study used the Human Behaviour model
of Ajzen and Fishbein (1980274) where knowledge and attitudes are related to individual‘s
beliefs and changes in beliefs are reflected in behaviours. Based on Ajzen and Fishbein‘s
model, Kim (2000275) developed a framework conceptualizing that workplace financial
education could positively change financial knowledge and financial behaviours, which
in turn could improve financial well-being. Further, workplace financial education could
impact employees‘ absenteeism and job satisfaction by improving their financial well-
being. Overall, participants perceived that their financial well-being improved as a result
of the workshops, which was similar to Hira and Loibl‘s (2005276) study. Although the
mean scores for saving for retirement in the financial behaviour scale did not change,
273 Kim, J. (2007). Workplace financial education program: Does it have an impact on employees' personal
finances? Journal of Family and Consumer Science, 99(1), 43-47. 274 Ajzen, I. & Fishbein, M. (1980). Understanding attitudes and predicting social behaviour. Englewood
Cliffs, NJ: Prentice-Hall. 275 Kim, J. (2000). The effects of workplace financial education on personal finances and work outcomes.
Doctoral Dissertation, Blacksburg: Virginia Polytechnic Institute and State University. 276 Hira, T. K. & Loibl, C. (2005). Understanding the impact of employer-provided financial education on
workplace satisfaction. Journal of Consumer Affairs, 39(1), 173-194.
106
people felt more secure about their personal finances for retirement after the workshops.
Further, although satisfaction with their current financial situations did not change
significantly, their financial stress had decreased and their feelings of retirement security
and general perceived financial well-being improved. The results suggest that workplace
financial education not only impacts retirement planning, but also perceived financial
well-being. Studies have found that employees‘ subjective financial well-being influences
their absenteeism, job satisfaction, and organizational commitment.
2.11.11 Maarten van Rooji et al. (2007)
For better understanding of financial literacy and its relation to financial decision making,
Maarten van Rooji, Annamaria Lusardi and Rob Alessie (2007277) designed two modules:
1) Basic financial literacy and 2) Advanced financial literacy, covering the questions to
measure numeracy and basic knowledge related to the working of inflation and interest
rates, as well as questions to measure more advanced financial knowledge related to
financial market instruments (stocks, bonds, and mutual funds) before entering into the
stock market. They used the data from the 2005 DNB (De Nederlandsche Bank)
Household Survey (DHS). This data set was representative of the Dutch population, and
was contained over 2,000 households. This study found that lack of financial literacy
correlates with investors‘ decisions to participate in the stock market and also financial
literacy matters for stock market ownership, even after controlling a large set of
demographic variables, income and wealth. The estimates are also sizable: A one-
standard deviation increase in advanced literacy raises stock market participation by more
than eight percentage points (p. 16). Thus, overall study concluded that stock ownership
increases sharply with the financial literacy (p. 15).
2.11.12 Luigi and Japelli (2008)
277 van Rooij, M., Lusardi, A., & Alessie, R. (2007, October). Financial literacy and stock market
participation. Dartmouth College, MRRC Working Paper 2007-2162.
107
Luigi Guiso and Tullio Japelli (2008278) attempted to find out a correlation between
financial literacy and portfolio diversification by using the data of Unicredit Clients‘
Survey conducted between June and September 2007. They have developed a sample of
Italian clients possessing at least 10,000 euro of financial wealth of the Unicredit Group.
The survey collected data from the respondents of 1686 individuals having a checking
account in one of the banks of Unicredit Group. The objectives of the study were to
analyze the different levels of financial literacy, with reference to the information
required to choose among different sets of assets and understand the meaning of
diversification. After analyzing the impact of financial literacy and investors‘
characteristics on portfolio diversification, they found that measures of financial literacy
are strongly correlated with the degree of portfolio diversification, and infer that the
evidence is consistent with explanation of under diversification based on investors‘
limited financial literacy. They have given four types of explanations for the lack of
diversification. One, the limited diversification reflects some frictions in otherwise
traditional portfolio models: high transaction cost, search cost, small portfolio size and
constraints to buy in round lots. This approach stresses that under-diversification is a
rational choice, where individuals trade-off the benefits of diversification against the
costs of achieving it. The exact mechanisms through which these frictions operate vary:
they can restrict the ability of investors to hold a large number of assets (Perraudin and
Sorensen, 2000279), limit their information processing capacity (Van Nieuwerburgh and
Veldkamp, 2007280), or affect the way they value outside job options (p. 4). Second, they
argued that diversification may reflect behavioural biases (p. 4). And third, investors may
develop preferences or attractions towards certain types of stocks (p. 5). And lastly, the
poor financial literacy is a fourth important factor associated with lack of diversification.
The individuals with poor financial sophistication may undervalue the benefits of
diversification or ignore them altogether (p. 6).
278 Luigi, G. & Jappelli, T. (2008). Financial literacy and portfolio diversificaition. CSEF Working Papers
Series, Working paper No. 212, Centre for Studies in Economics and Finance (CSEF), University of
Naples, Italy. 279 Perraudin, W. R. & Soreson B. E. (2000). The demand for risky assets: Sample selection and household
portfolios. Journal of Econometrics, 97, 117-144 280 Nieuwerburgh, S.V., & Veldkamp, L. (2007). Information acquisition and under-diversification. Stern
School of Business, New York University, Mimeo.
108
2.11.13 Hussain et al. (2009)
Hussain et al. (2009281) studied the level of financial literacy of UAE individual investors
who invest in local financial markets and examined the relationship between financial
literacy and the influence of the factors that affect the investment decision.. The data
were collected from 290 national investors. The study found that financial literacy of
UAE investors is far from the needed level. The financial literacy level was found to be
affected by income level, education level and workplace activity. High-income
respondents hold high educational degrees, and those who work in the field of
finance/banking or investment had as expected a higher financial literacy level than
others, whereas, financial illiteracy exists regardless of the age of the respondents. A
significant difference in the level of financial literacy was found as well between the
respondents according to their gender. Specifically, women possess a lower level of
financial literacy than men. The study also concluded that there is a relationship between
financial literacy and investment decisions.
2.11.14 Seth et al. (2010)
Seth et al., (2010282) assessed the level of financial literacy among the investors of Delhi
and National Capital Region. The study attempted to analyze the relationship between
financial literacy and other factors like age, income, and education. The study indicated
that the financial literacy of investors in Delhi and NCR was different for different
financial instruments. Around 96% of them have savings account in the banks, but the
mere acquaintance with banks is not adequate, as only around 30% had knowledge about
National Savings Certificate & Public Provident Fund. While 98 % of the investors knew
about Life Insurance, only about 45% preferred Life Insurance as the most effective
financial instrument, which would be helpful at the time of contingencies. Around 92 %
281 Hussein A. H., Al-Tamimi & Al Anood B. K. (2009). Financial literacy and investment decisions of
UAE investors. The Journal of Finance, 10(5), 500- 516. 282 Seth, P., Patel, G., and Krishnan, K. K. (2010). Financial literacy and & investment decision of Indian
investors – A case of Delhi & NCR.
109
of the investors knew about Mutual Funds but only 24 % preferred them. Financial
literacy is found to be affected by age, income and educational level of the individuals.
High-income respondents had high financial literacy than lower income people. The
study also revealed that people consider Life Insurance as the most effective financial
instrument followed by Fixed Deposits in banks. It was also found that most of the people
relied on telecast in the T.V channels or advertisements put out in the newspapers and
magazines to learn about financial products followed by ―advice‖ from friends. But,
while investing in share market, around 21% people relied on brokers.
2.12 Conclusion
The review of literature presented in this chapter is evidence for that financial literacy
levels possessed by the individuals among the various countries is low irrespective of the
stage of development of the country. Among them, several studies have also established a
relationship between financial literacy and demographics of an individual. While, the
studies done by consumer economists attempted to establish the relationship of financial
literacy with various financial behaviours, as an outcome of financial education. The
studies documented in this chapter shows a low level of financial literacy and lack of
financial information/education affect the ability to perform responsible financial
behaviour. In the context of Indian Economy, the researcher has proceeded further with
an exploratory research to find out whether the investors in state of Gujarat, which is one
of the most progressing states of India does possess the sufficient level of financial
literacy and does it have any impact on their investment decision. The next chapter
discusses the research methodology adopted for this study.
Research
Methodology
Chapter 3
110
3.1 Introduction
3.2 Research Gap
3.3 Objectives of the study
3.4 Scope of the study
3.5 Research Methodology
3.5.1 Research Design
3.6 Sampling Design
3.6.1 Sampling Technique
3.6.2 Population
3.6.3 Sampling Unit
3.6.4 Sample
3.6.5 Sample Size
3.7 Data collection
3.7.1 Secondary Data
3.7.2 Primary Data
3.8 Research Instrument
3.9 Description of variables
3.9.1 Description of demographic and socio-economic variables used
under study
3.9.2 Description of variables used as investmentalternatives
3.9.3 Description of variables used as preferred source of
information used under study
3.9.4 Description of variables influencing investment decision used
under study
3.10 Coding of Variables
3.11 Data analysis
3.11.1. Univariate techniques
3.11.2 Bivariate techniques
3.11.3 Multivariate techniques
111
3.11.4 Available Methodologies to measure Financial Literacy
3.11.5 Methodology used to Measure Financial Literacy for Present
Study
3.12 Hypotheses of the study
3.13 Conclusion
112
3.1 Introduction
Research is a scientific and systematic search for pertinent information on a specific
topic283
. The Advance Lerner‘s Dictionary of Current English lays down the meaning of
research as ―a careful investigation or inquiry especially through search for new facts in
any branch of knowledge‖284
. Before executing any research study, its research
methodology should be laid down explicitly. Research methodology includes various
steps that are generally adopted by a researcher in studying his/her research problem
along with the topic logic behind it. Research methodology depends, to a large extent, on
the target population, and how easy or difficult to access it is. The second factor which
influences it is, of course the importance of the decision which is taken based on the
research. Major parts of the research methodology are problem statement, research
design, sampling plan, questionnaire design, field work plan and analysis plan285
. The
current chapter provides an in-depth view of research methodology discussing on how the
researcher has carried out the present research.
3.2 Research Gap
On the basis of literature review done in the previous chapters, three general conclusions
have been drawn by the researcher.
1. There is a low level of financial literacy among the various demographic groups of the
citizens of different countries, irrespective of stage of the economic development of a
country.
2. There is a strong relationship between financial literacy level and demographic factors.
The literature review supported that there is a strong relationship between financial
literacy levels and demographic factors such as age, gender, education, income level and
work experience.
283 Kothari, C. R. (1999). Research Methodology: Method and Techniques. New Delhi: New Edge
International Publication. p. 1. 284 The Advanced Learner‘s Dictionary of Current English, Oxford, 1952, p. 1069. 285 Nargundkar R. Marketing Research- Text and Cases (2nd Revised ed.), New Delhi: TATA McGraw Hill,
26.
113
3. There is a relationship between financial literacy of individuals and their financial
behaviors.
On the basis of above discussion, the researcher found out a gap which exists in the
previous research works presented here. The present study attempts to study the level of
financial literacy among investors of Gujarat State, who invest in the domestic financial
markets. The present study has focused on following demographic and socio economic
factors of the investors.
Age,
Gender,
Education,
Monthly Income,
Stage of family life cycle,
Employment Structure,
Type of workplace Activity,
Number of years of work experience,
Number of years of Investment Experience,
Number of times individual shop around and
Risk Tolerance Level.
This study has also examined the relationship of these factors on financial literacy level
of investors and the impact of financial literacy level on their monthly spending to
monthly income ratio, monthly saving to monthly income ratio and investment decision.
3.3 Objectives of the study
The broad objective of this research is to study the level of financial literacy of investors
of Gujarat State and its impact on their investment decision.
Specific Objectives of the research are:
114
1. To explore the financial literacy level among the investors of Gujarat State.
2. To study the relationship between demographic and socio economic factors of
investors and their financial literacy level.
3. To study the investors‘ preference towards various sources of information for
investment.
4. To study the difference between preferences for the selected informative variables
and their influence on investment decision under study.
5. To study the relationship between financial literacy level of investors and their
monthly expenditure to monthly income ratio of investors.
6. To study the relationship between financial literacy level of investors and their
monthly saving to monthly income ratio.
7. To study the impact of financial literacy level on investment decision of investors.
3.4 Scope of the study
As mentioned in the objectives of study, the researcher has explored the level of financial
literacy among the investors of Gujarat State. After doing an in-depth study of available
literature on chronological developments taken place while defining the term ―financial
literacy‖, the researcher has conceived the following operational definition of ―financial
literacy‖, which defines the scope of the term for the present study.
Financial Literacy:
Financial literacy is a set of skills and knowledge that allows an investor (individual) to
understand
- the financial principles that individual needs to know to make informed decisions
and
- the financial products that impact individual‘s financial well-being.
115
Here, financial literacy is concerned with the understanding of basic financial concepts,
principles, skills and ability to understand key financial products, to make good financial
choices.
To explore the level of financial literacy, basic financial literacy and advanced financial
literacy are used. The in-depth description of the scope of the terms basic and advanced
financial literacy is mentioned as follows.
A. Basic Financial Literacy (Financial Competence)
Knowledge and understanding of basic financial concepts, principles and
ability to understand key financial concepts.
1 Scope of the term investment 2 Financial worth
3 Concept of disposable income 4 Types of Bank accounts
5 Numeracy 6 Compounding
7 Concept of inflation 8 Time value of money
9 Functioning of stock market 10 Diversification
11 Risk return trade off 12 Risk
13 Risk return trade off of two assets 14 Relationship between investment time
horizon and asset growth
15 Relationship between investment
time horizon and fluctuation
16 Asset al.location
17 Relationship between interest and
asset prices
18 Consumer rights and responsibility
19 Regulatory body as a part of market
structure
20 KYC
B. Advanced Financial Literacy (Product Literacy)
Understanding the basic features of various financial services/investment
instruments
available to the individual investor in Indian Financial Systems.
116
For advanced financial literacy, 30 questions were asked to the respondents covering:
3.5 Research Methodology
Research methodology is a way to systematically solve research problem (Kothari,
1999286
). A system of models, procedures and techniques used to find the result of a
research problem is known as Research Methodology (Panneerselvam, 2010287
).
3.5.1 Research Design
Research design is an arrangement of data collection and analysis of data in a manner that
aims to combine relevance to the research purpose. It is a blue print for the collection,
measurement and analysis of data.
For this study Exploratory and Descriptive research design methods have been used,
wherein researcher has explored the financial literacy level of investors and then
described the impact of financial literacy level on investment decision of investors. Both
the types of source of data primary and secondary were used. The data collection method
is discussed in detail in the following sections.
3.6 Sampling Design
286 Kothari, C. R.(1999). Research Methodology: Method and Techniques (2nd revised ed.). New Delhi:
New Edge International Pvt. Ltd., p. 8. 287 Panneererselvam, R. (2010). Research Methodology (8th ed.). New Delhi: Prentice-Hall India Ltd., p.2.
1. Fixed deposit 2. National Saving Certificates
3. Public Provident Funds 4. Employee Provident Funds
5. Equity Shares 6. Preference Shares
7. Mutual Funds 8. Debentures and Bonds
9. Post office Monthly Income scheme 10. Insurance Plan
117
Sampling design is a definite plan for obtaining a sample from a given population.
Sampling plan means a method, decided before the survey is undertaken, of selecting the
objects out of the universe. It refers to the technique or the procedure a researcher adopts
in selecting a sample and the sample size.
3.6.1 Sampling Technique
Sampling techniques are broadly divided into two groups as probability sampling
techniques and non-probability sampling techniques. For the current study, non-
probability convenient sampling technique is chosen. In convenient sampling technique,
the researcher chooses the sampling units as per his/her convenience.
3.6.2 Population
For any research study, to identify or decide the target population is one of the most
important things. Target population is defined as a specific group of people or objects for
which the data can be gathered or observations made to develop required data structure
and information288
. As this study has been done in the state of Gujarat of India, the retail
individual investors289
(above the age of 18 years) of Gujarat State are considered as a
population for this study.
3.6.3 Sampling Unit
Sampling unit is an object for which the data being gathered. For this study, all
households in the state of Gujarat who make investments in financial instruments are
considered as sampling unit.
288
Hair, Bush and Ortinau, Marketing Research- Within a Changing Information Environment (2nd ed.).
Tata McGraw Hill Publication, p 43. 289
―Retail individual investor‖ means an investor who applies or bids for specified securities for a value of
not more than two lakhs rupees. This definition is given in Securities and Exchange Board of India (Issue
of Capital and Disclosure Requirements) Regulations, 2009 (Amended), dated August 26, 2012, p. 4.
118
3.6.4 Sample
For this study sample is an Individual.
3.6.5 Sample Size
Total sample size of 385 investors in the state of Gujarat is considered for the study.
During data collection, proper care was taken to collect the data in such a way that it
covers the entire state of Gujarat. The justification for sample size determination is given
below.
Following formula can be used to determine sample size (Nargundkar, 2003290
).
𝑛 = 𝑝 1 − 𝑝 𝑍
𝑒
2
𝑛 = 0.50 1 − 0.50 1.96
0.05
2
Where,
N = Sample Size
Z = Z value from the standard normal distribution for the confidence level desired
by the researcher. For this study, the researcher has assumed 95 percent
confidence level. Then from the standard distribution tables, the Z value is 1.96.
p = Frequency of occurrence of something expressed as proportion (0.50).
290 Nargundkar, R. (2003). Marketing Research: Text and Cases (2nd ed.). New Delhi: TATA McGraw Hill
Publication, pp. 94-95.
𝑛 = 385
119
E = Tolerance error. This can be decided by the researcher. For this study, the
researcher as assumed tolerance error 0.05.
Applying above formula, the sample size comes to 385. Hence, researcher has collected
the primary data from 385 respondents.
3.7 Data collection
For this study both primary as well as secondary data sources are used.
3.7.1 Secondary Data
To get insight into the research area and to develop the theoretical framework and
hypotheses, the information was collected from various books, magazines, journals,
newspapers, web sites, research projects, reports published by Government and private
research firms at national and international level. Speeches and lectures of officials of
various governmental authorities and policy makers around the world were also used as
secondary data sources.
3.7.2 Primary Data
A detailed Performance Test and Questionnaire were prepared and administered on
investors in the State of Gujarat. With the help of these, personal (face to face) interviews
of the respondents were performed, because of the advantages of this method over other
methods. By personal interviewing, the researcher gets some other information pertaining
to the respondent. Sometimes, if the respondent has any query/doubt, it can be traced by
the researcher or interviewer.
Total sample size of 385 investors in the state of Gujarat is considered for the study.
During primary data collection, proper care was taken to collect the data in such a way
that it covers the entire state of Gujarat. For the purpose of primary data collection, the
120
researcher has approached various investors‘ associations operating in the different cities
of Gujarat with the objective of investors‘ awareness. The researcher has approached the
individual retail investors (respondents) at various investors awareness programmes
conducted by these associations in collaboration with SEBI and/or The Bombay Stock
Exchange Ltd. under the Investor Education and Protection Fund (IEPF). The researcher
has distributed and collected the filled up questionnaires from the respondents, before the
investor awareness programmes would begin. The investors were invited to attend these
programmes by an open invitation given as an advertisement in the newspapers.
After the collection of primary data, it was found that some of the questionnaires were
not filled up properly or some of the questions were not attempted. Such questionnaires
were discarded and new questionnaires from other investors were again collected.
3.8 Research Instrument
The survey was developed to investigate the financial literacy level of investors and its
impact on investment decision. To serve this objective, the research instrument was
divided into four sections. The details of which are discussed in the next section.
Instrument Construction:
Primary data are the most important source for this study as it is given by the investors.
Performance Test and Questionnaire were used as a research instrument to collect this
data. So, preparation of Performance Test and Questionnaire is the most important stage
for this study. Initially, preliminary research instrument was prepared based on in-depth
literature review in the same and/or similar field. Then the research instrument was sent
to a total of 16 experts at national and international level. Out of which, eleven experts
were from finance disciplines in the academic field, four experts were senior officials of
Securities and Exchange Board of India (SEBI), two experts were academicians in U.K.
and U.S.A, and one expert who is a policy maker on financial literacy among OECD
member countries, Organization of Economic Cooperation and Development (OECD).
121
The changes were incorporated in the research instrument as suggested by the experts and
then pilot testing of this modified research instrument was performed. Some drawbacks
were identified from the pilot testing. After eliminating these drawbacks and simplifying
language, final research instrument was prepared for the study. The research instrument
was not only in English, but, considering that language should not be a barrier to measure
financial literacy level of investors and its impact on investment decision, the whole
research instrument was translated into regional language of the state of Gujarat, i.e.
Gujarati. The translated version of the data collection instrument was sent to reviewers
who are subject experts in the field of Gujarati language. As per their suggestions, some
of the basic words and sentences were modified; keeping in mind that the ultimate crux
of the variables (to be studied) would not be changed.
The final research instrument was divided into four sections. Out of which Section A and
section B comprised of performance test on objective type and true/false type questions
(total 50 questions) based on basic financial literacy (20 questions) and advanced
financial literacy (30 questions) respectively. The respondents‘ overall performance in
these two sections has generated result of financial literacy level possessed by him/her in
terms of score. To measure the literacy level, the score as a percentage of correct answers
(Lyons, 2007291
) is taken into consideration (Detailed discussion on this has been done in
the next section of the same chapter). Section C comprised of questionnaire for
investment decision and risk tolerance level and the last section of the research
instrument was section D which consisted of questions based on demographic and socio-
economic variables. The research instrument follows a specific format and each question
belonging from a particular section of a data collection tool with its objective is briefly
discussed as under.
Section A: To measure basic financial literacy level (skills, knowledge and
understanding of basic financial concepts, principles and numeracy that individual should
posses), 20 questions were asked to the respondents. Out of these, 13 questions were
291 Lyons, A., Rachlis, M., & Scherpf, E. (2007). What‘s in a Score? Differences in Consumers‘ Credit
Knowledge Using OLS and Quantile Regressions. Networks financial institute. Indiana University, 2007-
WP-01, retrieved on January 21, 2012 from www.networksfinancialinstitute.org.
122
taken from the scale developed by Maarten van Rooji, Annamaria Lusardi and Alessie
Rob (2008292
). These questions ranging from numeracy, interest compounding, inflation,
time value of money, functioning of stock market, diversification, risk-return trade off of
two assets, risk, money illusion, relationship between investment time horizon and
fluctuation, concept of asset al.location, relationship between interest and asset price. The
rest of the seven questions were based on investment concept, financial worth, disposable
income, understanding of types of accounts, consumer right and responsibility,
knowledge of authority and KYC.
Section B: To measure advanced financial literacy level (knowledge and understanding
of the investment alternatives available to the investors in Indian Financial Systems), 30
questions were framed in this section, on the basis of features of these investment
alternatives available for saving and/or investment. These are as follows:
Section C: The questionnaire comprised of eight questions following the objectives
under study. First two questions were introductory question and had served the two
purposes 1. Identification of a sampling unit and 2. To get the attention of the respondent
to fill up the questionnaire. Out of which the second question was based on the present
investment pattern of investors, where the respondents were given the eight multiple
choices. These are 1) Post office Savings Schemes 2) Insurance and Pension Plans, 3)
Bank Deposits 4) Mutual Funds, 5) Shares, 6) Debentures and Bonds, 7) Real Estate and
292 Van Rooji, M., , Lusardi, A. & Alessie, R. (2008). Financial literacy and stock market participation, in
Financial Literacy, Retirement Provisions and Household Portfolio Behavior by Van Rooji, M. 45-90.
1. Fixed deposit 2. National Saving Certificates
3. Public Provident Funds 4. Employee Provident Funds
5. Equity Shares 6. Preference Shares
7. Mutual Funds 8. Debentures and Bonds
9. Post office Monthly Income scheme 10. Insurance Plan
123
8) Precious metals (Gold and Silver only). The in-depth description of these variables are
given in Table 3.2. The objective of third question was to know the respondent‘s
preference towards various investment alternatives. For this, the respondents were given
eight multiple choices, which were similar to the previous question. These are 1) Post
office Savings Schemes 2) Insurance and Pension Plans, 3) Bank Deposits 4) Mutual
Funds, 5) Shares, 6) Debentures and Bonds, 7) Real Estate and 8) Precious metals (Gold
and Silver only). The respondents were asked to rank the each investment alternative on
the basis of their preference for investment. The fourth question gives information about
the reasons that restrict the individual for not to invest (This question was asked only to
those respondents who are non-investors). The objective of the fifth question was to
know the respondents‘ investment objectives in the manner of their importance. For this,
seven investment objectives are given to respondents and they were asked to rank each
investment objective on the basis of their importance. These investment objectives are 1)
Saving of Income-tax, 2)Buying/improving home, 3) Child‘s marriage/education/social
ceremonies, 4) Secured retirement, 5) Gift/ donation/ vacation/ pilgrims, 6) To meet
unexpected financial contingencies and 7) To safeguard against inflation/ capital
appreciation. The sixth and seventh questions are rating questions collected on 5-point
Likert scale ranging from the least preferred = 1 to highly preferred = 5 and the least
influence = 1 to highly influence = 5 respectively. The question six is on respondent‘s
preference towards particular source of information while collecting information for
investment decision, which contains sixteen variables and question seven, contains forty
four variables that may influence the respondent‘s investment decision. In questions
seven, the researcher has employed forty four variables modified from that used by
Hussein A. H. (2009293
). The in-depth description of the variables used in question six
and seven are given in Table 3.2 and 3.3 respectively. The last question of this section is
to find out the investment risk tolerance level of the respondent. For this, the scale was
adopted from SEBI‘s financial education material (2010294
) especially developed for
financial literacy initiative in India.
293 Hussein A. H. Al-Tamini & Al Anood B. K. (2009). Financial literacy and investment decisions of UAE
investors. The Journal of Risk Finance, 10 (5). 294Securities and Exchange Board of India (SEBI) (2010). Lessons on financial planning for young
investors. 30-31.
124
Section D: This section focuses on personal information of respondents. The questions
based on city, age, gender, higher level of education attempted, monthly income, stage of
family life cycle, employment structure, type of workplace activity, years of work
experience, proportion of monthly expenditure to monthly income, proportion of monthly
saving to monthly income, years of investment experience and number of times
respondents shop around/ make inquiry while investing are drawn from the previous
research studies done in the field of financial literacy and nearer to it. Table 3.1 shows the
description of demographic and socio-economic variables used in this study.
The questionnaire in English and its translated version in Gujarati language are given in
the annexure.
3.9 Description of variables
The in-depth description of variables used under this study is given below.
3.9.1 Description of demographic and socio-economic variables used under study
Table 3.1 shows the description of demographic and socio-economic variables used under
study.
125
Table 3.1 Description of Demographic and Socio-Economic Variables used Under Study
Variables Scale used to
measure a
variable
Type of
measurement used to
measure a variable
Interval Source
Gender Dichotomous Multiple Choice
Question
All male investors (decision makers)
All female investors (decision makers)
---
Age Nominal Multiple Choice
Question
18-25 years,
26-35 years,
36-45 years,
46-55 years,
56-65 years,
66 years & above.
NCAER-MNYL Report
(2007). How India Earns,
Spends and Saves: India
Financial Protection Survey.
Education Nominal Multiple Choice
Question
Primary,
Secondary,
Higher Secondary,
Graduation,
Post Graduation.
NCAER-MNYL Report
(2007) How India Earns,
Spends and Saves: India
Financial Protection Survey.
Monthly
Income
Nominal Multiple Choice
Question
Upto Rs. 10,000,
Rs. 10,001 to Rs. 15,000,
Rs. 15,001 to Rs. 20,000,
Rs. 20,001 to Rs. 25,000,
Rs. 25,001 and above.
NCAER and SEBI (2011),
How Households Save and
Invest: Evidence from
NCAER Household Survey
Stage of family
life cycle
Nominal Multiple Choice
Question
Young single,
Young married without children,
Young married with children,
Middle-age married with children,
Middle age married without dependent
children,
Older married,
Older unmarried.
Schiffman, L. & Kanuk, L.
(2008). Consumer Behavior.
Ninth Ed. Pearson
Education, Inc. New Delhi.
p. 38
126
Variables Scale used to
measure a
variable
Type of
measurement used to
measure a variable
Interval Source
Employment
structure
Nominal Multiple Choice
Question
Full time salaried,
Part time salaried,
Casual, Self employed,
Housewife, Retired, Unemployed,
Student.
Hussein A. H. et al. (2009)
Type of
workplace
activity
Nominal Multiple Choice
Question
Working in finance/account related
Multiple Choice Question activity,
Not working in finance related activity.
Hussein A. H. et al. (2009)
Years of Work
Experience
Nominal Multiple Choice
Question
Less than five,
6 to 10 years,
11 to 20 years,
21 to 30 years,
More than 30 years.
Hussein A. H. et al. (2009)
Years of
Investment
Experience
Nominal Multiple Choice
Question
Less than five,
6 to 10 years,
11 to 20 years,
21 to 30 years
More than 30 years.
Hussein A. H. et al. (2009)
Monthly
expenditure to
monthly income
ratio
Nominal Multiple Choice
Question
1% to 50%,
51% to 60%
61% to 70%,
71% to 80%,
81% to 90%,
More than 90%.
SEBI-NCAER (2000)
Survey of Indian Investors.
Part A.SEBI.
Table 3.1 Continued
127
Variables Scale used to
measure a
variable
Type of
measurement used to
measure a variable
Interval Source
Monthly saving
to monthly
income ratio
Nominal Multiple Choice
Question
1% to 50%,
51% to 60%
61% to 70%,
71% to 80%,
81% to 90%,
More than 90%.
SEBI (2000). Survey of
Indian Investors. Part A.
SEBI.
No. of times
investor shop
around295
/ make
inquiry while
investing
Nominal Multiple Choice
Question
Zero,
1 to 3,
4 to 6,
More than 6.
ACNeilson (2005). ANZ
Survey of Adult Financial
Literacy In Australia.
Risk Tolerance Nominal Multiple Choice
Question
Lowest risk takers,
Moderate risk takers,
High risk takers,
Highest risk takers.
SEBI (2010). Lessons on
financial planning for young
investors.
295 This was defined as “… shop around by comparing financial products from different organisations”. See, ACNeilson (2005). ANZ Survey of Adult
Financial Literacy in Australia. Australia.
Table 3.1 Continued
128
3.9.2 Description of variables used as investment alternatives
Table 3.2 shows the description of variables used as investment alternatives under this
study.
Table 3.2 Description of Variables Used as Investment Alternatives for this Study
Variable Operational Definition Source
Post Office
Saving
Schemes
Investments in Post office & other similar
Saving Schemes such as
POMIS/NSS/KVP/PPF/PF etc.
SEBI and NCAER
(2011)
Insurance and
Pension Plans
Insurance and pension plans of LIC and private
companies.
SEBI and NCAER
(2011)
Bank Deposits Fixed Deposits and Recurring Deposits with
commercial banks, Cooperative banks, Regional
Rural banks, financial institutions other than
above and Company Fixed Deposits
SEBI and NCAER
(2011)
Mutual Funds All the types of schemes provided by the mutual
fund companies.
SEBI and NCAER
(2011)
Shares Investment in types of shares through primary
market and/or secondary market
SEBI and NCAER
(2011)
Debentures All the type of debentures issued by companies. SEBI and NCAER
(2011)
Bonds Various types of bonds issued by Government
and Government Undertakings.
SEBI and NCAER
(2011)
Real Estate Real estate should not be used for
commercial/business purpose.
SEBI and NCAER
(2011)
Precious
Metals
Gold and Silver only (in any form) SEBI and NCAER
(2011)
129
3.9.3 Description of variables used as preferred source of information used under
study
Description of variables used as preferred source of information used under study is
presented in Table 3.3.
Table 3.3 Description of Variables used as Preferred Source of Information used
Under Study
Variables Scale used
to measure
variable
Type of
measurement used
to measure variable
Source
Certified
Financial
Planner
Interval Five Point Rating
Likert Scale
Sung and Sandager (1997),
Lin (2002)
Annual reports
of the company
Interval Five Point Rating
Likert Scale
Lin (2002)
Prospectus of a
company
Interval Five Point Rating
Likert Scale
Lin (2002)
Company‘s
website
Interval Five Point Rating
Likert Scale
Nick et. al (2010), NCAER
and SEBI (2011)
Distributers/agen
ts of financial
product
Interval Five Point Rating
Likert Scale
Lin (2002), NCAER and
SEBI (2011)
Rating agencies‘
reports
Interval Five Point Rating
Likert Scale
Lin (2002), Lee and
Hogarth (2002b), Capon
and Lutz (1979)
Company‘s
telephone
representatives
Interval Five Point Rating
Likert Scale
NCAER and SEBI (2011)
Family members Interval Five Point Rating
Likert Scale
Lin (2002), Nagy and
Obenberger (1994)
Friends and
relatives
Interval Five Point Rating
Likert Scale
NCAER and SEBI (2011),
Lin (2002)
Conversation/ex
changes of views
with professional
colleagues
Interval Five Point Rating
Likert Scale
Arnswald (2001), Nagy and
Obenberger (1994), Capon
and Lutz (1979), Lee and
Hogarth (2002b)
130
Variables Scale used
to measure
variable
Type of
measurement used
to measure variable
Source
Publication in
the financial
press and news
papers and
electronic media
Interval Five Point Rating
Likert Scale
Arnswald (2001), NCAER
and SEBI (2011), Lin
(2002), Lee and Hogarth
(2002b),
Conversation/
exchanges of
views with
company
executive and
sector experts
Interval Five Point Rating
Likert Scale
Arnswald (2001), Lin
(2002), Capon and Lutz
(1979), Lee and Hogarth
(2002b)
Corporate
forecast prepared
by independent
investment
company
Interval Five Point Rating
Likert Scale
Arnswald (2001), Capon
and Lutz (1979), Lee and
Hogarth (2002b)
Published reports
from research
agencies
Interval Five Point Rating
Likert Scale
Arnswald (2001), Lin
(2002), Capon and Lutz
(1979), Lee and Hogarth
(2002b)
Opinions from
existing
investors of
various
instruments
Interval Five Point Rating
Likert Scale
Lin (2002)
Financial
advisors/Broker
and analyst‘s
recommendation
Interval Five Point Rating
Likert Scale
NCAER and SEBI (2011),
Lin (2002), Krishnan and
Brooker (2002), Nagy and
Obenberger (1994)
Table 3.3 Continued
131
3.9.4 Description of variables influencing investment decision used under study
Description of variables used as influencing investment decision used under study is
presented in Table 3.4.
Table 3.4 Description of Variables Influencing Investment Decision used Under
Study
Variables Scale used to
measure
variable
Type of
measurement
used to measure
variable
Source
Condition of financial
statement
Interval Five Point Rating
Likert Scale
Hussein, et al.
(2009), Nagy and
Obenberger (1994)
Expected Corporate Earning Interval Five Point Rating
Likert Scale
Nagy and
Obenberger (1994)
Past performance of the firm
(in terms of profit and return
given to investors)
Interval Five Point Rating
Likert Scale
Hussein, et al.
(2009), Nagy and
Obenberger (1994)
Company‘ s position in the
industry
Interval Five Point Rating
Likert Scale
Hussein, et al.
(2009)
Insiders‘ information Interval Five Point Rating
Likert Scale
Hussein, et al.
(2009)
The result of fundamental
analysis
Interval Five Point Rating
Likert Scale
Chandra et al. (2011)
The result of technical
analysis
Interval Five Point Rating
Likert Scale
Chandra et al. (2011)
Expected return on
investment
Interval Five Point Rating
Likert Scale
Hussein, et al.
(2009), Nagy and
Obenberger (1994)
Feeling for a company‘s
products and services
Interval Five Point Rating
Likert Scale
Hussein, et al.
(2009), Merikas et
al. (2003)
Perceived ethics of company Interval Five Point Rating
Likert Scale
Hussein, et al.
(2009), Nagy and
Obenberger (1994)
Political party affiliation Interval Five Point Rating
Likert Scale
Hussein, et al.
(2009)
132
Variables Scale used to
measure
variable
Type of
measurement
used to measure
variable
Source
Contribution of a firm
towards social causes
Interval Five Point Rating
Likert Scale
Blumberg et al.
(1997)
Coverage in the press Interval Five Point Rating
Likert Scale
Hussein, et al.
(2009), Nagy and
Obenberger (1994)
Recent price movements in a
firm‘ s stock/NAV
Interval Five Point Rating
Likert Scale
Nagy and
Obenberger (1994)
Statements from politicians
and governmental officials
Interval Five Point Rating
Likert Scale
Hussein, et al.
(2009)
Fluctuations/developments
in the indices of the major
market
Interval Five Point Rating
Likert Scale
Hussein, et al.
(2009)
Current economic indicators Interval Five Point Rating
Likert Scale
Hussein, et al.
(2009), Nagy and
Obenberger (1994)
Reputation of a company in
domestic market
Interval Five Point Rating
Likert Scale
Hussein, et al.
(2009)
Nagy and
Obenberger (1994),
Chandra et al. (2011)
Reputation of a parent
company or sister concern
Interval Five Point Rating
Likert Scale
Nagy and
Obenberger (1994)
Environmental Record Interval Five Point Rating
Likert Scale
Nagy and
Obenberger (1994)
Market Capitalization of
company
Interval Five Point Rating
Likert Scale
Chandra et al. (2011)
Conversation/exchanges of
views with professional
colleagues
Interval Five Point Rating
Likert Scale
Arnswald (2001),
Nagy and
Obenberger (1994)
Publication in the financial
press, newspapers and
electronic media
Interval Five Point Rating
Likert Scale
Arnswald (2001)
Conversation/ exchanges of
views with company
executive and sector experts
Interval Five Point Rating
Likert Scale
Arnswald (2001)
Table 3.4 Continued
133
Variables Scale used to
measure
variable
Type of
measurement
used to measure
variable
Source
Studying the portfolio
investments of other market
players
Interval Five Point Rating
Likert Scale
Arnswald (2001)
Corporate forecast prepared
by independent investment
company
Interval Five Point Rating
Likert Scale
Arnswald (2001)
Economic forecasts by
research institutions
Interval Five Point Rating
Likert Scale
Arnswald (2001)
Study of Annual Reports Interval Five Point Rating
Likert Scale
Nagy and
Obenberger (1994)
Opinions from family
members
Interval Five Point Rating
Likert Scale
Hussein, et al.
(2009), Nagy and
Obenberger (1994)
Opinions from friends and
relatives
Interval Five Point Rating
Likert Scale
Hussein, et al.
(2009), Nagy and
Obenberger (1994)
Opinions from existing
investors
Interval Five Point Rating
Likert Scale
Hussein, et al.
(2009)
Financial advisors/Broker
and analyst‟s
recommendation
Interval Five Point Rating
Likert Scale
Hussein, et al.
(2009), Nagy and
Obenberger (1994)
Opinion of Credit Rating
agencies‟ analysis
Interval Five Point Rating
Likert Scale
Arnswald (2001)
Diversification needs Interval Five Point Rating
Likert Scale
Hussein, et al.
(2009), Nagy and
Obenberger (1994)
Liquidity associated with
investment
Interval Five Point Rating
Likert Scale
SEBI (2010)
Availing the benefit of
income tax deduction
Interval Five Point Rating
Likert Scale
Nagy and
Obenberger (1994)
Risk-return trade off Interval Five Point Rating
Likert Scale
SEBI (2010)
Minimizing risk Interval Five Point Rating
Likert Scale
Hussein, et al.
(2009)
Table 3.4 Continued
134
Variables Scale used to
measure
variable
Type of
measurement
used to measure
variable
Source
Ease of obtaining borrowed
fund
Interval Five Point Rating
Likert Scale
Hussein, et al.
(2009)
Preferred investment time
horizon
Interval Five Point Rating
Likert Scale
SEBI (2010)
Safety associated with
investment
Interval Five Point Rating
Likert Scale
SEBI (2010)
Affordable minimum
investment amount
Interval Five Point Rating
Likert Scale
Nagy and
Obenberger (1994)
Ease in Liquidity Interval Five Point Rating
Likert Scale
SEBI (2010)
Guaranteed return Interval Five Point Rating
Likert Scale
SEBI (2010)
For this study, the scope of the term ‗company‘ includes banks, insurance companies,
financial institutions, mutual fund asset management companies and non-banking
financial companies. An in-depth description of the same in given below.
Bank
As per section 5(c) of Banking Regulation Act, 1949 a ―Banking Company‖ means any
company which transacts the business of banking India.
As per section 5(b) of Banking Regulation Act, 1949, ―banking‖ means the accepting, for
the purpose of lending or investment, of deposits of money from the public, repayable on
demand or otherwise, and withdrawal by cheque, draft, order or otherwise. (Source:
Ministry of Finance, Government of India (1949). The Banking Regulation Act, 1949.
Part I: Preliminary. 10th March, 1949.New Delhi, India, p.2.)
Insurance company
Table 3.4 Continued
135
As per the section 2 (20) of Companies Act, 1956; ―Insurance Company‖ means a
company which carries on the business of insurance either solely or by in conjunction
with any other business or businesses. (Source: The Companies Act, 1956, p. 20)
As per section 7(a) ―Insurance Company‖ means any insurer being a company-
(a) which formed and registered under the Companies Act, 1956 (1 of 1956);
(b) in which the aggregate holding of equity shares by a foreign company, either by
itself or through its subsidiary companies or its nominees, do not exceed twenty-
six percent equity capital of such Indian insurance company;
(c) whose sole purpose is to carry on life insurance business or general insurance
business or re-insurance business.
The insurance company includes, Indian Insurance Company, Foreign Insurance
Company and Insurance Co-operative Society. (Source: Government of India (2002). The
Insurance Act, 1938 (As amended by Insurance (Amended Act, 2002). Part – I:
Preliminary. p. 2).
Investment Company
―Investment Company‖ means a company whose principle business is the acquisition of
shares, stock debentures or other securities. (Source: Government of India (2002).The
Insurance Act, 1938 (As amended by Insurance (Amended Act, 2002). Part – I:
Preliminary, p. 2).
Financial Institutions
Financial institutions are financial intermediaries that mobilize savings and facilitate the
allocation of funds in an efficient manner. Financial institutions are classified as banking
and non-banking financial institutions. Non-banking financial institutions are
Developmental Financial Companies, Non-banking Finance Companies, Housing
136
Finance Companies. The Financial Institutions also include term-finance institutions such
as, IDBI, ICICI, IFCI, SIDBI and IIBI. IDFC, NABARD, NHB, UTI, LIC, GIC are
specialized financial institutions (Source: Pathak, B. (2008). The Indian Financial
Systems: Markets, Instruments and Services (2nd
ed.). New Delhi: Pearson Education. p.
3.)
As per the section 4 (A) of Companies Act, 1956; each of the financial institutions shall
be regarded as company for the purpose of this act. Provided that no institution shall be
so specified unless it has been established or constituted by or under any Central Act.
Non-Banking Financial Company is a company registered under the Companies Act,
1956 (Source: The Companies Act, 1956, p. 20).
Mutual Fund Asset Management Company
As per section 2 (d) of SEBI (Mutual Funds) Regulations, 1996; ―asset management
company‖ means a company formed and registered under Companies Act, 1956 (1 of
1956) and approved as such as the Board under sub regulation (2) of regulations 21.
(Source: The Gazatte of India. Securities and Exchange Board of India (Mutual Funds)
Regulations, 1996, Part II- Section 3 Subsection (ii), Chapter 1 – Preliminary, p. 7.)
3.10 Coding of Variables
Codes assigned to each variable of preferred sources of information and variables
influencing investment decision of investors are shown in Table 3.5 and Table 3.6
respectively.
137
Table 3.5 Codes Assigned to Variables used as Preferred Source of Information
used Under Study
No. Variables Code
assigned
1 Certified Financial Planner IS1
2 Annual reports of the company IS2
3 Prospectus of a company IS3
4 Company‘s website IS4
5 Distributers/agents of financial product IS5
6 Rating agencies‘ reports IS6
7 Company‘s telephone representatives IS7
8 Family members IS8
9 Friends and relatives IS9
10 Conversation/exchanges of views with professional colleagues IS10
11 Publication in the financial press and news papers and electronic
media
IS11
12 Conversation/ exchanges of views with company executive and
sector experts
IS12
13 Corporate forecast prepared by independent investment company IS13
14 Published reports from research agencies IS14
15 Opinions from existing investors of various instruments IS15
16 Financial advisors/Broker and analyst‘s recommendation IS16
138
Table 3.6 Codes Assigned to Variables Influencing Investment Decision used Under
Study
No. Variables Code
assigned
1 Condition of financial statement ID1
2 Expected Corporate Earning ID2
3 Past performance of the firm (in terms of profit and return given to
investors)
ID3
4 Company‘ s position in the industry ID4
5 Insiders‘ information ID5
6 The result of fundamental analysis ID6
7 The result of technical analysis ID7
8 Expected return on investment ID8
9 Feeling for a company‘s products and services ID9
10 Perceived ethics of company ID10
11 Political party affiliation ID11
12 Contribution of a firm towards social causes ID12
13 Coverage in the press ID13
14 Recent price movements in a firm‘ s stock/NAV ID14
15 Statements from politicians and governmental officials ID15
16 Fluctuations/developments in the indices of the major market ID16
17 Current economic indicators ID17
18 Reputation of a company in domestic market ID18
19 Reputation of a parent company or sister concern ID19
20 Environmental Record ID20
21 Market Capitalization of company ID21
22 Conversation/exchanges of views with professional colleagues ID22
23 Publication in the financial press, newspapers and electronic
media
ID23
24 Conversation/ exchanges of views with company executive and
sector experts
ID24
25 Studying the portfolio investments of other market players ID25
26 Corporate forecast prepared by independent investment company ID26
27 Economic forecasts by research institutions ID27
28 Study of Annual Reports ID28
29 Opinions from family members ID29
30 Opinions from friends and relatives ID30
31 Opinions from existing investors ID31
32 Financial advisors/Broker and analyst‟s recommendation ID32
139
No. Variables Code
assigned
33 Opinion of Credit Rating agencies‟ analysis ID33
34 Diversification needs ID34
35 Liquidity associated with investment ID35
36 Availing the benefit of income tax deduction ID36
37 Risk-return trade off ID37
38 Minimizing risk ID38
39 Ease of obtaining borrowed fund ID39
40 Preferred investment time horizon ID40
41 Safety associated with investment ID41
42 Affordable minimum investment amount ID42
43 Ease in Liquidity ID43
44 Guaranteed return ID44
3.11 Data analysis
Raw data contained in the research instrument needs to be converted into suitable form so
that meaningful findings can be obtained. For this purpose, the data are to be coded and
transferred from research instrument to the designed format. Once, the data are
transferred properly, data analysis can be initiated. The data obtained against various
questions from 385 valid respondents were properly coded and transcribed into designed
format.
Analysis of data is the process by which data are converted into useful information.
Different data analysis techniques were used to get meaningful outcome from the data
obtained against different questions of the research instrument and transferred to the
format. Decisions as to which of the statistical techniques should be used were made on
the basis of the various criteria like (a) the scale and other characteristics of data, (b)
objectives of the study, (c) characteristics of the research design etc. Following
paragraphs provide a bird‘s eye view of data analysis techniques, which have been used
Table 3.6 Continued
140
for the overall analysis of this study. The detailing of these techniques has been done in
Chapter 4 at appropriate place for better understanding.
Various techniques of data analysis are available. Broadly, data analysis techniques are
divided into three categories296
.
1. Univariate, involving a single variable at a time,
2. Bivariate, involving two variables at a time and
3. Mutlivariate, involving three or more variables simultaneously.
3.11.1. Univariate techniques
Univariate techniques are appropriate when there is a single measurement of each
element in the sample or there are several measurements of each element but each
variable is analyzed in isolation. Univariate techniques can be classified based on
whether the data are metric or non-metric. Metric data are measured on an interval or
ratio scale. Non-metric data are measured on a nominal or ordinal scale.
Frequency distribution is most widely used in Univariate technique. For this study,
where one variable was to be considered at a time, frequency distribution was carried out,
to obtain a correct number of responses. Bar charts, pie charts, percentage etc. were used
for further analysis of such questions.
3.11.2 Bivariate techniques
Bivariabte techniques are appropriate when the researcher wants to analyze two variables
simultaneously. This technique is also used to find out the association between two
variables. Some Bivariate techniques like cross tabulation, chi-square tests, correlation
and regression analysis were used in the study.
296 Nargundkar R.(2004). Marketing Research- Text and Cases (2nd ed.). Tata McGraw Hill Publication,
120.
141
3.11.3 Multivariate techniques
Multivariate techniques are suitable for analyzing data when there are three or more
measurements of each element and where the variables are analyzed simultaneously.
Multivariate techniques are broadly classified as dependence techniques and
interdependence techniques.
Dependence techniques are used when one or more variables are designated as being
predicted by a number of independent (predictor) variables. For the present study binary
logistic regression analysis, as a dependency technique, was used to classify respondents
on the basis of their financial literacy level either as investor with lower financial literacy
or investor with higher financial literacy. Correlation and regression analysis were also
performed under this technique.
Interdependence techniques are concerned with the relationship of a set of variables in
which no one variable is designated as being predicted by other variables. For the present
study interdependence techniques like factor analysis and regression analysis were used.
3.11.4 Available Methodologies to Measure Financial Literacy
From the review of literature presented in the first chapter of this study, it is concluded
that there is diversity in conceptual definitions of the term ―financial literacy‖ and the
methods used to measure financial literacy under various study. On the basis of surveys
done across the various nations, it is found that there are two types of different
approaches used to measure financial literacy. One approach is to give respondents an
objective test (i.e. performance test) that measures their knowledge and understanding of
financial terms and ability to apply financial concepts to particular situations. These types
of surveys were conducted in the United States and Korea. The second approach is to ask
the respondents for self-assessment (i.e. self report methods), or for their perceptions, of
their financial understanding and knowledge, as well as their attitudes towards financial
142
instruments, decisions, information, and its receipts. This is the approach used by surveys
undertaken in the United Kingdom, Japan and Australia. From Table 3.7, it can be seen
that both the measurement methods have been employed by various researchers to
measure financial literacy in their studies.
Table 3.7 shows a particular methodology adopted by a variety of researchers, policy
makers and authorities to measure financial literacy across the several empirical studies
done in various countries. As mentioned above, both the measurement methods have
been employed to measure financial literacy. Performance tests are principally
knowledge based, reflecting conceptual framework and/or construction. Most of the
measurement of financial literacy has focused on the cognitive aspects of the concept and
what people know or understand about financial matters because ―to be financially
literate, individuals must demonstrate knowledge and skills needed to make choices
within a financial marketplace‖ (Huston 2010297
, p. 309–310). The performance test as a
method for measurement of financial literacy is most often conducted using a set of
multiple-choice test questions and/or true–false test questions that are included in a larger
survey instrument that asks about general or specific financial matters and behaviors
(e.g., Hilgert, Hogarth, and Beverly 2003298
; Lusardi and Mitchell 2007299
; Lusardi,
Mitchell, and Curto 2010300
).
In contrast, a self reported method assesses perceived knowledge or confidence in
knowledge (i.e. how much you think you know). The mismatch between self-assessed
financial knowledge and actual understanding of financial concepts may lead to the
overconfidence in the respondents/investors. A substantial academic literature in
cognitive psychology makes the case that people are usually overconfident and in
particular, they are overconfident about the precision of their knowledge (Odean,
297
Huston, S. J. (2010), Measuring Financial Literacy. Journal of Consumer Affairs, 44: 296–316. 298 Hilgert, M.A., Hogarth, J.M. & Beverly, S.G. (2003). Household financial management: The
connection between knowledge and behavior. Federal Reserve Bulletin, 89 (July): 309-322.Washinton,
D.C. 299 Lusardi, A. & Mitchell, O.S. (2007c).Financial Literacy and Retirement Planning: New Evidence from
the Rand American Life Panel. Michigan Retirement Research Center Working Paper 2007-157. 300 Lusardi, Annamaria, Olivia Mitchell, &Vilsa Curto (2010), ―Financial Literacy Among the
Young,‖ Journal of Consumer Affairs, 44(2), pp 358-380.
143
1998301
). Literature has also documented that in general; people tend to overestimate their
ability to do well at tasks, are unrealistically optimistic about future events and have
unrealistically positive self-evaluations. International financial literacy research studies
suggest that overconfidence regarding financial knowledge and understanding is a
significant issue that impacts upon both the degree to which people seek financial
information and advice, and the financial decisions that they subsequently make (Lusardi,
A. and O Mitchell, 2006302
). An OECD report (2005) discusses research across 12
countries that found that respondents felt they knew more about financial matters than
was actually the case (consumers often think that they know more than they actually do
(ACNielsen, 2005303
). This was particularly clear in research which combined objective
tests (that measured knowledge and understanding of financial terms and ability to apply
financial concepts to particular situations), with self-assessment (respondents‘
perceptions of their financial understanding and knowledge (OECD, 2005304
)). However,
it is a common finding that has been demonstrated not only in financial matters, but
across a wide range of knowledge and abilities (Alba & Hutchinson, 2000305
).
301 Odean, T. (1998). Volume, Volatility, Price and Profit when all Traders are Above Average, Graduate
School of Management, University of California -Davis, United States of America, draft April 1998. 302 Lusardi, A. & Mitchell, O. (2006). Financial Literacy and Retirement Preparedness: Evidence and
implications for financial education programs, Michigan Retirement Research Centre, Working paper
2006-144, November 2006, p. 8. 303 ACNielsen Research (2005). ANZ Survey of Adult Financial Literacy in Australia. Melbourne,
November 2005. 304
OECD, op. cit., pp. 43-44. In the 2005 ANZ survey of financial literacy, 67% of respondents said that
they understood the principle of compound interest, but only 28% were rated with a ‗good level‘ of
comprehension when they solved an actual problem. 305 Alba, J., & Hutchinson, J.W. (2000). Knowledge calibration: What consumers know and what they think
they know. Journal of Consumer Research, 27, 123-156.
144
Table 3.7 Methodology Adopted by Various Researchers for Measuring Financial
Literacy
Research studies
on measuring
financial literacy
Particulars Methodology adopted in
a research study
Self-
assessment
questions
Performance
Test
Volpe, Chen, &
Pavlicko (1996)306
Percent correct answers on 10
multiple-choice items
--- Yes
Chen & Volpe
(1998)307
Percent correct on 36 multiple-choice
items
--- Yes
Volpe, Kotel, &
Chen (2002)308
Correct responses on 10 multiple-
choice items
--- Yes
Hilgert, Hogarth, &
Beverley (2003)309
Percent correct on a knowledge test Yes Yes
FINRA (2003)310
Correct responses to 10 true/false
items
Yes Yes
Moore (2003)311
Financial knowledge: Number of
correct responses to 12 binary-choice
items.
Financial experiences: Report having
financial experiences across 14 items.
Financial behaviour: Report engaging
in positive and negative behaviours
across 15 items.
Debt confidence: Responding
―completely‖ or ―very confident‖
regarding debt considerations
Yes Yes
306
Volpe, R. P., Chen, H. & Pavlicko, J.J. (1996). Personal investment literacy among college students: A
survey. Financial Practice & Education, 6, 86-94. 307 Chen, H. & Volpe, R. P. (1998). Analysis of personal financial literacy among college students.
Financial Services Review, 7, 107-128. 308 Volpe, R.P., Kotel, J.E. & Chen H. (2002). A survey of investment literacy among online investors.
Financial Counseling and Planning, 13, 1-16. 309 Hilgert Hilgert, M.A., Hogarth, J.M. & Beverly, S.G. (2003) Household financial management: The
connection between knowledge and behavior. Federal Reserve Bulletin, 89 (July): 309-322.Washinton, D.C. 310 FINRA (2003). NASD investor literacy research: Executive summary. Accessed March 30, 2010 at
http://www.finrafoundation.org/surveyexecsum.pdf. 311
Moore, D. (2003). Survey of financial literacy in Washington State: Knowledge, behavior, Attitudes, and
Experiences. Technical Report # 03-39. Social and Economic Sciences Research Center, Washington State
Department of Financial Institutions, Olympia, WA . Puulman: Washington State University.
145
Research studies
on measuring
financial literacy
Particulars Methodology adopted in
a research study
Self-
assessment
questions
Performance
Test
Mandell (2004)312
Percent correct on a 31-item
knowledge test
Yes
Agnew & Szykman
(2005)313
Number of correct responses to 10
multiple choice and true/false items.
Also, self-rated investment knowledge
relative to others on 1-10 scale.
Yes Yes
(NCEE) (2005)314
Percent correct on 24-item knowledge
test
--- Yes
Lusardi & Mitchell
(2006315
, 2008316
);
Correct responses to 3 multiple-choice
and true/false items
--- Yes
Lusardi & Mitchell
(2007a)317
Correct responses to 3 computational
items
Yes
Lusardi & Mitchell
(2007b)318
Single weighted average of
correct/incorrect responses (based on
factor analysis) of 5 multiple-choice
basic financial literacy items and 8
multiple choice sophisticated financial
literacy items. Separately considered a
7-pointitem on perceived knowledge.
--- Yes
312 Mandell, L. (2004). Financial Literacy: Are we improving? Jump$tart Coalition for Personal Financial
Literacy. 313 Agnew, J.R. & Szykman, L.R. (2005). Asset al.location and information overload: The influence of
information display, asset choice, and investor experience. Journal of Behavioral Finance, 6, 57-70. 314 National Council for Economic Education (NCEE) (2005), What American teens & adults know about
economics. Accessed March 30, 2010 at
http://www.ncee.net/cel/WhatAmericansKnowAboutEconomics_042605-3.pdf. 315 Lusardi, A., & Mitchell, O.S. (2006, October). Financial literacy and planning: Implications for
retirement wellbeing. Wharton School, University of Pennsylvania, Pension Research Council Working
Paper n. 1. 316 Lusardi, A., & Mitchell, O.S. (2008). Planning and financial literacy: How do women fare? American
Economic Review: Papers & Proceedings, 98, 413-417. 317 Lusardi, A., & Mitchell, O.S. (2007a). Baby boomer retirement security: The roles of planning, financial
literacy, and housing wealth. Journal of Monetary Economics, 54, 205-224. 318 Lusardi, A., & Mitchell, O.S. (2007b, October). Financial literacy and retirement planning: New
evidence from the RAND American Life Panel. MRRC Working Paper No. 2007-157.
Table 3.7 Continued
146
Research studies
on measuring
financial literacy
Particulars Methodology adopted in
a research study
Self-
assessment
questions
Performance
Test
Mandell (2007)319
Percent correct on a knowledge test --- Yes
van Rooij, Lusardi,
& Alessie (2007)320
Two weighted averages of
correct/incorrect responses (based on
factor analyses) for (a) 5 multiple-
choice basic financial literacy items
and (b) 11 multiple-choice
sophisticated financial literacy items.
Separately considered a 7-point item
on perceived knowledge.
--- Yes
Lusardi & Tufano
(2008)321
Correct responses to 3 individual
multiple-choice items
Yes Yes
ANZ Bank
(2008)322
Mean score, based on target responses
to 26 questions derived from an
operational framework
Yes Yes
Hussein et al.
(2009)323
Percent of correct answers 18 exam
type questions
--- Yes
3.11.5 Methodology used to measure Financial Literacy for Present Study
On basis of above explanation regarding various methodologies available to measure
financial literacy and to overcome the limitation of overconfidence reflected in the self-
reported method of measuring financial literacy, the researcher has not used questions
based on self-assessment to measure financial literacy level. Therefore researcher has
319 Mandell, L. (2007). Financial literacy of high school students. In J.J. Xiao (Ed.), Handbook of Consumer
Finance Research (pp. 163-183). New York, NY: Springer. 320 van Rooij, M., Lusardi, A., & Alessie, R. (2007, October). Financial literacy and stock market
participation. Dartmouth College, MRRC Working Paper 2007-162. 321 Lusardi, A. & Tufano, P. (2008). Debt literacy, financial experiences, and overindebtedness. Dartmouth Working Paper. 322 ANZ Bank (2008). ANZ survey of adult financial literacy in Australia. Accessed March 30, 2010 at
http://www.anz.com/Documents/AU/Aboutanz/AN_5654_Adult_Fin_Lit_Report_08_Web_Report_full.pdf 323 Hussein A. H. Al- Tamini & Al Anood Bin Kali (2009). Financial literacy and investment decision of
UAE investors. The Journal of Risk Finance, 10 (5), Emerald Group Publishing limited, 508
Table 3.7 Continued
147
gone for the performance test. To measure the financial literacy of respondents,
investor/respondent‘s total score was calculated as the percentage of correct answers324
,
by attempting the total 50 questions. Out of these, basic financial literacy consisted of 20
questions and advanced financial literacy consisted of 30 questions. The median
percentage of correct answers of the sample was considered to frame financial literacy
level and/or to classify the subgroups. The respondents with scores above median are
considered as respondents with higher financial literacy and hence classified as higher
financially literate and respondents with equal and/or lower than median are considered
as respondents with relatively lower level of financial literacy and hence classified as
lower financially literate. The in-depth explanation of these categories is given in chapter
4.
To generate a score to measure financial literacy, each statement/questions in the
performance test has been assigned 1 mark. If a respondent gave a correct answer to a
question, he/she has been awarded one mark for that question. No mark was given for
incorrect answers, for refusing to answer questions, or for answers ―don‘t know‖.
In this survey, the respondents were instructed to answer the questions on the basis of
their own understanding on the subject. They were neither allowed to consult others for
additional information nor allowed to use calculator/similar device to answer the
questions.
324
Lyons, A., Rachlis, M., & Scherpf, E. (2007). What‘s in a Score? Differences in Consumers‘ Credit
Knowledge Using OLS and Quantile Regressions. Networks financial institute. Indiana University, 2007-
WP-01, retrieved on January 21, 2012 from www.networksfinancialinstitute.org
148
3.12 Hypotheses of the study
1. Ho : There is no significant association between investors‘ demographic and
socio-economic variables and their financial literacy level.
H1 : There is a significant association between investors‘ demographic and
socio-economic variables and their financial literacy level.
1.1 Ho There is no significant association between investors‘ age and their
financial literacy level.
H1 There is a significant association between investors‘ age and their
financial literacy level.
1.2 Ho There is no significant association between investors‘ gender and their
financial literacy level.
H1 There is a significant association between investors‘ gender and their
financial literacy level.
1.3 Ho There is no significant association between investors‘ education and
their financial literacy level.
H1 There is a significant association between investors‘ education and their
financial literacy level.
1.4 Ho There is no significant association between investors‘ monthly income
and their financial literacy level.
H1 There is a significant association between investors‘ monthly income
and their financial literacy level.
1.5 Ho There is no significant association between investors‘ stage in family
life cycle and their financial literacy level.
H1 There is a significant association between investors‘ stage in family life
149
cycle and their financial literacy level.
1.6 Ho There is no significant association between investors‘ employment
structure and their financial literacy level.
H1 There is a significant association between investors‘ employment
structure and their financial literacy level.
1.7 Ho There is no significant association between investors‘ type of work
place activity and their financial literacy level.
H1 There is a significant association between investors‘ type of work place
activity and their financial literacy level.
1.8 Ho There is no significant association between investors‘ years of work
experience and their financial literacy level.
H1 There is a significant association between investors‘ years of work
experience and their financial literacy level.
1.9 Ho There is no significant association between investors‘ years of
investment experience and their financial literacy level.
H1 There is a significant association between investors‘ years of investment
experience and their financial literacy level.
1.10 Ho There is no significant association between number of times investors
shop around and their financial literacy level.
H1 There is a significant association between number of times investors
shop around and their financial literacy level.
1.11 Ho There is no significant association between investors‘ financial risk
tolerance and their financial literacy level.
H1 There is a significant association between investors‘ financial risk
tolerance and their financial literacy level.
150
2. Ho : There is no difference between preference for the selected informative
variables and their influence on investment decision under study.
H : There is a difference between preference for the selected informative
variables and their influence on investment decision under study.
3. Ho : There is no significant impact of investors‘ demographic and socio-
economic variables on their financial literacy level.
H1 : There is a significant impact of investors‘ demographic and socio-
economic variables on their financial literacy level.
4. Ho : There is no significant association between the financial literacy level of
investors and their monthly expenditure to monthly income ratio.
H1 : There is a significant association between the financial literacy level of
investors and their monthly expenditure to monthly income ratio.
5. Ho : There is no significant association between the financial literacy level of
investors and their monthly saving to monthly income ratio.
H1 : There is a significant association between the financial literacy level of
investors and their monthly saving to monthly income ratio.
6. Ho : There is no significant impact of financial literacy level on investment
decision of investors.
H1 : There is a significant impact of financial literacy level on investment
decision of investors.
3.13 Conclusion
The present chapter has provided in-depth idea of research methodology adopted by the
researcher for this study. Later, data analysis begins with the preliminary check of all the
questionnaires for its completeness. Examination of filled up questionnaire was done to
detect the error, omission of half-filled and un-qualified questionnaires and to correct the
151
errors wherever possible, to ensure accuracy, consistency and uniformity of data. Then
numerical codes have been assigned to represent a specific response to a specific question.
After this, the data were tabulated, i.e. arranged in a logical manner in columns and rows
for further analysis. In-depth discussion on data analysis is presented in the next chapter.
Data Analysis and
Interpretation
Chapter 4
152
4.1 Introduction
4.2. Frequency distribution showing profile of study‟s respondents
4.3 Analysis of Financial Literacy Questions
4.4 Analysis of existing investment pattern of investors
4.5 Cross Tabulation
4.5.1 Cross Tabulation of ranks given by the respondents to investment
objectives
4.5.2 Cross Tabulation of ranks given for preferred Investment
Alternative
4.6 Preference given to the variables as a Source of Information
4.7 Preference towards selected sources of information and their influence on
investment decisions
4.7.1 Three most preferred variables as source of information
4.7.2 Three least preferred variables as source of information
4.7.3 Three most influencing informative factors on investment
Decision
4.7.4 Three most influencing informative factors on investment
Decision
4.7.5 Gap Analysis
4.7.6 Paired „t‟ test
4.8 Reliability and Normality of data
4.8.1 Reliability of Measurement
4.8.2 Data quality and normality check
4.9 Factor Analysis
4.9.1 Factor analysis for factors preferred by respondents as source of
Information
4.9.1.1 Bartlett‟s test of Sphericity
4.9.1.2 Kaiser-Meyer-Olkin Test for Sampling Adequacy
4.9.1.3 Anti-Image Correlation Matrix
153
4.9.1.4 Communalities
4.9.1.5 Variance explained
4.9.1.6 Factor Extraction
4.9.1.7 Factor Loading
4.9.1.8 Rotated Factor Matrix
4.9.1.9 Naming of the factors
4.9.1.10 Mean score of extracted factors
4.9.2 Factor analysis for variables influencing investment decision
4.9.2.1 Bartlett‟s test of sphericity
4.9.2.2 Kaiser-Meyer-Olkin Test for Sampling Adequacy
4.9.2.3 Measure of Sampling Adequacy
4.9.2.4 Communalities
4.9.2.5 Variance explained
4.9.2.6 Factor Extraction
4.9.2.7 Naming of factors
4.9.2.8 Mean score of extracted factors influencing investment decision
4.10 Cross Tabulation and statistical tests
4.10.1 Association between investors‟ age and their financial literacy level
4.10.2 Association between investors‟ gender and their financial literacy
Level
4.10.3 Association between investors‟ education and their financial literacy
Level
4.10.4 Association between investors‟ monthly income and their financial
literacy level
4.10.5 Association between investors‟ stage in family life cycle and their
financial literacy level
4.10.6 Association between investors‟ employment structure and their
financial literacy level
4.10.7 Association between investors‟ type of workplace activity and their
financial literacy level
4.10.8 Association between investors‟ years of work experience and their
154
financial literacy level
4.10.9 Association between investors‟ years of investment experience and
their financial literacy level
4.10.10 Association between numbers of times investors shop around and
their financial literacy level
4.10.11 Association between risk tolerance level of investors and their
financial literacy level
4.11 Regression analysis (Binary Logistic Regression)
4.12 Association between financial literacy level of investors and their monthly
expenditure to monthly income ratio.
4.13 Association between financial literacy level of investors and their monthly
saving to monthly income ratio.
4.14 Regression Analysis: Impact of financial literacy on investment decision
4.14.1 Regression analysis: Financial literacy level and Factor 1-
Personal Financial Need
4.14.2 Regression analysis: Financial literacy level and Factor 2-
Accounting, Business and Financial Information
4.14.3 Regression analysis: Financial literacy level and Factor 3-
Economic and Regulatory Environment
4.14.4 Regression analysis: Financial literacy level and Factor 4 –
Operational Feedback
4.14.5 Regression analysis: Financial literacy level and Factor 5-
Advocate Recommendation
4.14.6 Regression analysis: Financial literacy level and Factor 6-
Overall Group Performance
4.14.7 Regression analysis: Financial literacy level and Factor 7-
Credit Features
4.14.8 Regression analysis: Financial literacy level and Factor 8-
Personal Inclination
4.14.9 Regression analysis: Financial literacy level and Factor 9-
Monetary Expectation
155
4.14.10 Regression analysis: Financial literacy level and sum of
Investment Decision Factors
4.15 Conclusion
156
4.1 Introduction
The purpose of this chapter is to analyze the raw data and convert them into some useful
information. This chapter includes compilation of primary data that is collected through
fieldwork. Broad observations are made after analyzing the data. The data were analyzed
using Statistical Package for Social Sciences version 16.0 and MS-Excel. Different types
of statistical tests were performed depending on the variables under study. The inferences
and conclusion are drawn from the output of these tests are presented in this chapter.
4.2. Frequency distribution showing profile of study‟s respondents
The research instrument required each respondent to provide his/her demographic and
socio-economic data that includes gender, age, level of education attempted, stage of
family life cycle, employment structure, type of workplace activity, years of work
experience, proportion of monthly expenditure to monthly income, proportion of monthly
saving to monthly income, number of times respondent shop around/make inquiry while
investing and years of investment experience.
Table 4.1 shows profile of the respondents of this study. Form the Table, it can be seen
that the sample has a gender distribution of approximately 79.2% male (n=305) and
20.8% female (n=80). The age distribution of sample respondents is heavily dominated
by age group 36 - 45 years as its weight is 24.93% (n=96), in comparison with
respondents fall under the age group between 18-25 years who represent 23.11% (n=89),
respondents belonging from the age group of 26-35 years who represent 19.74% (n=76),
respondent in age between 46 – 55 are 19.48% (n=75) and respondents belonging to age
group 56 years and above are 12.72% (n=49).
Table 4.1 also shows the highest level of education attempted by the respondents, where
31.70% are post graduates (n=122), 30.90% are graduates (n=119). 11.94% (n=46) and
11.68% (n=45) respondents have completed higher secondary education and secondary
157
education respectively. Respondents, who have completed diploma and primary
education are comprised of 7.53% (n=29) and 6.23% (n=24) respectively.
With respect to monthly income of respondents, from the Table 4.1, it can be seen that
the highest proportion of respondents i.e. 24.70% earn monthly income of less than Rs.
10,000 (n=95), Monthly income between Rs. 10,001-Rs. 15,000 was earned by 22.81%
respondents (n=85). About 21.60% respondents and 20.30% respondents earn monthly
income between Rs. 15,001-Rs. 20,000 (n=83) and Rs. 20,001-Rs. 25,000 (n=78)
respectively. Only 11.40 % respondents earn monthly income more than Rs. 25,000
(n=44).
It is evident from Table 4.1 that sample is divided in the various stages of family life
cycle. Out of total sample size (n=385), 26.23% respondents are in the stage of middle
age married with dependent children (n=101), 24.67% respondents are younger married
with children (n=95), 17.40% respondents are young single (n=67). The respondents who
are young married without children are 17.40% (n=67) and the respondents who are
middle age married without dependent children are 8.57% (n=33) and the rest of the
respondents i.e. 5.71 % respondents are older married (n=22).
The distribution of sample respondents on the basis of employment structure is heavily
dominated by full time salaried group as its weight is 59.22 % (n=228) in comparison
with respondents of self employed who represent 17.14% (n=67), respondents belonging
to part time salaried group represent 10.90% (n=42), respondents in category of retired
are 5.19% (n=20), respondents belonging to casual are of 2.59% (n=10), housewives
represent 3.63% (n=14) and respondents of in the category of unemployed and others are
0.51% (n=2) and 0.51% (n=2) respectively. Sample distribution with regard to years of
work experience 18.44% respondents possess work experience of less than one year
(n=71), 16.36% (n=63) and 20.00% (n=77) of the total respondents possess work
experience between 6 to 10 years and 11 to 20 years respectively. The 29.35% (n=113)
and 15.85% (n=61) respondents possess the total work experience of 21 to 30 years and
more than 30 years respectively. With respect to type of workplace activity, the
158
respondents working in finance related industry (i.e. Bank, Chartered Accountant,
Certified Financial Planner, Mutual Fund, Insurance Company, Investment Company
and/or any other financial institution) represent 34.50% (n=133), whereas respondents not
working in finance related industry (other than mentioned above) represent 62.90%
(n=242), while other respondents represent 2.60% (n=10).
Table 4.1 also shows the number of times respondents shop around/make inquiry while
investing his/her saving. Out of total respondents, 39.22 % shop around in between 1-3
times (n=151), 28.83% respondents shop around in between 4-6 times (n=111). 22.60%
(n=87) do not shop around/make inquiry at all while investing and 9.35% (n=36)
respondents shop around/make inquiry more than 6 times. The distribution of sample
according to number of years of investment experience is constituted by 34.55%
respondents who have been investing for 1-5 years (n=133) of the total sample, 27.01%
respondents who have been investing for 6-10 years (n=104), while 20.26% (n=78) and
18.18% (n=70) respondents have been investing for more than 10 years and less than 1
year respectively.
With regard to monthly expenditure to monthly income ratio of respondents, from Table
4.1, it can be seen that the highest proportion i.e. 32.73% (n=126) respondents‘ monthly
expenditure to monthly income ratios was between 1% to 50%. In the same way 23.11%
(n=89) and 21.56% (n=83) of total respondents‘ monthly expenditure to monthly income
ratio was 51% to 60% and 61% to 70% respectively. While 13.51% (n=52) and 7.79%
(n=30) respondents‘ monthly expenditure to monthly income ratio was 71% to 80% and
81% to 90% respectively. The 1.30% (n=5) respondents‘ monthly expenditure to monthly
income ratio was more than 90%.
Similarly, for monthly saving to monthly income ratio of respondents, from Table 4.1, it
can be seen that the highest proportion i.e. 56.37 % (n=217) of respondents‘ monthly
saving to monthly income ratios was 1% to 50%. In the same way, 32.99% (n=127) and
10.39% (n=40) respondents‘ monthly saving to monthly income ratio was 51% to 60%
and 61% to 70% respectively. Only 0.26% (n=1) respondents‘ monthly saving to monthly
159
income ratio was 71% to 80%. None of the respondent‘s monthly saving to monthly
income ratio was more than 81%.
With regard to risk tolerance level of respondents, 6.20% are lowest risk takers (n=24),
17.90% are moderate risk takers (n=69), 36.40% are high risk takers (n=140) and 39.50%
are highest risk takers (n=152).
Table 4.1 Respondents‟ Profile
Variables Categories Frequency Percentage
(in %)
Gender Male 305 79.23
Female 80 20.77
Age Group 18 to 25 years 89 23.11
26 to 35 years 76 19.74
36 to 45 years 96 24.93
46 to 55 years 75 19.48
56 to 65 years 49 12.72
66 years and above 0 0.00
Education Primary 24 6.23
Secondary 45 11.68
Higher secondary 46 11.94
Diploma 29 7.53
Graduation 119 30.90
Post-graduation 122 31.70
Monthly Income
(in Rs.)
Rs. 10,000 and less 95 24.70
Rs. 10,001 to Rs. 15,000 85 22.10
Rs. 15,001 to Rs. 20,000 83 21.60
Rs. 20,001 to Rs. 25,000 78 20.30
Rs. 25,001 and above 44 11.40
Stage in family life
cycle
Young single 67 17.40
Young married without children 67 17.40
Young married with children 95 24.67
Middle age married with children 101 26.23
Middle age married without
dependent children 33 8.57
Older married 22 5.71
Older unmarried 0 0.00
160
Variables Categories Frequency Percentage
(in %)
Employment
Structure
Full time salaried 228 59.22
Part time salaried 42 10.90
Casual 10 2.59
Self Employed 67 17.14
Housewife 14 3.63
Retired 20 5.19
Unemployed 2 0.51
Others 2 0.51
Type of workplace
activity
Finance related work activity 133 34.50
Non finance related work activity 242 62.90
Other 10 2.60
Years of work
experience
Less than five 71 18.44
6 Years to 10 years 63 16.36
11 years to 20 years 77 20.00
21 years to 30 years 113 29.35
More than 30 years 61 15.85
Number of times
shop around/make
inquiry while
investing
Zero 87 22.60
1 to 3 151 39.22
4 to 6 111 28.83
More than 6 36 9.35
Years of Investment
Experience
Less than 1 70 18.18
1 to 5 Years 133 34.55
5 to 10 Years 104 27.01
More than 10 Years 78 20.26
Monthly
expenditure to
monthly income
ratio
1 % to 50 % 126 32.73
51 % to 60 % 89 23.11
61 % to 70 % 83 21.56
71 % to 80 % 52 13.51
81 % to 90 % 30 7.79
More than 90 % 5 1.30
Monthly
expenditure to
monthly income
ratio
1 % to 50 % 217 56.37
51 % to 60 % 127 32.99
61 % to 70 % 40 10.39
71 % to 80 % 1 0.26
81 % to 90 % 0 0.00
More than 90 % 0 0.00
Table 4.1 Continued
161
Variables Categories Frequency Percentage
(in %)
Risk tolerance level Lowest risk takers 24 6.20
Moderate risk takers 69 17.90
High risk takers 140 36.40
Highest risk takers 152 39.50
4.3 Analysis of Financial Literacy Questions
The in-depth explanation for methodology used to measure the financial literacy level of
respondents for present study is given in Chapter 3. Following this, to measure the
financial literacy of respondents, investor/respondent‘s total score was calculated as the
percentage of correct answers (Lyons, 2007325
), by attempting the total 50 questions. Out
of these, basic financial literacy consisted of 20 questions and advanced financial literacy
consisted of 30 questions. The median percentage of correct answers of the sample was
considered to frame financial literacy level and/or to classify the subgroups. The
respondents with scores above median are considered as respondents with higher
financial literacy and hence classified as higher financially literate and respondents with
equal and/or below median are considered as respondents with relatively lower level of
financial literacy and hence classified as lower financially literate.
Table 4.2 shows the values of mean and median percentage of correct scores for the
entire survey, calculated on the basis of survey responses collected from the each investor
(respondent). The result shows that on an average, respondent answered 56.90 per cent of
the questions correctly. The median percentage of correct scores is 56.00. As explained in
the Chapter 3, this median percentage of correct scores of the sample was considered to
frame financial literacy level and/or to classify the respondents in to different subgroups.
The respondents with scores above median (i.e. 56 per cent) are considered as
respondents (investors) with higher financial literacy and hence classified in the first
325
Lyons, A., Rachlis, M., & Scherpf, E. (2007). What‘s in a Score? Differences in Consumers‘ Credit
Knowledge Using OLS and Quantile Regressions. Networks financial institute. Indiana University, 2007-
WP-01, retrieved on January 21, 2012 from www.networksfinancialinstitute.org
Table 4.1 Continued
162
category, i.e. investors with relatively higher level of financial literacy and respondents
with equal to and lower than median (i.e. 56 per cent) are considered as respondents with
relatively lower level of financial literacy and hence classified into second category, i.e.
investors with relatively lower level of financial literacy
Table 4.2 Overall Financial Literacy of Respondents
Central tendency Value
Mean 56.90
Median 56.00
Mode 56
Std. Deviation 14.258
Minimum 20
Maximum 98
The overall performance of the respondents towards 20 questions of basic financial
literacy is presented in Table 4.3. The second column of the same table represents the
percentage of total respondents who answered each question correctly. On the basis of
percentage of correct answers to each question, sorting was done and the rank was
assigned. From Table 4.3, it can be seen that the respondents earned highest score on the
question of numeracy, suggesting that investors know this concept very well. Nine other
subject questions had scores higher than the median. In the ascending order (on the basis
of ranks assign) these subjects are consumer rights and responsibility, concept of know
your customer (KYC), interest compounding, functioning of stock market, relationship
between investment time horizon and fluctuation in asset value, diversification, inflation,
risk-return trade off and risk-return trade off of two assets. On the other hand, the subject
questions on which the respondents scored less than the median are concept of
investment, financial worth, risk, relationship between investment time horizon and asset
growth, personal finance, time value of money, relationship between interest and asset
prices, regulatory as a part of market structure, concept of asset allocation and disposable
income.
163
Table 4.3 Summary of Answers given by the Respondents under Basic Financial Literacy Test
Basic Financial Literacy Subject Question No. of
Respondents
given
Correct/True
Answers
Percentage of
Respondents
given
Correct/True
Answers
No. of
Respondents
given False
Answers
Percentage
of
Respondents
given False
Answers
No. of
Respondents
given answer
of Don‟t
know
Percentage of
Respondents
given answer
of Don‟t
know
Rank
Investment concept 200 51.95 172 44.67 13 3.38 11
Financial worth 193 50.13 154 40.00 38 9.87 12
Disposable income 87 22.60 213 55.32 85 22.08 20
Personal finance 162 42.08 160 41.56 63 16.36 15
Numeracy 311 80.78 59 15.32 15 3.90 1
Interest compounding 295 76.62 66 17.14 24 6.23 4
Inflation 265 68.83 82 21.30 37 9.61 8
Time value of money 160 41.56 176 45.71 49 12.73 16
Functioning of stock market 282 73.25 80 20.78 23 5.97 5
Concept of diversification 266 69.09 96 24.94 23 5.97 7
Risk-return trade off 253 65.71 93 24.16 39 10.13 9
Risk 186 48.31 180 46.75 19 4.94 13
Risk-return trade off of two assets 251 65.19 91 23.64 43 11.17 10
Relationship between investment time
horizon and asset growth 181 47.01 166 43.12 38 9.87 14
Investment time horizon and fluctuation in
asset value 275 71.43 86 22.34 24 6.23 6
concept of asset allocation 106 27.53 208 54.03 71 18.44 19
Relationship between interest and asset
prices 159 41.30 191 49.61 35 9.09 17
Consumer rights and responsibility 309 80.26 60 15.58 16 4.16 2
Regulatory body as a part of market structure 107 27.79 192 49.87 86 22.34 18
Concept of KYC 304 78.96 39 10.13 42 10.91 3
164
Table 4.4 shows overall performance of the respondents towards 30 questions of
advanced financial literacy. With regard to advanced financial literacy, as can be seen
from Table 4.4, respondents scored highest score on the product i.e. fixed deposits (
74.89% of correct answers), followed by insurance plans (62.86% of correct answers),
mutual funds (60.69% of correct answers), national savings certificates (60.00% of
correct answers), preference shares (56.23% of correct answers). The respondents are less
knowledgeable on the investment alternatives, i.e. equity shares (54.81% of correct
answers), employee provident fund (51.00% of correct answers), Post office monthly
income schemes (50.22% of correct answers), public provident fund (49.87% of correct
answers) and Debentures and Bonds (48.74% of correct answers), which is far from
median score. And hence it can be concluded that respondents are less financially
literate/knowledgeable on these investment alternatives.
165
Table 4.4 Summary of Answers given by the Respondents under Advanced Financial Literacy Test
Advanced financial literacy
question subject
No. of
correct/
true
answers
No. of
correct/
true
answers
No. of false
answers
Percentage
of false
answers
No. of Don‟t
know
answers
Percentage
of Don‟t
know
answers
Rank
Fixed deposits 865 74.89 201 17.40 89 7.71 1
National saving certificates 693 60.00 256 22.16 206 17.84 4
Public provident fund 576 49.87 357 30.91 222 19.22 9
Employee provident fund 589 51.00 424 36.71 142 12.29 7
Equity shares 633 54.81 407 35.24 115 9.96 6
Preference shares 433 56.23 203 26.36 134 17.40 5
Mutual funds 701 60.69 235 20.35 219 18.96 3
Debentures and Bonds 563 48.74 378 32.73 214 18.53 10
Post office monthly income
schemes 580 50.22 362 31.34 213 18.44
8
Insurance plans 968 62.86 394 25.58 178 11.56 2
166
The overall results shows that out of 385 respondents 40.78% respondents (n=157)
scored higher than the median, which is 56.00, and hence these respondents are
considered as investors with higher level of financial literacy. The rest of the 59.22% of
respondents (n=228) have scored equal and/or lower than median. These investors are
considered as respondents with relatively lower level of financial literacy and hence
classified as lower financially literate. Fig. 4.1 shows the classification of respondents on
the basis of their financial literacy level.
Fig. 4.1 Pie chart of classification of respondents
4.4 Analysis of existing investment pattern of investors
Table 4.5 shows the investment alternatives in which the investors have invested their
savings. From the same table, it can be seen that out of 385 total respondents, 318
respondents have invested in bank deposits, followed by insurance and pension plans.
The 279 respondents have invested in precious metals (gold and silver), while 157
respondents have invested in mutual funds.148, 134 and 122 respondents have invested in
shares, post office saving schemes and real estate respectively. While only 33 investors
have invested in bonds and debentures.
59%
41%
Low High
167
Table 4.5 Existing Investment Pattern of Investors
Investment Alternative Male Female Total
Post Office Saving Schemes 102 32 134
Insurance and pension plans 243 56 299
Bank deposits 247 71 318
Mutual funds 135 22 157
Shares 132 16 148
Debentures and Bonds 26 06 033
Real Estate 104 18 122
Precious metals (Gold and silver) 217 62 279
4.5 Cross Tabulation
In this section, simultaneous analysis of two variables is carried out through cross-
tabulation.
4.5.1 Cross tabulation of ranks given by the respondents to investment objectives
The respondents were asked to give ranks to given investment objectives. The rankings
given by the respondents are presented in Table 4.6. Table 4.6 presents the cross
tabulation of ranks given by the respondents to the given investment objectives. From the
same table, it can be seen that 35.32% (n=136) of the total respondents have given the
first rank to saving of income tax as an investment objective, while only 4.16% (n=16)
have given the seventh rank to the same investment objective. The Table 4.6 also shows
that 72.99% (n=281) respondents have given the seventh rank to
gift/donation/vacation/pilgrim as an investment objective, 21.82% (n=84) respondents
have given second rank to child‘s marriage/child‘s education/social ceremony. The
12.21% (n= 47) respondents have given first rank to secured retirement and safeguarding
against inflation/capital appreciation. With regard to buying/improving home and
meeting unexpected financial needs, only 9.87% (n= 38) and 14.55% (n=56) respondents
168
have given first rank respectively. The graphical representation of this data in percentage
value is given in Fig. 4.2.
Table 4.6 Cross Tabulation of Ranks given to Investment Objectives
Investment
Objectives
Rank
1 2 3 4 5 6 7
Saving income tax 136
(35.32)
33
(8.57)
36
(9.35)
57
(14.81)
54
(14.03)
53
(13.77)
16
(4.16)
Child‘s marriage/
child‘s education/
social ceremony
50
(12.99)
84
(21.82)
68
(17.66)
58
(15.06)
44
(11.43)
66
(17.14)
15
(3.90)
Buying/improving
home
38
(9.87)
38
(9.87)
38
(9.87)
46
(11.95)
98
(25.45)
105
(27.27)
22
(5.71)
Secured
retirement
47
(12.21)
85
(22.08)
80
(20.78)
79
(20.52)
51
(13.25)
30
(7.79)
13
(3.38)
Gift/donation/vaca
tion/
Pilgrim
4
(1.04)
6
(1.56)
13
(3.38)
8
(2.08)
19
(4.94)
54
(14.03)
281
(72.99)
Meeting
unexpected
financial needs
56
(14.55)
80
(20.78)
86
(22.34)
85
(22.08)
50
(12.99)
20
(5.19)
8
(2.08)
Safeguard against
inflation/ capital
appreciation
47
(12.21)
58
(15.06)
73
(18.96)
55
(14.29)
66
(17.14)
57
(14.81)
29
(7.53)
Note: Figures in parenthesis shows the percentage of respondents
169
Fig. 4.2 Bar chart of ranks given by the respondents to investment Objectives (in per cent)
35.32
12.99
9.87
12.21
1.04
14.55
12.21
8.57
21.82
9.87
22.08
1.56
20.78
15.06
9.35
17.66
9.87
20.78
3.38
22.34
18.96
14.81
15.06
11.95
20.52
2.08
22.08
14.29
14.03
11.43
25.45
13.25
4.94
12.99
17.14
13.77
17.14
27.27
7.79
14.03
5.19
14.81
4.16
3.90
5.71
3.38
72.99
2.08
7.53
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Saving income tax
Child’s marriage/child’s education/social ceremony
Buying/improving home
Secured retirement
Gift/donation/vacation/pilgrim
Meeting unexpected financial needs
Safeguard against inflation/ capital appreciation
Rank 1 Rank 2 Rank 3 Rank 4 Rank 5 Rank 6 Rank 7
35.32
12.99
9.87
12.21
1.04
14.55
12.21
8.57
21.82
9.87
22.08
1.56
20.78
15.06
9.35
17.66
9.87
20.78
3.38
22.34
18.96
14.81
15.06
11.95
20.52
2.08
22.08
14.29
14.03
11.43
25.45
13.25
4.94
12.99
17.14
13.77
17.14
27.27
7.79
14.03
5.19
14.81
4.16
3.90
5.71
3.38
72.99
2.08
7.53
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Saving income tax
Child’s marriage/child’s education/social ceremony
Buying/improving home
Secured retirement
Gift/donation/vacation/pilgrim
Meeting unexpected financial needs
Safeguard against inflation/ capital appreciation
Rank 1 Rank 2 Rank 3 Rank 4 Rank 5 Rank 6 Rank 7
170
Total score of each investment objective is calculated on the basis of ranks given by the
respondents to each investment objective. Seven points are given to the first rank, six
points are given to the second rank and in the same way, one point is given to the seventh
rank. Based on these points, mean scores are calculated by adding the rank points and
divided by the total number of respondents. These mean scores of investment objectives
are presented in Table 4.7 and Fig. 4.3. From Table 4.7, it is found that saving of income
tax and meeting unexpected financial needs have the highest mean score i.e. 4.78,
followed by investment objective secured retirement having a mean score of 4.63. Other
investment objectives as per the descending order on mean score are Child‘s marriage/
child‘s education/social ceremony, Safeguard against inflation/capital appreciation,
Buying/improving home and Gift/ donation/ vacation/ vacation/ pilgrim.
Table 4.7 Mean Score of Investment Objectives
Investment Objective Mean score
Saving income tax 4.78
Child‘s marriage/ child‘s education/social ceremony 4.43
Buying/improving home 3.62
Secured retirement 4.63
Gift/ donation/ vacation/ pilgrim 1.58
Meeting unexpected financial needs 4.78
Safeguard against inflation/ capital appreciation 4.16
Fig. 4.3 Bar chart of mean score of investment objectives
4.78
4.43
3.62
4.63
1.58
4.78
4.16
0 1 2 3 4 5 6
Saving income tax
Child’s marriage/ child’s education/social ceremony
Buying/improving home
Secured retirement
Gift/ donation/ vacation/ pilgrim
Meeting unexpected financial needs
Safeguard against inflation/ capital appreciation
Mean score
171
4.5.2 Cross Tabulation of ranks given for preferred Investment Alternative
Table 4.8 shows the cross tabulation of ranks given by the respondents to the various
investment alternatives according to their preference. From Table 4.8, it can be seen that
bank deposits are the most preferred investment alternative, as 38.96% (n=150)
respondents have given it the first rank. The second and third most preferred investment
alternatives are precious metals (gold and silver) and insurance and pension plans 15.32%
(n=59) and 14.03% (n=54) respondents have given them the first ranks accordingly.
From Table 5.8, it can be also seen that only 2.60% (n=10) respondents have given first
rank to bonds and debentures. The graphical representation of the same is done in Fig.
4.4.
Table 4.8 Cross Tabulation of Rank given for Preferred Investment Alternative
Investment
Alternatives
Rank
1 2 3 4 5 6 7 8
Post office
saving schemes
(POMIS/NSC/
KVP/PPF etc.)
41
(10.65)
75
(19.48)
31
(8.05)
42
(10.91)
44
(11.43)
30
(7.79)
53
(13.77)
69
(17.92)
Insurance and
pension
Plans
54
(14.03)
59
(15.32)
107
(27.79)
88
(22.86)
41
(10.65)
25
(6.49)
7
(1.82)
4
(1.04)
Bank Deposits 150
(38.96)
53
(13.77)
50
(12.99)
23
(5.97)
23
(5.97)
50
(12.99)
27
(7.01)
9
(2.34)
Mutual funds 16
(4.16)
30
(7.79)
30
(7.79)
59
(15.32)
95
(24.68)
81
(21.04)
61
(15.84)
13
(3.38)
Shares 23
(5.97)
36
(9.35)
26
(6.75)
33
(8.57)
43
(11.17)
68
(17.66)
72
(18.7)
84
(21.82)
Debentures and
bonds
10
(2.60)
7
(1.82)
21
(5.45)
16
(4.16)
43
(11.17)
61
(15.84)
83
(21.56)
145
(37.66)
Real Estate 40
(10.39)
33
(8.57)
31
(8.05)
49
(12.73)
60
(15.58)
55
(14.29)
74
(19.22)
43
(11.17)
Precious metal
(Gold Silver)
59
(15.32)
94
(24.42)
84
(21.82)
73
(18.96)
40
(10.39)
12
(3.12)
8
(2.078)
15
(3.90) Note: Figures in parenthesis shows the percentage of respondents
172
Fig. 4.4 Bar chart for ranks given by the respondents to investment alternatives
10.65
14.03
38.96
4.16
5.97
2.60
10.39
15.32
19.48
15.32
13.77
7.79
9.35
1.82
8.57
24.42
8.05
27.79
12.99
7.79
6.75
5.45
8.05
21.82
10.91
22.86
5.97
15.32
8.57
4.16
12.73
18.96
11.43
10.65
5.97
24.68
11.17
11.17
15.58
10.39
7.79
6.49
12.99
21.04
17.66
15.84
14.29
3.12
13.77
1.82
7.01
15.84
18.70
21.56
19.22
2.08
17.92
1.04
2.34
3.38
21.82
37.66
11.17
3.90
0.00 20.00 40.00 60.00 80.00 100.00 120.00
Post
Insurance
Bank Deposits
MF
Shares
Debentures
Real Estate
Gold Silver
Rank 1 Rank 2 Rank 3 Rank 4 Rank 5 Rank 6 Rank 7 Rank 8
173
Total score of each investment alternative is calculated on the basis of ranks given by the
respondents on the basis of their preference to each investment alternative. Eight points
are given to the first rank, seven points are given to the second rank and in the same way,
and one point is given to the eighth rank. Based on these points, mean scores are
calculated by adding the rank points and divided by the total number of respondents.
These mean score of each investment alternative on the basis of their preference are
presented in Table 4.9 and Fig. 4.5. From Table 4.9, it is found that bank deposits have
the highest mean score i.e. 5.96, followed by the precious metal (gold and silver) having
mean score 5.78. Other investment alternatives as per the descending order in mean score
are insurance/pension plans, Post office saving schemes (POMIS/NSC/KVP/PPF etc.),
Mutual funds, Real estates, Shares and Debentures and bonds.
Table 4.9 Mean Score of Each Investment Alternatives on the basis of Ranks given
by the Respondents According to their Preference
Investment Alternatives Mean score
Post office saving schemes
(POMIS/NSC/KVP/PPF etc.)
4.38
Insurance and pension plans 5.66
Bank Deposits 5.96
Mutual funds 4.09
Shares 3.54
Debentures and bonds 2.57
Real Estate 4.09
Precious metals (Gold and Silver) 5.78
174
Fig. 4.5 Bar chart of mean score of each investment alternatives on the basis of
ranks given by the respondents according to their preference
4.6 Preference given to the variables as a preferred source of information
There are various sources of information, from where investors gather the information.
For this study, 16 variables were identified as a source of information and the preference
of investors towards these informative variables were measured as per the data given in
Table 4.10 and Fig. 4.6. Both the number of respondents and percentage of respondents
are shown in the Table 4.10. Five point Likert scale (1= Not at all preferred, 2 = Least
preferred, 3 = Neutral, 4 = Preferred and 5 = The most preferred) was used to identify the
preference of investors towards these 16 variables as a source of information. From Table
4.10, it is observed that majority of the respondents preferred all the variables as source
to collect information under study.
4.38
5.66
5.96
4.09
3.54
2.57
4.09
5.78
0 2 4 6 8
Post office saving schemes …
Insurance and pension plans
Bank Deposits
Mutual funds
Shares
Debentures and bonds
Real Estate
Precious metals (Gold and Silver)
Mean score
175
Table 4.10 Preference given by the Respondents towards Variables as Preferred
Source of Information
Variables Not
Preferred
Least
Preferred
Neutral Preferred Most
Preferred
N % N % N % N % N %
Certified Financial
Planner
0 0 12 4.21 39 10.13 195 50.65 139 36.10
Annual reports of the
company
9 2.34 22 7.72 112 29.09 181 47.01 61 15.84
Prospectus of a
company
23 5.97 59 20.70 105 27.27 134 34.81 64 16.62
Company‘s website 3 0.78 30 10.53 124 32.21 194 50.39 34 8.83
Distributers/agents of
financial product
0 0 0 0.00 103 26.75 207 53.77 75 19.48
Rating agencies‘ reports 0 0 39 13.68 133 34.55 168 43.64 45 11.69
Company‘s telephone
representatives
0 0 9 3.16 192 49.87 172 44.68 12 3.12
Family members 0 0 8 2.81 151 39.22 144 37.40 82 21.30
Friends and relatives 5 1.30 29 10.18 83 21.56 190 49.35 78 20.26
Conversation/exchanges
of views with
professional colleagues
0 0 15 5.26 88 22.86 217 56.36 65 16.88
Publication in the
financial press and news
papers
0 0 30 10.53 132 34.29 176 45.71 47 12.21
Conversation/
exchanges of views with
company executive and
sector experts
0 0 21 7.37 51 13.25 229 59.48 84 21.82
Corporate forecast
prepared by independent
investment company
12 3.12 88 30.88 126 32.73 188 48.83 31 8.05
Published reports from
research agencies
18 4.68 40 14.04 117 30.39 156 40.52 54 14.03
Opinions from existing
investors of various
instruments
0 0 6 2.11 51 13.25 230 59.74 98 25.45
Financial
advisors/Broker and
analyst‘s
recommendation
0 0 9 3.16 94 24.42 208 54.03 74 19.22
176
Fig. 4.6 Bar chart of preference given by respondents towards factors as preferred source of information
0
2.34
5.97
0.78
0
0
0
0
1.30
0
0
0
3.12
4.68
0
0
4.21
7.72
20.70
10.53
0.00
13.68
3.16
2.81
10.18
5.26
10.53
7.37
30.88
14.04
2.11
3.16
10.13
29.09
27.27
32.21
26.75
34.55
49.87
39.22
21.56
22.86
34.29
13.25
32.73
30.39
13.25
24.42
50.65
47.01
34.81
50.39
53.77
43.64
44.68
37.40
49.35
56.36
45.71
59.48
48.83
40.52
59.74
54.03
36.10
15.84
16.62
8.83
19.48
11.69
3.12
21.30
20.26
16.88
12.21
21.82
8.05
14.03
25.45
19.22
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Certified Financial Planner
Annual reports of the company
Prospectus of a company
Company’s website
Distributers/agents of financial product
Rating agencies’ reports
Company’s telephone representatives
Family members
Friends and relatives
Conversation/exchanges of views with professional colleagues
Publication in the financial press and news papers
Conversation/ exchanges of views with company executive …
Corporate forecast prepared by independent investment …
Published reports from research agencies
Opinions from existing investors of various instruments
Financial advisors/Broker and analyst’s recommendation
Not Preferred Least Preferred Neutral Preferred Most preferred
177
4.7 Preference towards selected sources of information and their influence on
investment decisions
Table 4.11 Preference towards Selected Sources of Information and their Influence
on Investment Decisions
Variables Preference
(Mean score)
Influence
(Mean
score)
Gap
(Preference-
Influence)
Family members 3.78 3.58 0.20
Friends and relatives 3.80 3.73 0.07
Annual reports 3.68 4.09 -0.41
Conversation with Professional
Colleagues
3.86 2.94 0.92
Financial Press and Newspapers 3.62 2.97 0.65
Conversation /exchanges of views with
company executives and sector experts
3.98 3.97 0.01
Corporate forecast prepared by
independent investment company
3.51 2.48 1.03
Present investors 4.09 2.88 1.21
Advisor/brokers/analyst 3.90 3.67 0.23
Rating agencies‘ reports 3.57 4.17 -0.60
The mean score of each variable is calculated, which is based on the number assigned to
the different response category. For preference towards the variables, points are assigned
on a 1 to 5 Likert scale with point 1 representing ‗not at all preferred‘ and point 5
representing ‗the most preferred‘ are given to the respondents. Similarly, for influence of
factors, points are assigned on a 1 to 5 Likert scale with point 1 representing ‗the least
influence‘ and point 5 representing ‗the most significantly influence‘. The mean score of
each variable ranges from 0 to 5. The mean values obtained for each response category is
presented in Table 4.11.
4.7.1 Three most preferred variables as source of information
Mean score of each variable is as given in Table 4.11. The response given by the
respondents to the variables represents their preference towards a particular source of
178
information. From Table 4.11, it can be seen that three most important variables (sources
of information) from the investors‘ point of view, are present investors, conversation
/exchanges of views with company executives & sector experts and
advisors/brokers/analyst having a mean score of 4.09, 3.98 and 3.90 respectively.
4.7.2 Three least preferred variables as source of information
Some of the variables as a source of information are not so important and hence, least
preferred by the investors. These three least preferred variables as a source of information
are corporate forecast prepared by independent investment company rating agencies‘
reports and Financial Press and newspapers, each is having a mean score of 3.51, 3.57
and 3.62 respectively.
4.7.3 Three most influencing informative variables on investment decision
For identifying the influence level of each variable mentioned in the Table 4.11, the mean
score of each variable‘s influence level was identified. Three variables that most
influence the investment decision are rating agencies‘ reports (mean score 4.17), annual
reports (mean score 4.09) and conversation /exchanges of views with company
executives and sector experts (mean score 3.97).
4.7.4 Three least influencing informative factors on investment decision
Out of total variables mentioned Table 4.11, three variables that least influence the
investment decision are corporate forecast prepared by independent investment company,
present investors and conversation/exchanges of views with professional colleagues
having mean score of 2.48, 2.88 and 2.94 respectively.
4.7.5 Gap Analysis
179
Gap in the mean score was identified by subtracting the mean score of variables for
which responses given on the basis of influence of these variables on investment decision
from the mean score of variables for which responses given on the basis of preference
towards a variable as a source of information. From the Table 4.11, it can be seen that
except two variables all the variables have positive gap, means except these two
variables, all other variables do not significantly influence the investors in their
investment decisions, as per their own preference given to the variables. The three most
positive gaps are for the variables ‗present investors‘ (1.21), ‗corporate forecast prepared
by independent investment company‘ (1.03) and ‗conversation/exchanges of views with
professional colleagues‘ (0.92). The highest gap is for the variable ‗present investors‘
which indicates that the preference of respondents is very high as compared to the
influence derived from this. Even respondents prefer to have more conversation with
professional colleagues with regard to investment decision, but the impact/influence of
this conversation on the investment decisions of investors is not profound, and hence, this
variable shows its presence among the top three gaps.
4.7.6 Paired „t‟–test
A paired sample t-test is the test for differences in the means of paired samples
(Malhorta, 2008326
). The t-test for differences between group mean can be conceptualized
as the difference between the mean divided by the variability of random means. The t-
value is a ratio of the difference between the two sample means and the standard error.
The t-test tries to provide a rational way of determining if the difference between the two
sample means occurred by chance (Hair et al., 2003327
). Some researchers refer to this
test as matched-pair test, or t test for related measures, or the correlated test (Black,
2006328
).
326 Malhotra, N.K. (2008). Marketing Research- An Applied Orientation(5th ed.). New Delhi: Pearson
Education, p. 453. 327 Hair J., Bush F., Robert, P., & Ortinau D. J. (2003). Marketing Research Within a Changing Information
Environment. Tata McGraw Hill Publication, New Delhi, Second edition, p. 542. 328 Black K. (2006). Business Statistics for Contemporary Decision Making (4th ed.). New Delhi: Wiley
India publication, p. 361.
180
Table 4.12 Paired Sample Statistics
Pairs Variables Mean Std.
Deviation
Std.
Error
Mean
Pair 1 Family members (P)* 3.78 0.801 0.041
Family members (I)** 3.58 1.059 0.054
Pair 2 Friends and relatives (P) 3.80 0.893 0.046
Friends and relatives (I) 3.73 1.155 0.059
Pair 3 Annual reports of the company(P) 3.68 0.889 0.045
Annual reports of the company(I) 4.09 0.693 0.035
Pair 4 Conversation /exchanges of views with professional
colleagues (P)
3.86 0.732 0.037
Conversation /exchanges of views with professional
colleagues (I)
2.94 1.123 0.057
Pair 5 Financial Press, newspapers and electronic media (P) 3.62 0.798 0.041
Financial Press, newspapers and electronic media (I) 2.97 1.049 0.053
Pair 6 Conversation /exchanges of views with company
executives and sector experts (P)
3.98 0.755 0.038
Conversation /exchanges of views with company
executives and sector experts (I)
3.97 0.649 0.033
Pair 7 Corporate forecast prepared by independent investment
company (P)
3.51 0.863 0.044
Corporate forecast prepared by independent investment
company (I)
2.48 0.857 0.044
Pair 8 Present investors (P) 4.09 0.665 0.034
Present investors (I) 2.88 1.111 0.057
Pair 9 Financial advisors/brokers and analyst‘s
recommendation (P)
3.90 0.722 0.037
Financial advisors/brokers and analyst‘s
recommendation (I)
3.67 0.598 0.030
Pair 10 Rating agencies‘ reports (P) 3.57 0.827 0.042
Rating agencies‘ reports (I) 4.17 0.601 0.031
P* stands for preference, I** stands for influence
For this study, pairs of ten variables between preference and influence on investment
decisions are used. The hypotheses used for this paired t-test are as under.
181
H0: There is no significant difference between preference for the selected informative
variables and their influence on investment decision under study.
H1: There is a significant difference between preference for the selected informative
variables and their influence on investment decision under study.
Paired t-test was performed for all the ten pairs under study and the output is represented
in Table 4.12.
Table 4.12 represents mean, standard deviation and standard error mean for all the ten
pairs of informative variables under study. From the standard deviation column, it is
found that for majority of variables these values are below 1.00. This indicates that the
variations in responses are low.
Table 4.13 represents the t-test values. In Table 4.13, second, third and fourth column
show the differences in mean, standard deviation and standard error of mean respectively.
Last column of the Table 4.13 represent the significance value for two tailed test.
182
Table 4.13 Paired Samples t-test values
Variables
Paired Differences
t-
value
Sig.
(2 tailed)
Mean
Std.
Deviation
Std.
Error
Mean
90%
Confidence
Interval of the
Difference
Lower
Upper
Family members 0.992 1.332 0.068 0.859 1.126 14.619 0.000*
Friends and
relatives
0.953 1.415 0.072 0.811 1.095 13.216 0.000*
Annual reports of
the company
-0.410 1.093 0.056 -0.520 -0.301 -7.364 0.000*
Conversation
/exchanges of
views with
professional
colleagues
0.925 1.351 0.069 0.789 1.060 13.430 0.000*
Financial Press,
newspapers and
electronic media
0.652 1.376 0.070 0.514 0.790 9.295 0.000*
Conversation
/exchanges of
views with
company
executives and
sector experts
0.008 1.022 0.052 -0.095 0.110 0.150 0.881
Corporate
forecast prepared
by independent
investment
company
1.031 1.237 0.063 0.907 1.155 16.356 0.000*
Present investors 1.216 1.272 0.065 1.088 1.343 18.754 0.000*
Financial
advisors/brokers
and analyst‘s
recommendation
0.031
0.935
0.048
-0.063
0.125
0.654
0.513
Rating agencies‘
reports
-0.603 0.960 0.049 -0.699 -0.506 -0.506 0.000*
*p<0.05, significant at 5%
The null hypothesis mentioned is tested at 95% level of confidence. From the last column
of Table 4.13, it is found that only for two pairs the significance value is greater than 0.05
and that is for ‗conversation/ exchanges of views with company executives and sector
183
experts‘ and ‗financial advisors/brokers and analyst‘s recommendation‘. For these
variables, null hypothesis is not rejected and it may be concluded that for conversation/
exchanges of views with company executives and sector experts and financial
advisors/brokers and analyst‘s recommendation, there is no significant difference
between their preference for and influence on investment decisions. For all other
variables, excluding two mentioned above, the difference between preference and
influence is significant at 5% level of significance, as the two tailed significance value is
less than 0.05.
4.8 Reliability and Normality of data
There were two questions in which the respondents were asked to give their 1) preference
to the variables as preferred source of information and 2) rate the influence of each
variable on their investment decisions. Before using these data for various
interdependence techniques, it is necessary to check whether the data is reliable, valid
and normally distributed.
4.8.1 Reliability of Measurement
Reliability is an assessment of the degree of consistency between multiple measurements
of a variable (Hair et al., 2009329
). It has to do with the accuracy and precision of a
measurement procedure (Cooper and Schindler, 2008330
).
Reliability is concerned with estimates of the degree to which a measurement is free of
random or unstable error. Reliable instruments can be used with confidence that transient
and situational factors are not interfering. They are robust; they work well at different
times under different conditions (Cooper and Schindler, 2009331
).
329 Hair, J. F., William, B. C., Barry, B. J., Anderson, R. E.& Tatham, R. L. (2009). Multivariate Data
Analysis (6th ed.). New Delhi: Pearson Education, p. 161. 330
Cooper, D. & Schindler, P. (2009). Business Research Methods (9th ed.). New Delhi : McGraw Hill
Publication, p. 320. 331 Ibid, p. 321.
184
The assessment of the consistency of the entire scale can be measured through reliability
coefficient. The most widely used reliability measure is Cronbach‘s alpha. Cronbach‘s
alpha is the average of all possible split half coefficients resulting from different ways of
the scale items. Hair et al. (2009) suggested the generally agreed upon lower limit for
Cronbach‘s Alpha is 0.7, although it may decrease to 0.60 in exploratory research (Hair
et al., 2009332
). The Cronbach‘s Alpha coefficient value for the scale 1) preference to the
16 variables as preferred source of information is 0. 708 as shown in Table 4.14 and 2) 44
variables influencing investment decision is 0.794 as shown in Table 4.15, indicates that
high level of internal consistency in the items. The values of Cronbach‘s Alpha is
acceptable and desirable, as these values are more than 0.700, confirming that both the
scales are reliable enough to be used for further analysis.
Table 4.14 Cronbach‟s Alpha for the Variables of Preferred Source of Information
Cronbach's Alpha Number of Items
0.708 16
Table 4.15 Cronbach‟s Alpha for Variables Influencing Investment Decision
Cronbach's Alpha Number of Items
0.794 44
4.8.2 Data quality and normality check
Data quality is examined by using skewness, Kurtosis and t-test values as shown in the
Table 4.16 and Table 4.17. A close examination of fifth column in Table 4.16 and Table
4.17 reveal that Kurtosis for majority of variables is below than 1. Out of 16 variables of
source of information, for all the variables, Kurtosis is below than 1 (see Table 4.16) and
out of 44 variables influencing investment decision only Kurtosis of eight factors are
greater than one and approaching to 2.00, a level beyond which non-normality of
distribution becomes a concern (see Table 4.17).
332 Hair, J. F., William, B. C., Barry, B. J., Anderson, R. E.& Tatham, R. L. (2009). Multivariate Data
Analysis (6th ed.). New Delhi: Pearson Education, p. 161.
185
Table 4.16 Data Quality for Variables of Preferred Sources of Information
Variables
Mean
Std.
Deviation Skewness Kurtosis
t- test Sig.
(2
Tailed)
Certified financial planner 4.20 0.741 -0.796 0.638 111.11 0.00
Annual reports of the company 3.68 0.889 -0.632 0.591 81.310 0.00
Prospectus of a company 3.41 1.115 -0.391 -0.563 59.99 0.00
Company‘s website 3.59 0.789 -0.465 0.249 89.15 0.00
Distributer/ Agents of financial
products 3.93 0.677 0.089 -0.813
113.83
0.00
Rating agencies‘ report 3.57 0.827 -0.138 -0.505 84.71 0.00
Company‘s telephone
representatives 3.49 0.600 0.164 -0.374
114.02 0.00
Family members 3.78 0.801 0.175 -0.976 92.60 0.00
Friends and relatives 3.80 0.893 -0.672 0.329 83.44 0.00
Conversation /exchanges of
views with professional
colleagues
3.86 0.732 -0.381 0.093
103.54
0.00
Publication in financial press,
newspapers and electronic
media
3.62 0.798 -0.146 -0.407
89.12
0.00
Conversation /exchanges of
views with company executives
and sector experts
3.98 0.755 -0.728 0.717
103.38
0.00
Corporate forecast prepared by
independent investment
company
3.51 0.863 -0.740 0.773
79.88
0.00
Published reports of the
research agencies 3.49 1.011 -0.546 -0.017
67.73
0.00
Present investors 4.09 0.665 -0.424 0.410 120.71 0.00
Financial advisors/brokers and
analyst‘s recommendation 3.90 0.722 -0.225 -0.227
106.00
0.00
186
Table 4.17 Data Quality for Variables Influencing Investment Decision
Variables
Mean
Std.
Deviation Skewness Kurtosis
t- test Sig. (2-
Tailed)
Condition of financial
statement 4.05 0.65 -0.05 -0.59 123.07 0.00
Expected Corporate
Earning 2.72 1.04 0.92 0.14 51.09 0.00
Past performance of the
firm 3.98 0.68 0.02 -0.83 114.86 0.00
Company‘s position in the
industry 3.91 0.69 0.12 -0.91 110.86 0.00
Insider‘s information 3.87 0.72 0.20 -1.07 105.09 0.00
Result of fundamental
analysis 2.94 1.19 0.46 -0.82 48.39 0.00
Result of technical
analysis 2.88 1.23 0.61 -0.75 45.90 0.00
Expected return on
investment 4.03 0.63 -0.02 -0.44 126.40 0.00
Feeling for a company 3.29 1.00 0.09 -0.96 64.29 0.00
Perceived ethics of
company 4.11 0.66 -0.12 -0.73 121.59 0.00
Political party affiliation 3.20 1.01 -0.02 -0.56 62.34 0.00
Contribution of a firm
towards social causes 3.22 0.96 0.04 -0.65 65.99 0.00
Coverage in the press 3.22 1.27 0.43 -1.51 49.53 0.00
Recent price movements
in a firm‘s stock/NAV 2.83 1.28 0.64 -0.86 43.44 0.00
Statements from
politicians and
governmental officials
3.29 0.87 0.07 -0.31 74.38 0.00
Fluctuations/developments
in the indices of the major
market
3.64 0.95 -0.35 -0.78 75.22 0.00
Current economic
indicators 3.26 0.82 -0.12 -0.28 78.31 0.00
Reputation of a company
in the domestic market 3.38 0.90 -0.36 -0.79 74.02 0.00
Reputation of a parent
company or sister concern 4.07 0.61 -0.04 -0.36 129.92 0.00
Environmental record 3.64 0.64 0.50 -0.67 111.03 0.00
Market capitalization of
company 3.83 0.83 -0.54 0.35 91.00 0.00
Conversation/exchanges
of views with professional
colleagues
2.94 1.12 0.89 -0.66 51.32 0.00
187
Variables
Mean
Std.
Deviation Skewness Kurtosis
t- test Sig. (2-
Tailed)
Publication in the
financial press,
newspapers and electronic
media
2.97 1.05 0.79 -0.50 55.57 0.00
Conversation/exchanges
of views with company
executives and sector
experts
3.97 0.65 0.03 -0.61 120.03 0.00
Studying the portfolio
investments of other
market players
3.77 0.65 0.26 -0.69 114.68 0.00
Corporate forecast
prepared by independent
investment company
2.48 0.86 0.79 1.08 56.84 0.00
Economic forecasts by
research institutions 2.90 1.12 0.68 -0.43 50.73 0.00
Study of Annual Reports 4.09 0.69 -0.13 -0.91 115.83 0.00
Family members 3.58 0.95 -0.40 -0.25 74.07 0.00
Friends and relatives 3.73 0.95 -0.63 0.08 77.44 0.00
Present investors 3.67 0.89 -0.16 -0.73 80.53 0.00
Financial advisors/Broker
and analyst‘s
recommendation
3.87 0.60 0.05 -0.30 126.90 0.00
Rating agencies‘ reports 4.17 0.60 -0.09 -0.38 136.20 0.00
Diversification needs 4.14 0.71 -0.20 -0.98 115.05 0.00
Liquidity associated with
investment 2.91 1.26 0.08 -1.10 45.22 0.00
Availing the benefit of
income tax deduction 3.44 1.25 0.09 -1.62 54.11 0.00
Risk-return trade off 3.98 0.61 0.01 -0.33 127.20 0.00
Minimizing risk 4.01 0.64 -0.01 -0.51 123.79 0.00
Ease of obtaining
borrowed fund 3.03 1.42 -0.01 -1.38 41.67 0.00
Preferred investment time
horizon 2.74 1.15 0.42 -0.60 46.65 0.00
Safety associated with
investment 2.98 1.31 0.18 -1.16 44.59 0.00
Affordable minimum
investment amount 2.77 1.49 0.31 -1.40 36.55 0.00
Ease in liquidity 4.03 0.65 -0.03 -0.63 121.44 0.00
Guaranteed return 2.57 1.16 0.83 0.04 43.50 0.00
Table 4.17 Continued
188
Thus, according to Kurtosis, data for all the 16 variables of preferred sources of
information and 44 variables influencing investment decision are normally distributed.
Also, referring to fourth column of Table 4.16 and Table 4.17 indicate that, Skewness for
all the variables is less than 1 for 16 variables of preferred sources of information and the
Skewness for all the 44 variables influencing investment decision is less than 0.94, far
smaller than the lower bound of four or five. Thus, both Kurtosis and Skewness of the
variables provide indication that the data are distributed normally. The t-test scores, as in
the last column of Table 4.16 and Table 4.17, indicate that the mean score of the
respondents on five point Likert scale are significantly different for all 16 variables of
preferred sources of information and 44 variables influencing investment decision
respectively.
4.9 Factor Analysis
One of the most interdependent techniques for data reduction is factor analysis.
According to Luck and Rubin, factor analysis seeks to identify a set of dimensions that is
not readily observed in a large set of variables (Luck and Rubin, 2003333
). The analysis
summarizes a majority of the information in the data set in terms of relatively new few
categories, known as factors. Two basic reasons using factor analysis are (i) to simplify a
set of data by reducing a large number of measures (in which some may be interrelated
causing multicollinearity) for set of respondents to a smaller manageable number of
factors (which are not interrelated) that still retain most of the information found in the
original data set and (ii) to identify the underlying structure of the data in which a large
number of variables may really be measuring a small number of basic characteristics of
the sample.
According to Hair et al. (2003334
), factor analysis is a multivariate statistical technique
that is used to summarize the information contained in a large number of variables into a
333 Luck, D. &Rubin, D. (2003). Marketing Research (7th ed.). New Delhi: Prentice Hall of India Pvt. Ltd.,
542-548. 334 Hair, J. F., Bush, R. P. & Ortinau, D. J. (2003). Marketing Research – Within a Changing Information
Environment (2nd ed.). New Delhi: Tata McGraw Hill Publishing Company Ltd., p. 601.
189
smaller number of subsets or factors. Reasons given by Malhotra Naresh K. (2008335
) for
using factor analysis are (1) to identify underlying dimensions or factors, which explain
the correlation among a set of variables, (2) to identify a new smaller set of uncorrelated
variables to replace the original set of correlated variables and (3) to identify a smaller set
of salient variables from a larger set. For the current study, factor analysis is used to
reduce the number of variables that (1) are preferred by respondents as source of
information and (2) may influence investment decision of respondents. Respondents were
asked to give their preference (on the five point Likert scale, where 1- Not preferred and
5- the most preferred) for source of information on 16 variables. On the other hand,
respondents were asked to rate the influence of 44 variables (on the five point Likert
scale, where 1- Least influence and 5-Most significantly influence) on their investment
decision. Factor analysis was performed separately for these two types of variables under
study.
4.9.1 Factor analysis for variables preferred by investors as source of information
Following the Cronbach‘s Alpha for the 16 variables of preferred sources of information
as shown in Table 4.14, which is 0.708 and data quality, which is examined by using
Skewness, Kurtosis and t-test values as shown in Table 4.16 explained both Kurtosis and
Skewness of the variables provide indication that the data are distributed normally. The t-
test values, as shown in the last column of Table 4.16, indicate that the mean scores of the
respondents on five point Likert scale are significantly different for all 16 variables of
preferred source of information. Hence, these allow the researcher to perform factor
analysis for these 16 variables of preferred source of information.
4.9.1.1 Bartlett‟s test of Sphericity
The method of determining the appropriateness of the factor analysis examines the entire
correlation matrix. The Bartlett‘s test of Sphericity is a statistical test for measuring the
335 Malhotra, N.K. (2008). Marketing Research – An Applied Orientation (5th ed.). New Delhi: Pearson
Education, pp. 640-641.
190
presence of correlations among the variables (Hair et al., 2009336
). It is used to test the
null hypothesis that the variables are uncorrelated in the population (Malhotra, 2008337
).
Means, it provides the statistical significance that the correlation matrix has significant
correlations among at least some of the variables. As, shown in Table 4.18, the Bartlett‘s
test of Sphericity value 1.671 with significance level of is 0.000, satisfies the required
condition. This indicates the statistical significance that correlation matrix has significant
correlation among other variables.
Table 4.18 KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy. 0.664
Bartlett's Test of Sphericity Approx. Chi-Square 1.674
Df 120
Sig. 0.000
4.9.1.2 Kaiser-Meyer-Olkin Test for Sampling Adequacy
The Kaiser-Meyer-Olkin (KMO) Measure for Sampling Adequacy is an index used to
examine the appropriateness of factors analysis. It is an index to quantify the degree of
inter-correlations among the variables. This examines the appropriateness of factor
analysis. It compares the magnitudes of observed correlation coefficients to magnitude of
partial correlation coefficients. The KMO value varies from 0 to 1. High value (between
0.5 to 1.0) indicates factor analysis is appropriate. Small values of KMO Statistic indicate
that correlations between pairs of variables cannot be explained by other variables, and
hence, factor analysis is not suitable (Malhotra, 2008338
). As shown in Table 4.18, the
KMO value for variables preferred by respondents as source of information is 0.664,
which is nearer to 1.0. Hence, this value is acceptable and justifies the appropriateness of
factor analysis.
336 Hair, J. F., William, B. C., Barry, B. J., Anderson, R. E.& Tatham, R. L. (2009). Multivariate Data Analysis (6th ed.). New Delhi: Pearson Education, 138. 337 Malhotra, N. (2008). Marketing Research: An Applied Orientation (5th ed.). New Delhi: Pearson
Education, p. 644. 338 Malhotra N. (2008). Marketing Research – An Applied Orientation (5th ed.). New Delhi: Pearson
Education, p. 642.
191
4.9.1.3 Anti-Image Correlation Matrix
It is the matrix of the partial correlations among variables after factors analysis,
representing the degree to which the factors explain each other in the results (Hair et al.,
2009339
). The diagonal values in the Anti-Image Correlation Matrix represent MSA
value. The variable(s) with the value less than 0.5 should be omitted from factor analysis
one by one, with the smallest being considered omitted first. The values which are not on
the diagonal represent the partial correlations among the variables.
Table 4.19 indicates that all the variables have MSA value more than 0.5. Hence,
researcher can proceed further.
Table 4.19 Anti- Image Correlation Matrix
IS1 IS2 IS3 IS4 IS5 IS6 IS7 IS8 IS9 IS10 IS11 IS12 IS13 IS14 IS15 IS16
IS1 0.636 0.032 0.060 -0.166 0.070 -0.039 -0.046 0.242 -0.258 -0.084 -0.029 -0.408 -0.242 -0.048 -0.200 0.040
IS2 0.032 0.687 -0.283 -0.132 0.131 -0.133 0.173 -0.141 -0.317 0.053 -0.321 -0.147 0.075 -0.292 0.143 0.201
IS3 0.060 -0.283 0.733 -0.361 -0.182 -0.032 -0.076 0.028 0.274 -0.228 -0.091 -0.118 -0.216 -0.050 -0.022 0.034
IS4 -0.166 -0.132 -0.361 0.757 0.072 -0.173 -0.171 -0.053 -0.022 -0.057 -0.216 0.168 0.119 0.077 0.131 -0.162
IS5 0.070 0.131 -0.182 0.072 0.530 0.081 0.251 -0.048 -0.148 -0.018 0.122 0.119 0.162 -0.259 -0.082 -0.260
IS6 -0.039 -0.133 -0.032 -0.173 0.081 0.738 -0.007 0.038 -0.150 0.019 0.183 0.016 -0.230 -0.161 0.114 -0.117
IS7 -0.046 0.173 -0.076 -0.171 0.251 -0.007 0.611 -0.070 -0.048 -0.002 -0.106 -0.036 0.018 -0.108 -0.152 0.223
IS8 0.242 -0.141 0.028 -0.053 -0.048 0.038 -0.070 0.526 -0.261 -0.033 0.102 -0.023 -0.046 -0.025 -0.186 0.056
IS9 -0.258 -0.317 0.274 -0.022 -0.148 -0.150 -0.048 -0.261 0.558 0.053 -0.029 -0.097 0.197 0.174 -0.274 -0.096
IS10 -0.084 0.053 -0.228 -0.057 -0.018 0.019 -0.002 -0.033 0.053 0.767 -0.006 0.070 0.082 -0.129 -0.057 0.069
IS11 -0.029 -0.321 -0.091 -0.216 0.122 0.183 -0.106 0.102 -0.029 -0.006 0.737 0.239 0.061 -0.128 0.004 -0.137
IS12 -0.408 -0.147 -0.118 0.168 0.119 0.016 -0.036 -0.023 -0.097 0.070 0.239 0.588 0.020 -0.053 0.064 -0.220
IS13 -0.242 0.075 -0.216 0.119 0.162 -0.230 0.018 -0.046 0.197 0.082 0.061 0.020 0.565 -0.120 -0.147 -0.002
IS14 -0.048 -0.292 -0.050 0.077 -0.259 -0.161 -0.108 -0.025 0.174 -0.129 -0.128 -0.053 -0.120 0.733 -0.092 -0.158
IS15 -0.200 0.143 -0.022 0.131 -0.082 0.114 -0.152 -0.186 -0.274 -0.057 0.004 0.064 -0.147 -0.092 0.626 -0.139
IS16 0.040 0.201 0.034 -0.162 -0.260 -0.117 0.223 0.056 -0.096 0.069 -0.137 -0.220 -0.002 -0.158 -0.139 0.561
4.9.1.4 Communalities
Communality is the amount of variance a variable explains with all the factors being
considered. This is also the percentage of total variance explained by the common
factors (Malhotra, 2008340
). The method selected for conducting factor analysis here is
339 Hair, J. F., William, B. C., Barry, B. J., Anderson, R. E.& Tatham, R. L. (2009). Multivariate Data
Analysis (6th ed.). New Delhi: Pearson Education, p. 125. 340 Malhotra N. (2008). Marketing Research – An Applied Orientation (5th ed.). New Delhi: Pearson
Education, p. 561.
192
Principal Component Analysis. In this method, the total variance in the data is
considered. The initial communalities for Principal Component Analysis are 1. However,
the primary concern is the extracted communalities, which are achieved after extraction
of factors. The communalities can be found mathematically by squaring the factor
loading of a variable across all variables and then summing these figures. This term may
be interpreted as a measure of ‗uniqueness‘. In the present study, for the variables
preferred by respondents as source of information, the communalities are calculated with
the help of computer software as shown in Table 4.20. A low communality figure
indicates that the variables is statistically independent and cannot be combined with other
variables.
4.20 Communalities
Variables Initial Extraction
Communalities
IS1 1.000 0.656
IS2 1.000 0.734
IS3 1.000 0.693
IS4 1.000 0.642
IS5 1.000 0.716
IS6 1.000 0.444
IS7 1.000 0.603
IS8 1.000 0.530
IS9 1.000 0.773
IS10 1.000 0.483
IS11 1.000 0.581
IS12 1.000 0.582
IS13 1.000 0.609
IS14 1.000 0.575
IS15 1.000 0.711
IS16 1.000 0.586
Extraction Method: Principal Component Analysis.
The close observation of Table 4.20 shows that the communality for variable IS6 (i.e.
Rating agencies‘ reports) and IS10 (i.e. Conversation/exchanges of views with
professional colleagues) is 0.444 and 0.483 respectively, which are removed from the list
193
of 16 variables of information sources and Anti-Image Correlation Matrix is to be
developed, MSA values are observed (Table 4.21), and revised communalities extracted
and presented in Table 4.22, shows that revised extracted communalities for the rest of
the variables are high (greater than 0.5), and hence, acceptable for all variables to
proceed.
Table 4.21 Revised Anti- Image Correlation Matrix
IS1 IS2 IS3 IS4 IS5 IS7 IS8 IS9 IS11 IS12 IS13 IS14 IS15 IS16
IS1 0.613 0.032 0.041 -0.181 0.072 -0.047 0.242 -0.264 -0.023 -0.404 -0.253 -0.066 -0.203 0.042
IS2 0.032 0.656 -0.285 -0.156 0.145 0.175 -0.135 -0.348 -0.304 -0.151 0.041 -0.317 0.165 0.185
IS3 0.041 -0.285 0.702 -0.395 -0.190 -0.079 0.022 0.293 -0.091 -0.105 -0.215 -0.088 -0.033 0.048
IS4 -0.181 -0.156 -0.395 0.726 0.087 -0.175 -0.049 -0.046 -0.191 0.178 0.088 0.044 0.151 -0.183
IS5 0.072 0.145 -0.190 0.087 0.524 0.252 -0.052 -0.137 0.109 0.120 0.188 -0.254 -0.094 0.252
IS7 -0.047 0.175 -0.079 -0.175 0.252 0.595 -0.070 -0.050 -0.107 -0.036 0.017 -0.112 -0.153 0.224
IS8 0.242 -0.135 0.022 -0.049 -0.052 -0.070 0.530 -0.257 0.097 -0.021 -0.035 -0.024 -0.194 0.064
IS9 -0.264 -0.348 0.293 -0.046 -0.137 -0.050 -0.257 0.551 -0.001 -0.100 0.165 0.162 -0.259 -0.120
IS11 -0.023 -0.304 -0.091 -0.191 0.109 -0.107 0.097 -0.001 0.766 0.242 0.109 -0.104 -0.018 -0.118
IS12 -0.404 -0.151 -0.105 0.178 0.120 -0.036 -0.021 -0.100 0.242 0.574 0.018 -0.043 0.067 -0.225
IS13 -0.253 0.041 -0.215 0.088 0.188 0.017 -0.035 0.165 0.109 0.018 0.555 -0.154 -0.120 -0.037
IS14 -0.066 -0.317 -0.088 0.044 -0.254 -0.112 -0.024 0.162 -0.104 -0.043 -0.154 0.710 -0.084 -0.174
IS15 -0.203 0.165 -0.033 0.151 -0.094 -0.153 -0.194 -0.259 -0.018 0.067 -0.120 -0.084 0.640 -0.123
IS16 0.042 0.185 0.048 -0.183 -0.252 0.224 0.064 -0.120 -0.118 -0.225 -0.037 -0.174 -0.123 0.557
Table 4.21 shows that all variables in the table have MSA value more than 0.5. So,
researcher can proceed further.
Table 4.22 Revised Communalities
Variables Initial Extraction
Communalities
IS1 1.000 0.738
IS2 1.000 0.714
IS3 1.000 0.697
IS4 1.000 0.644
IS5 1.000 0.707
IS7 1.000 0.593
IS8 1.000 0.644
IS9 1.000 0.784
IS11 1.000 0.607
IS12 1.000 0.679
IS13 1.000 0.707
194
IS14 1.000 0.609
IS15 1.000 0.679
IS16 1.000 0.596
Extraction Method: Principal Component Analysis.
4.9.1.5 Variance explained
The most commonly used method to answer the key question: How many factors to
extract or retain?
The rational for the latent roots or eigenvalues criterion is that any individual factor
should account for the variance of at least a single variable if it is to be retained for
interpretation. It is required that the scale constructed and the components extracted
should be able to explain maximum variance in the data. For this study, an analysis of the
Eigen values is required. Eigenvalues are the sum of the variances of the factor values.
Eigen value represents the total variance explained by each factor. Hence, only factors
with a variance greater than 1.0 are considered significant; all factors with eigenvalues
less than 1.0 are considered insignificant and are disregarded (Malhotra, 2008341
). Using
eigenvalues for establishing a cut off is most reliable when number of variables is
between 20 to 50 (Hair, 2009342
).
The percentage of variance is an approach based on achieving a specified cumulative
percentage of total variance extracted by successive factors. The purpose is to ensure
practical significance for the derived factors by ensuring that they explain at least
specified amount of variance. In the social science, it is common to consider a solution
that accounts for 60 percent of the total variance as satisfactory (Hair, 2009343
).
341 Malhotra N. (2008). Marketing Research – An Applied Orientation (5th ed.). New Delhi: Pearson
Education, p. 561. 342 Hair, J. F., William, B. C., Barry, B. J., Anderson, R. E.& Tatham, R. L. (2009). Multivariate Data
Analysis (6th ed.). New Delhi: Pearson Education, p. 144. 343 Ibid, 144.
195
Table 4.23 shows that the Eigenvalues of all the factors that can be extracted. Table 4.23
also shows the cumulative variance. However, it is required that the maximum amount of
variance should be explained in minimum number of components – for this reason
extraction of the components is required. Only those factors are extracted for which
Eigen values are greater than one. Thus, the factors extracted out of 14 variables under
study are five in number and together contribute 67.12% of total variance. This is a fair
percentage of variance to be explained and assumes the appropriateness of the factor
analysis. Thus, researcher has extracted 5 dimensions from total 14 variables preferred by
respondents as source of information for investment decision.
Further, Table 4.23 shows the extraction sum of squared loadings of the scale constructed
for variables/factors preferred by respondents. However, a careful look at Table 4.23
shows that 67.12% variance is not uniformly distributed across all the components, where
only first component accounts for 20.13% of variance. Thus, in order for the variance to
be uniformly distributed across the components, a rotation of the components matrix is
required.
196
Table 4.23 Total Variance Explained
Comp
onent
Initial Eigenvalues
Extraction Sums of
Squared Loadings
Rotation Sums of Squared
Loadings
Total
% of
Variance
Cumulative
% Total
% of
Variance
Cumulative
% Total
% of
Variance
Cumulative
%
1 3.008 21.483 21.483 3.008 21.483 21.483 2.819 20.135 20.135
2 2.284 16.316 37.798 2.284 16.316 37.798 1.874 13.385 33.520
3 1.640 11.718 49.516 1.640 11.718 49.516 1.717 12.264 45.785
4 1.413 10.093 59.609 1.413 10.093 59.609 1.617 11.549 57.333
5 1.052 7.511 67.120 1.052 7.511 67.120 1.370 9.787 67.120
6 0.877 6.268 73.388
7 0.678 4.845 78.233
8 0.655 4.676 82.909
9 0.589 4.208 87.118
10 0.471 3.361 90.479
11 0.408 2.913 93.392
12 0.393 2.810 96.202
13 0.280 2.001 98.203
14 0.252 1.797 100.000
Extraction Method: Principal Component Analysis.
4.9.1.6 Factor Extraction
There are two types of extraction methods.
1. Principal Component Analysis and 2. Common Factor Analysis
Principal component analysis considers the total variance in the data and derives the
factors than contain small proportions of unique variance and in some instances, error
variance. This method is most appropriate when data reduction is primary concern,
focusing on the minimum number of factors needed to account for the maximum portion
of the total variance represented in the original set of the variables (Hair, 2009344
).
344 Hair, J. F., William, B. C., Barry, B. J., Anderson, R. E.& Tatham, R. L. (2009). Multivariate Data
Analysis (6th ed.). New Delhi: Pearson Education, p. 142.
197
In common factor analysis, the factors are estimated based only on the common or shared
variance, assuming that both the unique and error variance are not of interest in defining
the structure of the variables. Communalities are inserted in the diagonal of the
correlation matrix to employ common variance in the estimation of the factors. This
method is most appropriate when primary objective is to identify the underlying
dimensions and the common variance is of interest (Hair, 2009345
).
The objective of this study is to extract the factors and so the data reduction becomes the
primary concern. Hence, the principal component analysis method is used to extract the
unrotated component matrix.
Table 4.24 Component Matrixa
Variables
Components
1 2 3 4 5
IS2 0.770 -0.066 0.146 0.217 -0.221
IS4 0.740 -0.268 0.083 0.094 -0.090
IS3 0.728 -0.289 0.119 -0.223 0.138
IS11 0.662 -0.311 0.188 0.172 -0.082
IS14 0.141 0.616 0.280 -0.278 0.231
IS15 0.064 0.673 -0.089 0.121 0.446
IS9 0.193 0.667 0.027 0.499 -0.228
IS12 0.238 0.574 -0.317 -0.206 -0.338
IS1 0.415 0.523 -0.459 -0.183 -0.220
IS16 0.135 0.503 0.452 -0.330 -0.106
IS5 -0.112 0.350 0.716 -0.180 0.164
IS7 0.353 -0.124 -0.506 0.300 0.326
IS8 0.117 0.321 0.169 0.600 0.373
IS13 0.282 0.115 -0.403 -0.520 0.427
Extraction Method: Principal Component Analysis.
a. 5 components extracted. 345 Ibid, 142
198
4.9.1.7 Factor Loading
A factor loading represents the correlations between the factors and variables. It shows
the strength of the variables that compose the factor. The larger the absolute value of the
factor loading, the factor and the variable are more closely related. Means, the more
important role the variable plays in interpreting the factor analysis (Malhotra, 2008346
).
Table 4.25 guides the researcher for identifying significant factor loadings based on
sample size.
Table 4.25 Guidelines for Identifying Significant Factor Loadings based on Sample
Sizea
Factor Loading Sample size needed for
significance
0.30 350
0.35 250
0.40 200
0.45 150
0.50 120
0.55 100
0.60 85
0.65 70
0.70 60
0.75 50 (a Significance is based on a .05 significance level, a power level of 80 percent, and
standard errors assumed to be twice those of conventional correlation coefficient)
(Source: Hair, et al. (2009). Multivariate Data Analysis (6th ed.).Pearson Education,
New Delhi, p. 152)
The sample size taken by researcher is 385 for this study. Hence, factor loading 0.30 is
sufficient. But, the factor loading value greater than 0.50 is generally considered
necessary for practical significance (Hair, 2009347
). Hence, the researcher has considered
factor loading 0.5 for extracting the factors from the list of total 16 variables as preferred
346
Malhotra N. (2008). Marketing Research – An Applied Orientation (5th ed.). New Delhi: Pearson
Education, p. 561. 347 Hair, J. F., William, B. C., Barry, B. J., Anderson, R. E.& Tatham, R. L. (2009). Multivariate Data
Analysis (6th ed.). New Delhi: Pearson Education, p. 152.
199
by respondents as source of information. He further added that a ―Although factor
loading of +.30 to +.40 are minimally acceptable for its significance. To be considered
significant, a smaller loading is needed given either a larger sample size or on a larger
number of variables being analyzed‖ (p. 153). Hence, researcher has considered factor
loading of 0.4 for extracting the factors from the list of total 44 variables that influencing
investment decision of investors.
4.9.1.8 Rotated Factor Matrix
Components matrix (unrotated matrix) is the loadings of various variables to the
extracted components, which is shown in Table 4.24. Although the initial or unrotated
matrix as shown in Table 4.24 indicates the relationship between the factors and
individual variables, it seldom results in factors that can be interpreted, because the
factors are correlated with many variables. In such a complex matrix, it is difficult to
interpret the factors. Therefore, through rotation, the factor matrix is transformed into a
simpler one that is easier to interpret (Malhotra, 2008348
). Rotation of the factors
improves interpretation by reducing some of the ambiguities that often accompany initial
unroated factor solutions. Therefore, researcher must employ a rotational method to
achieve simpler and theoretically more meaningful factor solutions.
Mainly, there are two types of factor rotation methods:
1. Orthogonal factor rotation: In orthogonal factor rotation, the axes are maintained at
90 degrees. It is also possible to rotate the axes and not retain the 90-degree angle
between the reference axes. This is the simplest and more widely used approach for
factor rotation.
2. Oblique factor rotation: When 90-degree is not maintained it is called oblique factor
rotation. This method is not widely used because the analytical procedures for
348 Malhotra N. (2008). Marketing Research – An Applied Orientation (5th ed.). New Delhi: Pearson
Education, p.647.
200
performing the oblique rotations are not as well developed and are still subject to
some controversy.
Therefore, researcher has employed the orthogonal factor rotation method.
One of the three types of orthogonal rotation methods are: (1) Quartimax rotation – The
ultimate goal of Quartimax rotation is to simplify the row of a factor matrix, i.e.,
Quartimax focuses on rotating the initial factor so that a variable loads high on one factor
and as low as possible on all other factors. (2) Varimax rotation – This is method of
factor rotation that minimizes the number of variables with higher loadings on a factor,
thereby enhancing the interpretability of factors and has proved successful as an
analytical approach to obtain an orthogonal rotation of factors. Furthermore, the Kaiser‘s
experiment indicates that the factor pattern obtained by Varimax rotation tends to be
more invariant than pattern obtained by the Quarimax method when different subsets of
variables are analyzed. (3) Equipmax rotation – This approach is a compromise between
the Quarimax and Varimax approaches. Rather than concentrating either on
simplification of the rows or on simplification of columns, it tries to accomplish some of
each. This method has not gained widespread acceptance and is used infrequently
(Malhotra, 2008349
).
Therefore, for this study, researcher has used VARIMAX rotation method, which is the
most commonly used rotation method. The variance explained by each component before
and after the rotation method is shown in Table 4.23. It can be seen from this table that
the variance is now evenly distributed in a range of 20.13% - 9.78%, which was
previously 21.48% - 7.51%.
An analysis of factor loadings in the rotated factor matrix helps in interpreting and
naming the five factors that have been extracted in the earlier section. Interpretation is
done by identifying the variables that have very high loading in the same component.
349 Malhotra N. (2008). Marketing Research – An Applied Orientation (5th ed.). New Delhi: Pearson
Education, pp. 648-649.
201
These factors can be interpreted in terms of the variables that load highly on it. Table
4.26 shows the rotated component matrix.
The relationship between the observed variables and the newly produced factors is
revealed in the form of factor loadings. These are the coefficients within the matrix that
indicate the importance of the factor. These loadings have the lower limit of -1.0 and an
upper limit of +1.0. For better data reduction, those variables that have the factor loadings
more than 0.50 were considered under each factor. Fortunately, all the 14 variables have
factor loading more than 0.50, so all the 14 variables are considered for loading on
extracted five factors.
Table 4.26 Rotated Component Matrixa
Variables
Components
1 2 3 4 5
IS2 0.802 0.201 -0.023 0.105 -0.138
IS4 0.792 0.041 -0.113 -0.028 0.025
IS11 0.768 -0.073 -0.077 0.001 -0.072
IS3 0.740 -0.058 0.028 -0.124 0.360
IS12 -0.018 0.817 0.087 -0.006 0.055
IS1 0.108 0.811 -0.075 0.079 0.239
IS14 0.077 0.538 0.363 0.124 0.409
IS5 -0.058 -0.181 0.793 0.206 0.002
IS16 0.064 0.272 0.713 0.068 0.061
IS7 0.230 0.036 -0.615 0.295 0.271
IS8 0.083 -0.119 -0.005 0.781 -0.114
IS15 -0.193 0.248 0.138 0.679 0.317
IS9 0.062 0.516 0.098 0.601 -0.378
IS13 0.022 0.202 -0.089 -0.047 0.810
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization. a. Rotation converged in 5 iterations.
Table 4.26 represents the rotated component matrix. Loadings of all the variables are
more than 0.5 with five factors extracted. The eigenvalues of the first five factors as
shown in Table 4.23 is more than 1 and total variance explained by these five factors is
67.12 percent.
202
The factors extracted, their respective items with the assigned codes and their
corresponding factor loading are given in Table 4.27.
Table 4.27 Composition of Each Factor Identified in Factor Analysis
Factor Items Factor
loadings
Factor 1
Published
Operational
Information
Annual Reports of the company (IS2) 0.802
Company‘s website (IS4) 0.792
Publication in the financial press, newspaper and
electronic media (IS11) 0.768
Prospectus of the company (IS3) 0.740
Factor 2
Independent
Information
Conversation/exchanges of views with company
executives and sector experts (IS12) 0.817
Certified Financial Planner (IS1) 0.811
Published reports of research agencies (IS14) 0.538
Factor 3
Advocate
Recommendation
Distributers/agents of financial products (IS5) 0.793
Financial advisors/brokers/ and analyst‘s
recommendations (IS16) 0.713
Company‘s telephonic representatives (IS7) -0.601
Factor 4
Accessibility
Information
Family members (IS8) 0.781
Existing investors (IS15) 0.679
Friends and relatives (IS9) 0.601
Factor 5
Performance
Forecast
Corporate forecast prepared by independent investment
company (IS13) 0.810
4.9.1.9 Naming of the factors
The following five factors were identified as per the factor loading in Table 4.27. The
explanation for the same is given below.
Factor 1: Published Operational Information
The four variables were identified under factor 1 are ‗annual reports of the company‘
(0.802), ‗company‘s website‘ (0.792), ‗publication in the financial press‘, ‗newspapers
203
and electronic media‘ (0.768) and ‗prospectus of the company‘ (0.740). The group of
these variables is named as ―Published Operational Information‖, as these variables
indicate the operational performance of a company and therefore are a key information
source for an investor looking to invest. Moreover, this operational performance is
published as information and available at free of cost to the investors. This extracted
factor explains 20.13 % of variance, which is the highest among the all factors.
Lin (2002) suggests investors collect the financial information from impersonal sources
that are written material published in books, brochures, reports, magazine; media such
as, TV, radio programs and the Internet.
Factor 2: Independent Information
The three variables identified under Factor 2 are ‗conversation/ exchanges of views with
company executives and sector experts‘ (0.817), ‗published reports from research
agencies‘ (0.811) and ‗certified financial planner‘ (0.538). By considering this, the name
of this factor is given as ―Independent Information‖. The variables under this factor are
related with an outside expert‘s view about the firm‘s position and what they perceive
about the firm and its prospects. In addition to this, the variables under this factor give
neutral and/or independent opinion to the investors with regard to the investment
alternatives/asset specific information about various investment choices available for
investment. This extracted factor explains 13.39 % of variance.
Arlen et al. (2007350
) emphasized that due to lack of confidence, investors heavily rely on
financial advice. Sung and Sandager (1997351
) prefer those sources of information, which
are be affiliated with an independent financial firm and provide neutral opinion.
Factor 3: Advocate Recommendation
350 Arlen, C. Ponston, L. R. & Akbulut, A. Y. (2007). Advice availability and gender differences in risky
decision making: A study of online retirement planning. Proceedings of the 40th Hawai International
Conference on System Sciences. 351 Sung, B. C. & Sandager, J. P. (1997). What consumers look for in financial planners? Association for
Financial Counseling and Planning Education.
204
The three variables identified under Factor 3 are ‗distributers/agents of financial
products‘ (0.793), ‗financial advisors/brokers and analyst‘s recommendation‘ (0.713) and
‗company‘s telephone representatives‘ (-0.615). The name of this factor is identified as
―Advocate Recommendation‖. The variables under this factor seek to give an inside
view of the firm, where views of people who have a direct or indirect stake in the
company are taken into consideration. These three variables together explain 12.26 % of
total variance.
Lin (2002) suggests that investors collect the financial information from personal sources
such as, professional financial services providers, brokers, distributers and third party
agents. Nick et. al (2010352
) found that telephone representatives of a company are one of
the major source to get the financial information.
Factor 4: Accessibility Information
The three variables identified under Factor 4 are ‗family members‘ (0.781), ‗existing
investors‘ (0.679) and ‗friends and relatives‘ (0.601). The name of this factor is identified
as ―Accessibility Information‖. These variables underpin information which can be
easily accessed by the investor although such information may need to be independently
verified. This extracted factor explains 11.59 % of variance.
Chandra et al. (2011353
) has stated that investors tend to rely heavily on the easily
available and accessible information. They don‘t tend to check the reliability of this
information and prefer to those piece of information which are easy to incorporate into
their decisions. They also added that investors tend to discount the information that seems
complex to incorporate into their decisions-making process, and adopt only those
information, which are easily available, accessible and adjustable in nature. Thus, they
352 Chater, N., Huck, S. &Inderest, R. (2010). Consumer Decision-Making in Retail Investment Services: A
Behavioural Economics Perspective. Decision Technology Ltd. 353
Chandra, A. &Kumar, R. (2011). Determinants of individual investor behaviour: An orthogonal linear
transformation approach. Munich Personal RePEc Archive No. 29722, accessed on April 15, 2011 at
http://mpra.ub.uni-muenchen.de/29722/
205
heavily rely on the easily available and accessible information. This accessible
information is easily available from family members, friends and relative and existing
investors.
Factor 5: Performance Forecast
The only one variable identified under factor 5 is corporate forecast prepared by
‗corporate forecast prepared by independent investment company‘ (0.810). This factor is
related to the business performance forecast of the company prepared by an independent
investment company. Hence, the name given to this extracted factor is “Performance
Forecast”, as financial information is supposed to facilitate the prediction of firm‘s
future cash flows and help the investors to assess the future securities risk and return.
This extracted factor explains 9.79 % of variance.
The psychological phenomenon explains the reasons for which why do investors behave
irrationally. One of the reasons is representativeness heuristic, which explains that after a
series of positive earnings, the investor is most likely started to believe that the next
period earnings will again be positive and fail to consider the probability of a decrease.
For this reason, they always want independent performance forecast of the firm, in which
they want to invest.
4.9.1.10 Mean score of extracted factors
Table 4.28 represents the factor wise mean preference score. The score is derived by
taking the grand mean of variables clubbed in each factor. From Table 4.28, it is found
that for factor 2 and factor 4, the mean score is highest, i.e. 3.89. This indicates that the
independent information and accessibility information are the most preferred by the
respondents, which is followed by the mean score of factor 3, i.e. advocate
recommendation, for which the preference mean score is 3.77. The least score is for
factor 5 and it is 3.51. This indicates that performance forecast is the least preferred by
206
the respondents to gather the information for their investment decision. The mean score
of the factor 1, i.e. ―Published operational information‖ is on the middle of the road.
Table 4.28 Mean Preference Score of Extracted Factors
Factor Factor Name Grand Mean Value
Factor 1 Published operational information 3.58
Factor 2 Independent information 3.89
Factor 3 Advocate recommendation 3.77
Factor 4 Accessibility information 3.89
Factor 5 Performance forecast 3.51
4.9.2 Factor analysis for variables influencing investment decision
Following the Cronbach‘s Alpha for the 44 variables influencing investment decision as
shown in Table 4.15, which is 0.794 and data quality, which is examined by using
Skewness, Kurtosis and t-test values as shown in Table 4.17 explained both Kurtosis and
Skewness of the variables provide indication that the data are distributed normally. The t-
test values, as shown in the last column of Table 4.17, indicate that the mean scores of the
respondents on five point Likert scale are significantly different for all 44 variables
influencing investment decision. Hence, all allow the researcher to perform factor
analysis for the 44 variables influencing investment decision of the respondents.
4.9.2.1 Bartlett‟s test of sphericity
The Bartlett‘s Test of Sphericity for measuring the presence of correlations among the
variables influencing investment decision is shown in Table 4.29. As shown in the Table
4.29 Bartlett's Test of Sphericity is 4.998 with significant value of 0.000. Hence,
researcher can proceed for the factor analysis.
4.9.2.2 Kaiser-Meyer-Olkin Test for Sampling Adequacy
KMO measure of sampling adequacy needs to be greater than 0.5 as per standards which
are present in this case. As shown in Table 4.29 the KMO value for factors influencing
207
investment decision is 0.690, which is nearer to 1.0. Hence, this value is acceptable and
justifies the appropriateness of factor analysis.
Table 4.29 KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy 0.690
Bartlett's Test of
Sphericity
Approx. Chi-Square 4.998
df 946
Sig. 0.000
4.9.2.3 Measure of Sampling Adequacy
The MSA values for all the variables under study are checked with the help of Anti-
image correlation matrix and it is found that all variables have MSA value more than 0.5.
Hence, researcher can proceed further.
4.9.2.4 Communalities
In the present study, for the variables influencing investment decision, the factor analysis
has been run on all 44 variables. Anti- image correlation matrix is derived and
communalities for all 44 variables are calculated with the help of computer software as
shown in Table 4.30. A low communality figure indicates that the variable is statistically
independent and cannot be combined with other variables.
208
Table 4.30 Communalities
Variables Initial Extraction
ID1 1.000 0.661
ID2 1.000 0.467
ID3 1.000 0.681
ID4 1.000 0.596
ID5 1.000 0.754
ID6 1.000 0.632
ID7 1.000 0.623
ID8 1.000 0.587
ID9 1.000 0.581
ID10 1.000 0.639
ID11 1.000 0.677
ID12 1.000 0.706
ID13 1.000 0.557
ID14 1.000 0.520
ID15 1.000 0.655
ID16 1.000 0.434
ID17 1.000 0.711
ID18 1.000 0.674
ID19 1.000 0.435
ID20 1.000 0.496
ID21 1.000 0.554
ID22 1.000 0.517
ID23 1.000 0.613
ID24 1.000 0.604
ID25 1.000 0.555
ID26 1.000 0.471
ID27 1.000 0.658
ID28 1.000 0.634
ID29 1.000 0.659
ID30 1.000 0.710
ID31 1.000 0.712
209
Variables Initial Extraction
ID32 1.000 0.641
ID33 1.000 0.621
ID34 1.000 0.612
ID35 1.000 0.635
ID36 1.000 0.505
ID37 1.000 0.480
ID38 1.000 0.587
ID39 1.000 0.702
ID40 1.000 0.578
ID41 1.000 0.667
ID42 1.000 0.733
ID43 1.000 0.599
ID44 1.000 0.392
Extraction Method: Principal Component Analysis.
A look at Table 4.30 shows the communality for ID2 (expected corporate earning) 0.467;
ID16 (fluctuations/developments in the indices of the major market), 0.434; ID19
(reputation of a parent company or sister concern), 0.435); ID20 (environmental record)
0.496; ID26 (corporate forecast prepared by independent investment company), 0.471;
ID37 (risk-return trade off), 0.480 and ID44 (guaranteed return), 0.392 are lower than
0.5. Hence, these variables are removed from the list of 44 variables and further analysis
was done. The revised Anti-image correlation matrix was studied by considering
remaining variables after removing above mentioned seven variables and new
communalities were found as shown in Table 4.31.
Table 4.30 Continued
210
Table 4.31 Revised Communalities
Variables Initial Extraction
ID1 1.000 0.665
ID3 1.000 0.607
ID4 1.000 0.538
ID5 1.000 0.450
ID6 1.000 0.614
ID7 1.000 0.638
ID8 1.000 0.590
ID9 1.000 0.534
ID10 1.000 0.612
ID11 1.000 0.599
ID12 1.000 0.697
ID13 1.000 0.457
ID14 1.000 0.469
ID15 1.000 0.519
ID17 1.000 0.733
ID18 1.000 0.476
ID21 1.000 0.557
ID24 1.000 0.516
ID23 1.000 0.441
ID24 1.000 0.626
ID25 1.000 0.391
ID27 1.000 0.489
ID28 1.000 0.616
ID29 1.000 0.602
ID30 1.000 0.686
ID31 1.000 0.586
ID32 1.000 0.648
ID33 1.000 0.558
ID34 1.000 0.564
ID35 1.000 0.581
ID36 1.000 0.523
211
Variables Initial Extraction
ID38 1.000 0.590
ID39 1.000 0.665
ID40 1.000 0.551
ID41 1.000 0.681
ID42 1.000 0.718
ID43 1.000 0.486
Extraction Method: Principal Component
Analysis.
Table 4.31 shows that out of remaining variables considered for factor analysis, the
communalities of seven variables are low (i.e. less than 0.5). These variables with their
respective communalities are: ID5 (Insider‘s information), 0.450; ID13 (coverage in the
press), 0.457; ID14 (recent price movements in a firm‘s stock/NAV), 0.469; ID18
(reputation of a company in the domestic market), 0.476; ID23 (publication in the
financial press, newspapers and electronic media), 0.441; ID25 (studying the portfolio
investments of other market players), 0.391; ID27 (economic forecasts by research
institutions), 0.489; and ID43 (ease in liquidity), 0.486. These seven variables are
removed from the rest of the variables. Further analysis is done with remaining 29
variables. An anti-image correlation matrix derived and new communalities are found for
these 29 variables influencing investment decision of investors, which is presented in
Table 4.32 and Table 4.33 respectively.
Table 4.32 indicates that all the variables have MSA value more than 0.5. Hence,
researcher can proceed further.
Table 4.31 Continued
212
Table 4.32 Anti Image Correlation Matrix (for 29 variables) ID1 ID3 ID4 ID6 ID7 ID8 ID9 ID10 ID11 ID12 ID15 ID17 ID21 ID22 ID24 ID25 ID29 ID30 ID31 ID32 ID33 ID34 ID35 ID36 ID38 ID39 ID40 ID41 ID42
ID1 0.753 -0.339 -0.144 -0.030 -0.039 0.042 0.189 -0.056 -0.076 -0.022 -0.167 0.199 -0.219 0.022 -0.088 -0.169 0.003 -0.085 -0.054 -0.037 -0.061 -0.134 -0.017 -0.097 0.015 0.021 0.051 0.052 -0.082
ID3 -0.339 0.719 -0.025 0.014 0.013 -0.039 0.042 0.072 -0.227 0.057 0.062 -0.040 -0.006 0.058 0.091 -0.323 0.015 -0.115 0.141 -0.030 -0.188 -0.125 -0.001 0.016 -0.058 -0.035 0.010 0.011 0.055
ID4 -0.144 -0.025 0.620 0.067 0.069 -0.002 -0.009 -0.228 -0.064 -0.215 -0.214 -0.015 0.117 0.043 -0.087 -0.058 -0.060 -0.066 0.182 0.017 0.036 -0.124 0.059 -0.141 0.064 -0.071 0.008 -0.101 0.065
ID6 -0.030 0.014 0.067 0.751 -0.315 -0.108 -0.053 0.099 0.029 0.049 0.112 -0.108 0.012 0.114 0.061 -0.085 -0.035 -0.087 0.160 -0.070 -0.007 0.114 -0.168 0.076 0.016 -0.044 -0.131 -0.045 -0.024
ID7 -0.039 0.013 0.069 -0.315 0.739 0.045 0.035 -0.079 -0.168 0.112 0.038 0.068 -0.141 -0.020 0.045 -0.018 0.046 -0.095 0.036 -0.285 -0.039 -0.004 -0.041 -0.015 0.026 -0.145 -0.005 0.016 0.015
ID8 0.042 -0.039 -0.002 -0.108 0.045 0.581 0.150 -0.048 0.090 0.045 -0.173 -0.146 0.062 -0.034 -0.181 -0.091 -0.077 -0.063 -0.007 0.177 0.051 -0.129 0.022 -0.110 0.147 -0.050 0.068 -0.037 0.055
ID9 0.189 0.042 -0.009 -0.053 0.035 0.150 0.571 -0.274 -0.201 -0.204 -0.250 0.169 0.080 0.016 -0.031 -0.023 0.041 -0.011 0.008 0.077 -0.137 -0.184 -0.009 -0.101 0.173 -0.022 -0.022 0.081 0.053
ID10 -0.056 0.072 -0.228 0.099 -0.079 -0.048 -0.274 0.501 0.073 0.098 0.209 -0.165 -0.121 0.047 -0.106 0.017 0.142 -0.137 -0.064 0.060 -0.163 -0.019 -0.057 0.180 0.072 0.006 -0.008 0.066 -0.145
ID11 -0.076 -0.227 -0.064 0.029 -0.168 0.090 -0.201 0.073 0.528 -0.302 -0.007 -0.220 0.046 -0.129 -0.004 0.040 -0.059 0.085 -0.184 0.357 0.304 0.060 0.074 0.049 0.006 0.024 0.013 0.066 -0.084
ID12 -0.022 0.057 -0.215 0.049 0.112 0.045 -0.204 0.098 -0.302 0.578 0.057 -0.196 -0.069 0.145 0.048 0.007 -0.090 -0.035 -0.043 -0.312 0.016 0.232 -0.035 -0.056 0.020 -0.017 0.053 -0.090 0.052
ID15 -0.167 0.062 -0.214 0.112 0.038 -0.173 -0.250 0.209 -0.007 0.057 0.539 -0.301 -0.161 -0.053 -0.088 0.186 -0.014 -0.002 0.029 -0.014 -0.049 0.121 -0.079 0.082 -0.034 0.047 0.002 -0.026 -0.039
ID17 0.199 -0.040 -0.015 -0.108 0.068 -0.146 0.169 -0.165 -0.220 -0.196 -0.301 0.584 -0.161 -0.088 0.159 0.084 0.155 -0.025 -0.058 -0.180 -0.040 -0.056 0.038 0.103 -0.084 -0.070 -0.065 0.067 -0.002
ID21 -0.219 -0.006 0.117 0.012 -0.141 0.062 0.080 -0.121 0.046 -0.069 -0.161 -0.161 0.614 -0.097 0.040 -0.165 0.043 0.089 -0.032 0.119 0.057 0.045 0.015 -0.178 -0.008 0.025 -0.003 -0.065 0.136
ID22 0.022 0.058 0.043 0.114 -0.020 -0.034 0.016 0.047 -0.129 0.145 -0.053 -0.088 -0.097 0.734 -0.095 0.005 -0.062 -0.029 0.044 -0.154 -0.204 -0.045 -0.056 -0.074 -0.013 -0.015 0.046 -0.078 -0.110
ID24 -0.088 0.091 -0.087 0.061 0.045 -0.181 -0.031 -0.106 -0.004 0.048 -0.088 0.159 0.040 -0.095 0.626 -0.137 0.061 -0.103 0.073 -0.192 -0.022 0.159 -0.053 -0.039 -0.004 -0.061 0.014 0.039 -0.020
ID25 -0.169 -0.323 -0.058 -0.085 -0.018 -0.091 -0.023 0.017 0.040 0.007 0.186 0.084 -0.165 0.005 -0.137 0.763 0.017 0.169 -0.194 -0.126 -0.037 0.044 0.062 -0.014 -0.135 0.018 0.004 0.039 -0.087
ID29 0.003 0.015 -0.060 -0.035 0.046 -0.077 0.041 0.142 -0.059 -0.090 -0.014 0.155 0.043 -0.062 0.061 0.017 0.565 -0.314 -0.099 0.031 -0.172 0.030 0.070 -0.022 -0.063 -0.057 0.035 0.038 -0.073
ID30 -0.085 -0.115 -0.066 -0.087 -0.095 -0.063 -0.011 -0.137 0.085 -0.035 -0.002 -0.025 0.089 -0.029 -0.103 0.169 -0.314 0.494 -0.370 0.104 0.155 0.038 -0.095 -0.017 -0.030 0.142 -0.072 -0.035 0.077
ID31 -0.054 0.141 0.182 0.160 0.036 -0.007 0.008 -0.064 -0.184 -0.043 0.029 -0.058 -0.032 0.044 0.073 -0.194 -0.099 -0.370 0.546 -0.150 -0.125 -0.119 -0.013 -0.051 0.028 -0.049 0.025 -0.015 0.013
ID32 -0.037 -0.030 0.017 -0.070 -0.285 0.177 0.077 0.060 0.357 -0.312 -0.014 -0.180 0.119 -0.154 -0.192 -0.126 0.031 0.104 -0.150 0.546 -0.008 -0.095 0.055 0.058 -0.077 0.042 0.007 -0.012 0.013
ID33 -0.061 -0.188 0.036 -0.007 -0.039 0.051 -0.137 -0.163 0.304 0.016 -0.049 -0.040 0.057 -0.204 -0.022 -0.037 -0.172 0.155 -0.125 -0.008 0.582 0.048 -0.033 -0.009 -0.042 0.035 0.026 -0.036 0.096
ID34 -0.134 -0.125 -0.124 0.114 -0.004 -0.129 -0.184 -0.019 0.060 0.232 0.121 -0.056 0.045 -0.045 0.159 0.044 0.030 0.038 -0.119 -0.095 0.048 0.592 -0.009 0.044 -0.361 -0.036 0.008 -0.025 0.022
ID35 -0.017 -0.001 0.059 -0.168 -0.041 0.022 -0.009 -0.057 0.074 -0.035 -0.079 0.038 0.015 -0.056 -0.053 0.062 0.070 -0.095 -0.013 0.055 -0.033 -0.009 0.820 -0.077 -0.060 -0.481 0.028 -0.068 -0.043
ID36 -0.097 0.016 -0.141 0.076 -0.015 -0.110 -0.101 0.180 0.049 -0.056 0.082 0.103 -0.178 -0.074 -0.039 -0.014 -0.022 -0.017 -0.051 0.058 -0.009 0.044 -0.077 0.753 -0.084 -0.006 -0.185 0.041 -0.148
ID38 0.015 -0.058 0.064 0.016 0.026 0.147 0.173 0.072 0.006 0.020 -0.034 -0.084 -0.008 -0.013 -0.004 -0.135 -0.063 -0.030 0.028 -0.077 -0.042 -0.361 -0.060 -0.084 0.730 0.021 -0.093 0.061 0.021
ID39 0.021 -0.035 -0.071 -0.044 -0.145 -0.050 -0.022 0.006 0.024 -0.017 0.047 -0.070 0.025 -0.015 -0.061 0.018 -0.057 0.142 -0.049 0.042 0.035 -0.036 -0.481 -0.006 0.021 0.822 -0.031 -0.178 -0.219
ID40 0.051 0.010 0.008 -0.131 -0.005 0.068 -0.022 -0.008 0.013 0.053 0.002 -0.065 -0.003 0.046 0.014 0.004 0.035 -0.072 0.025 0.007 0.026 0.008 0.028 -0.185 -0.093 -0.031 0.862 -0.220 -0.215
ID41 0.052 0.011 -0.101 -0.045 0.016 -0.037 0.081 0.066 0.066 -0.090 -0.026 0.067 -0.065 -0.078 0.039 0.039 0.038 -0.035 -0.015 -0.012 -0.036 -0.025 -0.068 0.041 0.061 -0.178 -0.220 0.832 -0.434
ID42 -0.082 0.055 0.065 -0.024 0.015 0.055 0.053 -0.145 -0.084 0.052 -0.039 -0.002 0.136 -0.110 -0.020 -0.087 -0.073 0.077 0.013 0.013 0.096 0.022 -0.043 -0.148 0.021 -0.219 -0.215 -0.434 0.810
Measures of Sampling Adequacy (MSA)
213
Table 4.33 Revised Communalities
Variables Initial Extraction
ID1 1.000 0.698
ID3 1.000 0.645
ID4 1.000 0.518
ID6 1.000 0.628
ID7 1.000 0.612
ID8 1.000 0.684
ID9 1.000 0.600
ID10 1.000 0.720
ID11 1.000 0.654
ID12 1.000 0.696
ID15 1.000 0.617
ID17 1.000 0.763
ID21 1.000 0.524
ID22 1.000 0.573
ID24 1.000 0.574
ID28 1.000 0.650
ID29 1.000 0.612
ID30 1.000 0.690
ID31 1.000 0.573
ID32 1.000 0.680
ID33 1.000 0.517
ID34 1.000 0.700
ID35 1.000 0.588
ID36 1.000 0.505
ID38 1.000 0.586
ID39 1.000 0.684
ID40 1.000 0.526
ID41 1.000 0.683
ID42 1.000 0.720
Extraction Method: Principal Component
Analysis.
214
A close look at revised communalities as presented in Table 4.33 shows that for all the
variables extracted communalities are high (greater than 0.5), and hence, acceptable for
all variables to proceed for factor analysis.
4.9.2.5 Variance explained
Following the earlier explanation given in previous factor analysis performed for factors
preferred by respondents as source of information, an analysis of the Eigen values is
required. Eigen value represents the total variance explained by each factor (Malhotra,
2008354
). Eigen value depicts explanatory power of each factor. So, the factor which is
having higher Eigen value should be selected first. Table 4.34 shows the Eigen values of
all the variables that can be extracted. From Table 4.34, it can be seen that total 9 factors
have been identified at the end of the factor analysis with more than 1.0 Eigen value and
62.11 % of explained variance. Following this, total 9 factors are extracted for this study.
Rotation sums of squared loadings are used for analysis. Table 4.35 presents the
component matrix.
354 Malhotra N. (2008). Marketing Research – An Applied Orientation (5th ed.). New Delhi: Pearson
Education, p. 561.
215
Table 4.34 Total Variance Explained
Com
pone
nt
Initial Eigenvalues
Extraction Sums of
Squared Loadings
Rotation Sums of Squared
Loadings
Total % of
Variance Cumulative
% Total % of
Variance Cumulative
% Total % of
Variance Cumulative
%
1 4.173 14.391 14.391 4.173 14.391 14.391 3.649 12.583 12.583
2 2.988 10.305 24.696 2.988 10.305 24.696 2.644 9.119 21.702
3 2.491 8.590 33.286 2.491 8.590 33.286 1.908 6.578 28.280
4 1.641 5.658 38.944 1.641 5.658 38.944 1.782 6.145 34.425
5 1.540 5.312 44.255 1.540 5.312 44.255 1.772 6.110 40.535
6 1.431 4.934 49.189 1.431 4.934 49.189 1.716 5.919 46.454
7 1.325 4.567 53.756 1.325 4.567 53.756 1.614 5.567 52.021
8 1.251 4.313 58.069 1.251 4.313 58.069 1.553 5.354 57.374
9 1.172 4.040 62.109 1.172 4.040 62.109 1.373 4.735 62.109
10 0.998 3.444 65.553
11 0.946 3.265 68.818
12 0.838 2.890 71.708
13 0.807 2.782 74.490
14 0.760 2.621 77.111
15 0.720 2.482 79.593
16 0.673 2.322 81.915
17 0.634 2.185 84.100
18 0.581 2.003 86.103
19 0.515 1.777 87.880
20 0.466 1.608 89.488
21 0.444 1.531 91.019
22 0.432 1.491 92.510
23 0.417 1.436 93.947
24 0.386 1.330 95.277
25 0.317 1.093 96.370
26 0.289 0.997 97.367
27 0.268 0.926 98.293
28 0.255 0.878 99.171
29 0.240 0.829 100.000
Extraction Method: Principal Component Analysis.
216
4.9.2.6 Factor Extraction
To extract the factors, principal component analysis method is used, as it considers the
total variance in the data and derives the factors that contain small proportions of unique
variance and in some instances, error variance. Table 4.35 shows component matrix (un-
rotated factor matrix).
Table 4.35 Component Matrixa
Variables Component
1 2 3 4 5 6 7 8 9
ID39 0.723 -0.383 -0.020 0.020 0.011 0.007 -0.038 0.104 0.044
ID42 0.713 -0.398 -0.011 -0.036 -0.064 -0.131 0.001 0.143 -0.101
ID41 0.684 -0.427 -0.022 -0.007 -0.052 -0.082 0.065 0.095 -0.099
ID35 0.672 -0.332 -0.037 -0.042 0.039 0.092 -0.035 0.065 0.085
ID40 0.576 -0.335 -0.072 0.040 -0.163 -0.103 0.032 0.181 -0.066
ID7 0.514 0.076 -0.147 0.272 -0.092 0.305 -0.280 -0.161 0.201
ID6 0.489 -0.170 -0.258 0.194 -0.169 0.184 -0.206 -0.237 0.307
ID36 0.472 -0.007 0.185 -0.159 -0.096 -0.367 0.010 -0.030 -0.262
ID22 0.411 0.030 0.101 -0.007 0.233 0.134 0.386 -0.035 -0.265
ID3 0.272 0.642 0.200 0.084 -0.076 -0.238 -0.153 0.057 0.149
ID28 0.397 0.629 0.034 0.067 0.021 -0.143 -0.248 -0.090 -0.035
ID1 0.410 0.572 0.302 -0.019 0.024 -0.260 -0.178 -0.079 0.070
ID38 0.280 0.505 -0.107 0.124 -0.135 -0.014 0.327 0.305 -0.087
ID33 0.207 0.368 -0.013 -0.186 0.365 0.336 0.013 0.049 -0.235
ID4 0.090 -0.074 0.598 -0.085 0.228 -0.233 -0.137 0.108 -0.052
ID11 -0.173 -0.134 0.594 0.272 -0.344 -0.162 -0.135 0.098 0.082
ID12 -0.121 -0.182 0.592 0.323 -0.206 0.111 -0.159 -0.126 -0.311
ID15 -0.010 -0.221 0.532 0.145 0.311 -0.060 0.376 -0.145 0.017
ID9 -0.254 -0.283 0.400 -0.073 0.181 0.158 -0.334 0.323 -0.129
ID17 0.007 -0.128 0.404 0.549 0.075 0.272 0.389 0.038 0.222
ID29 0.105 0.090 0.272 -0.478 -0.437 0.137 0.166 -0.174 -0.152
ID30 0.127 0.050 0.390 -0.460 -0.365 0.331 0.031 -0.098 0.235
ID21 0.238 0.300 0.243 0.370 0.115 -0.152 0.009 -0.176 0.123
217
ID24 0.285 0.023 0.150 -0.302 0.442 -0.008 -0.146 -0.375 -0.145
ID10 0.079 -0.066 0.248 -0.166 0.435 0.333 -0.353 0.340 0.284
ID32 0.321 0.332 -0.089 0.322 0.105 0.442 0.015 -0.186 -0.339
ID31 0.130 0.286 0.362 -0.188 -0.342 0.424 0.062 0.079 -0.020
ID34 0.199 0.465 -0.023 -0.053 0.042 0.002 0.238 0.596 0.166
ID8 0.168 -0.050 0.175 -0.295 0.215 -0.131 0.346 -0.299 0.513
Extraction Method: Principal Component Analysis.
a. 9 components extracted.
To extract the factors and to name them, an analysis of factors loadings in the rotated
factor matrix is used in present factor analysis. The interpretation of factors is done by
identifying the variables that have very high loadings on the same component.
For better reduction of variables, factor loadings more than or/and equal to 0.40 were
considered under each factor. Fortunately, all the variables got the factor loading greater
than 0.40. Total nine factors have been extracted. Finally, all the variables are grouped
under the 9 factors. Naming of factors is carried out based on Table 4.36.
The factors extracted, their respective items with the assigned codes and their
corresponding factor loading are given in Table 4.37.
218
Table 4.36 Rotated Component Matrix
Variables Component
1 2 3 4 5 6 7 8 9
ID42 0.848 0.024 -0.015 0.000 0.019 0.015 -0.007 0.004 -0.001
ID41 0.821 -0.037 0.042 -0.001 0.050 0.040 -0.023 -0.035 0.019
ID39 0.793 0.018 0.044 -0.014 0.054 0.188 -0.007 0.100 0.069
ID40 0.709 -0.020 -0.013 -0.005 -0.060 0.064 0.087 -0.062 -0.059
ID35 0.701 -0.007 0.016 0.037 0.108 0.228 -0.010 0.129 0.122
ID36 0.487 0.342 -0.069 0.115 0.037 -0.297 -0.098 -0.153 -0.021
ID1 0.065 0.812 -0.015 0.131 0.080 -0.027 0.059 0.032 0.076
ID3 -0.065 0.755 -0.032 0.082 -0.036 0.041 0.244 0.001 0.010
ID28 0.013 0.740 -0.159 0.018 0.206 0.140 0.093 -0.052 -0.062
ID21 -0.001 0.516 0.345 -0.124 0.036 0.121 -0.005 -0.072 0.048
ID17 -0.013 -0.092 0.834 -0.009 -0.030 0.165 0.164 0.053 0.006
ID15 0.057 -0.023 0.658 -0.015 0.076 -0.302 -0.174 0.067 0.219
ID11 -0.028 0.159 0.552 0.190 0.006 -0.141 -0.102 0.125 -0.275
ID12 -0.015 0.058 0.475 0.216 -0.142 -0.106 -0.359 0.076 -0.403
ID30 0.029 0.022 0.014 0.786 -0.073 0.083 -0.028 0.146 0.190
ID29 0.064 0.015 -0.089 0.727 0.046 -0.168 -0.077 -0.187 0.027
ID31 -0.050 0.121 0.108 0.675 0.114 0.082 0.195 0.094 -0.144
ID33 -0.054 0.146 -0.092 0.088 0.650 -0.005 0.130 0.191 -0.013
ID32 0.012 0.186 0.161 0.018 0.608 0.354 0.033 -0.144 -0.321
ID22 0.314 0.032 0.257 0.063 0.511 -0.123 0.093 -0.115 0.071
ID24 0.141 0.233 -0.073 0.024 0.444 -0.107 -0.442 0.162 0.249
ID7 0.273 0.238 0.012 0.027 0.112 0.678 -0.027 0.039 -0.068
ID6 0.380 0.058 -0.043 -0.008 -0.059 0.669 -0.117 -0.069 0.091
ID4 0.151 0.289 0.242 0.055 -0.074 -0.408 -0.181 0.381 0.016
ID34 0.026 0.234 -0.043 0.040 0.099 -0.088 0.772 0.140 0.084
ID38 0.063 0.285 0.019 0.058 0.204 -0.011 0.618 -0.254 -0.090
ID10 0.033 0.005 0.015 0.014 0.113 0.103 0.057 0.824 0.114
ID9 -0.042 -0.179 0.097 0.042 -0.095 -0.249 -0.153 0.619 -0.281
ID8 0.070 0.076 0.188 0.136 -0.011 -0.029 -0.081 -0.009 0.782
Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization.
a. Rotation converged in 14 iterations.
219
Table 4.37 Composition of Each Factor Identified in Factor Analysis
Factor Items Factor
loadings
Factor 1
Personal Financial
Need
Affordable minimum investment amount (ID42) 0.848
Safety associated with investment (ID41) 0.821
Ease of obtaining borrowed fund (ID39) 0.793
Preferred investment time horizon (ID40) 0.709
Liquidity associated with investment (ID35) 0.701
Availing the benefit of income tax deduction (ID36) 0.487
Factor 2
Accounting, Business
& Financial
Information
Condition of financial statement (ID1) 0.812
Past performance of the firm (ID3) 0.755
Study of Annual Reports (ID28) 0.740
Market capitalization of company (ID21) 0.516
Factor 3
Economic &
Regulatory
Environment
Current economic indicators (ID17) 0.834
Statements from politicians and governmental officials (ID15) 0.658
Political party affiliation (ID11) 0.552
Contribution of a firm towards social causes (ID12) 0.475
Factor 4
Operational
Feedback
Opinions from friends and relatives (ID30) 0.786
Opinions from family members (ID29) 0.727
Opinions from existing investors (ID31) 0.675
Factor 5
Advocate
Recommendation
Opinion of credit rating agencies‘ reports (ID33) 0.650
Financial advisors/Broker and analyst‘s recommendation (ID31) 0.608
Conversation/exchanges of views with professional colleagues
(ID22) 0.511
Conversation/exchanges of views with company executives and
sector experts (ID24) 0.444
Factor 6
Overall Group
Performance
Result of technical analysis (ID7)) 0.678
Result of fundamental analysis (ID6) 0.669
Company‘s position in the industry (ID4) -0.408
Factor7
Credit features
Diversification needs (ID34) 0.772
Minimizing risk (ID38) 0.618
Factor 8
Personal Inclination
Perceived ethics of company(ID10) 0.824
Feeling for a company‘s products and services (ID9) 0.619
Factor9
Monetary Expectation
0.782 Expected return on investment (ID8)
220
4.9.2.7 Naming of factors
According to the loading of variables on these nine factors, they can be explained as:
Factor 1: Personal Financial Need
Total six variables were identified under factor 1 are ‗availing the affordable minimum
investment amount‘ (0.848), ‗safety associated with investment‘ (0.821), ‗ease of
obtaining borrowed fund‘ (0.793), ‗preferred investment time horizon‘ (0.709), ‗liquidity
associated with investment‘ (0.701) and ‗availing the benefit of income tax deduction‘
(0.487). These variables are grouped under ―Personal Financial Need”, as these
variables reflect each individual investor‘s needs as to what is safety, liquidity, time
horizon and tax benefit according to his/her personal and/or unique circumstances. Every
individual investor looks at variables from his/her own point of view, which would be
differing from investor to investor. This extracted factor explains 12.58 % of variance,
which is the highest among all the factors.
Nagy and Obenberger (1994355
), Hussein et al. (2009356
) examined that personal financial
needs of an individual investor mostly influence the investment decision. Nagy and
Obenberger (1994357
) also added that ―perhaps sophisticated investors view investment
capital and consumption expenditures independent entities‖ (p. 67).
Factor 2: Accounting, Business and Financial Information
The four variables were identified under factor 2 are ‗condition of financial statement‘
(0.812), ‗past performance of the firm‘ (0.755), ‗study of annual reports‘ (0.740), and
‗market capitalization of company‘ (0.516). The group of these variables is named as
355 Nagy, R. A. & Obenberger, R. W. (1994). Factors affecting investors behaviour. Financial Analyst
Journal, 50, 63-68. 356 Hussein A. H. Al- Tamini & Al Anood Bin Kali (2009). Financial literacy and investment decision of
UAE investors. The Journal of Risk Finance, 10 (5), Emerald Group Publishing limited, p. 508 357 Nagy, R. A. &Obenberger, R. W. (1994). Factors affecting investors‘ behaviour. Financial Analyst
Journal, 50, 63-68.
221
“Accounting, Business and Financial Information”, as these variables are related with
an individual firm‘s accounting, business and financial performance. This extracted factor
explains 9.12 % of variance.
Chandra et al. (2011358
) said that ―accounting, business and financial information of the
firms and their past performance also has its bearing to the individual investors to some
extent, and the individuals strongly rely on this while investing. However, these are said
to be the part of informational sources. This study also added that majority of the
investors surveyed do not consider financial statements, as individual investors facing
complexity of interpreting the financial statements‖ (p.22).
Factor 3: Economic and Regulatory Environment
The four variables identified under factor 3 are ‗current economic indicators‘ (0.834),
‗statement from politicians and government officials‘ (0.658), ‗political party affiliation‘
(0.552), and ‗contribution of a firm towards social causes‘ (0.475). These variables are
grouped under “Economic and Regulatory Environment”, as these variables seek to
examine the firm‘s presence of business in the context of the economic, regulatory and
social environment under which the firm operates. This extracted factor explains 6.58 %
of variance.
Aregbeyan and Mbadiugha (2011359
) explained that along with the social, psychological
factors, and cultural factors, investors always rely on economic factors and their
investment decisions are always influences by these economic factors. Hussein et al.
(2009360
) also suggested that statement from government officials and current economic
indicators do have their influence on investment decision of individual investors.
358
Chandra, A. & Kumar, R. (2011). Determinants of individual investor behaviour: An orthogonal linear
transformation approach. Munich Personal RePEc Archive No. 29722, accessed on April 15, 2011 at http://mpra.ub.uni-muenchen.de/29722/ 359 Aregbeyen, O. &Mbadiugha, O. S. (2011). Factors influencing investors decisions in shares of quoted
companies in Nigeria. The Social Sciences, 6 (3), p.1. 360 Hussein A. H. Al- Tamini & Al Anood Bin Kali (2009). Financial literacy and investment decision of
UAE investors. The Journal of Risk Finance, 10 (5), Emerald Group Publishing limited, 508
222
Blumberg et al. (1997361
) added that the stakeholders are always interested to the extent
to which the company is investing in social and related issues. They always want to see
company as a going concern.
Factor 4: Operational Feedback
Total three variables identified under factor 4 are ‗opinions from friends and relatives‘
(0.786), ‗opinions from family members‘ (0.727) and ‗opinions from existing investors‘
(0.675). The group of these variables is named as “Operational Feedback”. The
investor generally gets this some operational feedback from their family members,
friends and existing investors, which may support their individual investor‘s views
towards investment alternative and/or firm‘ operation to make investment decision. This
factor seeks to give insider‘s view of the firm who have either an interest in the company
or have done some analysis of the company. This extracted factor explains 6.15 % of
variance.
Chandra et al. (2011362
) has stated the group of these variables as informational
asymmetry. This study further added that set of variables explaining this component leads
to infer that individual investors are suffered from information inferiority complex. They
tend to rely heavily on the easily available and accessible information. They are
influenced by the information hovering around them and which can be easily used by
investors for their decisions; rather they don‘t tend to check the reliability of this
information (as operational feedback) and prefer to those piece of information which are
easy to incorporate into their decisions. Instead of incorporating all the publically
available information as suggested by the standard definition of efficient market theory,
investors tend to discount the information that seems complex to incorporate into their
decisions-making process, and adopt only those information, which are easily available
361 Blumberg, J., Korswold, A. & Blum, G. (1996). Environmental Performance and Shareholder Value
World Business Council for Sustainable Development Geneva. 362
Chandra, A. & Kumar, R. (2011). Determinants of individual investor behaviour: An orthogonal linear
transformation approach. Munich Personal RePEc Archive No. 29722, accessed on April 15, 2011 at
http://mpra.ub.uni-muenchen.de/29722/
223
and adjustable in nature. These are the operational feedbacks easily available from family
members, friends and relative and existing investors.
Factor 5: Advocate Recommendation
There are four variables identified under factor 5. These variables are ‗rating agencies‘
reports‘ (0.650), ‗financial advisors/brokers and analyst‘s recommendation‘ (0.608),
‗conversation/ exchanges of views with professional colleagues‘ (0.511) and
‗conversation/exchanges of views with company executives and sector experts‘ (0.444).
These variables are grouped under “Advocate Recommendation”, as these variables are
related to the recommendations that the investor gets from company insiders, outsider
and professionals. This recommendation provides outside independent expert‘s views
about the firm‘s position and what they think about the firm and its prospect, which is
objective/neutral in nature and it is perceived to be unbiased. This extracted factor
explains 6.11 % of variance.
Nagy and Obenberger (1994363
) said that ―advocate recommendation includes purchase
recommendations from brokerage houses, individual stock brokers and co-workers. This
study also added that these information sources could be constructed as a
recommendation from sources with vested interested in the investor‘s ultimate actions.
Although, many investors obviously rely on professional expertise‖ (p. 67).
Factor 6: Overall Group Performance
The three variables identified under factor 6 are ‗result of technical analysis‘ (0.678),
‗result of fundamental analysis‘ (0.669) and ‗company‘s position in the industry‘ (-
0.408). The group of these variables is named as “Overall Group Performance”, as
these variables impinge on firm‘s performance. This factor shows that while investing,
the individual investor do not rely only on company‘s position in the domestic market,
363 Nagy, R. A. & Obenberger, R. W. (1994). Factors affecting investor‘ behaviour. Financial Analyst
Journal, 50, 63-68.
224
but also give due importance to the technical analysis and to what extent the
company/firm is fundamentally strong. This extracted factor explains 5.92 % of variance.
Chandra et al. (2011364
) said that the group of these variables shows prudence and
precaution attitude. They also added that each variable included under this factor is
associated with different behavioural attitude. These variables underline the symmetric
behaviorual attitude of risk aversion. These behavioural factor traduces the prudent and
cautions attitude of individual investors. The study said that investors tend to use trend
analysis and their decision are always based on market share and reputation of the firm/
company (p. 21).
Factor 7: Credit Features
Two variables identified under factor 7 are ‗diversification need‘ (0.772) and ‗minimizing
risk‘ (0.618). These variables are grouped under “Credit Features”, as these variables
are associated with creditability of an investor. The investors‘ community often tries to
minimize their total risk associated with their whole investment portfolio through
diversification by studying the risk-return trade-off of various investment alternatives.
This extracted factor explains 5.57 % of variance.
Hussein et al. (2009365
) suggested that diversification needs and minimizing the risk are
two important variables that heavily influence investment decision of investors. Nagy and
Obenberger (1994366
) also suggest that investors often look for the diversification need
while investing.
Factor 8: Personal Inclination
364 Chandra, A. & Kumar, R. (2011). Determinants of individual investor behaviour: An orthogonal linear
transformation approach. Munich Personal RePEc Archive No. 29722, accessed on April 15, 2011 at
http://mpra.ub.uni-muenchen.de/29722/ 365
Hussein A. H. Al- Tamini & Al Anood Bin Kali (2009). Financial literacy and investment decision of
UAE investors. The Journal of Risk Finance, 10 (5), Emerald Group Publishing limited, 500-516. 366 Nagy, R. A. & Obenberger, R. W. (1994). Factors affecting investors‘ behaviour. Financial Analyst
Journal, 50, 63-68.
225
The two variables identified under factor 8 are ‗firm‘s perceived ethics of a company‘
(0.824) and ‗feelings for a company‘s products and services‘ (0.619). The group of these
variables is named as “Personal Inclination”, as these variables are related to the
individual investor‘s internal inclination towards the firm, its operation and business
practices. This inclination may be due to lack of widespread knowledgeable information
about a particular investment alternative, along with the resulting herd behavior, thus
contributing towards the relative neglect of consideration of significant traditional values.
This extracted factor explains 5.35 % of variance.
Draft Green Paper on Consumer Policy Framework (DTI) (2004367
) states that more and
more consumers are interested in the world behind the products, the production process
and the ethics of the company that produces goods and services. Hussein et al. (2009368
)
and Nagy and Obenberger (1994369
) also suggested that the self image factor/ firm image
coincidence which consisting of variables such as ‗perceived ethics of firm‘ and ‗feeling
for firm‘s products and services‘ influence the investment decision of investors (p. 513).
Factor 9: Monetary Expectation
Only one variable was identified under factor 9 ‗expected return on investment‘ (0.782).
It is named as “Monetary Expectation” as while making investment decision, individual
always looks to collect the information about the pattern of profit distribution and starts
to expect the return to compensate the risk associated with investment. This extracted
factor explains 4.74 % of total variance.
Nagy and Obenberger (1994370
) suggested that monetary expectation can be considered
as classic factor that focuses on wealth maximization criterion.
367 Government Gazette (2004). Draft Green Paper on Consumer Policy Framework. Republic of South
Africa, 471 (9), September 9, 2004, Pretoria. 368
Hussein A. H. Al- Tamini & Al Anood Bin Kali (2009). Financial literacy and investment decision of
UAE investors. The Journal of Risk Finance, 10 (5), Emerald Group Publishing limited, 500-516. 369
Nagy, R. A. & Obenberger, R. W. (1994). Factors affecting investors‘ behaviour. Financial Analyst
Journal, 50, 63-68. 370 Nagy, R. A. & Obenberger, R. W. (1994). Factors affecting investors‘ behaviour. Financial Analyst
Journal, 50, 63-68.
226
4.9.2.8 Mean score of extracted factors influencing investment decision
Table 4.38 represents the factor wise mean influencing score. The score is derived by
taking the grand mean value of variables clubbed in each factor. From Table 4.38, it is
found that for factor 7, the mean score is highest, i.e. 4.07. This indicates that the factor
named ‗credit features‘ is the most influencing factor on investment decision of investors.
This shows that investors continuously try to decrease the total risk associated with their
investment portfolio by diversifying their portfolio. This factor is followed by the mean
score 4.03 of factor 9, i.e. ‗Monetary Expectation‘, which shows that while diversifying
their total portfolio, investors do consider the monetary expectation by way of expected
return to compensate the risk associated with their portfolio. The third important factor
that influences investment decision is Factor 2: ‗Accounting, Business and Financial
Information‘, having a mean score of 3.98, which indicates that the investors rely and
emphasize on rational decision making criteria. This factor is followed by the factor 5
with mean score of 3.74; ―Advocate Recommendation‖. This shows that published and
non published information does have their influence investment decision. The least score
is for factor 1 i.e. Personal Financial Need having mean score of 3.02.
Table 4.38 Mean Score of Extracted Factors
Factor Factor Name Grand Mean
Value
Factor 1 Personal financial need 3.02
Factor 2 Accounting, business and financial information 3.98
Factor 3 Economic and regulatory environment 3.25
Factor 4 Operational feedback 3.66
Factor 5 Advocate recommendation 3.74
Factor 6 Overall group performance 3.24
Factor 7 Credit features 4.07
Factor 8 Personal inclination 3.70
Factor 9 Monetary expectation 4.03
Table 4.39 indicates the mean score of 29 variables from which 9 factors that influence
the investment decision of investors. From Table 4.39, it can be seen that the most
227
influencing variables on investment decision of investors is ‗rating agencies‘ reports‘
(4.17) (which shows that investors‘ investment decision is highly influenced by the
independent opinion given by the rating agencies) followed by the variable
‗diversification needs‘ (4.14) (this shows that investors look for the various investment
alternatives to spread/ to decrease their risk).
Table 4.39 Mean Score of Each Variable within Each Factor
Variables within each factor Mean
value
Factor 1: Personal financial need
Affordable minimum investment amount 2.77
Safety associated with investment 2.98
Ease of obtaining borrowed fund 3.03
Preferred investment time horizon 2.74
Liquidity associated with investment 2.91
Availing the benefit of income tax deduction 3.44
Grand mean value 3.02
Factor 2: Accounting, Business and Financial Information
Condition of financial statements 4.05
Past performance of the firm 3.98
Study of annual report 4.09
Market capitalization of a company 3.83
Grand mean value 3.98
Factor 3: Economic and regulatory environment
Current economic indicators 3.26
Statements from politicians and Government officials 3.29
Contribution of firm towards social causes 3.22
Political party affiliation 3.20
Grand mean value 3.25
Factor 4: Operational feedback
Friends and Relatives 3.73
Family members 3.58
Present Investors 3.67
Grand mean value 3.66
Factor 5: Advocate recommendation
Rating agencies‘ report 4.17
Advisor/brokers/ analyst‘s recommendation 3.87
Conversation/exchanges of views with Professional colleagues 2.94
228
Conversations/exchanges of views with company executives and sector
experts 3.97
Grand mean value 3.66
Factor 6: Overall group performance
Result of technical analysis 2.88
Result of fundamental analysis 2.94
Company‘s position in the industry 3.91
Grand mean value 3.24
Factor 7: Credit features
Diversification needs 4.14
Minimizing risk 4.01
Grand mean value 4.07
Factor 8: Personal inclination
Perceived ethics of a company 4.11
Feeling for company's products/services 3.29
Grand mean value 3.70
Factor 9: Monetary expectation
Expected return on investment 4.03
Grand mean value 4.03
5.10 Cross Tabulation and statistical tests
In this section, simultaneous analysis of two variables is carried out through cross
tabulation. Hypothesis testing is also included in this section.
Association between explainable variables and financial literacy level
First, to test the significant association between various explainable variables such as
investors‘ age, gender, education, income, stage of family life cycle, monthly income,
employment structure, workplace activity, number of years of work experience, number
of years of investment experience, risk tolerance level and number of times investors
shop around and financial literacy level, the Chi-Square test was performed. Chi-square
Table 4.39 Continued
229
test enables to explain whether or not two attributes are associated. Kothari (2009371
)
suggested that ―Chi-square test can be used to determine if categorical data shows
dependency or the two classifications are independent. It can be also used to make
comparisons between theoretical populations and actual data when categories are used.
Thus, the chi-square test is applicable in large number of problems. The test is, in fact, a
technique through the use of which it is possible for all researchers to (i) test the
goodness of fit, (ii) test the significance of association between two attributes, and (iii)
test the homogeneity or the significance of population variance‖.
4.10.1 Association between investors‟ age and their financial literacy level
The data regarding investors‘ age and their financial literacy level is given in Table 4.40.
Table 4.40 Cross Tabulation of Investors‟ Age and their Financial Literacy Level
Financial
literacy level
Respondents‟ age Total
18 to 25 26 to 35 36 to 45 46 to 55 56 to 65
Low 85
(95.5)
65
(85.53)
35
(38.46)
16
(21.33)
27
(55.10)
228
(59.22)
High 4
(2.5)
11
(14.47)
61
(67.03)
59
(78.67)
22
(44.90)
157
(40.78)
Total 89
(100)
76
(100)
96
(100)
75
(100)
49
(100)
385
(100) Note: Figures in parenthesis shows the percentage of respondents
Hypotheses for the data shown in Table 4.40 for chi-square test are as under:
H0: There is no significant association between investors‘ age and their financial literacy
level.
H1: There is a significant association between investors‘ age and their financial literacy
level.
371 Kothari, C.R. (2009). Research Methodology: Methods and Techniques (2nd ed.). New Delhi: New Age
International Publishers, p. 233.
230
Table 4.41 Chi-Square Tests
Value df Sig.
Pearson Chi-Square 1.358 4 0.000
Likelihood Ratio 153.949 4 0.000
Linear-by-Linear Association 82.464 1 0.000
Number of Valid Cases 385
The test was performed at 5% level of significance. The output of Chi-square test is as
presented in Table 4.41. The Pearson Chi-square significance value is 0.000 with degree
of freedom 4. Therefore, null hypothesis is rejected and hence, it is found that there is a
significant association between investors‘ age and their financial literacy level. It might
be also concluded that investors‘ age and their financial literacy level are not independent
of each other. In other words, these two variables are significantly associated with each
other.
Chi-square test shows the statistical significance between the two variables. It does not
tell us the strength of association between two variables in the cross tabulation. To
measure the strength of this association, The Cramer‘s V, one of the measures of indexes
of agreement is used, which is shown in Table 4.42.
Table 4.42 Symmetric Measures
Value Sig.
Nominal by
Nominal
Phi 0.594 0.000
Cramer's V 0.594 0.000
Contingency Coefficient 0.511 0.000
Number of Valid Cases 385
From Table 4.42, it can be seen that the value of Cramer‘s V is significant with 0.000 and
the degree of association between these two variables is 59.4%.
231
4.10.2 Association between investors‟ gender and their financial literacy level
The data regarding the investors‘ gender and their financial literacy level is given in
Table 4.43.
Table 4.43 Cross Tabulation of Investors‟ Gender and their Financial Literacy
Level
Financial
literacy level
Respondents‟ gender
Total Male Female
Low 157
(51.48)
71
(88.75)
228
(59.22)
High 148
(48.52)
9
(11.25)
157
(40.78)
Total 305
(100)
80
(100)
385
(100)
Note: Figures in parenthesis shows the percentage of respondents
Hypotheses for the data shown in Table 4.43 for chi-square test are as under.
H0: There is no significant association between investors‘ gender and their financial
literacy level.
H1: There is a significant association between investors‘ gender and their financial
literacy level.
Table 4.44 Chi-square tests
Value df Sig.
Pearson Chi-Square 36.462 1 0.000
Continuity Correction 34.965 1 0.000
Likelihood Ratio 41.727 1 0.000
Linear-by-Linear Association 36.367 1 0.000
Number of Valid Cases 385
Cramer's V 0.308 0.000
Number of valid cases 385
232
The test was performed at 5% level of significance. The output of Chi-square test is as
presented in Table 4.44. The Pearson Chi-square significance value is 0.000 and degree
of freedom is 1. Therefore, null hypothesis is rejected and hence, it is found that there is a
significant association between investors‘ gender and their financial literacy level. It
might be also concluded that investors‘ gender and their financial literacy level are not
independent of each other. In other words, these two variables are significantly
associated. From the last row of the same table, it is also seen that Cramer‘s V value is
significant with 0.000 and the degree of association between these two variables is
30.8%.
4.10.3 Association between investors‟ education and their financial literacy level
The data regarding investors‘ education their financial literacy level is given in Table
4.45.
Table 4.45 Cross Tabulation of Investors‟ Education and their Financial Literacy
Level
Financial
literacy
level
Respondents‟ education
Total
Primary Secondary
Higher
secondary Diploma Graduation
Post-
graduation
Low 18
(75)
22
(48.89)
30
(65.22)
18
(62.07)
65
(52.42)
75
(61.48)
228
(59.22)
High 6
(25)
23
(51.11)
16
(34.78)
11
(37.93)
54
(45.38)
47
(38.52)
157
(40.78)
Total 24
(100)
45
(100)
46
(100)
29
(100)
119
(100)
122
(100)
385
(100)
Note: Figures in parenthesis shows the percentage of respondents
Hypotheses for the data shown in Table 4.45 for chi-square test are as under.
H0: There is no significant association between investors‘ education and their financial
literacy level.
233
H1: There is a significant association between investors‘ education and their financial
literacy level.
Table 4.46 Chi-Square Tests
Value df Sig.
Pearson Chi-Square 6.545 5 0.257
Likelihood Ratio 6.670 5 0.246
Linear-by-Linear Association 0.096 1 0.757
Number of Valid Cases 385
The test was performed at 5% level of significance. The output of Chi-square test is as
presented in Table 4.46. From the table, it can be seen that the Pearson Chi-square
significance value is 0.257 and degree of freedom is 5. Therefore, null hypothesis is not
rejected and hence, it is found that there is no significant association between the
investors‘ education and their financial literacy level. It might be also concluded that
investors‘ education and their financial literacy level are independent of each other. In
other words, these two variables are not significantly associated.
4.10.4 Association between investors‟ monthly income and their financial literacy
level
The data regarding the investors‘ monthly income and their financial literacy level is
given in Table 4.47.
234
Table 4.47 Cross Tabulation of Investors‟ Monthly Income and their Financial
Literacy Level
Financial
literacy
level
Respondents‟ monthly income
Upt0 Rs.
10,000
Rs. 10,001 to
Rs. 15,000
Rs. 15,001 to
Rs, 20,000
Rs. 20,001 to
Rs. 25,000
Rs. 25,001
and above
Total
Low 80
(84.21)
62
(72.94)
51
(61.45)
25
(32.05)
10
(22.73)
228
(59.22)
High 15
(15.79)
23
(27.06)
32
(38.56)
53
(67.95)
34
(77.27)
157
(40.78)
Total 95
(100)
85
(100)
83
(100)
78
(100)
44
(100)
385
(100)
Note: Figures in parenthesis shows the percentage of respondents
Hypotheses for the data shown in Table 4.47 for chi-square test are as under.
H0: There is no significant association between the investors‘ monthly income and their
financial literacy level.
H1: There is a significant association between the investors‘ monthly income and their
financial literacy level.
Table 4.48 Chi-Square Tests
Value Df Sig.
Pearson Chi-Square 79.469 4 0.000
Likelihood Ratio 82.740 4 0.000
Linear-by-Linear Association 75.892 1 0.000
Cramer‘s V 0.304 0.000
Number of Valid Cases 385
The Chi-square test was performed at 5% level of significance. The output of Chi-square
test is as presented in Table 4.48. The Pearson Chi-square significance value is 0.000 and
degree of freedom is 4. Therefore, null hypothesis is rejected and hence, it is found that
there is a significant association between the investors‘ monthly income and their
financial literacy level. It might be also concluded that investors‘ monthly income and
235
their financial literacy level are not independent of each other. In other words, these two
variables are significantly associated. From the last row of the same table, it is also seen
that Cramer‘s V value is significant with 0.000 and the degree of association between
these two variables is 30.4%.
4.10.5 Association between investors‟ stage in family life cycle and their financial
literacy level
The data regarding investors‘ stage in family life cycle and their financial literacy level is
given in Table 4.49.
Table 4.49 Cross Tabulation of Investors‟ Stage in Family Life Cycle their Financial
Literacy Level
Financial
literacy
level
Respondents‟ stage in family life cycle Total
Young
single
Young
married
without
children
Young
married
with
children
Middle
age
married
with
children
Middle age
married
without
dependent
children
Older
married
Low 56
(83.58)
48
(71.64)
55
(57.89)
44
(43.56)
16
(48.48)
9
(40.91)
228
(59.22)
High 11
(16.42)
19
(20.36)
40
(42.11)
57
(56.44)
17
(51.51)
13
(59.09)
157
(40.78)
Total 67
(100)
67
(100)
95
(100)
101
(100)
33
(100)
22
(100)
385
(100)
Note: Figures in parenthesis shows the percentage of respondents
Hypotheses for the data shown in Table 4.49 for chi-square test are as under.
H0: There is no significant association between investors‘ stage in family life cycle and
their financial literacy level.
H1: There is a significant association between investors‘ stage in family life cycle and
their financial literacy level.
236
Table 4.50 Chi-Square Tests
Value df Sig.
Pearson Chi-Square 35.696 5 0.000
Likelihood Ratio 37.672 5 0.000
Linear-by-Linear Association 31.211 1 0.000
Cramer‘s V 0.304 0.000
Number of Valid Cases 385
The Chi-square test was performed at 5% level of significance. The output of Chi-square
test is as presented in Table 4.50. The Pearson Chi-square significance value is 0.000 and
degree of freedom is 5. Therefore, null hypothesis is rejected and hence, it is found that
there is a significant association between the investors‘ stage in family life cycle and their
financial literacy level. It might be also concluded that investors‘ stage in family life and
their financial literacy level are not independent of each other. In other words, these two
variables are significantly related. From the last row of the same table, it is also seen that
Cramer‘s V value is significant with 0.000 and the degree of association between these
two variables is 30.4%.
4.10.6 Association between investors‟ employment structure and their financial
literacy level
The data regarding investors‘ employment structure and their financial literacy level is
given in Table 4.51.
Table 4.51 Cross Tabulation of Investors‟ Employment Structure and Financial
Literacy Level
Financial
literacy
level
Full
time
salaried
Part
time
salaried Casual
Self
Employed
House
wife Retired
Un-
employed Other Total
Low 134 29 7 41 8 6 1 2 228
High 94 13 3 26 6 14 1 0 157
Total 228 42 10 67 14 20 2 2 385
237
To check the association between these two variables statistically, Chi-square test is
performed. Although, there are no assumptions made as to the shape of data distribution
for this non parametric technique, there are restrictions on its applications. As a rule of
thumb, if the degree of freedom is greater than one, not more than 20% of the cells
should have expected frequencies of less than 5. If this requirement can‘t be met,
researcher should attempt to combine cells, until it conforms to this rule, but only if the
combination would not render the data meaningless (Luck and Rubin, 2003372
). Based on
this rule, for the data in the above table, not more than 2 cells should have expected
frequency less than 5. But, for this data exactly cells have expected frequency less than
5, as indicted in the Table 4.51. Hence regrouping is not done by combining. The new
revised table free from this limitation is as shown below (see Table 4.52).
Table 4.52 Revised Cross Tabulation of Investors‟ Employment Structure and their
Financial Literacy Level
Financial
literacy
level
Respondents‟ employment structure Total
Full time
salaried
Part
time
salaried Casual
Self
Employed Housewife
Retired,
Unemployed
and others
Low 134
(58.77)
29
(69.05)
7
(70.00)
41
(61.19)
8
(51.14)
9
(37.50)
228
(59.48)
High 94
(41.23)
13
(30.95)
3
(30.00)
26
(38.81)
6
(42.85)
15
(62.50)
157
(40.78)
Total 228
(100)
42
(100)
10
(100)
67
(100)
14
(100)
24
(100)
385
(100)
Note: Figures in parenthesis shows the percentage of respondents
Hypotheses for the data shown in Table 4.52 for chi-square test are as under.
H0: There is no significant association between investors‘ employment structure and their
financial literacy level.
372 Luck, D. &Rubin, D. (2003). Marketing Research (7th ed.). New Delhi: Prentice Hall of India Pvt. Ltd.
p. 347
238
H1: There is a significant association between investors‘ employment structure and their
financial literacy level.
Table 4.53 Chi-Square Tests
Value Df Sig.
Pearson Chi-Square 7.001 5 0.221
Likelihood Ratio 6.972 5 0.223
Linear-by-Linear Association 1.181 1 0.277
Number of Valid Cases 385
The Chi-square test was performed at 5% level of significance. The output of Chi-square
test is as presented in Table 4.53. The Pearson Chi-square significance value is 0.221 and
degree of freedom is 4. Therefore, null hypothesis is not rejected and hence, it is found
that there is no significant association between investors‘ employment structure and their
financial literacy level. It might be also concluded that investors‘ employment structure
and their financial literacy level are independent of each other. In other words, these two
variables are not significantly associated.
4.10.7 Association between investors‟ type of workplace activity and their financial
literacy level
The data regarding investors‘ type of workplace activity and their financial literacy level
is given in Table 4.54.
239
Table 4.54 Cross Tabulation of Investors‟ Type of Workplace Activity and their
Financial Literacy Level
Financial
literacy
level
Respondents‟ type of workplace activity
Finance
related work
place activity
Non finance
related work
place activity
Other Total
Low 37
(27.82)
182
(75.21)
9
(90.00) 228
(59.22)
High 96
(72.18)
60
(24.79)
1
(10.00) 157
(40.78)
Total 133
(100)
242
(100)
10
(100) 385
(100)
Note: Figures in parenthesis shows the percentage of respondents
Hypotheses for the data shown in Table 4.54 for chi-square test are as under.
H0: There is no significant association between investors‘ type of workplace activity and
their financial literacy level.
H1: There is a significant association between investors‘ type of workplace activity and
their financial literacy level.
Table 4.55 Chi-Square Tests
Value df Sig.
Pearson Chi-Square 83.835 2 0.000
Likelihood Ratio 85.717 2 0.000
Linear-by-Linear Association 80.080 1 0.000
Cramer‘s V 0.467 0.000
Number of Valid Cases 385
The Chi-square test was performed at 5% level of significance. The output of Chi-square
test is presented in Table 4.55. The Pearson Chi-square significance value is 0.000 and
degree of freedom is 2. Therefore, null hypothesis is rejected and hence, it is found that
there is a significant association between investors‘ type of workplace activity and their
240
financial literacy level. From the last row of the same table, it is also seen that Cramer‘s
V value is significant with 0.000 and the degree of association between these two
variables is 46.7%.
4.10.8 Association between investors‟ years of work experience and their financial
literacy level
The data regarding investors‘ years of work experience and their financial literacy level is
given in Table 4.56.
Table 4.56 Cross Tabulation of Investors‟ Years of Work Experience and Their
Financial Literacy Level
Financial
literacy
level
Respondents‟ years of work experience
Total
Less than
five
6 Years to
10 years
11 years to
20 years
21 years to
30 years
More than
30 years
Low 57 44 39 51 37 228
(80.28) (69.84) (50.65) (45.13) (60.66) (59.22)
High 14 19 38 62 24 157
(19.71) (30.16) (49.35) (54.88) (39.34) (40.78)
Total 71 63 77 113 61 385
(100) (100) (100) (100) (100) (100)
Note: Figures in parenthesis shows the percentage of respondents
Hypotheses for the data shown in Table 4.56 for chi-square test are as under.
H0: There is no significant association between investors‘ years of work experience and
their financial literacy level.
H1: There is a significant association between investors‘ years of work experience and
their financial literacy level.
241
Table 4.57 Chi-Square Tests
Value df Sig.
Pearson Chi-Square 27.665 4 0.000
Likelihood Ratio 28.835 4 0.000
Linear-by-Linear Association 15.170 1 0.000
Cramer‘s V 0.268 0.000
Number of Valid Cases 385
The Chi-square test was performed at 5% level of significance. The output of Chi-square
test is as presented in Table 4.57. The Pearson Chi-square significance value is 0.000 and
degree of freedom is 4. Therefore, null hypothesis is rejected and hence, it is found that
there is a significant association between investors‘ years of work experience and their
financial literacy level. It might be also concluded that investors‘ years of work
experience and their financial literacy level are not independent of each other. In other
words, these two variables are significantly related. From the last row of the same table, it
is also seen that Cramer‘s V value is significant with 0.000 and the degree of association
between these two variables is 26.8%.
4.10.9 Association between investors‟ years of investment experience and their
financial literacy level
The data regarding investors‘ years of investment experience and their financial literacy
level is given in Table 4.58.
242
Table 4.58 Cross Tabulation of Investors‟ Years of Investment Experience and their
Financial Literacy Level
Financial
literacy level Years of investment experience
Total
Less than
1year 1-5 years 6-10 years
More than
10 years
Low 69
(98.57)
109
(81.95)
29
(27.88)
21
(26.92)
228
(59.22)
High 1
(1.43)
24
(18.05)
75
(72.12)
57
(73.08)
157
(40.48)
Total 70
(100)
133
(100)
104
(100)
78
(100)
385
(100)
Note: Figures in parenthesis shows the percentage of respondents
Hypotheses for the data shown in Table 4.58 for chi-square test are as under.
H0: There is no significant association between investors‘ years of investment experience
and their financial literacy level.
H1: There is a significant association between investors‘ years of investment experience
and their financial literacy level.
Table 4.59 Chi-Square Tests
Value df
Asymp. Sig.
(2-sided)
Pearson Chi-Square 1.493 3 0.000
Likelihood Ratio 170.524 3 0.000
Linear-by-Linear Association 128.615 1 0.000
Cramer‘s V 0.623 0.000
Number of Valid Cases 385
The Chi-square test was performed at 5% level of significance. The output of Chi-square
test is as presented in Table 4.59. The Pearson Chi-square significance value is 0.000 and
degree of freedom is 3. Therefore, null hypothesis is rejected and hence, it is found that
243
there is a significant association between investors‘ years of investment experience and
their financial literacy level. It might be also concluded that investors‘ years of
investment experience and their financial literacy level are not independent of each other.
In other words, these two variables are significantly associated with each other. From the
last row of the same table, it is also seen that Cramer‘s V value is significant with 0.000
and the degree of association between these two variables is 62.3%.
4.10.10 Association between numbers of times investors shop around and their
financial literacy level
The data regarding numbers of times investors shop around and their financial literacy
level is given in Table 4.60.
Table 4.60 Cross Tabulation of Numbers of Times Investors Shop Around and their
Financial Literacy Level
Financial
literacy
level
Numbers of times respondents shop around
Total Zero 1 to 3 4 to 6 More than 6
Low 81
(93.10)
122
(80.79)
21
(18.92)
4
(11.11)
228
(59.22)
High 6
(6.70)
29
(19.21)
90
(81.08)
32
(88.89)
157
(40.78)
Total 87
(100)
151
(100)
111
(100)
36
(100)
385
(100)
Note: Figures in parenthesis shows the percentage of respondents
The hypotheses for the data shown in Table 4.60 for chi-square test are as under.
H0: There is no significant association between numbers of times investors shop around
and their financial literacy level.
H1: There is a significant association between numbers of times investors shop around
and their financial literacy level.
244
Table 4.61 Chi-Square Tests
Value Df Sig.
Pearson Chi-Square 1.79 3 0.000
Likelihood Ratio 196.358 3 0.000
Linear-by-Linear Association 153.857 1 0.000
Number of Valid Cases 385
Cramer‘s V 0.683 0.000
The Chi-square test was performed at 5% level of significance. The output of Chi-square
test is as presented in Table 4.61. The Pearson Chi-square significance value is 0.000 and
degree of freedom is 3. Therefore, null hypothesis is rejected and hence, it is found that
there is a significant association between number of times investors shop around and their
financial literacy level. It might be also concluded that number of times investors shop
around and their financial literacy level are not independent of each other. In other words,
these two variables are significantly associated with each other. From the last row of the
same table, it is also seen that Cramer‘s V value is significant with 0.000 and the degree
of association between these two variables is 68.3%.
4.10.11 Association between risk tolerance level of investors and their financial
literacy level
The data regarding risk tolerance level of investors and their financial literacy level is
given in Table 4.62.
245
Table 4.62 Cross Tabulation of Risk Tolerance Level of Investors and their
Financial Literacy Level
Financial
literacy
level
Respondents‟ risk tolerance level
Total
Lowest Risk
Taker
Moderate
Risk Taker
High Risk
Taker
High Risk
Taker
Low 17
(70.83)
50
(72.46)
73
(52.14)
88
(57.89)
228
(59.22)
High 7
(29.17)
19
(27.54)
67
(47.86)
64
(42.11)
157
(40.78)
Total 24
(100)
69
(100)
140
(100)
152
(100)
385
(100)
Note: Figures in parenthesis shows the percentage of respondents
Hypotheses for the data shown in Table 4.62 for chi-square test are as under.
H0: There is no significant association between risk tolerance level of investors and their
financial literacy level.
H1: There is a significant association between risk tolerance level of investors and their
financial literacy level.
Table 4.63 Chi-Square Tests
Value Df Sig.
Pearson Chi-Square 9.366 3 0.025
Likelihood Ratio 9.629 3 0.022
Linear-by-Linear Association 3.682 1 0.055
Cramer‘s V 0.156 0.025
Number of Valid Cases 385
The Chi-square test was performed at 5% level of significance. The output of Chi-square
test is as presented in Table 4.63. The Pearson Chi-square significance value is 0.025 and
degree of freedom is 3. Therefore, null hypothesis is rejected and hence, it is found that
there is a significant association between risk tolerance level of investors and their
financial literacy level. It might be also concluded that risk tolerance level of investors
246
and their financial literacy level are not independent of each other. In other words, these
two variables are significantly associated with each other. From the last row of the same
table, it is also seen that Cramer‘s V value is significant with 0.025 and the degree of
association between these two variables is 15.6%.
4.11 Regression analysis
Some of the most interesting questions of statistical analysis revolve around the
relationship among the variables, which is established by regression analysis. Regression
analysis is a tool with several important applications. First, it is a way of testing
hypothesis concerning the relationship between two types of variables. Second, it is a
way of estimating the specific nature of such a relationship. Third, it allows us to predict
the values of one variable, if we know or estimate the other variables.
Logistic Regression
Logistic regression is regularly used rather than discriminant analysis, when there are two
categories of dependent variable. Logistic regression is also easier to use with SPSS than
discriminant analysis when there is a mixture of numerical and categorical independent
variables, because it includes the procedure for generating necessary dummy variables
automatically, requires fewer assumptions, and is more statistically robust (Hair et al.
1999). Discriminant analysis strictly requires the continuous independent variables
(though dummy variables can be used as in multiple regression). Thus, in instances where
the independent variables are categorical, or a mix of continuous and categorical, and
dependent variable is categorical, logistic regression is necessary.
Binomial (or binary) logistic regression is a form of regression which uses binomial
probability theory, does not require linearity of relationship between the independent
variables and the dependent and does not require normally distributed variables.
247
The binary logistic model commonly deals with the issue of how likely an observation is
to belong to each group. It estimates the probability of an observation belonging to a
particular group (Malhotra, 2008373
)
To test the following hypotheses, the logistic regression was used.
H0: There is no significant impact of demographic and socio-economic variables of
investors on their financial literacy level.
H1: There is a significant impact of demographic and socio-economic variables of
investors on their financial literacy level.
To perform logistic regression, for deciding the independent/explanatory variables, the
in-depth review literature that establishes the association between investors‘/consumers‘
demographic and socio-economic variables with their financial literacy level is studied.
The prior research has shown that the level of financial literacy varies with demographic
and socio-economic variables of people. For example, on the variable of gender, female
are less financially literate than male (Chen & Volpe, 2002; Beal & Delpachitra, 2003;
OECD studies, 2005; Lusardi & Mitchell, 2009; Hussein et al., 2009), female are less
knowledgeable in some areas of personal finances (Bakken, 1967; Danes & Hira, 1987;
HSR, 1993; Volpe et al., 1996; Chen & Volpe, 1998) and women experience more
problems in managing their finances than men (Martinez, 1994; Genasci, 1995; Lewin,
1995). With regard to age, prior studies found that not only those who are under the age
of 30 years (Chen & Volpe, 1998; Commonwealth Bank Study, 2004; OECD (U.K.)
Study, 2005), but also, who are at both the extremes of age profile (ANZ Bank Study,
2003; OECD (Australia) Study, 2005) possess lower financial literacy than others.
There is evident that financial literacy does vary with education of an individual. The
studies identified those having lower level of education are less financially literate (Vole
et al., 2002; ANZ Bank Study, 2003; Commonwealth Bank Study, 2004; OECD
373 Malhotra, N. (2008). Marketing Research – An Applied Orientation (5th ed.). New Delhi: Pearson
Education, p. 625.
248
(Australia & Korean) Study, 2005; Hussain et al., 2009). While on the variables of
monthly income, those with lower monthly income are found to be less financially
literate than those withdrawing higher monthly income (Chen & Volpe, 1998; Beal &
Delpachitra, 2003; ANZ Bank Study, 2003; OECD (Australia & U.K.) Studies, 2005).
Studies show that the stage of life cycle is also one of the important predictors of
financial literacy. Past studies found that those who are young single (Chen & Volpe,
1998; Vole et al., 2002; ANZ Bank Study, 2003; Commonwealth Bank Study, 2004;
OECD (Australia & U.K.) Studies, 2005), single parents (Schegen & Lines, 1996) are
less financially knowledgeable than others.
With regard to employment structure, the review of literature concludes employment
structure is one of the important predictor for financial literacy (ANZ Bank Study, 2003;
Commonwealth Bank Study, 2004; OECD (Australia) Study; Hussain et.al., 2009). Prior
literature also suggest that individual‘s financial literacy does vary with years of work
experience they possess (Chen & Volpe, 1998; Beal & Delpachitra, 2003) and type of
workplace activity in which they are engaged in (ANZ Bank Study, 2008; Hussain et. al,
2003).
Several studies have also found the association between financial literacy and years of
investment experience (ANZ Bank Study, 2005) and number of times investors shop
around/make inquiry while investing (ANZ Bank Study, 2005). Prior studies also found
that financial literacy does vary with the risk tolerance of individuals (Beal &
Delpachitra, 2003).
Following the review of literature, in present study, investors‘ demographic and socio-
economic variables such as, investors‘ age, gender, education, monthly income, stage of
family life cycle, employment structure, type of workplace activity, years of work
experience, number of times shop around while investing and years of investment
experience are considered as determinants of financial literacy for performing logistic
regression. The logistic regression was used to identify the effect of these independent
variables on financial literacy level.
249
The level of financial literacy was used as the dependent variable As explained in
Chapter 3, to measure the financial literacy level of respondents, the respondents‘ total
score was calculated as the percentage of correct answers (Lyons, 2007374
). The survey
responses from each respondent were used to calculate the median percentage of correct
scores for entire survey. The overall scores were grouped into two categories according to
the median percentage of correct scores of all participants of the survey. Accordingly, a
median percentage of correct answers of the sample was considered as a base to frame
financial literacy level of the respondents and/or to classify the subgroups. The
respondents with the scores equal to or below median are considered as respondents with
relatively lower level of financial literacy and hence classified into the first category, i.e.
investors with relatively lower level of financial literacy and respondents with scores
above median are considered as respondents (investors) with higher financial literacy and
hence classified in the second category, i.e. investors with relatively higher level of
financial literacy and hence classified into second category (Volpe et al.,2002375
, Hussein
et al., 2009376
). This dichotomous variable of financial literacy level was used as the
dependent variable.
The independent variables used in the logistic regression are shown in Table 4.64, with
their codes used to get impact on financial literacy. From the Table, it can be seen that the
explanatory variables comprise only categorical. In logistic regression, the metric
variables are treated as ‗covariates‘ and non-metric or categorical variables are treated as
‗factors‘. All the independent variables are fed accordingly in SPSS.
374 Lyons, A., Rachlis, M., & Scherpf, E. (2007). What‘s in a Score? Differences in Consumers‘ Credit
Knowledge Using OLS and Quantile Regressions. Networks financial institute. Indiana University, 2007-WP-01, retrieved on January 21, 2012 from www.networksfinancialinstitute.org 375 Volpe, R. P., Kotel, J. E. & Chen, H. (2002). A survey of investment literacy among online investors,
Financial Counseling and Planning, 13(1), 1-13. 376 Classification/subgroups are made on the basis of Hussein A. H. Al- Tamini & Al Anood Bin Kali
(2009). Financial literacy and investment decision of UAE investors. The Journal of Risk Finance, 10 (5),
Emerald Group Publishing limited, p. 508
250
Table 4.64 Study Variables: Dependent and Independent Variables for Logistic
Regression
Variables
Dependent variables (DV)
Financial Literacy Categorical variables ‗0‘ if respondent possesses lower level
of financial literacy, ‗1‘ if respondent possesses higher level
of financial literacy
Independent (Explanatory) variables (IV)
Gender Dichotomous variable ‗1‘ for male, ‗2‘ for female
Age Multinomial variable with value of ‗1‘ for 18 to 25 years, ‗2‘
for 26 to 35 years, ‗3‘ for 36 to 45 years, ‗4‘ for 46 to 55 years
and ‗5‘ for 56 years and above (ordinal)
Education Multinomial variable with value of ‗1‘ for primary, ‗2‘ for
secondary, ‗3‘ for higher secondary, ‗4‘ for diploma and ‗5‘ for
graduation, ‗6‘ for post graduation (ordinal)
Monthly income Multinomial variable with value of ‗1‘ for upto Rs. 10,000, ‗2‘
for Rs. 10,001 to 15,000, ‗3‘ for Rs. 15,001 to Rs. 20,000, ‗4‘
for Rs. 20,001 to Rs. 25,000 and ‗5‘ for Rs. 25,001 and above
(ordinal)
Stage in family life
cycle
Multinomial variable with value of ‗1‘ for young single, ‗2‘ for
young married without children, ‗3‘for young married with
children, ‗4‘ for middle age married with children and ‗5‘ for
middle age married without dependent children, ‗6‘ for Older
married (ordinal)
Employment
structure
Multinomial variable with value of ‗1‘ full time salaried, ‗2‘
part time salaried, ‗3‘for casual, ‗4‘ for self employed and ‗5‘
for housewife and ‗6‘ retired, unemployed and others
Type of workplace
activity
Dichotomous variable ‗1‘ for working in financial (services)
related industry, ‗2‘ for working in non-financial (services)
related industry
251
(Table 4.64 Continued)
Years of work
experience
Multinomial variable with value of ‗1‘ for less than 5 years, ‗2‘
for 6 to 10 years, ‗3‘ for 11 to 20 years, ‗4‘ for 21 to 30 years
and ‗5‘ for years and more (ordinal)
Number of times
shops around
Multinomial variable with value of ‗1‘ for zero , ‗2‘ for 1 to 3
times, ‗3‘ for 4 to 6 times, ‗4‘ for more than 6 times (ordinal)
Years of investment
experience
Multinomial variable with value of ‗1‘ for less than 1 year, ‗2‘
for 1 to 5 years, ‗3‘ for 6 to 10 years, ‗4‘ for more than 10 years
(ordinal)
Risk tolerance level Multinomial variable with value of ‗1‘ for lowest risk
tolerance, ‗2‘ for moderate risk tolerance, ‗3‘ for high risk
tolerance, ‗4‘ for highest risk tolerance (ordinal)
The logistic regression was used to identify the effect of each of above mentioned
predictors (independent variables/explainable variables) on financial literacy level
(dependent variable). Each independent variable has as many parameters as categories,
but one is redundant, so, researcher needs to specify a reference category. The equation 1,
the coefficients in the regression function represent the effect of each subgroup compared
with a reference group, which is arbitrarily selected. For example, for gender, the
reference category is male, for age, the reference category is age group of 18 to 25 years;
for education, the reference category is Primary, i.e. who has completed primary
education; for monthly income, the reference category is first, i.e. the respondents who
earn monthly income less than Rs. 10,000. Similarly for stage in family life cycle, the
reference category is young single; for employment structure, reference category is
respondent who is full time salaried; for type of workplace activity the reference group is
respondents working in financial services related industry. Reference category for years
of work experience is respondents having a work experience is less than 5 years; for
number of times investor shop around while investing, the reference category is first, i.e.
zero (respondents who do not shop around at all/ do not make inquiry at all while
investing); reference category for years of investment experience is first, i.e. less than 1
252
year. Lastly for risk tolerance, the reference group is respondents with low risk tolerance
level.
In logistic regression, where explanatory variable is categorical, the use of dummy
variables to contrast the different categories are required. For each variable, logistic
regression requires to choose a baseline category and then to contrast all remaining
categories with the base line. If an explanatory variable has k categories, then the
researcher needs k-1 dummy variables to investigate all the differences in the categories
with respect to the dependent variable. For present logistic regression SPSS has created
dummy variables for the researcher from categorical explanatory variables, the in-depth
explanation for dummy variables used in logistic regression is given after the logistic
regression equation formed.
Table 4.65 Case Processing Summary
Unweighted Casesa N Percent
Selected Cases Included in Analysis 385 100.0
Missing Cases 0 0.0
Total 385 100.0
Unselected Cases 0 0.0
Total 385 100.0
a. If weight is in effect, see classification table for the total
number of cases.
While running the logistic regression, the classification Table (Table 4.66 in block 0)
Beginning Block is important to interpret, before deriving final model.
Table 4.65 shows that all 385 cases are considered to perform logistic regression, under
study. As explained in chapter 3, this median percentage of correct scores (i.e. 56) of the
sample was considered to frame financial literacy level and/or to classify the cases in to
different subgroups. The respondents with scores above median were considered as
respondents with higher financial literacy and hence classified as higher financially
literate and respondents with scores equal to and lower than median were considered as
respondents with relatively lower level of financial literacy and hence classified as lower
253
financially literate. To perform the logistic regression, the dependent variable is
considered as financial literacy level of respondents and coded as 0 for ―lower level of
financial literacy‖ and 1 for ―higher level of financial literacy‖. For the present analysis,
the cut off point for classifying the respondents in two categories is 0.56.i.e. median
percentage of correct answers.
Block 0- Beginning Block: Block 0 presents the results with the constant included before
any coefficients (i.e. those relating to independent variables) are entered into the
equation. Logistic regression compares this model with a model including all the
predictors (independent variables) to determine whether the later model is more
appropriate or not. Table 4.66 suggests that if we knew nothing about the predicted
variables under study and guessed that a person would not fall under higher level of
financial literacy, then the result would that only 59.2% cases are correctly classified, as
shown in Table 4.66. Table 4.67 (Variables not in the equation table) shows that whether
each category of independent variables improves the model by its significant value shown
in last column of the Table 4.67, and if these independent/explainable variables/
predictors are included in the table, it would add to the predictive power of the final
model. If they had not been significant and able to contribute to the prediction, then the
termination of the analysis would obviously occur, at this point.
Table 4.66 Classification Tablea,b
Observed
Predicted
Level Percentage
Correct Low High
Step 0 Level Low 228 0 100.0
Higher 157 0 0.0
Overall Percentage 59.2 a. Constant is included in the model. b. The cut value is 0.56
254
Table 4.67 Variables Not in the Equation
Score df Sig.
Step 0 Variables Gender(1) 36.462 1 0.000*
Age 135.818 4 0.000*
Age(1) 27.133 1 0.000*
Age(2) 27.438 1 0.000*
Age(3) 55.365 1 0.000*
Age(4) 0.394 1 0.530
Education 6.545 5 0.257
Education(1) 2.252 1 0.133
Education(2) 0.778 1 0.378
Education(3) 0.105 1 0.746
Education(4) 1.508 1 0.219
Education(5) 0.376 1 0.540
Income 79.469 4 0.000*
Income(1) 8.503 1 0.004*
Income(2) 0.217 1 0.641
Income(3) 29.900 1 0.000*
Income(4) 27.395 1 0.000*
Stageoflife 35.696 5 0.000*
Stageoflife(1) 5.182 1 0.023*
Stageoflife(2) 0.092 1 0.762
Stageoflife(3) 13.897 1 0.000*
Stageoflife(4) 1.723 1 0.189
Stageoflife(5) 3.240 1 0.072**
Employmnt_structure 7.001 5 0.221
Employmnt_structure(1) 1.885 1 0.170
Employmnt_structure(2) 0.494 1 0.482
Employmnt_structure(3) 0.131 1 0.718
Employmnt_structure(4) 0.026 1 0.872
Employmnt_structure(5) 5.000 1 0.025*
Workactivity 83.835 2 0.000*
Workactivity(1) 68.944 1 0.000*
255
Workactivity(2) 4.027 1 0.045*
WorkExperience 27.665 4 0.000*
WorkExperience(1) 3.518 1 0.061**
WorkExperience(2) 2.928 1 0.087**
WorkExperience(3) 13.145 1 0.000*
WorkExperience(4) 0.062 1 0.804
No.oftimesshoparound 179.618 3 0.000*
No.oftimesshoparound(1) 47.881 1 0.000*
No.oftimesshoparound(2) 104.899 1 0.000*
No.oftimesshoparound(3) 38.062 1 0.000*
InvestmentExperience 149.327 3 0.000*
InvestmentExperience(1) 43.487 1 0.000*
InvestmentExperience(2) 57.938 1 0.000*
InvestmentExperience(3) 42.252 1 0.000*
RiiskTolerance 9.366 3 0.025*
RiiskTolerance(1) 6.105 1 0.013*
RiiskTolerance(2) 4.564 1 0.033*
RiiskTolerance(3) 0.183 1 0.669
Overall Statistics 278.295 39 0.000*
*p<.05,
**p<0.1
Table 4.68 shows, at initial step (i.e. step 0), in the constant model without considering
any independent variables in the equation of logistic regression. The significant value is
0.000, which is less than 0.05. Hence, this shows that the researcher can proceed further
for defining final model.
Table 4.68 Variables in the Equation
B S.E. Wald df Sig. Exp(B)
Step 0 Constant -0.373 0.104 12.943 1 0.000* 0.689
*p<0.05
(Table 4.67 Continued)
256
As initial step (step 0), permitted the researcher to go further to explain the effect of
independent variables under study on financial literacy level. Hence, researcher has
proceeded with including the independent variables under study, in the constant logistic
regression model, as explained above.
Once the significance of constant model checked, the researcher has developed the final
model by considering all the independent variables under study, which may determines
the impact of multiple independent variables presented simultaneously to predict
membership of one or other of the two dependent variable categories. Further to assess
whether each of the independent variables included in the model make a significant
contribution to the model, to evaluate the model fit and its significance as well, of the
final model of logistic regression, a variety of statistical tests for model fit are performed,
as shown below.
Model Chi-square
The overall significance is tested by Model Chi square, which is derived from the
likelihood of observing the actual data under the assumption that the model that has been
fitted accurately. Two hypotheses were developed to test in relation to the overall fit of
the model.
H0: The predictors do not have significant effect.
H1: The predictors have a significant effect.
As shown in Table 4.69, present case model chi square has 39 degrees of freedom, a
value of 420.876, with a significant value of 0.000, which is less than 0.05 as shown in
Table 4.69. Hence, we reject the null hypotheses and conclude the predictors do have a
significant effect, with the model containing only the constant indicating that the
predictors do have a significant effect and create essentially a different model. So, we
257
need to look closely at predictors and from the later tables determine whether the
independent variables the significance of predictors.
Table 4.69 Omnibus Tests of Model Coefficients
Chi-square df Sig.
Step 1 Step 420.876 39 0.000
Block 420.876 39 0.000
Model 420.876 39 0.000
Logistic regression employs maximum likelihood procedure through iterations to find out
the most likely estimates for the coefficients. Instead of minimizing the sum of squared
deviations (or least squares differences in ordinary least square method) in multiple
regression, the logistic regression maximizes the likelihood that an event will occur. This
likelihood value is used for assessing the measure of overall model fit. Logistic
regression measures the model estimation fit with the value of -2 time the log of the
likelihood values, referred to as -2LL or -2 log likelihood.
The likelihood ratio test is based on -2LL ratio. It is a test of the significance of the
difference between the likelihood ratio (-2LL) for the researcher‘s model with predictors
(called model chi square) minus the likelihood ratio for baseline model with only a
constant in it. Significance at the 0.05 level or lower means the researcher‘s model with
the predictors is significantly different from the one with the constant only (all ‗b‘
coefficients being zero). It measures the improvement in fit that the explanatory variables
make compared to the null model. Chi square is used to assess significance of this ratio.
The difference between –2LL for the best-fitting model and –2LL for the null hypothesis
model (in which all the b values are set to zero in block 0) is distributed like chi squared,
with degrees of freedom equal to the number of predictors; this difference is the Model
chi square that SPSS refers to. As explained above, the likelihood-ratio test uses the ratio
of the maximized value of the likelihood function for the full model (L1) over the
maximized value of the likelihood function for the simpler model (L0). This log
258
transformation of the likelihood functions yields a chi-squared statistic. Very
conveniently, the difference between -2LL values for models with successive terms
added also has a chi-squared distribution, so when going to a stepwise procedure,
researcher may use chi-square tests to find out if adding one or more extra predictors.
In other words, the overall test of relationship among the independent variables and
groups defined by the dependent is based on the reduction in the likelihood values for a
model which does not contain any independent variables and model that contains the
independent variables. This difference in likelihood follows chi-square distribution as
discussed above, and is referred to as model chi-square. The significance test for the final
model chi-square (after independent variables added) is the researcher‘s statistical
evidence of the presence of a relationship between the dependent variables and the
combinations of the independent variables.
H- L Test
An alternative to model chi-square is the Hosmer and lemeshow test which divides
subject into 10 observed groups of subjects and then compared the number actually in the
each group (observed) to the number predicted by the logistic regression model
(predicted) (Table 4.70).
Table 4.70 Contingency Table for Hosmer and Lemeshow Test
Level = Low Level = High
Total Observed Expected Observed Expected
Step 1 1 39 39.000 0 0.000 39
2 39 38.999 0 0.001 39
3 39 38.989 0 0.011 39
4 38 38.883 1 0.117 39
5 37 37.382 2 1.618 39
6 31 26.529 8 12.471 39
7 4 7.699 35 31.301 39
8 1 0.494 38 38.506 39
259
9 0 0.024 39 38.976 39
10 0 0.001 34 33.999 34
From Table 4.70, it can be seen that the 10 ordered groups are created based on their
estimated probability; those with estimated probability below 0.1form one group, and so
on, up to those with probability 0.9 to 1.0. Each of these categories is further divided into
two groups based on actual observed outcome variable (success, failure). The expected
frequencies for each of the cells are obtained from model. A probability (p) value is
computed from the chi-square distribution with 8 degree of freedom to test the fit of the
logistic model. If H-L goodness of-of-fit test statistic is greater than 0.05, as researcher
wants for well-fitting model, the researcher fails to reject null hypothesis that there is not
difference between observed and model-predicted values, implying that the model‘s
estimates fit the data at an acceptable level. That is, well-fitting models show non-
significance on the H-L goodness-of-fit test. This desirable outcome of non-significance
indicates that model prediction does not significantly differ from the observed.
Table 4.71 Model Fitting Information:
Test of Significance between Independent and Dependent Variable:
Hosmer-Lemeshow Test
Model Chi-square df Sig.
Final 11.930 8 0.154*
*p>0.05
Table 4.71 summarizes the existence of relationship between the independent variables
and the independent variable was supported. Furthermore, the probability of the model
chi-square (11.930 is significant with 0.154), which is higher than the required level of
significance i.e. 5%. This enables the researcher not to reject the null hypothesis and to
conclude that there is no difference between observed and model-predicted values,
implying that the model‘s estimates fit the data at an acceptable level. That is, well fitting
models show non-significance on the H-L goodness-of-fit test concludes that there is no
difference between researcher‘s model (observed) and model predicted by the logistic
regression (predicted) (that SPSS refers to).
260
Thus, the researcher may conclude that the dependent variable is significantly explained
by the given set of independent variables. In other words, independent variables
(predictors) have significant effects in predicting the dependent variable.
Amount of variation explained by the model
After assessing the model fit, it is required to check the strength of relationship between
the independent variables and dependent variable.
Although, there is no close analogous statistic in logistic regression to the coefficient of
determination R2, the logistic regression computes correlation measures to estimate the
strength of the relationship. In logistic regression, commonly used measures of model fit
are based on the likelihood function and are Cox & Snell R square and Nagelkerke’s R
square. Both these measures are similar to R2 in multiple regression and these correlation
measures do not really tell us much about the accuracy or errors associated with the
model. In Table 4.72, the model summary provides approximation only. Cox and Snell‘s
R-square attempts to imitate multiple R-square based ―likelihood‘. The Cox and Snell R
square is constrained in such a way that it cannot equal 1.0, even if the model perfectly
fits the data. This limitation is overcome by the Nagelkerke‘s R square (Malhotra, 2008).
Moreover, Nagelkerke‘s R2
is widely used and the most reported measure of Pseudo R-
square measures among the others such as Cox and Snell. The Nagelkerke modification
that does range from 0 to 1 is a more reliable measure of the relationship. Nagelkerke‘s
R2 is normally be higher than the Cox and Snell measure. For present study, from Table
4.72, indicates that 66.5% of the variation in the dependent variables is explained by the
logistic model. It is also found that Nagelkerke‘s R2 value was (0.897), indicating a
strong relationship of 89.7% between the predictors and the prediction or high level of
percentage variance is explained by the independent variables.
261
Table 4.72 Strength of the Relationship Model
Step
-2 Log
likelihood
Cox & Snell
R Square
Nagelkerke R
Square
1 99.678a 0.665 0.897
a. Estimation terminated at iteration number 8 because
parameter estimates changed by less than 0.001.
Variables in the equation:
The variables in the equation Table (Table 4.73), has several important elements. The
Wald statistic and associated probabilities provide an index of significance of each
predictor in the equation. The Wald statistic has a chi-square distribution.
In Table 4.73, in the second column, the value of ‗B‘ is the estimated co-efficient, with
standard error, S.E. (refer third column of the same Table). The ratio of ‗B‘ to S.E.,
squared, equals to Wald statistic. The simplest way to assess Wald statistic is to take the
significant values (as shown in the sixth column of the same table) and if it is less than
0.05, then reject the null hypothesis as the variable does make a significant contribution
and further it is to be concluded that the parameter is useful to the model.
―Exp(B)‖ column in Table 4.73 represents the extent to which raising the corresponding
measure by one unit influences the odd ratio. In other words, ―Exp(B)‖, or odd ratio, is
the predicted change in odds for a unit increase in the predictor. The ―exp‖ refers to the
exponential value of B. When Exp(B) is less than 1, increasing values of the variable
correspond to the decreasing odds of the event‘s occurrence. When Exp(B) is greater than
1, increasing values of the variables correspond to increasing odds of the event‘s
occurrence. In other words, if value of Exp(B) exceeds 1, then odds of an outcome
occurring increase; if the figure is less than 1, any increase in the predictor leads to a drop
in the odds of the outcome occurring.
A Wald test is used to test the statistical significance of each coefficient (b) in the model.
A Wald test calculates a Z statistic. This Z value is then squared, yielding a Wald statistic
with a chi-square distribution.
262
Table 4.73 Significance of each Individual Predictor on Dependent Variable
Categorical Variables B S.E. Wald df Sig. Exp(B)
GENb -- -- -- -- -- --
GEN1 -5.382 1.549 12.074 1 0.001* 0.005
AGEb -- -- -- -- -- --
AGE1 0.512 1.309 0.153 1 0.696 1.668
AGE2 5.648 1.530 13.634 1 0.000* 283.707
AGE3 4.457 1.717 6.734 1 0.009* 86.204
AGE4 1.218 1.790 0.463 1 0.496 3.381
EDUb -- -- -- -- -- --
EDU1 1.544 1.427 1.169 1 0.280 4.681
EDU2 0.790 1.457 0.294 1 0.588 2.203
EDU3 -1.516 1.790 0.717 1 0.397 0.220
EDU4 0.649 1.391 0.217 1 0.641 1.913
EDU5 -2.500 1.715 2.126 1 0.145 0.082
INCb -- -- -- -- -- --
INC1 0.668 1.124 0.353 1 0.553 1.950
INC2 -0.674 1.160 0.338 1 0.561 0.509
INC3 2.428 1.213 4.007 1 0.045* 11.338
INC4 2.349 1.334 3.100 1 0.078**
10.471
LIFESTAGEb -- -- -- -- -- --
LIFESTAGE1 -4.079 1.782 5.241 1 0.022* 0.017
LIFESTAGE2 -2.203 1.593 1.912 1 0.167 0.110
LIFESTAGE3 -3.512 1.797 3.823 1 0.051* 0.030
LIFESTAGE4 -3.014 1.978 2.322 1 0.128 0.049
LIFESTAGE5 -4.453 2.449 3.306 1 0.069**
0.012
EMPTb -- -- -- -- -- --
EMPT1 -1.056 1.323 0.637 1 0.425 0.348
EMPT2 -0.413 2.124 0.038 1 0.846 0.662
EMPT3 -1.555 1.008 2.380 1 0.123 0.211
EMPT4 5.191 2.236 5.388 1 0.020* 179.672
EMPT5 -1.283 1.462 0.770 1 0.380 0.277
263
Categorical Variables B S.E. Wald df Sig. Exp(B)
WORKACTIb -- -- -- -- -- --
WORKACTI1 -2.897 0.759 14.575 1 0.000* 0.055
WORKACTI2 -4.937 2.198 5.044 1 0.025* 0.007
WORKEXb -- -- -- -- -- --
WORKEX1 -1.500 1.241 1.460 1 0.227 0.223
WORKEX2 -1.486 1.309 1.288 1 0.256 0.226
WORKEX3 -2.523 1.634 2.385 1 0.123 0.080
WORKEX4 -0.975 1.605 0.369 1 0.543 0.377
NOOFTIMESHOPb -- -- -- -- -- --
NOOFTIMESHOP1 0.521 0.915 0.324 1 0.569 1.683
NOOFTIMESHOP2 6.100 1.369 19.854 1 0.000* 446.063
NOOFTIMESHOP3 5.092 1.756 8.413 1 0.004* 162.760
INVESTEXb -- -- -- -- -- --
INVESTEX1 1.737 1.625 1.143 1 0.285 5.683
INVESTEX2 8.029 2.128 14.233 1 0.000* 3.0693
INVESTEX3 7.423 2.126 12.193 1 0.000* 1.6743
RTb -- -- -- -- -- --
RT1 -1.927 1.377 1.959 1 0.162 0.146
RT2 -0.463 1.573 0.086 1 0.769 0.630
RT3 -1.427 1.819 0.616 1 0.433 0.240
Constant -3.078 3.090 0.992 1 0.319 0.046
Note: b reference category,
*p<0.05, **p<0.1
From Table 4.73, it can be seen that variables GENDER1, AGE2, AGE3, INC3, INC4,
LIFESTAGE1, LIFESTAGE3, LIFESTAGE5, EMPT4, WORKACTI1, WORKACTI2,
NOOFTIMESHOP2, NOOFTIMESHOP3, INVESTEX2 and INVESTEXP3 are found to
be significantly related to financial literacy level.
The co-efficient of GEN1 is negative (B=-5.382) and also negative relationship is
displayed from other measure i.e. value of exponential coefficient (0.005), which is less
Table 4.73 Continued
264
than 1. This indicates that lower the value of GEN1, predicted probability of gender
belonging to higher level of financial literacy group will decrease by 0.005 times. In other
words, the GEN1(Female) is found to be significant (as p=0.000) with the coefficient
value (-5.382), which indicates that female are more likely to fall under the lower level of
financial literacy group, as its Exp(B) values is 0.005, which is less than 1.
With regard to age, those falling under the age groups of 26 to 35 years (AGE1) are
found to be non-significant, as p=0.696. In fact, those falling under the age group of 36 to
45 (AGE2) and under the age groups of 46 to 55 (AGE3) are found to be significant with
coefficient value of (5.648) (p=0.000) and 4.457 (p=0.009) respectively, and likely to fall
into higher level of financial literacy group. It indicates that increase in age also increases
the probability of having higher level of financial literacy among the age group of 36-45
years and 46 to 55 years. AGE4 (years of 56 and above) (p=0.496) are non significant to
financial literacy level.
Considering the variable education, all the groups of this variable are found to be non-
significant predictors for financial literacy level.
With regard to monthly income, those investors whose monthly income is Rs. 15,001 to
Rs. 20,000 (INC3) and those whose monthly income is in between Rs. 20,001 to Rs.
25,000 (INC4) are found to be significant predictors with coefficient value of 2.428
(p=0.045) and 2.349 (p=0.078) respectively and are likely to fall into higher level of
financial literacy group. Monthly income more than Rs. 25,001 (INC4) is not a
significant factor for financial literacy level.
With respect to stage of family life cycle, LIFESTAGE1 is significant with coefficient
value of -4.079 (p=0.022) indicating that young married without children are likely to fall
in low financial literacy. Similarly, middle age married with dependent children
(LIFESTAGE3) is found to be significant (p=0.051) with a coefficient value of -3.512
and older married (LIFESTAGE5) with coefficient value of -4.453 is found be significant
(p=0.069) are among the group who are likely to have more tendency to have a low level
of financial literacy. It was also found that LIFESTAGE2 and LIFESTAGE4 i.e. young
265
married with children and middle age married without dependent children are non-
significant predictors for financial literacy level.
Table 4.73 also shows that among the categorical variable belonging to investors‘
employment structure, EMPT1 (those who are part time salaries) (p=0.425), EMPT2
(those who are casual) (p=0.846), EMPT3 (self employed) (p=0.123) and EMPT5
(retired, unemployed, and other) (p=0.380) are found to be non-significant predictors for
financial literacy level, as their respective p values are higher than 0.05 and 0.10. Among
the all the groups of employment structure, only EMPT4, i.e housewife with coefficient
value of 5.191 is found to be positively significant (p=0.020), and which is likely to have
more tendency to fall into higher level of financial literacy, as its Exp(B) value is 179.67,
which is higher than 1.
All the groups of variables of type of workplace activity, both the group variables are
found to be significant predictors for predicting financial literacy level. WORKACTI1
(not working in financial services related industry) and WORKACTI2 (Other) are found
to be significant predictors for financial literacy level, with their coefficient value of -
2.897 (p=0.000) and -4.937 (p=0.025) respectively. Both of those are likely to have more
tendency to belong to lower level of financial literacy as their Exp.(B) values are 0.055
and 0.007 respectively,
Similarly, with regard to number of years of work experience of respondents, all the
group variables are found to be non-significant predictors for financial literacy level.
From the Table 4.73, it can be also seen that the among all the groups of number of times
investors shops around/ make inquiry while investing, the groups NOOFTIMESHOP2
(those who shop around for 4 to 6 times while investing) and NOOFTIMESHOP3 (those
who shop around for more than 6 times while investing) are found to be significant
predictors for financial literacy level, with their coefficient value of 6.100 (p=0.000) and
5.092 (p=0.004) respectively. Both of those are likely to have more tendency to belong to
higher level of financial literacy as their Exp.(B) value is 446.06 and 162.76 respectively,
266
which are far higher than 1. From the same table, it is also found that all these groups are
positively significantly related to the level of financial literacy. Among all, the group
NOOFTIMESHOP1 (those who shop around for 1 to 3 times while investing) is found to
be non-significant predictors for financial literacy level.
With regard to years of investment experience of investors, INVESTEXP2 (those having
the years of investment experience of 6 to 10 years) and INVESTEXP3 (those having the
years of investment experience of more than 10 years) are found to be positively
significant with their coefficient value of 8.029 (p=0.000) and 7.423 (p=0.000), are likely
to fall into relatively higher level of financial literacy as Exp(B) value is 3.07 and 1.67
respectively, which are higher than 1. Among all, the group INVESTEXP1 (those having
the years of investment experience of less than 1 years) is non-significant predictor for
financial literacy level as the p value of this group is 0.285.
Similarly, for risk tolerance, all the group variables are found to be non-significant
predictors for predicting financial literacy level.
Overall, among the all the sub groups, AGE1, AGE4, EDU1, EDU2, EDU3, EDU4,
EDU5, INC1, INC2, INC4, LIFESTAGE2, LIFESTAGE4, EMPT1, EMPT2, EMPT3,
EMPT5, WORKEX1, WORKEX2, WORKEX13, WORKEX4, NOOFTIMESHOP1,
INVESTEX1, RT1, RT2 and RT3are found to be non-significant predictors for financial
literacy level.
Logistic regression gives each predictor (independent variable) a coefficient ‗B‘ which
measures its independent contribution to variations in the dependent variable, the
dependent variable can only takes on one of the two values: 0 or 1.
The logistic regression model is as follows:
267
𝐿𝑜𝑔 𝑝
1 − 𝑝
= B0 + B1(GEN1) + B2(AGE1) + B3(AGE2) + B4(AGE3) + B5(AGE4)
+ B6(EDU1) + B7 (EDU2) + B8(EDU3) + B9(EDU4) +B10(EDU5)
+ B11(INC1) + B12(INC2) + B13(INC3) + B14(INC4) + B15(LIFESTAGE1)
+ B16(LIFESTAGE2) + B17(LIFESTAGE3) + B18(LIFESTAGE4)
+ B19(LIFESTAGE5) + B20(EMPT1) + B21(EMPT2) + B22(EMPT3)
+ B23(EMPT4) + B24(EMPT5) + B25(WORKEX1) + B26(WORKEX2)
+ B27(WORKEX3) + B28(WORKEX27) + B29(NOOFTIMESHOP)
+ B30(NOOFTIMESHOP2) + B31(NOOFTIMESHOP3) + B32(INVESTEX)
+ B33(INVESTEX2) + B34(INVESTEX3) +B35(RT1) + B36(RT2) + B37(RT3)
+ B38(WORKACTI1) + B39(WORKACTI2)
where,
FL = Financial literacy level
P = The probability of a respondent with relatively higher level of
financial literacy
GEN = 1, if respondent is a female, 0 otherwise,
AGE1 = 1, if a respondent is in age group of 26 to 35 years, 0
otherwise,
AGE2 = 1, if a respondent is in age group of 36 to 45 years, 0
otherwise,
AGE3 = 1, if a respondent is in age group of 46 to 55years, 0 otherwise,
AGE4 = 1, if a respondent is in age group of 56 years and above, 0
otherwise,
EDU1 = 1, if a respondent has completed secondary education, 0
otherwise,
EDU2 = 1, if a respondent has completed higher secondary education, 0
otherwise,
EDU3 = 1, if a respondent has completed diploma education, 0
otherwise,
EDU4 = 1, if a respondent has completed graduation , 0 otherwise,
EDU5 = 1, if a respondent has completed post graduation education, 0
268
otherwise,
INC1 = 1, if a respondent‘s monthly income is Rs. 10,000 to Rs.
15,000, 0 otherwise,
INC2 = 1, if a respondent‘s monthly income is Rs. 15,001 to Rs.
20,000, 0 otherwise,
INC3 = 1, if a respondent‘s monthly income is Rs. 20,001 to Rs.
25,000, 0 otherwise,
INC4 = 1, if a respondent‘s monthly income is Rs. 25,0001 and above,
0 otherwise,
LIFESTAGE1 = 1, if a respondent is young married without children, 0
otherwise,
LIFESTAGE2 = 1, if a respondent is young married with children, 0 otherwise,
LIFESTAGE3 = 1, if a respondent is middle age married with dependent
children, 0 otherwise,
LIFESTAGE4 = 1, if a respondent is middle age married without dependent
children, 0 otherwise,
LIFESTAGE5 = 1, if a respondent is older married, 0 otherwise,
EMPT1 = 1, if a respondent is part time salaried, 0 otherwise,
EMPT2 = 1, if a respondent is casual, 0 otherwise,
EMPT3 = 1, if a respondent is self employed, 0 otherwise,
EMPT4 = 1, if a respondent is housewife, 0 otherwise,
EMPT5 = 1, if a respondent is retired, unemployed and others, 0
otherwise,
WORKEXP1 = 1, if a respondent‘s work experience is 6 to 10 years, 0
otherwise,
WORKEXP2 = 1, if a respondent‘s work experience is 11 to 20 years, 0
otherwise,
WORKEXP3 = 1, if a respondent‘s work experience is 21 to 30 years, 0
otherwise,
WORKEXP4 = 1, if a respondent‘s work experience is more than 30 years, 0
otherwise,
269
NOOFTIMESHOP1 = 1, if respondent shop around 1 to 3 times, 0 otherwise
NOOFTIMESHOP2 = 1, if respondent shop around 4 to 6 times, 0 otherwise
NOOFTIMESHOP3 = 1, if respondent shop around more than 6 times, 0 otherwise
INVESTEXP1 = 1, if a respondent‘s investment experience is 1 to 5 years, 0
otherwise,
INVESTEXP2 = 1, if a respondent‘s work experience is 6 to 10 years, 0
otherwise,
INVESTEXP3 = 1, if a respondent‘s work experience is more than 10 years, 0
otherwise,
RT1 = 1, if a respondent is moderate risk tolerance is , 0 otherwise,
RT2 = 1, if a respondent is higher risk tolerance, 0 otherwise,
RT3 = 1, if a respondent is highest risk tolerance, 0 otherwise,
WORKACTI1 = 1, if a respondent not working in finance related workplace
activity, 0 otherwise,
WORKACTI2 = 1, if a respondent‘s work activity is other, 0 otherwise,
Model fit
How good the classification model (after including all the independent variables under
study) is? The answer to this question is given in classification table as shown in the
Table 4.74. The classification table helps the researcher to assess the performance of the
model by cross-tabulating the observed response categories with the predicted response
categories. From Table 4.74, it is found that 95.6 % respondents were correctly classified.
The table also shows that out of 235 cases predicted to be under low financially literate
group, 223 cases were observed to be in low financially literate group, while 12 were in
high financially literate group. Similarly, out of 150 cases predicted to be high financially
literate group, 145 were correctly classified as high financially literate, while only 5 cases
are under low financially literate group. So, out of total 385 cases, 368 (223+145) were
correctly classified and only 17(12+5) cases were misclassified. From this, it can be said
that (385-17)/385, or 95.60% of the cases were correctly classified with this model.
270
Table 4.74 Classification Tablea
Observed
Predicted
Level Percentage
Correct Low High
Step 1 Level Low 223 05 97.80
High 12 145 92.40
Overall Percentage 95.60
a. The cut off value is 0.56
By adding all the independent variables under study in the constant logistic regression, as
explained above, it was found that after adding all the independent variables under study,
now overall 95.60 % of respondents are correctly classified, as shown in Table 4.74. In
other words, researcher can predict the final model with 95.60 % of accuracy, which was
earlier 59.20% only (see Table 4.66).
Thus, to sum up, a logistic regression analysis was conducted to predict the investors‘
financial literacy i.e. higher level of financial literacy or low level of financial literacy by
using the responses from 385 respondents‘ age, gender, education, monthly income, stage
of family life cycle, employment structure, type of workplace activity, years of work
experience, number of times respondents shop around, years of investment experience
and risk tolerance level as predictors (explainable variables/ independent variables). A
test of final model against constant model was statistically significant, indicating that the
predictors as a set reliably distinguished between higher and lower level of financial
literacy (chi square = 420.876, p < 0.05, with df =39).
Nagelkerke‘s R2
with value of 0.897 indicated that a strong relationship between
prediction and grouping. The overall prediction success was 95.60% (97.8% for lower
level of financial literacy and 92.4% for higher level of financial literacy). The Wald
criterion demonstrated that except education, monthly income, and risk tolerance level,
all the predictors are important to predict financial literacy level.
271
Classification Plot
The Fig. 4.7 is a classification plot or histogram of predicted probabilities provides a
visual demonstration of the correct and incorrect predictions. Also called the ‗classplot‘
or the ‗plot of observed groups and predicted probabilities‘. From the graph, it can be
seen that X axis is the predicted probability from 0.0 to 1.0 of the dependent being
classified ‗1‘. The Y axis is frequency: the number of cases classified. A researcher must
look for the following points while analyzing the classification plot.
1) This classification plot should be U –shaped. In logistic regression, U-shaped rather
than Normal distribution is desirable. A U-shaped distribution indicates the
predictions are well-differentiated with cases clustered at each end showing correct
classification.
2) There should be few errors. The ‗H‘s‘ to the left are false positive. The ‗L‘s‘ to the
right are false negatives.
272
Step number: 1
Observed Groups and Predicted Probabilities
160 ┼H ┼
│L │
│L │
F │L │
R 120 ┼L ┼
E │L │
Q │L H│
U │L H│
E 80 ┼L H┼
N │L H│
C │L H│
Y │L H│
40 ┼L H┼
│L H│
│LH H│
│LLLL HH│
Predicted ─────────┼─────────┼─────────┼─────────┼─────────┼─────────┼─────────┼─────────┼─────────┼──────────
Prob: 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1
Group: LLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHH
Predicted Probability is of Membership for High
The Cut Value is .56
Symbols: L - Low
H - High
Each Symbol Represents 10 Cases.
Fig. 4.7 Classification Plot
273
4.12 Association between financial literacy level of investors and their monthly
expenditure to monthly income ratio.
The data regarding investors‘ financial literacy level and their monthly expenditure to
monthly income ratio is given in Table 4.75.
Table 4.75 Cross Tabulation of Investors‟ Financial Literacy Level and their
Monthly Expenditure to Monthly Income Ratio
Monthly expenditure
to monthly income
ratio
Respondents with
lower level of
Financial Literacy
Respondents with
higher level of
Financial Literacy Total
1% to 50% 88 38 126
51% to 60% 57 32 89
61% to 70% 40 43 83
71% to 80% 22 30 52
81% to 90% 18 12 30
More than 90 % 3 2 5
Total 228 157 385
To check the association between these two variables statistically, Chi-square test is
performed. Although there are no assumptions made as to the shape of data distribution
for this non parametric technique, there are restrictions on its applications. As a rule of
thumb, if the degree of freedom is greater than one, not more than 20% of the cells
should have expected frequencies of less than 5. If this requirement can‘t be met,
researcher should attempt to combine cells, until it conforms to this rule, but only if the
combination would not render the data meaningless (Luck and Rubin, 2003377
). Based on
this rule, for the data in the above table, not more than 2 cells should have expected
frequency less than 5. But, for this data exactly cells have expected frequency less than
5, as indicated in the Table 4.75. Hence regrouping is done by combining the cells, which
has expected count less than 5. The new table free from this limitation is as shown below.
377 Luck, D. & Rubin, D. (2003). Marketing Research (7th ed.). New Delhi: Prentice Hall of India Pvt. Ltd.,
p. 347
274
Table 4.76 Revised Cross Tabulation of Investors‟ Financial Literacy Level and
their Monthly Expenditure to Monthly Income Ratio
Monthly expenditure to
monthly income ratio
Investors with
lower level of
Financial
Literacy
Investors with
higher level of
Financial
Literacy Total
1% to 50% 88 38 126
51% to 60% 57 32 89
61% to 70% 40 43 83
71% to 80% 22 30 52
81% to 90% and more than 90% 21 14 35
Total 228 157 385
Hypotheses for the data presented in Table 4.76 for chi-square test are as under
H0: There is no significant association between financial literacy level of investors and
their monthly expenditure to monthly income ratio.
H1: There is a significant association between financial literacy level of investors and
their monthly expenditure to monthly income ratio.
Table 4.77 Chi-Square Tests
Value df Sig.
Pearson Chi-Square 17.091a 4 0.002
Likelihood Ratio 17.101 4 0.002
Linear-by-Linear Association 9.662 1 0.002
Phi 0.211 0.002
Cramer's V 0.211 0.002
Contingency Coefficient 0.206 0.002
Number of Valid Cases 385
The test was performed at 5% level of significance. The output of Chi-square test is as
presented in Table 4.77. The Pearson Chi-square significance value is 0.002 and degree
of freedom is 4. Therefore, null hypothesis is rejected and hence, it found that there is a
significant association between financial literacy level of investors and their monthly
275
expenditure to monthly income ratio. It might be concluded that there is an association of
financial literacy level of investors with their monthly expenditure to monthly income
ratio or financial literacy level of investors and their monthly expenditure to monthly
income ratio are not independent of each other. In other words, these two variables are
significantly associated with each other.
Chi square test shows the statistical significance between the two variables. It does not
tell us the strength of association between two variables in the cross tabulation. To
measure the strength of this association, The Cramer‘s V, one of the measures of indexes
of agreement is used, which is shown in the Table 4.77. From the table, it can be seen that
the value of Cramer‘s V significant with 0.002 and the degree of association is between
the variables is 21.1%.
4.13 Association between financial literacy level of investors and their monthly
saving to monthly income ratio.
The data regarding the investors‘ financial literacy level and their monthly saving to
monthly income ratio is given in Table 4.78.
Table 4.78 Cross Tabulation of Investors‟ Financial Literacy and their Monthly
Saving to Monthly Income Ratio
Monthly saving to
monthly income
ratio
Investors with
lower level of
Financial Literacy
Investors with
higher level of
Financial Literacy Total
1% to 50% 183 34 217
51% to 60% 43 84 127
61% to 70% 2 38 40
71% to 80% 0 1 1
Total 228 157 385
To check the association between these two variables statistically, Chi-square test is
performed. Although there are no assumptions made as to the shape of data distribution
for this non parametric technique, there are restrictions on its applications. As a rule of
276
thumb, if the degree of freedom is greater than one, not more than 20% of the cells
should have expected frequencies of less than 5. If this requirement can‘t be met,
researcher should attempt to combine cells, until it conforms to this rule, but only if the
combination would not render the data meaningless (Luck and Rubin, 2003378
). Based on
this rule, for the data in the above table, not more than 2 cells should have expected
frequency less than 5. But, for this data exactly cells have expected frequency less than
5, as indicted in the Table 4.78. Hence regrouping is not done by combining. The new
Table free from this limitation is as shown below.
Table 4.79 Revised Cross Tabulation of Investors‟ Financial Literacy Level and
their Monthly Saving to Monthly Income Ratio
Monthly saving to
monthly income ratio
Respondents with
lower level of
Financial Literacy
Respondents with
higher level of
Financial Literacy Total
1% to 50% 183 34 217
51% to 60% 43 84 127
61% to 80% and more
than that 02 39 41
Total 228 157 385
Hypotheses for the data presented in Table 4.79 for chi-square test are as under:
H0: There is no significant association between financial literacy level of investors and
their monthly saving to monthly income ratio.
H1: There is a significant association between financial literacy level of investors and
their monthly saving to monthly income ratio.
378 Luck, D. & Rubin, D. (2003). Marketing Research (7th ed.). New Delhi: Prentice Hall of India Pvt. Ltd.,
p. 347
277
Table 4.80 Chi-Square Tests
Value df Sig.
Pearson Chi-Square 1.406 2 0.000
Likelihood Ratio 153.578 2 0.000
Linear-by-Linear
Association 137.105 1 0.000
Phi 0.604 0.000
Cramer's V 0.604 0.000
Contingency Coefficient 0.517 0.000
Number of Valid Cases 385
The test was performed at 5% level of significance. The output of Chi-square test is as
presented in Table 4.80. The Pearson Chi-square significance value is 0.000 and degree
of freedom is 2. Therefore, null hypothesis is rejected and hence, it found that there is a
significant association between financial literacy level of investors and their monthly
saving to monthly income ratio. It might be concluded that there is an association of
financial literacy level of investors with their monthly saving to monthly income ratio or
financial literacy level of investors and their monthly saving to monthly income ratio are
not independent of each other. In other words, these two variables are significantly
associated with each other.
As, we have discussed earlier, Chi-square test shows the statistical significance between
the two variables. It does not tell us the strength of association between two variables in
the cross tabulation. To measure the strength of this association, The Cramer‘s V, one of
the measures of indexes of agreement is used, which is shown in the Table 4.80. From the
Table 4.80, it can be seen that the value of Cramer‘s V significant with 0.000 and the
degree of association is between the variables is 60.4%.
278
4.14 Regression Analysis: Impact of financial literacy on investment decision
To test the last hypothesis, the simple linear regression model was used.
Regression is a simple statistical tool used to model the dependence of a variable on one
(or more) explanatory variables. This functional relationship may then be formally stated
as an equation, with associated statistical values that describe how well this equation fits
the data. Usually, the investigator seeks to ascertain the causal effect of one variable upon
another. To explore such issues, the investigator assembles data on the underlying
variables of interest and employs regression to estimate the quantitative effect of the
causal variables upon the variable that they influence. The investigator also typically
assesses the ―statistical significance‖ of the estimated relationships, that is, the degree of
confidence that the true relationship is close to the estimated relationship.
Linear regression analysis is a bi-variate statistical technique used to examine the
relationship between a single dependent variable and single independent variable. The
objective of linear regression is to use independent variable whose value is known to
predict the single dependent value selected by the researcher. The independent variable
weighted by the regression analysis procedure to ensure its maximal prediction on the
value of dependent variable. The weight denotes the relative contribution of independent
variable to the overall prediction and facilitate interpretation as to the influence of
independent variable in making the prediction.
Thus, regression analysis with a single explanatory variable is termed ―simple
regression.‖ Then, the hypothesized relationship between financial literacy and
investment decision may be written as:
I = + E +
Where,
279
The variable I is termed the ―dependent‖ variable (I = investment decision); E is termed
the ―independent,‖ or ―explanatory,‖ variable (E = financial literacy level); is the
―constant term‖ and the ―coefficient‖ of the variable E (financial literacy level).
One of the most widely used statistical techniques is simple linear regression. This
technique is used to relate a measured response variable, Y, to a single measured
predictor (explanatory) variable, X, by means of a straight line. It uses the principle of
least squares to come up with values of the ―best‖ slope and intercept for a straight line
that approximates the relationship. The purpose of simple linear regression is to come up
with a straight line that captures the relationship between the predictor and the response
variable.
Measurement used in simple linear regression analysis
Regression co-efficient: Regression co-efficient is the standardized regression co-
efficient that allows for a direct for direct comparison between coefficients as to their
relative explanatory power of dependent variable (Malhotra and Dash, 2010379
).
Coefficient of determination: It is a measure of the proportion of the variance of the
dependent variable about its mean that is explained by the independent or predictable
variable/s. The strength of association is measure by the coefficient of determination.
Its value varies from 0 to 1. The researcher can infer that the higher the value of R2,
greater the explanatory power of the regression equation and therefore the better the
prediction of the dependent variable (Malhotra and Dash, 2010).
Adjusted R2: It is the modified measure of the coefficient of determination that takes
into account the number of independent variables included in the regression equation
and the sample size. It explains whether or not the inclusion of additional independent
379 Malhotra, N. & Dash, S. (2010). Marketing Research: An Applied Orientation (6th ed.).New Delhi:
Pearson Education.
280
variables in regression equation may increase or reduce the overall coefficient of
determination (Malhotra and Dash, 2010)
ANOVA (F-test): The F-test is used to test the significance of the overall regression
equation. It is used to test that the coefficient of multiple determination in the
population is non zero (Malhotra and Dash, 2010).
To test the impact of financial literacy level on investment decision, the following
hypotheses are framed.
H0: There is no significant impact of financial literacy level on investment decision of
investors.
H1: There is a significant impact of financial literacy level on investment decision of
investors.
As explained above, to test this hypothesis, the simple liner regression model is
performed as under. To perform the regression analysis, financial literacy level is
considered as an independent variable and nine factors extracted by factor analysis
independently and simultaneously are taken as factors investment decision and are
considered as dependent variables for regression analysis.
Further, to show the overall impact of financial literacy level (FLL) on investment
decision, the sum of all nine investment factors are considered as dependent variable to
run the regression analysis.
4.14.1 Regression analysis: Financial literacy level and Factor 1-Personal Financial
Need
To check the impact of financial literacy level on investment decision, the financial
literacy level is considered as independent variable and Factor 1: Personal financial need
281
(as a factor for investment decision) is considered as dependent variable. The results of
correlation and regression are shown in following tables with interpretation.
Table 4.81 Correlations between FLL and Personal Financial Need
** Correlation is significant at the 0.05 level (2-tailed).
Table 4.81 shows that the correlation coefficient of two variables under study is 0.882.
This value of r suggests a strong positive linear correlation since the value is positive and
close to 1. Since, there is a strong positive linear relationship between financial literacy
level and Factor 1: personal financial need. Hence, the researcher may go for linear
simple regression analysis. The results of regression analysis performed for these two
variables are presented below.
Table 4.82 Model Summary
Model R R Square Adjusted R2 Std. Error of the Estimate
1 0.882a 0.778 0.777 0.46174
a. Predictors: (Constant), FLL
Table 4.83 ANOVAb
Model Sum of Squares df
Mean
Square F Sig.
1 Regression 285.11 1 285.511 1.339 0.000a
Residual 81.656 383 0.213
Total 367.167 384
a. Predictors: (Constant), FLL
b. Dependent Variable: FACTOR1
FLL Factor 1
FLL Pearson Correlation 1 0.882**
Sig. (2-tailed) 0.000
N 385 385
Factor 1 Pearson Correlation 0.882**
1
Sig. (2-tailed) 0.000
N 385 385
282
Table 4.84 Coefficientsa
Model
Unstandardized
Coefficients
Standardized
Coefficients
T Sig. B Std. Error Beta
1 (Constant) 0.512 0.071 7.174 0.000
Level 1.752 0.048 0.882 36.595 0.000
a. Dependent Variable: FACTOR1
The regression analysis was done to measure the variation in personal financial need
(dependent variable), based on variation in financial literacy level (FLL) (independent
variable). From Table 4.84, the following regression equation is derived.
Personal Financial Need = 0.512 + 1.752 (FLL)
In the present regression analysis, the measure of strength of association is given by the
coefficient of determination denoted by R2. From Table 4.82, it can be seen that the R
2
value is 0.778, which shows 77.8 % of variance in investor‘s personal financial need (as a
factor of investment decision) can be predicted by his/her financial literacy level. In other
words, 22 % (1.000-0.778=0.222) in investor‘s personal financial need cannot be
predicted by his/her financial literacy level. The regression equation appears to be very
useful for making predictions, since the value of R2 is close to 1. The last column of the
same table ―standard error of estimation‖ provides a measure of how accurately the
regression equation predicts values of dependent variable. The smaller the value of
standard error of estimation is, better one can predict that the independent variable (i.e.
Financial literacy level) account for the variance in the dependent variable.
The t-test value for the significance of individual independent variable indicates the
significance at 95% confidence level. Table 4.84 shows that financial literacy level (FLL)
is statistically significant with a value of 0.000, which is less than 0.05. The result of F-
test shown in Table 4.83 is also significant with value of 0.000, which allows a researcher
to determine whether or not the linear regression was statistically significant. This
indicates that model is statistically significant at a confidence level of 95%.
283
4.14.2 Regression analysis: Financial literacy level and Factor 2- Accounting,
Business and Financial Information
To check the impact of financial literacy level on investment decision, the financial
literacy level is considered as independent variable and Factor 2: Accounting, Business
and Financial Information (as a factor for investment decision) is considered as a
dependent variable. The results of correlation and regression are shown in following
tables with interpretation.
Table 4.85 Correlations between FLL and Accounting, Business and Financial
Information
FLL Factor 2
FLL Pearson Correlation 1 0.042
Sig. (2-tailed) 0.416
N 385 385
Factor 2 Pearson Correlation 0.042 1
Sig. (2-tailed) 0.416
N 385 385
Table 4.85 shows that the correlation coefficient of two variables under study is 0.042
with a significant value of 0.416, which is higher than 0.05 at 5 % level of significance.
This value of r suggests that there is no correlation between the variables under study.
Therefore, it is concluded that there is no relationship between financial literacy level and
Factor 2: Accounting, business and financial information, and hence, the researcher
should not go for regression analysis.
4.14.3 Regression analysis: Financial literacy level and Factor 3- Economic and
Regulatory Environment
To check the impact of financial literacy level on investment decision, the financial
literacy level is considered as an independent variable and Factor 3: Economic and
284
regulatory environment (as a factor for investment decision) is considered as a dependent
variable. The results of correlation and regression analysis are shown in following tables
with interpretation.
Table 4.86 Correlations between FLL and Economic and Regulatory Environment
FLL Factor 3
FLL Pearson Correlation 1 -0.022
Sig. (2-tailed) 0.673
N 385 385
Factor 3 Pearson Correlation -0.022 1
Sig. (2-tailed) 0.673
N 385 385
Table 4.86 shows that the correlation coefficient of two variables under study is -0.022
with significant value of 0.673. This value is higher than 0.05 at 5% level of significance.
This value of r suggests that there is no correlation between the variables under study.
Therefore, it is concluded that there is no relationship between financial literacy level and
Factor 3: Economic and regulatory environment. Hence, the researcher should not go for
regression analysis.
4.14.4 Regression analysis: Financial literacy level and Factor 4 – Operational
Feedback
To check the impact of financial literacy level on investment decision, the financial
literacy level is considered as an independent variable and Factor 4: Operational
Feedback (as a factor for investment decision) is considered as a dependent variable. The
results of correlation and regression analysis are shown in following tables with
interpretation.
285
Table 4.87 Correlations between FLL and Operational Feedback
FLL Factor 4
FLL Pearson Correlation 1 0.042
Sig. (2-tailed) 0.416
N 385 385
Factor 4 Pearson Correlation 0.042 1
Sig. (2-tailed) 0.416
N 385 385
Table 4.87 shows that the correlation coefficient of two variables under study is 0.042
with a significant value of 0.416, which is higher than 0.05 at 5% level of significance.
This value of r suggests that there is no correlation between the variables under study.
Therefore, it is concluded that there is no relationship between financial literacy level and
Factor 4: Operational feedback. Hence, the researcher should not go for regression
analysis.
4.14.5 Regression analysis: Financial literacy level and Factor 5- Advocate
recommendation
To check the impact of financial literacy level on investment decision, the financial
literacy level is considered as an independent variable and Factor 5: Advocate
recommendation (as a factor for investment decision) is considered as a dependent
variable. The results of correlation and regression analysis are shown in the following
tables with interpretation.
Table 4.88 Correlations between FLL and Advocate recommendation
FLL Factor 5
FLL Pearson Correlation 1 0.261**
Sig. (2-tailed) 0.000
N 385 385
Factor 5 Pearson Correlation 0.261**
1
Sig. (2-tailed) 0.000
N 385 385
**Correlation is significant at the 0.05 level (2-tailed).
286
Table 4.88 shows that the correlation coefficient of two variables under study is 0.261
with significant value of 0.000. This positive value of r suggests a positive linear
correlation between the variables under study. Since, there is a positive linear relationship
between financial literacy level and Factor 5: Advocate recommendation. Hence, the
researcher may go for linear simple regression analysis. The results of regression analysis
performed for these two variables are presented below.
Table 4.89 Model Summary
Model R R Square
Adjusted R
Square
Std. Error of
the Estimate
1 0.261a 0.068 0.066 0.4555
a. Predictors: (Constant), FLL
Table 4.90 ANOVAb
Model
Sum of
Squares df
Mean
Square F Sig.
1 Regression 5.833 1 5.833 28.112 0.000a
Residual 79.477 384 0.208
Total 85.310 385
a. Predictors: (Constant), FLL
Dependent Variable: FACTOR5
Table 4.91 Coefficientsa
Model
Unstandardized
Coefficients
Standardized
Coefficients
t Sig. B Std. Error Beta
1 (Constant) 3.384 0.070 48.044 0.000
Level 0.250 0.047 0.261 5.032 0.000
a. Dependent Variable: FACTOR5
The regression analysis was done to measure the variation in advocate recommendation
(dependent variable), based on variation in financial literacy level (FLL) (independent
variable). From Table 4.91, the following regression equation is derived.
287
Advocate recommendation = 3.384 + 0.250 (FLL)
In the present regression analysis, the measure of strength of association is given by the
coefficient of determination denoted by R2. From Table 4.88, it can be seen that the R
2
value is 0.068, which shows 6.8 % of variance of investor‘s advocate recommendation
(as a factor of investment decision) can be predicted by his/her financial literacy level. In
other words, 93 % (1.000 -0.068=0.932) in investor‘s advocate recommendation cannot
be predicted by his/her financial literacy level. The last column of the Table 4.89 shows
―standard error of estimation‖, that provides a measure of how accurately the regression
equation predicts values of dependent variable. The smaller the value of standard error of
estimation is, better one can predict that the independent variable (i.e. Financial literacy
level) account for the variance in the dependent variable.
The t-test value for the significance of individual independent variable indicates the
significance at 95% confidence level. Table 4.91 shows that financial literacy level (FLL)
is statistically significant with a value of 0.000, which is less than 0.05. The result of F-
test shown in Table 4.90 is also significant with value of 0.000, which allows a researcher
to determine whether or not the linear regression was statistically significant. This
indicates that model is statistically significant at a confidence level of 95%.
4.14.6 Regression analysis: Financial literacy level and Factor 6-Overall Group
Performance
To check the impact of financial literacy level on investment decision, the financial
literacy level is considered as an independent variable and Factor 6: Overall group
performance (as a factor for investment decision) is considered as a dependent variable.
The results of correlation and regression analysis are shown in following tables with
interpretation.
288
Table 4.92 Correlations between FLL and Overall Group Performance
FLL Factor 6
FLL Pearson Correlation 1 0.352**
Sig. (2-tailed) 0.000
N 385 385
Factor 6 Pearson Correlation 0.352**
1
Sig. (2-tailed) 0.000
N 385 385
** Correlation is significant at the 0.05 level (2-tailed).
Table 4.92 shows that the correlation coefficient of two variables under study is 0.352,
with significant value of 0.000, which is lower than 0.05, at 5 % level of significance.
This value of r suggests a linear correlation between financial literacy level and overall
Factor 6: Overall group performance. Since, there is a positive linear relationship
between variables; the researcher may go for linear simple regression analysis. The
results of regression analysis performed for these two variables are presented below.
Table 4.93 Model Summary
Model R R Square
Adjusted R
Square Std. Error of the Estimate
1 0.352a 0.124 0.122 0.65412
a. Predictors: (Constant), FLL
Table 4.94 ANOVAb
Model
Sum of
Squares df
Mean
Square F Sig.
1 Regression 23.226 1 23.226 54.283 0.000a
Residual 163.875 384 0.428
Total 187.101 385
b. Dependent Variable: FACTOR6
289
Table 4.95 Coefficientsa
Model
Unstandardized
Coefficients
Standardized
Coefficients
T Sig. B Std. Error Beta
1 (Constant) 2.540 0.101 25.107 0.000
Level 0.500 0.068 0.352 7.368 0.000
a. Dependent Variable: FACTOR6
The regression analysis was done to measure the variation in overall group performance
(dependent variable), based on variation in financial literacy level (FLL) (independent
variable). From Table 4.95, the following regression equation is derived.
Overall Group Performance = 2.540 + 0.500 (FLL)
In the present regression analysis, the measure of strength of association is given by the
coefficient of determination denoted by R2. From Table 4.93, it can be seen that the R
2
value is 0.124, which shows 12.4 % of variance in investor‘s overall group performance
(as a factor of investment decision) can be predicted by his/her financial literacy level. In
other words, 87.6 % (1.0 – 0.124=0.876) variation in overall groups performance cannot
be predicted by his/her financial literacy level. The regression equation appears to be very
useful for making predictions, since the value of R2 is close to 1. The last column of the
same table ―standard error of estimation‖ provides a measure of how accurately the
regression equation predicts values of dependent variable. The smaller the value of
standard error of estimation is, better one can predict that the independent variable (i.e.
Financial literacy level) account for the variance in the dependent variable.
The t-test value for the significance of individual independent variable indicates the
significance at 95% confidence level. Table 4.95 shows that financial literacy level (FLL)
is statistically significant with a value of 0.000, which is less than 0.05. The result of F-
test shown in Table 4.94 is also significant with value of 0.000, which allows a researcher
to determine whether or not the linear regression was statistically significant. This
indicates that model is statistically significant at a confidence level of 95%.
290
4.14.7 Regression analysis: Financial literacy level and Factor 7-Credit Features
To check the impact of financial literacy level on investment decision, the financial
literacy level is considered as an independent variable and Factor 7: Credit feature (as a
factor for investment decision) is considered as a dependent variable. The results of
correlation and regression analysis are shown in following tables with interpretation.
Table 4.96 Correlations between FLL and Credit Features
FLL Factor7
FLL Pearson Correlation 1 0.045
Sig. (2-tailed) 0.382
N 385 385
Factor7 Pearson Correlation 0.045 1
Sig. (2-tailed) 0.382
N 385 385
Table 4.96 shows that the correlation coefficient of two variables under study is 0.045,
with a significant value of 0.382, which is higher than 0.05 at 5% level of significance.
This value of r suggests that there is no correlation between the variables under study.
Therefore, it is concluded that there is no relationship between financial literacy level and
Factor 7: Credit features. Hence, the researcher should not go for regression analysis.
4.14.8 Regression analysis: Financial literacy level and Factor 8- Personal
Inclination
To check the impact of financial literacy level on investment decision, the financial
literacy level is considered as an independent variable and Factor 8: Personal inclination
(as a factor for investment decision) is considered as a dependent variable. The results of
correlation and regression analysis are shown in following tables with interpretation.
291
Table 4.97 Correlation between FLL and Personal Inclination
FLL Factor 8
FLL Pearson Correlation 1 -0.057
Sig. (2-tailed) 0.266
N 385 385
Factor 8 Pearson Correlation -0.057 1
Sig. (2-tailed) 0.266
N 385 385
Table 4.97 shows that the correlation coefficient of two variables under study is -0.057,
with a significant value of 0.266, which is higher than 0.05 at 5% level of significance.
This value of r suggests that there is no correlation between the variables under study.
Therefore, it is concluded that there is no relationship between financial literacy level and
Factor 8: Personal inclination. Hence, the researcher should not go for regression
analysis.
4.14.9 Regression analysis: Financial literacy level and Factor 9-Monetary
expectation
To check the impact of financial literacy level on investment decision, the financial
literacy level is considered as an independent variable and Factor 9: Monetary
expectation (as a factor for investment decision) is considered as a dependent variable.
The results of correlation and regression analysis are shown in following tables with
interpretation.
Table 4.98 Correlations between FLL and Monetary Expectation
FLL Factor 9
FLL Pearson Correlation 1 0.090
Sig. (2-tailed) 0.076
N 385 385
Factor 9 Pearson Correlation 0.090 1
Sig. (2-tailed) 0.076
N 385 385
p < 0.10
292
Table 4.98 shows that the correlation coefficient of two variables under study is 0.090
with a significant value of 0.076. This significant value is lower than 0.10 at 10% level of
significance. This value of r suggests that there is a correlation between the variables
under study. Since, there is a positive linear relationship between variables; the researcher
may go for linear simple regression analysis. The results of regression analysis performed
for these two variables are presented below.
4.99 Model Summary
Model R R Square
Adjusted R
Square
Std. Error of
the Estimate
1 .090a .008 .006 .491
a. Predictors: (Constant), FACTOR9
4.100 ANOVAb
Model
Sum of
Squares df Mean Square F Sig.
1 Regression .760 1 .760 3.157 .076a
Residual 92.216 383 .241
Total 92.977 384
a. Predictors: (Constant), FACTOR9
b. Dependent Variable: Level
4.101 Coefficientsa
Model
Unstandardized
Coefficients
Standardized
Coefficients
T Sig. B Std. Error Beta
1 (Constant) 1.121 .163 6.868 .000
FACTOR9 .071 .040 .090 1.777 .076
a. Dependent Variable: Level
The regression analysis was done to measure the variation in monetary expectation
(dependent variable), based on variation in financial literacy level (FLL) (independent
variable). From Table 4.101, the following regression equation is derived.
293
Monetary Expectation = 1.121 + 0.071 (FLL)
In the present regression analysis, the measure of strength of association is given by the
coefficient of determination denoted by R2. From Table 4.99, it can be seen that the R
2
value is 0.008, which shows 0.8 % of variance in investor‘s monetary expectation (as a
factor of investment decision) can be predicted by his/her financial literacy level. In other
words, 99.2 % (1.0 – 0.008=0.992) variation in monetary expectation cannot be predicted
by his/her financial literacy level. The last column of the same table ―standard error of
estimation‖ provides a measure of how accurately the regression equation predicts values
of dependent variable. The smaller the value of standard error of estimation is, better one
can predict that the independent variable (i.e. Financial literacy level) account for the
variance in the dependent variable. For these variables standard error of estimation is
49.1%.
The t-test value for the significance of individual independent variable indicates the
significance at 90% confidence level. Table 4.101 shows that financial literacy level
(FLL) is statistically significant with a value of 0.076, which is less than 0.10. The result
of F-test shown in Table 4.100 is also significant with value of 0.076, which allows a
researcher to determine whether or not the linear regression was statistically significant.
This indicates that model is statistically significant at a confidence level of 90%.
4.14.10 Regression analysis: Financial literacy level and sum of investment decision
factors
To check the impact of financial literacy level on investment decision, the financial
literacy level is considered as an independent variable and sum of investment decision
factors is considered as a dependent variable. The results of correlation and regression
analysis are shown in following tables with interpretation.
294
Table 4.102 Correlations between FLL and sum of Investment Decision Factors
FLL
Sum of
Investment
Decision Factors
FLL Pearson Correlation 1 0.513**
Sig. (2-tailed) 0.000
N 385 385
Sum of
Investment
Decision
Factors
Pearson Correlation 0.513**
1
Sig. (2-tailed) 0.000
N 385 385
*Correlation is significant at the 0.05 level (2-tailed).
Table 4.102 shows that the correlation coefficient of two variables under study is 0.513,
with a significant value of 0.000, which is lower than 0.05 at 5% level of significance.
This value of r suggests a strong positive linear correlation between financial literacy
level and sum of investment factors. Hence, the researcher may go for linear simple
regression analysis.
Table 4.103 Model Summary
Model R R Square
Adjusted R
Square
Std. Error of
the Estimate
1 0.513a 0.263 0.261 0.24539
a. Predictors: (Constant), FLL
Table 4.104 ANOVAb
Model
Sum of
Squares Df
Mean
Square F Sig.
1 Regression 8.237 1 8.237 136.794 0.000a
Residual 23.062 384 0.060
Total 31.299 385
a. Predictors: (Constant), FLL
b. Dependent Variable: OVERALLFACTOR
295
Table 4.105 Coefficientsa
Model
Unstandardized
Coefficients
Standardized
Coefficients
T Sig. B Std. Error Beta
1 (Constant) 3.118 0.038 82.171 0.000
Level 0.298 0.025 0.513 11.696 0.000
a. Dependent Variable: OVERALLFACTORS
The regression analysis was done to measure the variation in investment decision
(dependent variable), based on variation in financial literacy level (FLL) (independent
variable). From Table 4.105, the following regression equation is derived.
Investment Decision = 3.118 + 0.298 (FLL)
In the present regression analysis, the measure of strength of association is given by the
coefficient of determination denoted by R2. From the Table 4.103, it can be seen that the
R2 value is 0.263, which shows 26.30% of variance in investors‘ investment decision can
be predicted by his/her financial literacy level. In other words, 73.7 % (1-0.263=0.737) in
investors‘ investment decision cannot be predicted by his/her financial literacy level. The
regression equation appears to be very useful for making prediction, since value of R2
is
closer to 1. The last column of the same table ―standard error of estimation‖ provides a
measure of how accurately the regression equation predicts values of dependent variable.
The smaller the value of standard error of estimation is, better one can predict that the
independent variable (i.e. Financial literacy level) account for the variance in the
dependent variable. For these two variables the value of standard error of coefficient is
24.54 %.
The t-test value for the significance of individual independent variable indicates the
significance at 95% confidence level. Table 4.105 shows that financial literacy level
(FLL) is statistically significant with a value of 0.000, which is less than 0.05. The result
of F-test shown in Table 4.104 is also significant with value of 0.000, which allows a
296
researcher to determine linear regression is statistically significant. This indicates that
model is statistically significant at the confidence level of 95 %.
Hence, the null hypothesis ―There is no significant impact of financial literacy level on
investment decision of investors‖ is rejected, as significant value 0.000 is less than 0.05
at 5% level of significance. Hence, it may conclude that there is a significant impact of
financial literacy level on investment decision.
Table 4.106 summarizes the result of overall regression analysis done to study the impact
of financial literacy level on investment decision of investors.
297
Table 4.106 Summary of Regression Analysis of Financial Literacy Level and Investment Decision
Factors R2 Adj.
R2
S.E. of
estimates
Beta t -
value
Sig. F-test Sig.
Personal financial need 0.778 0.777 2.462 0.882 36.59 0.000* 1.339 0.000
*
Accounting, business and financial
information
0.002 0.000 0.526 0.042 0.814 0.416 0.663 0.416
Economic and regulatory environment 0.000 -0.002 0.622 -0.022 -0.423 0.673 0.179 0.673
Advocate recommendation 0.002 0.000 0.827 0.042 0.815 0.416 0.664 0.416
Operational feedback 0.068 0.066 0.456 0.261 5.032 0.000* 28.112 0.000
*
Overall group performance 0.124 0.122 0.654 0.352 7.368 0.000* 54.283 0.000
*
Credit features 0.002 0.000 0.566 0.045 0.876 0.382 0.767 0.382
Personal inclination 0.003 0.001 0.670 -0.057 -1.114 0.266 1.240 0.266
Monetary expectations 0.008 0.006 0.624 0.090 1.777 0.076**
3.157 0.076**
Sum of investment factors 0.263 0.261 0.245 0.513 11.696 0.000* 136.794 0.000
*
*p<0.05, significant at 0.05,
**p<0.1, significant at 0.1
298
4.15 Conclusion
In-depth analysis of data that was collected by administrating the research instrument to
retail individual investors in the state of Gujarat, is presented in this chapter by
employing various univariate, bivarate and multivariate techniques of data analysis. The
results of the statistical tests presented in this chapter are also supported with in-depth
interpretation and explanation of results of each statistical test. The next chapter deals
with the findings of this research study based on the data analysis presented in this
chapter.
Findings
and Conclusion
Chapter 5
299
5.1 Introduction
5.2 Summary of the study
5.3 Findings of the study
5.3.1 Findings for the analysis of financial literacy questions
5.3.2 Findings for analysis of existing investment pattern, investment
objectives and preferences for various investment alternatives
5.3.3 Findings for analysis of variables preferred as Sources of
Information while investment
5.3.4 Findings for Factor Analysis performed for variables influencing
Investment Decision of Investors
5.3.5 Findings for the Cross –tabulation and Chi-square test performed
to know association between each demographic and socio-economic
variable of investors and their financial literacy level
5.3.6 Findings for Logistic Regression performed to assess the combine
impact of Demographic and Socio-economic variables of Investors
on their Financial Literacy Level
5.3.7 Findings for the Cross –tabulation and Chi-square test performed
to know association between each financial literacy level of investors
and their monthly expenditure to monthly income ratio and
monthly saving to monthly income ratio
5.3.8 Findings of correlation and regression performed to assess the
impact of financial literacy level of investors on their investment
decision
5.4 Conclusion
5.5 Recommendations
5.5.1 For Policy makers and regulatory authorities
5.5.2 For Investors
5.5.3 For Financial Education Providers
5.6 Limitations of the study
5.7 Scope for further research
300
5.1 Introduction
The purpose of this chapter is to present the conclusions in light of the research
objectives presented in chapter 3. The chapter begins with the summary of the study
followed by conclusion and limitations of the study. Future scope for the research is also
discussed. Detailed discussions on major findings are the thrust of this chapter. The
significance of the research is also simultaneously presented.
5.2 Summary of the study
The chapter 1 has begun with the background of the research topic. Researcher has
explained the rationale for selecting the topics for the study. The detailed explanation
was given for need to be a financially literate in today‘s context. Before presenting the
historical developments those taken place while explaining the composite definition of
financial literacy, it is necessary to understand the meaning of ‗literacy‘ first and the
scope of this word. Hence, researcher has explained in detail the word ‗literacy‘. In this
chapter, researcher has also explained the term ‗literacy‘ was one that has been adopted
by practitioners from a variety of backgrounds to give more description to the term
―literacy‘ and following this, the term, ―financial literacy‖ was defined, and
conceptualized. This chapter has also covered the attempts made by various researchers/
authorities to explain and/or derive a composite definition of financial literacy. This
chapter has also discussed and justified the reason, why financial literacy has gained the
attention of governments, regulatory authorities and policymakers in various countries by
identifying its need and consequences of financial illiteracy. This chapter is concluded
with the scope of the term ―financial literacy‖.
Chapter 2 was aimed at developing theoretical framework for this study. The detailed
literature review was presented and theory building task was carried out in this chapter.
To get an in-depth idea for the topic under study and to support the academic research
base to the research topic, this chapter was divided into two sections. 1) Investors‘ Saving
and Investment Decision, and 2) Financial Literacy and Financial Behaviour. The first
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section of this chapter has begun with the relevance of financial systems to economic
development through the savings-investment process and incorporated the theoretical
framework for this study by summarizing behavioural finance models to explain investor
behaviour, followed by in-depth review of literature on investment motives and factors
that influence the investment decision of investors. This section also included studies
those had measured the association of demographic factors of investors and investment
decisions, importance of risk tolerance ability and need for/ sources of information search
in decision making. The second section of this chapter has included analysis of the
studies conducted in various countries for measuring the financial literacy of their
citizens. This section has also provided the theoretical framework for financial behavior
following the review of literature is presented on various studies those have attempted to
establish the relationship between financial education, financial literacy and various
financial behaviour.
Chapter 3 provides brief outline of the present research study. It gave detailed picture of
Research Methodology. Research gap, objectives and scope of this research study is also
included in this chapter. Exploratory and Descriptive research design was used. The
research has included the explanation about population of the study, sampling techniques,
sample unit, sample and sample size, construction of data collection instrument, different
sources of data used, and data collection methods. The discussion on data analysis
techniques is also presented. The review of literature was also done on available
methodologies to measure the financial literacy on an individual. The merits and demerits
of each method are also discussed, that is followed by the particular methodology
selected by a researcher to measure financial literacy of investors in present study. The
hypotheses framed for this study has concluded this chapter.
Data analysis and interpretation of primary data collected, following the objectives under
study are presented in Chapter 4. Different tools – frequency and percentage analysis,
cross tabulation, chi-square test, paired t-test, factor analysis, binary logistic regression
and simple linear regression is used for data analysis in details to draw a conclusion. The
analysis was divided into three major sections. The first section of this chapter included
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an in-depth analysis of survey responses collected from each respondent towards
performance test that was used to measure financial literacy of investors and to classify
the respondents (investors) into two categories: 1) investors with relatively low level of
financial literacy and 2) investors with relatively higher level of financial literacy. The
second part of this chapter included an in-depth analysis of investment objectives,
existing investment pattern of investors, investors‘ preference towards various investment
alternatives, analysis of informative variables on the basis of investors‘ preference, and
the influence of selected informative variables on investment decision of investors. The
analysis is done by applying factor analysis, identifying the mean score of each factor,
and later applying paired t-test. The factors analysis is also conducted to identify the
factors that may influence the investment decisions of investors. In the third section, the
categories of financial literacy levels, found in the first section of this chapter, are further
used to check the association between each demographic and socio-economic variable of
investors and their financial literacy level. The Chi-square test was used for this purpose.
To assess the combine impact of demographic and socio-economic variables of investors
on their financial literacy level, a binary logistic regression was performed. The chi-
square test also performed to check the association between financial literacy level of
investors and their monthly expenditure to monthly income ratio and similarly, to check
the relationship between financial literacy level of investors and their monthly saving to
monthly income ratio. The correlation and simple regression used to assess the impact of
financial literacy level of investment decision of investors have concluded this chapter.
The present chapter talks about the major findings and its implications in the research
word. The concluding part of this chapter talks on the applicability of this research to
academicians, financial education providers, N.G.O.s and policy maker to promote
financial literacy through financial education.
5.3 Findings of the study
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Findings are the outcomes of an in-depth analysis of data performed in chapter 4. All the
findings are in relation with the objectives of the study and data analysis performed in
this study are mentioned in the following paragraphs.
5.3.1 Findings for the analysis of financial literacy questions
The analysis of total 50 questions to measure financial literacy found that out of
385 respondents, on an average 56.90 % respondents answered correctly. The
minimum score was 20%, while maximum score was 98%.
To measure the financial literacy level of respondents, the respondents‘ total score
was calculated as the percentage of correct answers. The survey responses from
each respondent were used to calculate the median percentage of correct scores
for entire survey. The median of percentage of correct scores achieved by
respondents was calculated, and it was 56.00. The overall scores were grouped
into two categories according to the median percentage of correct scores of all
participants of the survey. Accordingly, a median percentage of correct answers
(i.e. 56.00) of the sample was considered as a base to frame financial literacy
level of the respondents and/or to classify the subgroups. The respondents with
the scores equal to or below median were considered as respondents with
relatively lower level of financial literacy and hence classified into the first
category, i.e. investors with relatively lower level of financial literacy and
respondents with scores above median are considered as respondents (investors)
with higher financial literacy and hence classified in the second category, i.e.
investors with relatively higher level of financial literacy and hence classified into
second category. It was found that out of total 385 respondents, 40.78%
respondents (n=157) scored higher than the median, i.e. 56.00, and hence these
respondents were considered as investors with higher level of financial literacy.
The rest of the 59.22% of respondents (n=228) have scored equal to and/or lower
than median. These investors were considered as respondents with relatively
lower level of financial literacy and hence classified as lower financially literate.
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On the basis of percentage of correct answers given by the respondents for each
question, sorting was done and rank was given to each subject question. Analysis
shows that for basic financial literacy, the respondents earned highest score on the
question of numeracy (80.78%, n=311), suggesting that investors know this
concept very well. Nine other subject questions had scores higher than the
median. In the ascending order, these subjects are ‗consumer rights and
responsibility‘ (80.26%, n= 309), ‗concept of know your client‘ (KYC) (78.96 %,
n=304), ‗interest compounding‘ (76.62%, n=295), ‗functioning of stock market‘
(73.25%, n=282), ‗relationship of investment time horizon and fluctuation in asset
value‘ (71.43%, n=275), ‗diversification‘ (69.09%, n=266), ‗inflation‘ (68.83%,
n=265), ‗risk-return trade off and risk-return trade off‘ (65.71%, n=253) and ‗risk
return trade off of two assets‘ (65.19%, n=251).
Under the basic financial literacy test, the subject questions where the respondents
scored less than the median are concept of ‗investment‘ (51.95%, n=200),
‗financial worth‘ (50.13%, n=193), ‗concept of risk‘ (48.31%, n=186),
‗relationship between investment time horizon and asset growth‘ (47.01%,
n=181), ‗personal finance‘ (42.08%, n=162), ‗time value of money‘ (41.56%,
n=160), ‗relationship between interest and asset prices‘ (41.30%, n=159),
‗regulatory as a part of market structure‘ (27.09%, n=107), ‗concept of asset
al.location‘ (27.53%, n=106) and ‗disposable income‘ (22.60%, n=87).
The result of analysis of advanced financial literacy test shows that respondents
scored highest score on the product i.e. fixed deposits (74.89%, n=865/1155),
followed by insurance plans (62.86%, n=968/1540), mutual funds (60.69%,
n=701/1155), national savings certificates (60.00%, n=693/1155), preference
shares (56.23%, n=433/770). The respondents are less literate on the investment
alternatives, i.e. Equity shares (54.81%, n=633/1155), employee provident fund
(51.00%, n=589/1155), Post office monthly income schemes (50.22%,
n=580/1155), public provident fund (49.87%, n=576/1155) and Debentures and
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Bonds (48.74%, n=563/1155), which is far from median score and concluded that
respondents are less financially literate/knowledgeable on these investment
alternatives.
By analysis of mean score of preferred investment alternatives, it was found that
the bank deposits are highly preferred by the investors (mean score 5.96),
followed by precious metals (gold and silver) (mean score 5.78) and insurance
and pension plans (mean score 5.66), while investment in shares are found to be
the least preferred by the investors having mean score of 3. 54.
5.3.2 Findings for analysis of existing investment pattern, investment objectives and
preferences for various investment alternatives
With regard to existing investment pattern, out of 385 respondents, 318
respondents have invested in bank deposits, followed by the insurance and
pension plans (n = 299). 279 respondents have invested in precious metal (gold
and silver), while 157 respondents have invested in mutual funds. 148, 134 and
122 respondents have invested in shares, post office saving schemes and real
estate respectively while, only 33 investors have invested in bonds and
debentures.
For ranks given to investment objectives, 35.32% (n=136) of the respondents
have given first rank to saving of income tax as an investment objective, while
only 4.16% (n=16) have given the seventh rank to the same investment objective.
Analysis of the data shows that 72.99% (n=281) respondents have given the
seventh rank to gift/donation/vacation/pilgrim as an investment objective, 21.82%
(n=84) respondents have given second rank to child‘s marriage/child‘s
education/social ceremony. 12.21% (n= 47) respondents have given first rank to
secured retirement and safeguarding against inflation/capital appreciation. With
regard to buying/improving home and meeting unexpected needs only 9.87% (n=
38) and 14.55% (n=56) respondents have given first rank respectively.
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By analysis of mean score of investment objectives, it was found that two
investment objectives i.e. saving of income tax and meeting unexpected financial
needs have secured the highest mean score i.e. 4.78, which is followed by the
secured retirement (mean score 4.63) and Child‘s marriage/ child‘s
education/social ceremony (mean score of 4.43). The investment objective Gift/
donation/ vacation/ pilgrim has secured the lowest mean score of i.e. 1.58.
With regard to ranks given to investment alternative, as per the preference of
respondents, out of total, 38.96% (n=150) respondents have given the first rank to
bank deposits as the most preferred investment alternative. The second and third
most preferred investment alternative are precious metals (gold and silver) and
insurance and pension plans preferred by 15.32% (n=59) and 14.03% (n=54)
respondents respectively, accordingly the second and third rank were given to the
precious metal and insurance/pension plans. While 37.66% (n=145) respondents
have given the eighth rank to bonds and debentures, hence, bonds and debentures
are considered to be the least preferred investment alternatives by the respondents.
5.3.3 Findings for analysis of preference of investors towards preferred source of
information while investment
With regard to information search, on the basis of mean score of sixteen variables
as a preferred source of information, certified financial planner (4.20) is found to
be the most preferred source of information, followed by opinions from the
present investors (4.09), conversation and exchanges of views with company
executives and sector experts (3.98) and distributer/ agents of financial
products/services (3.93) respectively.
On the basis of mean scores of sixteen variables for information search, the three
least preferred informative variables include prospectus of a company (3.41),
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published reports of the research agencies (3.47) and corporate forecast prepared
by independent investment companies (3.51).
On the basis of mean scores of selected variables for information search, the three
least preferred variables include corporate forecast prepared by independent
investment company, rating agencies‘ reports and publication in financial Press,
newspapers and electronic media, each having a mean score of 3.51, 3.57 and
3.62 respectively.
On the basis of mean scores of selected variables for information search, it was
found that the three most influencing informative variables on investment
decision were rating agencies‘ reports (mean score 4.17), annual reports (4.09)
and conversation /exchanges of views with company executives and sector
experts (mean score 3.97).
Among the selected variables of preference for information search, it was found
that the three least influencing informative variables in the investment decision
are corporate forecast prepared by independent investment company, present
investors and conversation/exchanges of views with professional colleagues, each
having a mean score of 2.48, 2.88 and 2.94 respectively.
The gap analysis was also performed. Gap in the mean score was identified by
subtracting the mean score of variables for which responses given on the basis of
influence of these variables on investment decision form the mean score of
variables for which responses given on the basis of preference towards a variable
as a source of information. The three most positive gaps found for the variables
present investors, corporate forecast prepared by independent investment
company and conversation/exchanges of views with professional colleagues.
These high gaps indicate that the preference of respondents towards various
variables of information is very high as compared to the influence derived from
this.
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To know the difference between the preference and influence of informative
variables on investment decision, the paired t-test was performed. It was found
that only for two pairs, the significance value is greater than 0.05 and that is for
the variables 1. Conversation/exchanges of views with company executives and
sector experts and 2. Financial advisors/brokers and analyst‘s recommendation.
For these two variables, null hypothesis was not rejected and concluded that for
conversation/exchanges of views with company executives and sector experts and
financial advisors/brokers and analyst‘s recommendation, there is no difference
between preference for these informative variables and their influence on
investment decision under study at 5% level of significance. For rest of variables,
the difference between preference for the informative variables and their influence
on investment decision is statistically significant at 5% level of significance.
Factor analysis was used to convert more number of variables into less number of
factors. The factor analysis was performed for sixteen variables preferred by
investors as source of information while making investment decision. Before
performing factor analysis the reliability of sixteen informative variables was
checked with Cronbach‘s alpha and was 0.708. The Bartlett‘s test of sphericity
was significant with the value of 0.000. The Measure of Sampling Adequacy was
checked with K.M.O. value, which shows 0.664. These permitted researcher to
proceed further to run factor analysis. With the help of factor analysis, five factors
were extracted from fourteen variables (as two variables out of sixteen variables
were removed due to their communalities were lower than 0.500) under study.
67.12% of the total variance was explained by these extracted factors. These five
factors are named as ―Published Operational Information‖, ―Independent
Information‖, ―Advocate Recommendation‖, ―Accessibility Information‖ and
―Performance Forecast‖.
The mean score of each of above extracted factor was also found. It was found
that for factor 2 (i.e. Independent Information) and factor 4 (i.e. Accessibility
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Information), the mean score is highest, i.e. 3.89. This indicates that these two
factors are the most preferred by investors to collect the information, which is
followed by the mean score of factor 3, i.e. advocate recommendation, for which
the preference mean score is 3.77. The least score is for factor 5, i.e. 3.51. This
indicates that performance forecast is the least preferred by the respondents to
gather the information for their investment decision.
5.3.4 Findings for Factor Analysis performed for variables influencing Investment
Decision of Investors
The factor analysis was also performed for forty four variables, influencing
investment decision of investors. Before performing factor analysis the reliability
of these forty four variables influencing investment decision of investors was
checked with Cronbach‘s alpha and it was found 0.794. The Bartlett‘s test of
sphericity was significant with the value of 0.000. The Measure of Sampling
Adequacy was checked with K.M.O. value, which shows 0.690. These permitted
researcher to proceed further to run factor analysis. With the help of this factor
analysis, nine factors were extracted from twenty nine variables (as fifteen
variables were removed due to their communalities lower than 0.500) under
study. 62.10% of the total variance was explained by the extracted factors. These
factors are ―Personal Financial Need‖, ―Accounting, Business And Financial
Information‖, ―Economic and Regulatory Environment‖, ―Operational Feedback‖,
―Advocate Recommendation‖, ―Overall Group Performance‖ , ―Credit Features‖
And ―Personal Inclination‖ and ―Monetary Expectations‖.
The grand value of mean score was also found for these extracted factors. It was
found that for factor 1: ―Credit features‖, the mean score is highest, and it is 4.07.
This indicates that this factor is the most influencing factor on investment
decision of investors. This factor is followed by the mean score 4.03 of factor 9,
i.e. Monetary expectation.
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5.3.5 Findings for the Cross –tabulation and Chi-square test performed to know
association between each demographic and socio-economic variable of investors and
their financial literacy level
With regard to age of investors and their financial literacy level, the chi-square
test found that a significant association between these two variables. For current
study, the strength of this association is found to be 59.40% as shown by
Cramer‘s V. The cross tabulation shows that out of total investors fall under both
the extremes (for age group of 18 years to 25 years ( n= 89), 26 years to 35 years
(n=76) and 56 years and above (n=49)), out of which majority of the investors
possess lower level of financial literacy (i.e. for the age group of 18 years to 25
years ( n= 85, 95.5%), 26 years to 35 years (n=65, 85.53%) and 56 years and
above (n=27, 55.10%)).These findings are consistent with Chen & Volpe (1998),
Volpe & Chen (2009), ANZ Bank (2003), Commonwealth Bank (2004), Lusardi
& Mitchell (2009), OECD (Australia) Studies (2005).
Cross tabulation performed for two variables, investors‘ gender and their financial
literacy level found that as compared to male investors, female investors possess
much lower level of financial literacy. This finding is consistent with the studies
done by Chen and Volpe (1998), Volpe and Chen (2002), Lusardi and Mitchell
(2009). Chi-square result also found a significant association between investors‘
gender and their financial literacy level. This finding is consistent with H. A.
Hassan and A.A. Bin Kali (2009). For these two variables, Cramer‘s V value is
30.8%, which shows that there is a low level of association between these two
variables.
Chi-square test performed to know association between investors‘ education and
financial literacy level found no significant association between the respondents‘
education and their financial literacy level. It may be concluded that ―educated
does not mean that to be financially literate‖. The cross tabulation performed for
these two variables shows that out of total investors who are non-graduate
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(investors with primary education (n=24), investors with secondary education
(n=45), investors with higher secondary education (n=46) and those with diploma
(n=29)), majority of them possess lower level of financial literacy. (i.e. investors
with primary education (n=18, 75.00%), investors with secondary education
(n=22, 48.89%), investors with higher secondary education (n=30, 65.22%) and
those with diploma (n=18, 62.07 %)). This finding is consistent with the findings
of Volpe & Chen (2002), ANZ Bank Study (2003), Commonwealth Bank Study
(2004).
Chi-square test performed to check association between monthly income of
investors and their financial literacy level found a statistical significant
association between monthly income of investors and their financial literacy level.
These findings are consistent with previous study H. A. Hassan and A. A. Bin
Kalli (2009). The strength of the association between these two variables is found
to be 30.4 % as measured by Cramer‘s V. The cross tabulation performed for
these two variables found that those investors whose monthly income is less than
Rs. 10,000 (n=95), out of which, 84.21% (n=80) investors possess lower level of
financial literacy. With regard to the investors whose monthly income is more
than Rs. 25,001 (n=44), out of which 77.27% (n=34) possess higher level of
financial literacy. The researcher may conclude that the investors with lower
monthly income demonstrate lower level of financial literacy than those with
higher monthly income. These finding is consistent with the findings of ANZ
Bank Study (2003), OECD Studies (2005), Beal & Delpachitra (2003) and
Commonwealth Bank Study (2004).
Chi-square test was performed to check association between investors‘ stage of
life cycle and their financial literacy level. The result found that these two
variables do have statistical significant association with each other. These
findings are consistent with the earlier studies done by Chen and Volpe (1998).
The strength of association between these two variables, as measured by Cramer‘s
V, is found to be 30.4%. The cross tabulation performed for these two variables
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also shows that the investors falling under the categories of young single (n=67)
and young married without children (n=67), of which the higher number of
investors (for young single 83.58% (n=56) and for young married without
children 71.64% (n=48)) possess lower level of financial literacy than those who
are married with children and/or older married. This finding is consistent with the
findings of Lusardi & Mitchell (2009), ANZ Bank Study (2003), OECD studies
and commonwealth Bank Study (2004).
Chi-square test performed to know the association between type of employment
structure of investors and their financial literacy level found that there is no
association between type of employment structure of investors and financial
literacy level. This finding is consistent with the findings of previous study of H.
A. Hassan and A. A. Bin Kalli (2009).
With regard to type of workplace activity of investors and their financial literacy
level, the results of cross tabulation shows that out of total investors engaged in
finance related work activity (n=133), out of these majority of investors, i.e.
72.18% (n=96), possess higher level of financial literacy. While out of total
investors engaged in non-finance related workplace activity (n=242), 75.21%
(n=182) possess lower level of financial literacy. It is concluded that the investors
who are engaged in finance related work place activity possess higher level of
financial literacy than those who are not engaged in finance related work activity.
The results of Chi-square test found a statistical significant association between
type of workplace activity of investors and their financial literacy level. These
findings are consistent with previous study of H. A. Hassan and A. A. Bin Kalli
(2009). The strength of the association between these two variables is 46.70%, as
indicated by Cramer‘s V.
The cross tabulation of two variables investors‘ years of work experience and
their financial literacy level found that those having a work experience of less
than five years posses lower level (n=71), of which majority of investors (n= 57,
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80.88%) possess lower level of financial literacy. This finding is similar and
consistent with the finding of Chen and Volpe (1998). Chi-square test was
performed to know association between investors‘ years of work experience and
their financial literacy level found a statistical significant association between the
investors‘ years of work experience and their financial literacy level. This finding
is consistent with the findings of Beal & Delpachitra (2003). The degree of the
association between these two variables is found to be 28.6%, as indicated by
Cramer‘s V.
The cross tabulation performed between two variables, i.e. investors‘ years of
investment experience and their financial literacy level shows that those having
investment experience of less than one year (n=70), of these majority of investors
(n= 69, 98.57%) possess lower level of financial literacy. The Cross tabulation
Table also shows that as the years of investment experience increases, the level of
financial literacy also increases. It was found that out of total investors who have
invested for the last more than 10 years (n=78), of these, majority of investors
(n=57, 73.8%) possess higher level of financial literacy. The Chi-square test was
performed to know association between investors‘ years of investment experience
and their financial literacy level. The result of this test shows a statistical
significant association between the investors‘ years of investment experience and
their financial literacy level and the strength of this association between these two
variables is 62.3%, as indicated by Cramer‘s V.
Similarly, the cross tabulation performed for two variables, i.e. number of times
investors shop around/make inquiry and their financial literacy level shows that
out of total investors who shop around/make inquiry on an average for more than
6 times (n=36), out of these majority of investors (i.e. n=32, 88.89% of investors)
possess relatively higher level of financial literacy as compared to those who do
not shop around at all (n=87), of these majority of investors (n=81, 93.10%)
possess lower level of financial literacy. The Cross tabulation also shows that as
the frequency of number of times investors shop around/make inquiry increases,
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the level of financial literacy also increases. The Chi-square test was also
performed to know association between these two variables, i.e. number of times
investors shop around and their financial literacy level. The result of this test
shows a statistical significant association between the number of times investors
shop around/make inquiry and their financial literacy level and the strength of the
association between these two variables is 68.30%, as indicated by Cramer‘s V.
The results of chi-square test performed to know the association between risk
tolerance level of investors and their financial literacy level show a statistical
significant association between risk tolerance level of investors and their financial
literacy level. The strength of the association between these two variables is found
to be 15.60%, as indicated by Cramer‘s V.
5.3.6 Findings for Logistic Regression performed to assess the combine impact of
Demographic and Socio-economic variables of Investors on their Financial Literacy
Level
The logistic regression was performed to study the impact of demographic and
socio-economic variables of investors (i.e. gender, age, education, income, stage
in family life cycle, monthly income, employment structure, workplace activity,
number of years of work experience, number of years of investment experience,
risk tolerance level, number of times investors shop around) on their financial
literacy level. Logistic regression shows that a financial literacy level of investors
varies with their demographic and other characteristics. A test of final model
against constant model is found to be statistically significant, indicating that the
predictors as a set reliably distinguished between higher and lower level of
financial literacy (chi square = 420.876, p < 0.05, with df =39). Nagelkerke‘s R2
is found to be 0.897 indicated a strong relationship between prediction and
grouping. The overall prediction success was 95.60% (97.8% for lower level of
financial literacy and 92.4% for higher level of financial literacy). The Wald
criterion demonstrated that except education, years of work experience and risk
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tolerance level, all the predictors are important predictors to predict financial
literacy level.
5.3.7 Findings for the Cross –tabulation and Chi-square test performed to know
association between each financial literacy level of investors and their monthly
expenditure to monthly income ratio and monthly saving to monthly income ratio
To check the association between financial literacy level of investors and their
monthly expenditure to monthly income ratio, the chi-square test was performed.
The results of this test found a significant association between financial literacy
level of investors and their monthly expenditure to monthly income ratio and the
strength of the association between these two variables is 21.10%, as indicated by
Cramer‘s V.
Similarly, to know the association between level of financial literacy of investors
and their monthly saving to monthly income ratio, chi-square test was performed.
The chi-square test shows a significant association between these two variables
and the strength of this association is found to be 64.40%, as indicated by
Cramer‘s V.
5.3.8 Findings of correlation and regression performed to assess the impact of
financial literacy level of investors on their investment decision
To study the impact of financial literacy level of investors on their investment
decision, all 9 factors extracted from factor analysis are taken individually as well
as sum of all investment factors extracted from factor analysis is taken as
dependent variable and financial literacy level as an independent variable. The
correlation and simple linear regression method was used. For individual variable,
it was found that financial literacy level does have positive correlation with the
factors 1 ‗Personal Financial Need‘, Factor 5 ‗Advocate Recommendation, and
Factor 6 ‗Overall Group Performance‘, and regression analysis found that
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financial literacy level does have its significant impact on above mentioned
factors. Correlation analysis also found that financial literacy level does not any
correlation with Factor 2 ‗Accounting, Business and Financial Information‘,
Factor 3 ‗Economic and Regulatory Environment‘, Factor 4 ‗Operational
Feedback, Factor 7 ‗Credit Features‘, Factor 8 ‗Personal Inclination‘ and Factor 9
‗Monetary Expectation‘.
For assessing the impact of financial literacy level on sum of investment decision
factors, the correlation and regression analysis is performed. It was found that
financial literacy level does have positive correlation with sum of investment
factors. The regression analysis found that financial literacy does have a statistical
significant impact on sum of investment decision factors. Thus, it may conclude
that a financial literacy level of investors does have a statistical significant impact
on their investment decision.
5.4 Conclusion
Based on data analysis some conclusions are presented as under.
All the respondents (investors) have parked their savings in various
investmentalternatives, though majority of the respondents (investors) possess lower
level of financial literacy. This shows that all the investors do not understand the
basics of investments and its calculation.
From the analysis of financial literacy questions, it is also found that the majority
investors are less financially literate on some of subjects of basic financial literacy
and advanced financial literacy and even some of them do not understand the
important concepts at all.
With regard to demographic and socio-economic profile of respondents and financial
literacy, it was found that females possess lower level of financial literacy, as
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compared to males. It was also found that investors on the basis of two extremes of
age i.e. 18 to 35 years and 56 years and above possess a lower level financial
literacy. On the basis of monthly income group of respondents, it is found that the
respondents falling under lower income groups possess a lower level of financial
literacy.
It is also concluded that for respondents with higher monthly income, higher number
of years of investment experience, and those who shop around maximum number of
times/ make maximum number of inquiries while investing, possess comparatively
higher level of financial literacy than others.
With regard to investment objective, for majority of investors (respondents), the first
investment objective is saving of income tax and meeting unexpected financial
needs.
It is also concluded that majority of the investors (respondents) invest in and/or
prefer conventional savings/investment alternatives to park their savings, which
renders them unable to cope up with the rate of inflation in a long run.
Further, it is also found that financial literacy does have statistically significant
impact on investment decision. It may be concluded that financial literacy may
empower the investors to make investment decision.
With regard to the relationship between financial literacy and financial behavior, it
was found that financial literacy leads to controlled spending behavior and
encouraged saving behavior and also have a significant impact on investment
decision of investors.
318
5.5 Recommendations
Increase in the life expectancy, changes in pension agreement and transfer of risk,
increase in an individual‘s responsibility, financial product and services Innovation,
globalization, privatization and deregulation of financial services industry, multifaceted
features of financial products, technological changes and market innovations have sought
the attention of educators, community groups, businesses, government agencies,
organizations, non government organizations, policy makers and regulatory authorities.
Based on findings of the present study, the researcher suggests the financial education
providers, investors and policy makers and regulatory authorities to promote financial
literacy.
5.5.1 For Policy makers and regulatory authorities
More emphasis on financial education should be given to college students as there is
a low level of literacy prevalent amongst the age group 18-25 and 26-35 years. Due
to lack of financial literacy, they may misallocate the private wealth, which may
restrict them to achieve their financial objectives in long run. Since college students
are the most vulnerable to be trapped in debt, Policy makers can design financial
education material focused on Personal finance.
With respect to retirees or the investors having the age of 56 years and above, this
group should be targeted with an objective to learn them the importance of
retirement planning as a part of financial planning so that these investors can teach
the importance of retirement planning to their children, if they have not done yet. In
line with the same, the awareness of future retirees about the need to assess the
financial adequacy of their current public or private pension schemes and to take
appropriate action when needed should be encouraged.
319
It is recommended that females should be targeted to provide financial education and
to empower and encourage them or to make them realize the importance of their
role/participation in investment decision making at household level.
There is a need to start the financial education program at an earlier stage in the life
cycle of people as that will ensure that the habit of savings and proper money
management and investment is ingrained into them right from their childhood.
There is a need to consider including financial education/ money management as a
specific subject in the syllabus at the primary and secondary school level.
The investors with lower income and those who work with non-financial services
industry mostly possess low level of FL. there is even more need to teach these
people about money management so that their limited income (monetary resource) is
put to optimum use. There is a need to provide financial education to these people at
their workplace.
To empower the investors on the subject of financial literacy, the issue of financial
literacy should be taken either as a policy perspective or as a pragmatic perspective
5.5.2 For Investors
Investors should start showing interest in financial education.
Females should start taking more initiative in financial matters and should be more
assertive in family financial matters
Schools, colleges, universities should introduce the syllabus of financial education to
enhance the financial literacy of their students. A common learning goal and
financial language should be established for financial literacy education across the
various masses of society, subject matter and levels. The development of financial
320
skills and knowledge should ideally begin at home and be continued in school,
college, workplace levels and the community as a whole. It should be easily and
widely accessible to all sectors of the community at all stages of the lifecycle.
At workplace, employer should encourage the workplace financial education
programmes.
The NGOs working for social up-liftment of women must ensure that the financial
education should be provided to them for their economic up-liftment.
5.5.3 For Financial Education Providers
Should provide the financial education as an ―information‖ and ―instruction‖ not as
an ―advise‖.
Further, it should be provided in fair and unbiased manner.
Companies should also emphasis on developing financial literacy of the community
by providing financial education to various masses as a part of Corporate Social
Responsibility.
For promoting and coordinating financial education, the national, regional, local public
and private initiatives should reach the population and raise awareness of the population
about the need to improve their understanding of financial risks and ways to protect
against financial risks through adequate savings, insurance and financial education.
Financial education complements the important aspects like greater transparency, policies
on consumer protection and regulation of financial institutions. Financial literacy should
be on a common structure and a common approach so that it can be spread in a
comprehensive manner. These efforts should aim at empowering consumers to
understand and select the financial products and services that best suit their needs, goals
321
and personal circumstances. The overall efforts by regulatory authorities, N.G.O.s and
community groups should be structured in the direction to enable the individuals to
develop the ability to make informed judgments, to be able to identify financial products
and services that address their needs, to take effective decisions regarding the use and
management of their money and to avoid to be a victim of bad selling.
However, financial literacy can only ensure individuals are informed and empower to
make financial decisions, it cannot ensure that 'right' decision is actually made, because,
individuals do not always make decisions based purely on economic rationality, personal
circumstances and social factors, which are beyond the control of an individual are also
considered while taking the decisions.
5.6 Limitations of the study
Care and attention has been taken to ensure that the research was designed and conducted
to optimize the ability to achieve the objectives of the research. However, researcher
sometimes was unable to conduct study with zero defects due to personal resources
constraints in terms of time, manpower and money, which results in error in data
collection and analysis. Some other limitations of this study are mentioned below.
The method adopted for primary data collection was non-probability convenience
sampling method, which does possess it own limitations, hence these limitations
automatically applies to the study.
The bias in respondents‘ views can‘t be ignored.
The investors who have just started to make investment, and those who make
investment decision on the basis of others‘ recommendation, without any analysis of
their own, it was difficult for them to answer the questions about financial literacy
and to give rating.
322
In this study, all the respondents (i.e. retail individual investors) were from urban
areas of Gujarat state. It may be possible that the views of respondents from rural
areas turn out to be different from the respondents belonging from urban areas.
While every care has taken to use, both primary and secondary sources of information
and efforts have been made to provide generic conclusions which would cover all
possible situations, still it needs to be mentioned that the conclusion of this study may
not be applicable uniformly in all situations and therefore, they would need to be
applied taking into account the prevailing circumstances in a particular case.
5.7 Scope for further research
No research in any subject can be complete in itself. In this study, an attempt was made
to explore the financial literacy level of investors and its impact on investment decision
among the investors of Gujarat State. The study is limited up to these two outcomes.
However, more areas can be explored for further research taking the present study as a
base. Some of the scopes for further research are as under:
As mentioned in the limitations, this study is based on the responses of retail
individual investors of urban areas in Gujarat state. One can expand the study by
including rural population of the Gujarat state. One can also perform a comparative
study on responses collected from rural investors and responses collected from urban
investors.
Research is performed on the basis of views collected from retail investors. One can
perform or expand this study by collecting the responses from HNIs (High Networth
Investors).
In the present study, the impact of financial literacy on investment decision of
investors is examined. There is enough scope to perform the research by examining
the impact of financial literacy on stock market participation, portfolio diversification,
323
individual saving behavior, individual spending behavior, overall financial behavior
as well as readiness to participate in retirement planning.
Nowadays, SHG members are empowered through financial education and their
socio-economic status is enhanced through micro-finance activities. Hence, one can
also perform this study by collecting the responses only from SHG members.
At broad spectrum, one can also perform the research on role of financial literacy and
demand elasticity in promoting a sound financial systems.
The present study is performed in the state of Gujarat. One can also perform the study
in other parts of India and/or at national level as a whole. Also, comparison of two or
more states‘ data can be possible.
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Appendix
347
ANNEXURE I
Dear respondent,
Season’s greetings to you,
Section A: Basic Financial literacy
1. Which of the following is correct? Investment can be made by
A. Government B. Households C. Business
Only A & B Only B & C
Only C & A All of above None of above
2. Financial worth: Suppose you have the total assets of Rs. 20,000; out of which investment comprising of Rs.
10,000; on the other hand you are owing total liabilities/debt of Rs. 10,000, then your financial worth will be
calculated by:
Total assets minus total liabilities Total investment minus total debt
Total worth of all the personal assets Don‟t know
3. Disposable income: Suppose your monthly income is Rs. 10,000, on which you are supposed to pay tax of
Rs. 1000, and your monthly saving is Rs. 1,500, what will be your disposable income differs from:
Monthly income = disposable income Personal taxes Personal saving
Personal consumption expenditure None of above Don‟t know
4. Out of following, which account cannot be open by layman individual?
Saving account Current account DEMAT account
Fixed Deposit account Recurring Deposit account Don‟t know
5. Numeracy: Suppose you have Rs. 100 in a saving bank account, earning an annual interest rate of 2 % under
simple interest rate method. After five years how much do you think to you would have in the account if you
have left the money to grow?
More than Rs. 102 Less than Rs. 102 Exactly Rs. 102
None of above Don‟t know
6. Interest Compounding: Suppose you had Rs. 100 in a saving account and the interest rate is 20 % per year
and you never withdraw money or interest payments. After 5 years, under compounding how much would you
have on this account in total?
More than Rs. 200 Exactly Rs. 200 Less than Rs. 200
None of above Don‟t know
7. Inflation: Imagine that the interest rate on your savings account was 1% per year and inflation was 2% per
year. After 1 year, how much would you be able to buy with the money in this account?
More than today Exactly the same Less than today Do not know
8. Time value of money: Assume a friend inherits Rs. 10000 today and his sibling inherits Rs. 10000 3 years
from now. Who is richer because of the inheritance?
My friend His sibling They are equally rich Do not know
I, Harsha Jariwala pursuing Ph.D. on “To study the level of financial literacy and its impact on investment
decision: An in-depth analysis of investors of Gujarat state”. Your valuable feedback will be helpful to me to
accomplish my study successfully. I will be thankful to you, if you will answer the given questions and provide
the comment for required information that best suit to your knowledge and information. I assure you; the
information given by you will be used only for the study purpose and will be kept confidential.
348
9. Functioning of Stock market: Which of the following statements describes the main function of the stock
market?
The stock market helps to predict stock earnings;
The stock market results in an increase in the price of stocks
The stock market brings people who want to buy stocks together with those who want to sell stocks
None of the above Don‟t Know
10. Diversification: When an investor spreads his money among different assets, does the risk of losing money:
Increase Decrease Stay the same None of above Don‟t know
11. Risk-return trade off: Buying a single company‟s stocks usually provides safer return than a stock mutual
fund.
True False None of above Don‟t know
12. Risk: The presence of risk means that
Investors will lose money.
More than one outcome is possible. .
All of above None of above Don‟t know
13. Risk-return trade off of two assets: Stocks are normally riskier than bonds.
True False Don‟t know None of above
14. Relationship between investment time horizon and asset growth: Considering a long time period (for example 10 or 20 years), which asset normally gives the higher return among the specified below?
Saving account Bonds Stocks
None of the above Don‟t know
15. Investment time horizon and fluctuation: Out of following; normally which asset displays the higher price
fluctuations over time?
Saving account Bonds Stocks
None of the above don‟t know
16. Asset Allocation: In Asset allocation the investor involves in
A. decision to allocate his corpus between a risk-free asset and a risky asset.
B. decision his corpus among different risky assets.
C. considerable security analysis.
None of the above don‟t know
17. Relationship between interest and asset prices: If interest rate falls, what should happen to bond prices?
Rise Fall Stay the same Don‟t know
18. Consumer rights & responsibility: If a person keeps PIN number of ATM card on a piece of paper in a wallet
along with ATM card. If the wallet is stolen, and card and PIN no. are used to take money from an account,
who will be responsible for money lost?
A bank only A card holder only both of them Don‟t know
19. Regulatory body as a part of market structure: Out of following which is not the regulatory authority.
SEBI IRDA RBI AMFI
None of above All of above Don‟t know
20. KYC: “Know your customer” (KYC) is a document, which is used for customer/client identification process.
True False Don‟t know
Total Score: /20
349
Section B- Advanced Financial Literacy
Following are the statements regarding the features of various investment alternatives available for individual investors.
On the basis of your understanding and knowledge about the investment alternatives answer the following (Tick any
one). There is no mark.
Total Score: /30 = _____/50
True False DK
Fixed Deposits
1. It is a lump sum amount in the bank at a certain rate of a interest for a fixed period of time.
2. Rate of interests offered on fixed deposits by various banks are same.
3. The rate of interest offered on fixed deposits is calculated under compound interest method.
National Savings Certificates (NSCs)
4. It is a long term risk free investment.
5. NSCs are issued by the corporate only.
6. Interest rate is constant and calculated twice in a year.
Public Provident Fund (PPF)
7. There is no minimum and maximum limit of investment made in PPF per year.
8. There is no lock in period for PPF.
9. The amount deposited during the year is eligible for tax deduction irrespective of the sum.
Employee Provident Fund (EPF)
10. Employee provident fund is applicable to salaried employees only.
11. Employee can invest the amount higher than mandatory contribution in EPF.
12. The whole amount contributed by employer is deposited in employee‟s EPF account.
Equity Shares
13. A equity shares represent the ownership of a company.
14. The return that the owner of a share receives is known as earning per share.
15. Dividend is paid on the basis of market price of the share.
Preference Shares
16. Preference shareholders get the preference over the equity shareholders at the time of dividend distribution.
17. Preference shareholders do not get the preference over the equity shareholders at the time of liquidation of a company and distribution of sales proceed.
Mutual Fund
18. When an investor invests in mutual fund, he receives units in return.
19. The price of a mutual fund unit is known as Net Asset Value.
20. Mutual funds pay a guaranteed rate of return which depends on their past performance.
Debentures and Bonds
21. Investment in infrastructure bonds is not eligible for income tax benefits.
22. The return on bonds and debentures is remain same over the investment period.
23. If a somebody buys a bond of a firm, then he owns a part of the same firm.
Post Office Monthly Income Scheme(POMIS)
24. Investment in POMIS is not guaranteed by the Government of India.
25. There is no lock in period.
26. The premature withdrawal does not carry any penalty charges after one years of investing in POMIS.
Insurance Policy
27. All the types of illnesses are covered under the life insurance.
28. Life insurance policy is transferrable in nature.
29. The life insurance premium payments do not enjoy the tax benefits under section 80 C.
30. The amount received on the maturity of life insurance policy is taxable.
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Section C: Investment Decision: Questionnaire
1. Do you invest your saving in any of the investment alternative available?
Yes (Go to question 2) No (Go to question 4)
2. Out of following, in which saving and/or investment alternative you have invested in?
Post Office Saving Schemes (POMIS/NSC/KVP/PPF etc.) Insurance and Pension Plans
Shares Mutual Funds
National Saving Certificates/KVP Debentures and Bonds
Real Estate Precious Metals (Gold & Silver)
3. If you have „n‟ amount of corpus, available with you, out of following, in which investment alternative would you like
to invest? (Rank them, where 1 is the most preferred)
Post Office Saving Schemes (POMIS/NSC/KVP/PPF etc.) Insurance and Pension Plans
Shares Mutual Funds
National Saving Certificates/KVP Debentures and Bonds
Real Estate Precious Metals (Gold & Silver)
4. Out of following reasons which restrict you to make the investment? Rank them. (Go to section D)
I prefer to have complete liquidity on my hands. There is no need to make an investment.
I don‟t possess investment knowledge I don‟t have enough money for investment.
I did never consider making an investment. I never have a time to arrange for investment.
I don‟t see any benefits of doing so. I don‟t trust the information given by the advisors/ company.
I find it all to confusing. For me, all the investment alternatives are too risky
Put off by high fees charged by advisors and companies.
.
5. Following are the objectives of investment. (Rank them, where 1 is the most preferred)
Saving income tax Children marriage/ Social ceremonies/ Child‟s education
Buying/ improving/ a home For secured retirement
Gift/Donation/ Vacation/Pilgrims To meet unexpected financial contingencies
Safeguard against inflation/ Capital appreciation
6. Before investing your savings, I prefer to collect the information from.
Not
Preferred
Least
Preferred
Neutral Preferred Most
Preferred
1 Certified Financial Planner
2 Annual reports of the company
3 Prospectus of a company
4 Company‟s website
5 Distributers/agents of financial product
6 Rating agencies‟ reports
7 Company‟s telephone representatives
8 Family members
9 Friends and relatives
10 Conversation/exchanges of views with professional colleagues
11 Publication in the financial press, news papers & electronic media
12 Conversation/ exchanges of views with company executive and sector experts
13 Corporate forecast prepared by independent investment company
14 Published reports from research agencies
15 Opinions from existing investors of various instruments
16 Financial advisors/Broker and analyst‟s recommendation
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7. Following are the variables that influence my investment decision, Rate them.
Least influence
Influence Neutral Significantly Influence
Most Significantly
Influence
1. Condition of financial statement
2. Expected corporate earning
3. Past performance of the firm(in terms of profit and return given to investors)
4. Company‟s position in the industry
5. Insiders‟ information
6. The result of fundamental analysis
7. The result of technical analysis
8. Expected return on investment
9. Feeling for a company‟s products or services
10. Perceived ethics of company
11. Political party affiliation
12. Contribution of a firm towards social causes
13. Coverage in the press
14. Recent price movements in a firm‟s stock/NAV
15. Statements from politicians and governmental officials
16. Fluctuations/developments in the indices of the major market
17. Current economic indicators
18. Reputation of a company in the domestic market
19. Reputation of a parent company or sister concern
20. Environmental Record
21. Market Capitalization of company
22. Conversation/exchanges of views with professional colleagues
23. Publication in the financial press, newspapers and electronic media
24. Conversation/ exchanges of views with company executive and sector experts
25. Studying the portfolio investments of other market players
26. Corporate forecast prepared by independent investment company
27. Economic forecasts by research institutions
28. Study of Annual Reports
29. Opinions from family members
30. Opinions from friends and relatives
31. Opinions from existing investors
32. Financial advisors/Broker and analyst‟s recommendation
33. Opinion of Credit Rating agencies‟ analysis
34. Diversification needs
35. Liquidity associated with investment
36. Availing the benefit of income tax deduction
37. Risk-return trade off
38. Minimizing risk
39. Ease of obtaining borrowed fund
40. Preferred investment time horizon
41. Safety associated with investment
42. Affordable minimum investment amount
43. Ease in Liquidity
44. Guaranteed return
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8. Given below the small questions to understand what sort of investor you are. Kindly tick one for every question.
1. What is your major investment objective?
a. High Return
b. Moderate return
c. Liquidity
d. Low risk
e. Safety
2. How would you allocate your funds?
Allocation of Funds (Options)
Stocks (%)
Mutual Funds (%)
Real Estate (%)
Debt up to (%)
Fixed Deposits (%)
a 100 0 0 00 0
b 70 10-20 10-20 05 2
c 50 15-25 10-20 10 5
d 30 20-25 10-20 20 10
e 10 20-30 10-20 25 20
3. What is your investment horizon?
a. 3 to 6 months
b. 6 months to 1 year
c. 1 years to 3 years
d. 3 years to 5 years
e. > 5 years
4. What are your expectations on return from investment?
a. 50 to 100 %
b. 40 to 50 %
c. 20 to 40 %
d. 10 to 20 %
e. 5 to 10 %
5. What type of investor do you consider yourself?
a. High risk taker
b. Opportunistic Risk taker
c. Moderate risk taker
d. Low risk taker
e. I don‟t like taking risk
Total Score for Q. 8 ____________
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Section D: Personal Information:
Name: _______________________________________________________________
City:____________________________________________________
1. Gender: Male Female
2. Age(in years): 18 to 25 26 to 35 36 to 45
46 to 55 56 to 65 66 and above
3. What is the highest level of education you have reached?
Primary Secondary Higher Secondary
Diploma Graduation Post graduation
4. Your monthly Income (Before tax): Upto Rs. 10,000 Rs. 10,001 to Rs. 15,000 Rs. 15,001 to Rs. 20,000 Rs. 20,001 to Rs. 25,000 Rs. 25,001 and above
5. Stage in family life cycle:
Young single Young married without children
Young married with children Middle-aged married with children
Middle age married without dependent children Older married
Older unmarried If other, please specify ___________________
6. Employment structure: Full Time Salaried Part Time Salaried Casual Self employed
Housewife Retired Unemployed Student
7. Workplace activity: Working in Finance related industry (Bank, Accountant, CFP, MF, Investment Co., Insurance Co. etc) Not working in Finance related industry (Other than shown above) Other
8. Would you please mind telling me your total number of years of works experience (in years)? Less than five 6 to 10 11 to 20 21 to 30 30 & more
9. Monthly expenditure to total monthly income?
1 %to 50 % 51% to 60% 61 % to 70% 71 % to 80 % 81% to 90% More than 90%
10. Monthly savings to total monthly income? 01 % to 50 % 51% to 60% 61 % to 70% 71 to 80 % 81% to 90% More than 90%
11. While investing your fund, usually how many times you shop around/visit the market?
Zero 1 to 3 4 to 6 More than six
12. How long have you invested your saving?
Less than1 year 1-5 years
6-10 years More than 10 years
--------------THANK YOU----------------
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ANNEXURE II
પ્રિમ ઉત્તયદાતા, શબેુચ્છાઓ વશ,
પ્રલબાગ અ: િાથપ્રભક આપ્રથિક વાક્ષયતા
૧. વાભાન્મ જ્ઞાન: નીચે આેર વલકલ્ોભાાંથી કમો વલકલ્ વાચો છે. યોકાણ _________ કયી ળકે છે.
અ. વયકાય ફ. વ્મક્તત ક. વ્મક્તત કાંની/ેઢી ભાત્ર અ. અને ફ. ભાત્ર ફ.અને ક. ભાત્ર ક. અને અ.
ઉય દળાાલેર ફધા જ ઉય દળાાલેરભાાંથી એકણ નહશ
૨. નાણાકીમ મલૂ્મ: ધાયો કે આની ાવે કુર વભરકતો રૂ. ૨૦,૦૦૦ ની છે, જેભાાંથી આે નાણાકીમ યોકાણના વલકલ્ો ભાાં રૂ. ૧૦,૦૦૦ યોકાણ(તયર વભરકત) છે; અને ફીજી ફાજુ આની ઉય રૂ. ૫,૦૦૦ નુાં દેવુાં છે, તો આ આની ચોખ્ખી/ખયી વભરકતોનુાં નાણાકીમ
મલૂ્મ કઈ યીતે ગણળો?
તયર વભરકત -- કુર દેવુાં કુર વભરકત -- કુર દેવુાં કુર વભરકત + તયર વભરકત કુર વભરકત + તયર વભરકત -- કુર દેવુાં ફાદ કયીને ઉય દળાાલેરભાાંથી એકણ નહશ ખફય નથી
૩. પ્રનકાર કયલામોગ્મ આલક: ધાયો કે આની ભાવવક આલક રૂ. ૧૦,૦૦૦ છે, જેના ઉય તભે રૂ. ૧,૦૦૦ નો આલક લેયો બયલા ાત્ર
છે, તો આની વનકાર કયલામોગ્મ આલક નીચે આેર વલકલ્ભાાંથી કમા વલકલ્થી પયક ડળે?
વ્મક્તતગત આલક લેયો વ્મક્તતગત ફચત વ્મક્તતગત ઘયગથ્થા ખચાા ભાવવક આલક= વનકાર કયલામોગ્મ આલક ઉય દળાાલેરભાાંથી એકણ નહશ ખફય નથી
૪. ફચતની મૂભતૂ જાણકાયી: નીચે દળાાલેર ખાતાઓના વલકલ્ોભાાંથી કયુાં ખાત ુાં એક વાભાન્મ વ્મક્તત ખોરી ળતતી નથી?
ફચત ખાત ુાં ચાલ ુખાત ુાં ડીભેટ ખાત ુાં
હપતવ હડોઝીટ ખાત ુાં યીકયીંગ હડોઝીટ ખાત ુાં ખફય નથી
૫. ગણતયી (વાદ ું વ્માજ): ધાયો કે આની ાવે આજે રૂ. ૧૦૦ ફેંકના ફચત ખાતાભાાં છે, જેના ય આને લાવિક ૨% રેખે વ્માજ વાદા વ્માજની દ્ધવતથી ભે છે, તો ફયાફય આજથી ૫ લા છી આના ખાતાભાાં કેટરા રૂવમા શળે, જો તભે તેભાાં વદૃ્ધદ્ધ ાભલા રૂવમા મકૂી યાખો તો?
રૂ. ૧૦૨ કયતા લધાયે રૂ. ૧૦૨ કયતા ઓછા રૂ. ૧૦૨
ઉય દળાાલેરભાાંથી એકણ નહશ ખફય નથી
૬. ગણતયી (ચક્રવદૃ્ધિ વ્માજ): ધાયો કે આની ાવે રૂ. ૧૦૦ ફેંક ફચત ખાતાભાાં છે, જેના ય આને લાવિક ૨૦% ના દયે ચક્રવદૃ્ધદ્ધ વ્માજ
ભે છે, અને આ ૫ લા સધુી તે રૂવમા ઉાડતા નથી, તો ૫ લાના અંતે આના ખાતાભાાં કુર કેટરા રૂવમા શળે?
રૂ. ૨૦૦ કયતા લધાયે રૂ. ૨૦૦ કયતા ઓછા રૂ. ૨૦૦ ઉય દળાાલેરભાાંથી એકણ નહશ ખફય નથી
હુાં, શાા જયીલારા, "To study the level of finacical literacy level and its impact on investment decision: An in-depth
analysis of investors in Gujarat state” ય ી.એચ.ડી. કરુાં છાં.આનો હકભતી જલાફ અને અભબપ્રામ ભને ભાયો અભ્માવ ણૂા કયલાભાાં ઉમોગી નીલડળે. હુાં આની આબાયી યશીળ, જો આ આના જ્ઞાન અને જાણકાયીને અનવુયીને નીચે આેર પ્રશ્નોના ઉત્તય અને ભાહશતી પ્રત્મેનો વાચો અભબપ્રામ આળો. હુાં તભને ખાતયી આુાં છાં કે આના દ્વાયા પ્રાપ્ત થમેર ભાહશતીનો ઉમોગ ભાત્ર અભ્માવના શતે ુઅથે જ કયલાભાાં આલળે અને તેને ણૂાણે ખાનગી યાખલાભાાં આલળે.
355
૭. ફૂગાલો: કલ્ના કયો કે આના ફચત ખાતા ય વ્માજ નો લાવિક દય ૧% છે, અને લાવિક ફૂગાલાનો દય ૨% છે, તો એક લા છી આના ફચત ખાતાભાાં યશરે રૂવમાથી નીચે આેર વલકલ્ોભાાંથી આ કેટલુાં ખયીદી ળકળો?
આજ કયતા લધાયે આજના જેટલુાં જ આજ કયતા ઓછા ખફય નથી
૮. નાણાુંન ું વભમમલૂ્મ: કલ્ના કયો કે એક વભત્રને આજે રૂ. ૧૦૦૦૦ લાયવાભાાં ભે છે, અને એની ફશનેને રૂ. ૧૦,૦૦૦ આજથી ૩ લા સધુી ભે છે, તો ફને્નભાાંથી લાયવાને કાયણે લધ ુૈવાદાય કોણ શળે?
વભત્ર એની ફશને ફને્ન ફયાફય ૈવાદાય ખફય નથી
૯. ળેય ફજાયની કાભગીયી: નીચે આેર વલકલ્ભાાંથી કમો વલકલ્ ળેય ફજાયની મખુ્મ કાભગીયી દળાાલે છે? ળેય ફજાયળેયની કભાણીનુાં બાલી જાણલા ભદદરૂ થામ છે,
ળેય ફજાય ળેયની કીભતભાાં લધાયો કયે છે,
ળેય ફજાય એલા રોકોને બેગા કયલાનુાં કાભ કયે છે જે ળેય ખયીદલા અને લેચલા ભાગતા શોમ,
ઉય દળાાલેરભાાંથી એકણ નહશ ખફય નથી
૧૦. લૈપ્રલદ્યકયણ: જમાયે યોકાણકાય ોતાના રૂવમા એક કયતા લધાયે જુદા જુદા યોકાણના વલકલ્ોભાાં યોકે છે, ત્માયે રૂવમા ગભુાલાનુાં જોખભ
____________ . લધે છે ઘટે છે એભ જ યશ ેછે ખફય નથી
૧૧. રયસ્ક-યીટનન ટે્રડ ઓપ: વાભાન્મ યીતે કોઈ એક કાંનીના ખયીદેર ળેય, ળેય મચુ્યરુ પાંડ કયતા સયુભિત લતય/યીટના આે છે.
વાચુાં છે ખોટુાં છે ખફય નથી
૧૨. જોખભ: યોકાણભાાં જોખભની શાજયી એટરે _________.
યોકાણકાય રૂવમા ગભુાલળે. એક કયતા લધાયે હયણાભ ળક્ય છે.
ઉય મજુફ દળાાલેર ફાંને ઉય દળાાલેરભાાંથી એકણ નહશ ખફય નથી
૧૩. ફે યોકાણના પ્રલકલ્ લચ્ચેનો રયસ્ક-યીટનન ટે્રડ ઓપ: વાભાન્મ યીતે ળેય ફોન્ડ કયતા લધાયે સયુભિત શોમ છે.
વાચુાં છે ખોટુાં છે ખફય નથી
૧૪. યોકાણના વભમગાા અને યોકાણ પ્રભરકતના મલૂ્મભાું વદૃ્ધિ લચ્ચેનો વુંફધ: એક રાાંફો વભમગાો વલચાયતા (દાખરા તયીકે ૧૦ થી ૨૦ લા), નીચે આેર કઈ યોકાણ વભરકત વાભાન્મ વાંજોગોભાાં લધાયે લતય/યીટના આળે?
ફચત ખાત ુાં ફોન્ડ ળેય
ઉય દળાાલેરભાાંથી એકણ નહશ ખફય નથી
૧૫.યોકાણનો વભમગાો અને યોકાણના મલૂ્મભાું અસ્સ્થયતા: વાભાન્મ વાંજોગોભાાં નીચે દળાાલેર કમા યોકાણનુાં મલૂ્મભાાં વભમાાંતયે લધાયે
અક્થથયતા જોલા ભે છે?
ફચત ખાત ુાં ફોન્ડ ળેય
ઉય દળાાલેરભાાંથી એકણ નહશ ખફય નથી
૧૬. પ્રભરકત/યોકાણની પાલણી: વભરકત/યોકાણની પાલણી એટરે _____ . અ. વનણામ કે જેભાાં યોકાણકાય ોતાના રૂવમા જોખભયહશત અને જોખભી યોકાણના વલકલ્ોભાાં પાલે છે.
ફ. વનણામ કે જેભાાં યોકાણકાય ોતાના રૂવમા અરગ અરગ જુદા જુદા જોખભી યોકાણના વલકલ્ોભાાં પાલે છે.
ક. ધ્માનલૂાક કયેર યોકાણનુાં વલશ્રેણ
અ. અને ફ. ફ.અને ક. ક. અને અ.
ઉય દળાાલેર ફધા જ ઉય દળાાલેરભાાંથી એકણ નહશ ખફય નથી
356
૧૭. વ્માજ અને યોકાણ/પ્રભરકતની કીભત લચ્ચેનો વફુંધ: જો અથાળાસ્ત્રભાાં વ્માજનો દય ઘટે તો, ફોન્ડની કીભતભાાં ______ થામ.
લધાયો ઘટાડો એભ જ યશ ે ખફય નથી
૧૮. ગ્રાશકના શક અને જલાફદાયી: જો એક વ્મક્તત ોતાની ાવે ોતાના ફેંક ખાતાનુાં એ.ટી.એભ. કાડા અને તેનો P.I.N.(ીન) નાંફય
ોતાના વાભાાં મકેુ છે, જો વા ચોયાઈ જામ/ખોલામ જામ, અને ફીજી કોઈ વ્મક્તત તેના ીન નાંફય અને એ.ટી.એભ. કાડા નો ઉમોગ
કયી તેના ખાતાભાાંથી રૂવમા ઊડ ેછે, તો આ વાંજોગોભાાં કોણ જલાફદાય યશળેે?
ભાત્ર ફેંક ભાત્ર કાડાધાયક ફેંક અને કાડાધાયક/ફેંક ખાતાનો ભાભરકફને્ન ખફય નથી
૧૯. પ્રનમભો ફનાલતી વુંસ્થાઓ નાણાકીમ ભાખાના બાગરૂ: નીચે આેર પ્રલકલ્ોભાુંથી કઈ વુંસ્થા ાવે કામદેવયની વત્તા નથી. વેફી ઈયડા આય.ફી.આઈ. એમ્પી
ઉય દળાાલેર ફધા જ ઉય દળાાલેરભાાંથી એકણ નહશ ખફય નથી
૨૦. Know your client (KYC):"તભાયા ગ્રાશકને જાણો", આ રેખખત ત્રકનો ઉમોગ ગ્રાશકના તાદ્રશ્તાને િસ્તાપ્રત કયલાભાું થામ
છે.
વાચુાં છે ખોટુાં છે ખફય નથી
પ્રલબાગ ફ: - એડલાુંવ આપ્રથિક વાક્ષયતા
નીચે મજુફ યોકાણકાયો ભાટે યોકાણના વલકલ્ોના રિણો આેર છે, આની વભજણ અને જ્ઞાન પ્રભાણે આ ભશયેફાની કયીને નીચે આેર
લાક્યો ખયા છે કે ખોટા તે કશો.(નોંધ: દયેક લાક્ય ભાટે કોઈ ણ એક વલકલ્ વાંદ કયો.)
ક્રભ યોકાણના પ્રલકલ્ોના રક્ષણો શા ના ખફય નથી રપક્વ રડોઝીટ
૧. આ ફેંકભાાં એકવાથે મકૂલાભાાં આલતી યકભ છે, જેના ય ચોક્કવ વભમ ભાટે ચોક્કવ દયે વ્માજ
ભે છે.
૨. જુદી જુદી ફેંકો દ્વાયા હપતવ હડોઝીટ ય આલાભાાં આલતો વ્માજનો દય એકવયખો શોમ
છે.
૩. હપતવ હડોઝીટ ય આલાભાાં આલતી વ્માજની ગણતયી ચક્રીવદૃ્ધી વ્માજની દ્ધવતથી કયલાભાાં આલે છે.
નેળનર વેપ્રલિંગ વટીપીકેટ
૪. આ રાાંફા વભમગાા ભાટે કયલાભાાં આલતુાં જોખભયહશત યોકાણ છે. .
૫. નેળનર વેવલિંગ વટીપીકેટવ કાંનીઓ દ્વાયા ફશાય ાડલાભાાં આલે છે.
૬. નેળનર વેવલિંગ વટીપીકેટવ ય વ્માજનો દય તેની યૂી મદુત સધુી એક જ યશ ેછે અને
વ્માજની ગણતયી લાભાાં ફે લખત કયલાભાાં આલે છે.
બ્લરક િોલીડુંટ પુંડ (ી.ી. એપ.)
૭. ી.ી. એપ. ભાાં લાવિક યોકાણ કયલા ભાટે કોઈ ભશત્તભ કે રઘતુ્તભ યોકાણભમાાદા નથી
૮. ી.ી. એપ. ભાાં યોકાણને રાગતો કોઈ રોક-ઇન-ીયીઅડ નથી.
૯. ી.ી. એપ. ભાાં કયેર લાવિક યોકાણ ય કોઈ ણ પ્રકાયની યોકાણ ભમાાદા લગય કયલેયાની ચકૂલાની લખતે યાશત આલાભાાં આલે છે.
એમ્પ્રોમી િોલીડુંટ પુંડ (ઈ.ી.એપ.)
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Total Score: /20 = /30 =_____/50
૧૦. ઈ.ી.એપ. ભાત્ર ગાય દાયોને રાગ ુડ ેછે
૧૧. ગાય દાય ઈ.ી.એપ. ભાાં પયજીમાતણે કયલાભાાં આલતા પાા કયતા લધાયે યકભ નુાં યોકાણ કયી ળકે છે.
૧૨. ઈ.ી.એપ. ભાાં ભાભરક દ્વાયા કયલાભાાં આલતી પાાની ફધી જ યકભ ગાયદાયના ઈ.ી.એપ.
ખાતાભાાં જામ છે.
ઇસ્ક્લટી ળેય
૧૩. ઇક્તલટી ળેયએ કાંનીની ભાભરકી દળાાલે છે.
૧૪. ઇક્તલટી ળેય ધાયક ને ઇક્તલટી ળેવા ય ભતુાં લતય/ હયટાન અવનિંગ ય ળેય કયીકે
ઓખલાભાાં આલે છે.
૧૫. ઇક્તલટી ળેય ય ડીલીડાંડની ચકૂલણી ળેયની ફજાય હકિંભત ય કયલાભાાં આલે છે.
િેપયન્વ ળેય
૧૬. પે્રપયન્વ ળેયધાયકોને ઇક્તલટી ળેવા ધાયકો કયતા ડીલીડાંડની ચકૂલણી લખતે પ્રથભ વાંદગી આલાભાાં આલે છે.
૧૭. પે્રપયન્વ ળેયધાયકોને કાંનીના વલવર્જન અને લેચાણ પ્રહક્રમાની આલકની લશચેણી કયતી લખતે
ઇક્તલટી ળેયધાયકો કયતા અને કાંનીની પ્રથભ વાંદગી આલાભાાં આલતી નથી
મ ચ્ય ર પુંડ
૧૮. જમાયે યોકાણકાય મચુ્યરુ પાંડભાાં યોકાણ કયે છે, ત્માયે વાભે તેને યવુનટ્વ આલાભાાં આલે છે.
૧૯. મચુ્યરુ પાંડના યવુનટની હકિંભત નેટ એથવેટ લેલ્ય ુતયીકે ઓખામ છે.
૨૦. મચુ્યરુ પાંડ એણે ફાશેંધયી આેર યીટનાના /લતયના દયે ચકૂલણી કયે છે, જે તેના ભતૂકાની કાભગીયી ય આધાહયત શોમ છે.
ફોન્ડઅને ડીફેન્ચય
૨૧. ફોન્ડ અને ડીફેન્ચયભાાં કયલાભાાં આલતુાં યોકાણ આલકલેયા ભાાં ભલાાત્ર યાશત ભાટે મોગ્મ
નથી.
૨૨. ફોન્ડ અને ડીફેન્ચય ય ભતુાં લતય/ યીટના ાકતી મદુત સધુી એકવયખુાં જ યશ ેછે.
૨૩. જો કોઈ વ્મક્તત કોઈ કાંનીના ડીફેન્ચય ખયીદે છે, તો એ ખયીદેર ડીફેન્ચય હકિંભત જેટરી કાંનીની ભાભરકી બોગલે છે.
ોસ્ટ ઓપીવ ભાપ્રવક આલક મોજના
૨૪. ોથટ ઓપીવ ભાવવક આલક મોજનાભાાં કયેર યોકાણ ભાટે બાયત વયકાય કોઈ ણ પ્રકાયની ફાશેંધયી આતી નથી.
૨૫. ોથટ ઓપીવ ભાવવક આલક મોજનાભાાં કોઈ ણ પ્રકાયની ફાાંધી મદુ્દત શોતી નથી.
૨૬. ોથટ ઓપીવ ભાવવક આલક મોજનાભાાં તેની મદુ્દતવભમ દયમ્માન યોકાણ કામાના એક લા છી જો નાણાાંનો ઉાડ કયલાભાાં આલે તો તે દાંડ ને ાત્ર નથી.
જીલન લીભા ોરીવી
૨૭. લીભા ોરીવીભાાં ફધા જ પ્રકાયની ફીભાયીઓને સયુભિત કયલાભાાં આલે છે.
૨૮. એક વ્મક્તતના નાભની જીલન લીભા ોરીવી ફીજી વ્મક્તતના નાભ ય તફદીર કયી ળકામ છે.
૨૯. જીલન લીભાના પ્રીભીંમભની ચકૂલણી આલકલેયા ની કરભ ૮૦-C ભાાં યાશત ભલાાત્ર નથી.
૩૦. જીલન લીભાની ાતતની મદુતે લીભેદાયને ચકૂલલાભાાં આલતી યકભ કયાત્ર છે.
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પ્રલબાગ ક: નાણાકીમ યોકાણ પ્રનણનમ: િશ્નાલરી
૧. શુાં તભે તભાયી ફચતભાાંથી યોકાણ કયો છો?
શા (પ્રશ્ન ક્રભાાંક ફે ય જાઓ) ના (પ્રશ્ન ક્રભાાંક ચાય ય જાઓ)
૨. નીચે આેરા ફચત/યોકાણના વલકલ્ોભાાંથી આે કમા વલકલ્ોભાાં યોકાણ કયુું છે?
ોથટ ઓપીવ ફચત મોજના (NSC/KVP/PPF etc.) લીભા ોરીવી ફેંક હડોઝીટ (થાણ) મચુ્યરુ પાંડ
ળેય ફોન્્વ અને ડીફેન્ચયવ
હયઅર ઇથટેટ વોનુાં અને ચાાંદી
૩. જો આની ાવે રૂ. ‘X’ નુાં બાંડો/ફચત શોમ તો નીચે આેરા વલકલ્ોભાાંથી આ કમા વલકલ્ભાાં રૂવમા યોકલાનુાં વાંદ કયળો? (ચઢતા ક્રભભાાં ગોઠલો, જેભાાં ભશત્તભ વાંદગી ને એકથી ળરુ કયો.) ોથટ ઓપીવ ફચત મોજના (NSC/KVP/PPF etc.) લીભા ોરીવી ફેંક હડોઝીટ (થાણ) મચુ્યરુ પાંડ
ળેય ફોન્્વ અને ડીફેન્ચયવ
હયઅર ઇથટેટ વોનુાં અને ચાાંદી
૪. નીચેઆેરા કાયણો ભાાંથી કયુાં કાયણ આને યોકાણ કયતા યોકે/અટકાલે છે?
ભને ભાયા શાથભાાં યેુયુી તયરતા જોઈએ છે . ભને યોકાણ કયલાની જરૂય નથી
ભાયી ાવે યોકાણનુાં જ્ઞ્ઞાન નથી ભાયી ાવે યોકાણ ભાટે યૂતા રૂવમા નથી
હુાં યોકાણ કયલાભાાં ભાનતો નથી હુાં યોકાણ કયલા ભાટે યુતો વભમ પાલી ળકતો નથી. ભને યોકાણ કયલાથી કોઈ રાબ દેખાતો નથી હુાં કોઈ ણ એડલાઈવય/કમ્નીએ આેર ભાહશતી ય વલશ્વાવ યાખતો નથી યોકાણની પ્રવવૃત્ત ભને ગ ૂાંચલણીબયી રાગે છે ભને યોકાણ ના ફધા જ વલકલ્ો જોખભી રાગે છે
એડલાઈવય/કમ્નીની ઊંચી પીવને કાયણે હુાં યોકાણ કયલાનુાં ડતુાં મકુૂાં છ. (પ્રલબાગ ડ ય જાઓ)
૫. નીચે મજુફ યોકાણ કયલાના શતે ુદળાાલેર છે, કૃા કયી આ આનો યોકાણ ાછનો શતે ુજણાલો. (ચઢતા ક્રભભાાં ગોઠલો, જેભાાં ભશત્તભ
વાંદગી ને એકથી ળરુ કયો.) ઇન્કભ ટેિભાાં યાશત ભાટે ભાયા ફાકના રગ્ન/વાભાજજક પ્રવાંગો/વળિણ ભાટે નાણાકીમ જોગલાઈભાટે
ભકાન ખયીદલા/ યીનોલેળન કયલા ભાટે વનવવૃત્ત દયમ્માન નાણાકીમ વનવિતતા/સયુિા ભેલલા ભાટે
બેટ/ડોનેળન/ આલા ભાટે/ માત્રા કયલા ભાટે બવલષ્મભાાં થનાયા અજાણતા ખચાાઓને શોચી લલા ભાટે
લતાભાન નાણાકીમ બાંડોની વદૃ્ધદ્ધ કયલા ભાટે/ ફૂગાલા વાભે નાણાકીમ સયુિા ભેલલા
359
૬.આ યોકાણ કયતા શરેા નીચે આેર કમા વલકલ્ોભાાંથી યોકાણની ભાહશતી રેલાની વાંદ કયળો?.
ક્રભ પ્રલકલ્ો વુંદ
નરશ કયીએ
ઓછી વુંદ
કયીશ ું
તટસ્થ વુંદ
કયીશ ું વૌથી લધાયે વુંદ
કયીશ ું 1 વહટિપાઈડ પાઈનાક્ન્વઅર પ્રાનય
2 કાંની ના લાવિક અશલેારો યથી
3 કાંની ના પ્રોથેતટવભાાંથી
4 કાંનીની લેફવાઈટ યથી
5 પાઈનાક્ન્વઅર પ્રોડતટ્વના ડીથરીબ્યટુવા
6 યેહટિંગ એજન્વીઓએ આેરા યીોટાવભાાંથી
7 કાંની ના ટેભરપોન યીપે્રઝેન્ટેટીલ ાવેથી
8 કુટુાંફના વભ્મો ાવેથી
9 વભત્રો અને વફાંધીઓ ાવેથી
10 પ્રોપેળનર કરીગ્વ વાથેના ચચાા/ લાતાારા દ્વાયા
11 પાઈનાક્ન્વઅર પે્રવ/લતાભાનત્રોભાાં કે ઈરેતરોવનક ભીહડમાથી થમેર બ્રીકેળન દ્વાયા
12 કાંની એતવીક્યટુીલ અને વેતટય એિટા વાથેના ચચાા/લાતાારા દ્વાયા
13 થલતાંત્ર ઇન્લેથટભેન્ટ કમ્નીએ દળાાલેર કાંની ના બાવલ વલે
14 યીવચા એજન્વીઓએ બ્રીળ કયેરા યીોટાવ/અશલેારોભાાંથી
15 પ્રલતાભાન કે શારના યોકાણકાયો ાવેથી
16 પાઈનાક્ન્વઅર એડલાઈવય/બ્રોકય એ આેરા સચૂનો યથી
૭. આના યોકાણના વનણામને અવય કયતા નીચે મજુફ કેટરાક હયફો આેર છે, તેઓ આના યોકાણ ના વનણામને કમા પ્રભાણ થી અવય
કયે છે, તે જણાલો.
ક્રભ રયફો અવય કયતા નથી
આંપ્રળક
અવય તટસ્થ ભશત્લ
ની અવય
કયે છે
વૌથી લધાયે/ વૌથી ભશત્લની અવય કયે છે
1 નાણાકીમ અશલેારોની ક્થથવત
2 કાંની/ેઢીની અંદાજીત એકવત્રત આલક
3 કાંની/ેઢીની ભતૂકાની કાભગીયી( કાંનીએ કયેર નપો અને યોકાણકાયોને
આેર લતયની દ્રષ્ટીએ)
4 ઔદ્યોભગક શયણપાભાાં કાંની/ેઢીનુાં થથાન
5 કાંની/ેઢીની અંદયની વ્મક્તતએ આેર ભાહશતી
6 મૂભતૂ વલષ્રેણ(fundamental analysis)નુાં હયણાભ
7 તકનીકી વલષ્રેણ(Technical analysis)નુાં હયણાભ
8 આના યોકાણ ય આે ધયેર લતય
360
અવય કયતા નથી
આંપ્રળક
અવય તટસ્થ ભશત્લ
ની અવય
કયે છે
વૌથી લધાયે/ વૌથી ભશત્લની અવય કયે છે
9 કાંની/ેઢીની પ્રોડતટ્વ/ વવલિવીવ યામે આની રાગણી/બાલ
10 કાંની/ેઢીના નૈવતક મલૂ્મોની પ્રત્મેની કદયની સઝૂ/ખ્માર
11 કાંની/ેઢી વાથે યાજકીમ ભાંડ(political party)વાથે જોડાણ
12 વાભાજજક િેત્રભાાં પાો
13 લતાભાન ત્રોભાાં આલતા અશલેારો યથી
14 થટોક ભાકેટભાાં કાંનીના ળેયની શારની હકિંભતભાાં થતી લધઘટ / એન.એ.લી
15 વત્તાલાય/અવધકાયી કે યાજકીમ િના નેતાએ આેર અશલેાર/ત્રક
16 દુવનમાના ભોટા ળેય ફજાયોભાાં પ્રલતાતી અક્થથયતા/ વલકાવ
17 અથાળાસ્ત્ર ના પ્રલતાભાન વનદેળનો
18 આણા દેળભાાં કાંનીની ળાખ
19 કાંનીની ેયન્ટ કાંની કે વવથટય કાંનીની ળાખ
20 માાલયણ ને રગતા કાંની અશલેારોની માદી
21 કાંનીનુાં ભાકેટ કેીટરાઈઝેળન
22 પ્રોપેળનર કરીગ્વ વાથે થમેર ચચાા/ લાતાારા
23 પાઈનાક્ન્વઅર પે્રવ કે ઈરેતરોવનક ભીહડમાથી થમેર બ્રીકેળન
24 કાંની ઇતઝેકુતીલ અને વેતટય એક્ષ્તા વાથે થમેર ચચાા/લાતાારા
25 ફજાયભાાં યોકાણ કયતા અન્મ યોકાણકાયો ના ોટાપોરીઓનો અભ્માવ
26 થલતાંત્ર ઇન્લેથત્ભેન્ત કમ્નીએ દળાાલેર કાંનીના બાવલ વલે
27 યીવચા એજન્વીઓએ ફનાલેર અથાળાસ્ત્રના બાવલ વલેના અશલેારો
28 કાંની ના લાવિક અશલેારોનો અભ્માવ
29 કુટુાંફના વભ્મોનો ભત/ભાંતવ્મ
30 વભત્રો અને વફાંધીઓનો ભત/ભાંતવ્મ
31 પ્રલતાભાન કે શારના યોકાણકાયોનો ભત/ભાંતવ્મ
32 પાઈનાક્ન્વઅર એડલાઈવય/બ્રોકય એ આેરા સચૂનો
33 યેહટિંગ એજન્વીઓએ આેરા યીોટાવના ભત/ભાંતવ્મ
34 આે કયેર કુર યોકાણભાાં લૈવલદ્યતાની જરૂહયમાત (કુર યોકાણભાાં ડાઈલવીપીકેળનની જરૂહયમાત)
35 કુર યોકાણને રગતી તયરતાની જરૂહયમાત
36 આલક લેયા(ઇન્કભ ટેિ)ભાાં ભતી યાશત
37 હયથક-યીટના રેડ ઓપ
38 કુર યોકાણ ને રગતા બમ (હયથક) ને ઓછાં કયલાની જરૂયીમાત
39 ઊછીના રૂવમા રેલાની વયતાની વ્મલથથા
40 યોકાણ વાથે વાંકામેર વભમમદુત
41 યોકાણના વલકલ્ વાથે વાંકામેર મદુ્દરની સયુિા
42 યોકાણના વલકલ્ને શોચી લલા યોકાણ કયલા જોઈતી ઓછાભાાં ઓછી યકભ
361
૮. આ કમા પ્રકાયના યોકાણકાય છો, તે જાણલા નીચે મજુફ નાના પ્રશ્નો આેર છે, આેર વલકલ્ોભાાંથી એક વલકલ્ વાંદ કયો: (નોધ:
વાંદ કયેર વલકલ્ ભાટે કોઈ ગણુ નથી.)
૧. નીચે આેર વલકલ્ોભાાંથી આનો યોકાણ કયલા ભાટે ભશત્લનો ઉદે્દશ્મ કમો છે? High Return
અ. ઊંચુાં યીટના/લતય ફ. ભધ્મભ યીટના/લતય ક. તયરતા ડ. ઓછાં જોખભ ઈ. સયુિા
૨. નીચે આેર વલકલ્ોભાાંથી આ ોતાની ફચતનુાં કઈ યીતે યોકાણ કયલાનુાં વાંદ કયળો?
કુર યોકાણની પાલણી (વલકલ્ો)
ળેય (%) મચુ્યરુ પાં્વ
(%)
હયઅર ઇથટેટ
(%)
ફોન્્વ અને
ડીફેન્ચયવ
(%)
પીિ ડીોવવટ
(%)
અ. ૧૦૦ ૦ ૦ ૦ ૦
ફ. ૭૦ ૧૦-૨૦ ૧૦-૨૦ ૫ ૨
ક. ૫૦ ૧૫-૨૫ ૧૦-૨૦ ૧૦ ૫
ડ. ૩૦ ૨૦-૨૫ ૧૦-૨૦ ૨૦ ૧૦
ઈ. ૧૦ ૨૦-૩૦ ૧૦-૨૦ ૨૫ ૨૦
૩. નીચે આેર વલકલ્ોભાાંથી આની યોકાણ ભાટે ભાટેની વભમ મદુત ભાટેની વાંદગી કઈ છે?
અ ૩ થી ૬ ભહશના ફ. ૧ લા થી ૩ લા ક. ૩ લા થી ૫ લા ઈ. ૫ લાથી લધ ુ
૪. નીચે આેર વલકલ્ોભાાંથી આે કયેર યોકાણથી આને કેટલુાં લતય/યીટના ભળે એભ આ ધાયો છો?
અ. ૫૦-૧૦૦ %
ફ. ૪૦-૫૦ %
ક. ૨૦-૪૦ %
ડ. ૧૦-૨૦%
૫. નીચે આેર વલકલ્ોભાાંથી આ આને કમા પ્રકાયના યોકાણકાય ભાનો છો?
અ. ઊંચુાં જોખભ રેનાય
ફ. તક ભે તો જોખભ રેલાની તૈમાયી ફતાલનાય
ક. ભાધ્મભ પ્રકાયનુાં જોખભ રેનાય
ડ. ઓછાં જોખભ રેનાય
ઈ. ભને જોખભ રેવુાં ગભતુાં નથી Total Score for Q. 8 ____________
43 રૂવમાભાાં રૂાાંતય કયલાની વયતા
44 યોકાણ ય ભનાય ગેયેન્ટીડ હયટના/ લતય
362
પ્રલબાગ ડ: વ્મસ્ક્તગત ભારશતી નાભ: _______________________________________________________________ ળશયે:_____________________
૧. જાપ્રત: પ ર સ્ત્રી
૨. ઉભય (લોભાું): ૧૮ થી ૨૫ ૨૬ થી ૩૫ ૩૬ થી ૪૫
૪૬ થી ૫૫ ૫૬ થી ૬૫ ૬૬ અને તેથી ઉય
૩. આન ું ભશત્તભ પ્રળક્ષણ: પ્રાથવભક ભાધ્મવભક ઉચ્ચત્તય ભાધ્મવભક
ડીપ્રોભાાં થનાતક થનાતકોત્તય
૪. આની ભાપ્રવક આલક (કયાત્ર):
રૂ. ૧૦,૦૦૦ અનેતેથી ઓછી રૂ. ૧૦,૦૦૧ થી રૂ. ૧૫,૦૦૦ રૂ. ૧૫,૦૦૧ થી રૂ.૨૦,૦૦૦
રૂ. ૨૦,૦૦૧ થી રૂ. ૨૫,૦૦૦ રૂ. ૨૫,૦૦૧ અને તેથી લધ ુ
૫. ક ટ ુંફજીલન ચક્રનો તફક્કો: યલુાન એકર યલુાન યભણત વન:વાંતાન
યલુાન યભણત વાંતાન વાથે ભધ્મભ ઉંભય યભણત વાંતાન વાથે
ભધ્મભ ઉંભય યભણત યાંત ુવાંતાન અલરાંભફત ના શોમ વદૃ્ધ યભણત
વદૃ્ધ અયભણત અન્મ, _________________
૬. યોજગાયીન ું ભાખ ું: ણૂા વભમ ગાયદાય ઓછા વભમ ગાયદાય છૂટક થલયોજગાય ગહૃશણી વનવતૃ્ત વલદ્યાથી અન્મ ______________
૭. કામનકે્ષત્રની િવપૃ્રત્ત પાઈનાન્વ વાંફાંધી કામાિેત્રભાાં પ્રવવૃત્તભમ (ફેંક,એકાઉટાંટ,મચુ્યરુ પાં્વ,વવતિહપઈડ પાઈનાક્ન્વઅર પ્રાનય,ઇન્લેથત્ભેન્ત,ઇન્થયયુન્વ કાંનીભાાં ઇ.) પાઈનાન્વ અવાંફાંધી કામાિેત્રભાાં પ્રવવૃત્તભમ (ઉય દળાાલેર િેત્રો વવલામનુાં કામાિેત્ર)
અન્મ
૮. આનો ક ર કાભનો તજ ફો (લોભાું): ૫ કે તેના કયાંતા ઓછા ૬ થી ૧૦ ૧૧ થી ૨૦
૨૧ થી ૩૦ ૩૧ કે તેથી લધ ુ
૯. આના ભાપ્રવક ખચાન આની ભાપ્રવક આલકના િભાણભાું: ૧% થી ૫૦% ૫૧% થી ૬૦% ૬૧ % to ૭૦%
૭૧ % to ૮૦% ૮૧% to ૯૦% ૯૧% કે તેથી લધ ુ
૧૦. આની ભાપ્રવક ફચત આની ભાપ્રવક આલકના િભાણભાું: ૧% થી ૫૦% ૫૧% થી ૬૦% ૬૧ % to ૭૦%
૭૧ % to ૮૦% ૮૧% to ૯૦% ૯૧% કે તેથી લધ ુ
૧૧. યોકાણ કયતી લખતે ભારશતી ભેલલાના ઉદે્દશ્મથી આ વાભાન્મ યીતે કેટરી લાય પછૂયછ કયો છો/ કે એ જ ઉદે્દશ્મથી કેટરીલાય ફશાય જાઓ છો?
શનૂ્મ લખત ૧ થી ૩ ૪ થી ૬ ૭ કે તેથી લધ ુ
૧૨. આ અંદાજે કેટરા લોથી ોતાની ફચતભાુંથી યોકાણ કયો છો?
૧ લાથી ઓછાં ૧ થી ૫ લાથી
૬ થી ૧૦ લાથી ૧૧ કે તેથી લધ ુલોથી
------આબાય-----
List of Publications
371
1) Jariwala, H. (2010). Strategic approach to investment: A new paradigm in
financial planning. In I. E. S.Centre (Ed.), The Role of Financial Innovation:
Corporate Sustenance and Growth. Mumbai: Excel India Publishers.
2) Jariwala, H. & Sharma, S. (2011). Financial literacy: A call for an attention.
International Journal of Academic Conference Proceedings, 1(1), Washington
DC, USA: Library of Congress.
3) Jariwala, H. (2010). Developing Financial Literacy among Indian Masses. In
Amarnani, N. & Danak, D. (Eds.), Sustaining Shareholder Value: Corporate
Finance Practices. Ahmedabad: Excel India Publishers.
4) Jariwala, H., Sharma, M., & Patel, H. (2012). Young students’ towards financial
education. GFJMR, IV (January- June), pp. 15-37.
5) Jariwala, H., & Pandya,K. (2012). Investors’ behavior of equity investment: An
empirical study of individual investors. GFJMR, V (July- December), pp.1-32.
6) Jariwala, H. & Sharma, M. (2013). Assessment of behavioral outcomes of
financial education workshops on financial behavior of participants: An
experimental study. Journal of Financial Services Marketing (Special Issue on
Financial Literacy) 3. U.K.: Pal grave Macmillan.