Chapter 3

Methodology

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CHAPTER 3

METHODOLOGY

A systematic and careful analysis of information is inevitable in any research. In

order to obtain reliable results, it is essential to evolve scientific methods of data

collection and apply appropriate and reliable techniques for the analysis of information.

The methodology followed in the current study is discussed under the following heads:

3.1 DATA COLLECTION

The study is purely based on secondary data. The data for the study has been

collected from the RBI Bulletins, Reports on Trend and Progress of Banking in India,

Schedule of Commercial Banks in India, Report on Currency and Finance, Statistical

Tables relating to Banks in India which were published by the Statistical Department of

RBI. Further, for the purpose of analysis, detailed information is collected from books

relating to Commercial Banks, Journals, Magazines, and Unpublished Reports.

3.2 PERIOD OF THE STUDY

The study covers a period of ten financial years from 2002-03 to 2011-12. The

financial year starts from 1st day of April of a calendar year and ends on the 31st day of

March of next year.

3.3 SAMPLING DESIGN

Keeping in view the problem and the scope of the study, the researcher has

decided to include select Indian Scheduled Commercial Banks (Public, Private and

Foreign Banks) functioning in India for the financial period from 2002-03 to 2011-12. The

researcher selected banks based on the criteria that the banks should have maintained

its identity and reported its accounts without any gap for the ten consecutive financial

years 2002-03 to 2011-12. However, commercial banks which discontinued or started

their operation in the middle of the period under review were not considered. Screening

on the basis of this criterion led to the selection of a sample of 67 banks. The selected

banks were grouped into five categories; namely SBI group (6 Banks), Nationalised banks

group (20 Banks), Old private sector banks group (13 Banks), New private sector banks

group (5 Banks) and Foreign banks group (23 Banks). The Regional Rural Banks

(RRBs) and Co-operative Banks are not included in this study. The detailed list of banks

selected under each bank group is as follows:

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TABLE 2

LIST OF COMMERCIAL BANKS SELECTED FOR THE STUDY

BANK GROUPS S.No NAME OF THE BANKS

SBI GROUP (6)

1 State Bank of India

2 State Bank of Bikaner & Jaipur

3 State Bank of Hyderabad

4 State Bank of Mysore

5 State Bank of Patiala

6 State Bank of Travancore

NATIONALISED BANKS

GROUP (20)

7 Allahabad Bank

8 Andhra Bank

9 Bank of Baroda

10 Bank of India

11 Bank of Maharashtra

12 Canara Bank

13 Central Bank of India

14 Corporation Bank

15 Dena Bank

16 IDBI Bank Ltd

17 Indian Bank

18 Indian Overseas Bank

19 Oriental Bank of Commerce

20 Punjab & Sind Bank

21 Punjab National Bank

22 Syndicate Bank

23 UCO Bank

24 Union Bank of India

25 United Bank of India

26 Vijaya Bank

NEW PRIVATE SECTOR

BANKS GROUP (5)

27 Axis Bank

28 Development Credit Bank

29 HDFC Bank

30 ICICI Bank

31 IndusInd Bank

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OLD PRIVATE SECTOR

BANKS (13)

32 Catholic Syrian Bank

33 City Union Bank

34 Dhanlaxmi Bank

35 Federal Bank

36 ING Vysya Bank

37 Jammu & Kashmir Bank

38 Karnataka Bank

39 Karur Vysya Bank

40 Lakshmi Vilas Bank

41 Nainital Bank

42 Ratnakar Bank

43 South Indian Bank

44 Tamil Nad Mercantile Bank

FOREIGN BANKS GROUP

(23)

45 AB Bank

46 Abu Dhabi Commercial Bank

47 Antwerp Diamond Bank

48 Bank of America

49 Bank of Bahrain & Kuwait

50 Bank of Ceylon

51 Bank of Nova Scotia

52 Bank of Tokyo Mitsubishi UFJ

53 Barclays Bank

54 BNP Paribas

55 Chinatrust Commercial Bank

56 Citibank

57 DBS Bank

58 Deutsche Bank

59 Hongkong & Shanghai Banking Corporation

60 Krung Thai Bank

61 Mashreq Bank

62 Mizuho Corporate Bank

63 Oman International BanK

64 Societe Generale Bank

65 Sonali Bank

66 Standard Chartered Bank

67 State Bank of Mauritius

A total of 67 Scheduled Commercial Banks are selected for the study.

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3.4 TOOLS AND TECHNIQUES USED FOR DATA ANALYSIS

To draw meaningful inferences, a sound methodology accompanied by appropriate

tools and techniques of analysis is necessary.

3.4.1 To assess the financial performance of scheduled commercial banks

The performance of a bank can be measured by a number of indicators. Among

these, profitability and efficiency are the most important and reliable indicators as it give

a broad indication of the capability of bank to increase its earning. To assess the

financial performance and overall efficiency among the SBI Group, Nationalised banks

group, Old private sector banks group, New private sector banks group and Foreign

banks group, the study employs the following three methods viz.,

a) Summary Statistics

b) Ratio Analysis

c) Indexed Ratios

a) Ratio Analysis

Ratio analysis was carried on a CRAMEL basis to find out the actual

performance of the banks individually and collectively among its group. Six sets of

CRAMEL ratios have been employed for assessing the financial performance of

scheduled commercial banks, viz., Capital adequacy ratios (C), Resource Deployed

Ratios (R), Asset Quality Ratios (A), Management Efficiency Ratios (M), Earnings

Quality Ratios (E) and Liquidity Ratios (L). These ratios were calculated for the individual

banks for 10 year period of 2002-03 to 2011-12 and then the average of individual banks

was calculated year wise. Thereafter, from this individual figure the average ratios for the

group were calculated. Therefore, the calculated ratios reveal a true and fair

representation of each group. The study uses following ratios to assess the profitability,

efficiency and liquidity of select scheduled commercial banks in India.

i) Capital Adequacy Ratios (C)

The capital base of financial institutions facilitates depositors in forming their risk

perception about the institutions. It is the ratio which determines the capacity of the

banks in terms of meeting the liabilities and others. Capital adequacy ultimately

determines how well financial institutions can cope with shock to their balance sheets.

The ratios considered under this category are:

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1. Advances to Assets (C1),

2. Government Securities to Total Assets (C2),

3. Government Securities to Investment (C3),

4. Capital Adequacy ratio (C4) and

5. Debt-Equity ratio (C5).

ii) Resource deployed ratios (R)

Resource deployment ratio analysis makes the balance sheet of a bank more

noteworthy. This is arrived as the total proportion of resources deployed in liquid assets,

which generates relatively low yields. Five resource deployed ratios to be employed in

the present study are:

1. Investment to Assets (R1),

2. Other Assets to Total Assets (R2),

3. Credit-Deposit Ratio( R3),

4. Fixed Assets to Total Assets (R4) and

5. Investment - Deposit Ratio (R5).

iii) Asset quality ratios (A)

Asset quality determines the health of financial institutions against loss of value

in the assets. The solvency of financial institutions is typically at risk when their assets

become impaired. So it is important to monitor the quality of assets and hence the study

seeks to analyse the following asset quality ratios:

1. Return on Advances (A1),

2. Return on Investment (A2),

3. Non-Performing loans to Loans (A3),

4. Priority Sector Advances to Advances (A4) and

5. Interest Income to Assets (A5).

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iv) Management Efficiency ratios (M)

Management efficiency is another important dimension in CRAMEL Model. The

following ratios are considered to capture the possible dynamics of management

efficiency which affects the financial performance of the banks.

1. Profit per Employee (M1),

2. Business per Employee (M2),

3. Intermediation cost to Total assets (M3),

4. Business per branch (M4),

5. Return on Net worth (M5).

v) Earnings Quality ratios (E)

Strong earnings and profitability profile of banks reflects the ability to support

present and future operations. More specifically, this determines the capacity to absorb

losses, finance its expansion, pay dividends to its shareholders and build an adequate

level of capital. Being front line of defense against erosion of the capital base from

losses, the need for high earnings and profitability can hardly be overemphasized. The

following ratios measure earnings quality:

1. Spread or Net Interest Income to Total Assets (E1),

2. Burden to Total Assets (E2),

3. Operating profit to Total Assets (E3),

4. Return on assets (E4),

5. Interest Income to Total Income (E5) and

6. Non- Interest Income to Total Assets (E6).

vi) Liquidity ratios (L)

An adequate liquidity position refers to a situation, where institutions can obtain

sufficient funds, either increasing liabilities or by converting its assets quickly at a

reasonable cost. Inability of the banks to manage its short term liquidity liabilities and

loan commitments can adversely affect the performance of the banks by substantially

increasing its cost and over exposure to unrated asset category. Therefore, the following

ratios are used in this study to measure liquidity.

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1. Deposits to Total Assets (L1),

2. Liquid Assets to Total Assets (L2),

3. Liquid Assets to Demand Deposits (L3),

4. Liquid Assets to Total Deposits (L4),

5. Provisions and Contingencies to Total Assets (L5) and

6. Cash -Deposit Ratio (L6).

To get a better picture of the performance of banks, these ratios shall be

analyzed and interpreted by calculating Mean (X), Standard Deviation (S.D) and

Co-efficient of variation (C.V) at three levels:

- At the individual bank level, i.e., by computing X, S.D and C.V for each year

under the period of study.

- At the bank group level, i.e. by computing X, S.D and C.V for each bank

group under the period of study.

- At Industry level, i.e., by computing X, S.D and C.V at the industry level.

Summary Statistics

The above discussed CRAMEL ratios are analyzed with help of summary statistics

like arithmetic mean, standard deviation and coefficient of variation.

i) Arithmetic Mean

A measure of the central tendency of a data set, the mean is the average value

in a data set. It is determined by adding all the values and dividing the sum by the

number of values in the data set.

Xi Mean = ------

n

where i = 1, 2, 3…n,

n = number of samples

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ii) Standard Deviation

The Standard Deviation is less affected than the range by extreme and untypical

values. It is a very accurate measurement for showing how closely the values in a list

cluster around or diverge from the average. The standard deviation is lower if the values

cluster closely around the mean and becomes higher the more they diverge from it. For

the mathematically inclined, the Standard Deviation is defined as the square root of the

variance, or

Standard Deviation = n

)xx( 2

= Sum of squares of deviation taken from an arithmetic average

number of items.

iii) Co-efficient of Variation

The co-efficient of variation (CV) is a measure of dispersion of a probability

distribution. It is defined as the ratio of the standard deviation to the mean. The

coefficient of variation is a dimensionless number that allows comparison of the variation

of populations that have significantly different mean values. It is used in this study, to

check the consistency of the variables among the selected bank groups.

SD

CV = ------- 100 Mean

a) Overall financial performance based on Indexed Ratios

In order to judge the overall financial performance of SBI group, Nationalised

banks group, Old private sector banks group, New private sector banks group and

Foreign banks group, indices be applied by converting the ratios into an index form. To

compute these indices, at first, the respective ratios will be calculated over the period

from 2002-03 to 2011-12, then, these ratios shall be averaged bank-wise and the

respective indices are computed by the method given below:

Average ratio for the concerned bank group Index = ------------------------------------------------------------------------------------------ Average ratio for aggregate of all Indian Scheduled Commercial banks

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Further, the study seeks to classify the banks with regard to these financial

performance indices at four performances level viz., excellent, good, fair and poor.

‘Excellent’ performance level includes the banks lying at the top 25 per cent area of the

normal distribution i.e., where the growth index value is greater than (X + 0.6745σ).

‘Good’ performance category stands for banks whose growth index score lies between

50 -75 per cent area of the normal distribution, i.e., where the growth index value lies in

between X to (X + 0.6745σ). ‘Fair’ category includes those banks whose growth index

value lies in between 25 – 50 per cent area under normal curve i.e., where growth index

value between (X - 0.6745σ) to X. ‘Poor’ category comprises the banks which show

their growth index value lying at the bottom 25 per cent area of the normal distribution

i.e., where growth index value lies below (X - 0.6745σ) (Stockson et al.1971).

3.4.2. To examine the determinants of profitability of scheduled commercial banks

Apart from assessing the financial performance of banks, the study also

attempts to examine the determinants of profitability of scheduled commercial banks. For

this purpose, a multivariate approach viz., Correlation Analysis, Step-wise Multiple

Regression Analysis, Factor Analysis and Path Analysis have been adopted. The data of

selected variables have been pooled together for each of the individual group i.e., SBI

Group, Nationalised banks group, Old private sector banks group, New private sector

banks group and Foreign banks group for the period of 2002-03 to 2011-12. The

variables identified by the researcher are grouped into

a) Capital Adequacy ratios

b) Resource Deployed ratios

c) Asset Quality ratios

d) Management Efficiency ratios

e) Earnings Quality ratios and

f) Liquidity ratios.

Table 3 shows the variables which have been selected for the multivariate

techniques.

* Value of ± 0.6745 σ refers to the standard normal distribution, which divides the

distribution at 25 per cent and 75 per cent respectively.

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TABLE 3

VARIABLES SELECTED FOR THE STUDY

Category Variables

Capital Adequacy

(C)

C1 - Advances to Assets

C2 - Government Securities to Total Assets

C3 - Government Securities to Investment

C4 - Capital Adequacy

C5 - Debt -Equity

Resource Deployed

(R)

R1 - Investment to Assets

R2 - Other Assets to Total Assets

R3 - Credit -Deposit Ratio

R4 - Fixed Assets to Total Assets

R5 - Investment -Deposit Ratio

Asset Quality

(A)

A1 - Return on Advances

A2 - Return on Investment

A3 - Non-Performing loans to Loans

A4 - Priority Sector Advances to Advances

A5 - Interest Income to Assets

Management Efficiency

(M)

M1 - Profit per Employee

M2 - Business per Employee

M3 - Intermediation cost to Total assets

M4 - Business per branch

M5 - Return on Net worth

Earnings Quality

(E)

E1 - Spread or Net Interest Income to Total Assets

E2 - Burden to Total Assets

E3 - Operating profit to Total Assets

E4 - Return on Assets

E5 - Interest Income to Total Income

E6 - Non- Interest Income to Total Assets

Liquidity

(L)

L1 - Deposits to Total Assets

L2 - Liquid Assets to Total Assets

L3 - Liquid Assets to Demand Deposits

L4 - Liquid Assets to Total Deposits

L5 - Provisions and Contingencies to Total Assets

L6 - Cash- Deposit Ratio

Out of the above mentioned variables the ratio of Return on assets (E4) is taken as

dependent variable and the remaining variables are considered as independent

variables.

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a) Correlation Analysis

Correlation analysis is applied to study the relationship that exists between two

variables and to test the hypothesis framed under this study. The correlation co-efficient

of the selected independent variables with the dependent variable (Return on assets)

has been worked out in order to identify the most important variable, which have

relationship with the dependent variable.

b) Step-wise Multiple Regression

The step-wise regression technique is used to have a better idea of the

independent contribution of each explanatory variable. Under these techniques, the

researcher adds the independent contribution of each explanatory variable into the

prediction equation one by one, computing betas and R2 at each step. Hence, step-wise

regression analysis consists of procedures via which a forward inclusion is combined

with the deletion of variables that no longer meet the pre-established criteria at each

successive step.

c) Path Analysis

The technique of path analysis is based on a series of multiple regression

analyses with the added assumption of a causal relationship between independent and

dependent variables. It is applied to find out the direct and indirect effect of independent

variables on the dependent variable.

d) Factor Analysis

It aims at studying the effect of two or more predictor variables on certain

evaluation criterion. One among it is the Factor Analysis which is intended to group the

original input variables into factors which underlie the input variables. Each factor will

account for one or more input variables. By performing factor analysis the total number

of factors in the study can be reduced by dropping the insignificant factors based on the

criterion.

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To find out the factors determining the efficiency of the banks, factor analysis was

applied.

Xi = Ai1 F1 + Ai2 F2 + Ai3 F3 + …… + Aim Fm + Vi Ui

where,

Xi = ith standardized variable,

Aij = Standardized multiple regression coefficient of variable i on

common factor j

F = Common factor,

Vi = Standardized regression coefficient of variable i on unique factor i

Ui = The unique factor for variable i

M = Number of common factors

The unique factors are uncorrelated with one another and with the common

factors. The common factors themselves can be expressed as linear combinations of the

observed variables.

Fi = Estimate of ith factor

Wi = Weight or factor score coefficient

K = Number of variables.

It is possible to select weights or factor score coefficients so that the first factor

explains the largest portion of the total variance. Then, a second set of weights can be

selected, so that the second factor accounts for most of the residual variance, subject to

being uncorrelated with the first factor. This same principle could be applied to selecting

additional weights for the additional factors. Thus, the factors can be estimated so that

their factor scores, unlike the value of the original variables, are not correlated.

Furthermore, the first factor accounts for the highest variance in the data, the second

factor for the second highest and so on.

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3.4.3 To evaluate the financial soundness of scheduled commercial banks

Following IMF (2000) recommendations and Shar et al. (2010), the researcher

used the procedure of a Bankometer Model to evaluate the financial soundness of

scheduled commercial banks in India. This procedure has the quality of minimum

number of parameters with maximum accurate results.

Bankometer Parameters:

1. Capital Adequacy Ratio : 40 %≤CAR≥08%

2. Capital to Assets Ratio (Capita / Asset) : ≥04%

3. Equity to total Assets (Equity / Asset) : ≥ 02%

4. NPLs to Loans (NPLs / Loans) : ≤15%

5. Cost to Income ratio (Cost / Income) : ≤40%

6. Loans to Assets (Loan / Asset) : ≤ 65%

These percentages explain a bank that;

has capital adequacy ratio between 8% to 40%,

has more than 4% capital to assets ratio,

has equity to assets ratio greater than 2%,

has controlled non-performing loans (NPLs) ratio below 15% and

has controlled cost to income ratio below 40%.

has maintained loans to assets ratio below 65%.

Under Bankometer model, the following formula is used to analyse the solvency of the

banks.

Where ‘S’ stands for solvency

CA stands for the capital assets ratio

EA stands for equity to assets

CAR stands for the capital adequacy ratio

S = 1.5*CA + 1.2*EA + 3.5*CAR + 0.6*NPL + 0.3*CI + 04*LA

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NPL stands for non -performing loans to loans

CI stands for cost to income

LA stands for loans to assets

Based on the calculated Solvency score (S), the banks were classified as follows:

‘S’ Value Classification of Banks

>70% Super sound banks

50% - 70% Grey Area

<50% Not solvent

Altman (1968); Sher et.al. (2010).

3.4.4 To analyse the growth and progress of scheduled commercial banks

The growth pattern of the bank has been analysed by adopting the following

techniques: a) Compound Growth Rate and b) Linear Trend Method.

The following variables have been adopted to assess the growth and trend

pattern of the commercial banks under this study.

1. Advance 2. Deposits 3. Borrowings

4. Interest Income 5. Total Income 6. Net worth

7. Total Assets 8. Liquid Assets 9. Fixed Assets

10. Provisions and Contingencies 11. Investment 12. Net Profit

a) Compound Annual Growth Rate (CAGR)

The general performance of the banks can be analysed more meaningfully and

objectively for a given period of time by comparing their growth patterns over the period

rather than on a year-to-year basis. The best measure available for such an exercise is

the compound growth rate technique.

Since the growth curves for many of the variables were non-linear, the compound

growth rates were estimated using the following equation.

Let,

Yt = Y0 (1 + r/100)t

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be the growth curve where y0 and yt are the initial and the tth period value of Y

respectively and r is the compound growth rate.

Taking logarithms on both sides,

Log Yt = log Y0 + t log ( 1 + r/100 )

i.e., log Yt = A + Bt

where B = log [ 1 + (r/100)] and A = log Y0. Using the value of B, r can be obtained from

the following formula:

r = [antilog (B) – 1] x 100

From the above, the resultant value is the Compound Growth Rate in per cent.

b) Linear Trend Analysis

Trend analysis reveals the direction of changes or it is a good guide to the

movement of facts and figures revealed while comparing the financial statements of

different periods. It is a mathematical method and with its help a trend line is fitted to the

data in such a manner that the following two conditions are satisfied:

a) ( Y – Yc ) = 0

i.e. the sum of deviations of the actual values of Y and the computed values of Y is zero.

b) (Y – Yc )2 is least

i.e., the sum of the squares of the deviations of the actual and computed values

is least from trend line. The method of least squares is used to fit a straight-line trend,

which is represented by the equation as:

Yc = a + bx

where,

Yc = Trend value; a = Constant;

b = Regression co-efficient; and x = Time.

3.5 Summary

It can be concluded that this study analysed the performance of five bank groups

in the categorisation of SBI group, Nationalised banks group, Old private sector banks

Group, New private sector banks group and Foreign banks group. The study assessed

49

profitability and efficiency of banks through CRAMEL ratios and financial soundness

through Bankometer model. To examine the determinants of profitability of banks, a

multivariate approach viz., Correlation analysis, Step-wise Regression analysis, Path

analysis and Factor analysis have been adopted. Besides the above mentioned tools of

analysis, charts, graphs and diagrams are used wherever necessary.