INSTITUTE OF PUBLIC POLICY AND MANAGEMENT

(IPPM)

THESIS

Evaluating default for Vietnamese listed enterprises from 2005 to

2010 using Multiple Discriminant Analysis (MDA)

Student : Tran Quoc Hoan – MD6B

Supervisor : Dr. Nguyen Trong Hoa

& Prof. Nguyen Khac Minh, PhD

A thesis submitted for the degree of master of Vietnam – Netherlands Center for

Development Economics and Public Policy

September 2011, Hanoi

i

Abstract

Using financial statements of over 600 enterprises listed on Ho Chi Minh City Stock

Exchange and Hanoi Stock Exchange provided by Stockplus Financial Media Corporation

from 2005 to 2010, this study apply Multiple Discriminant Analysis (MDA) to predict default

for Vietnamese listed enterprises.

This study aim to examine the validity that financial ratios can be used to predict default for

Vietnamese listed enterprises. The ratio “Total debts / Total assets” has positive relationship

with default while other ratios such as “Current assets / Total assets”, “Accounts Receivable /

Earnings before interest and tax ” and “Earning before taxes / Total assets” have negative

relationship with default.

The results of this thesis are consistent with economic theories and previous studies about

financial ratios analysis and default prediction.

Keywords: Default, Multiple Discriminant Analysis, MDA, Z-score; Vietnam Stock

Markets, financial ratios.

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Declaration

I hereby declare that this research paper for the degree of Master of Development Economics

and Public Policy. This is my own work with support from my supervisors based on data

sources and information what I have. The results and content of this research is honest and

never published through any work in anyway.

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Acknowledgments

I would like to thank my supervisors, Dr. Nguyen Trong Hoa and Prof. Nguyen Khac Minh,

Ph. D, for their great support, encouragement and valuable recommendations with this thesis.

I would like to express my thankfulness to Dr. Giang Thanh Long, Dr. Nguyen Thi Minh and

other researchers, teachers and officers of Institute of public policy and management (IPPM)

for their support.

I would also like to thanks managers and other employees of Maritime Bank Securities

Company for their useful feedbacks and helpful advices for this thesis.

Tran Quoc Hoan

Hanoi, September 2011

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TABLE OF CONTENT

ABSTRACT ............................................................................................................................................ I

DECLARATION .................................................................................................................................. II

ACKNOWLEDGMENTS ................................................................................................................... III

LIST OF TABLES ............................................................................................................................... VI

LIST OF FIGURES ............................................................................................................................ VII

CHAPTER 1: INTRODUCTION ........................................................................................................ 1

1.1 Background and Relevance of the Thesis ................................................................................................ 1

1.2 Scope, limitations and Research Questions ............................................................................................ 1

1.3 Structure of the Thesis ............................................................................................................................ 1

CHAPTER 2: LITERATURE REVIEW AND CURRENT SITUATION OF VIETNAM STOCK MARKET FROM 2005 TO 2010 ..................................................................................................... 3

2.1 Literature review .................................................................................................................................... 3

2.2 Financial situation of listed enterprises from 2005 to 2010..................................................................... 8

CHAPTER 3: DATA AND METHODOLOGY .............................................................................. 12

3.1 Default definition ..................................................................................................................................12

3.2 Data description ....................................................................................................................................14

3.3 Selection of the sample .........................................................................................................................14

3.4 Selection of variables .............................................................................................................................15

3.5 Methodology .........................................................................................................................................16

CHAPTER 4: ESTIMATION RESULTS ........................................................................................ 21

4.1 Estimation results ..................................................................................................................................21

4.2 Discussion about Estimation results.......................................................................................................25

CHAPTER 5: SUMMARY, LIMITATIONS AND SUGGESTIONS FOR FURTHER RESEARCHES.................................................................................................................................... 29

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5.1 Summary ...................................................................................................................................................29

5.2 Policy implications ....................................................................................................................................30

5.3. Limitations and suggestion for further researches: ..................................................................................31

REFERENCE ...................................................................................................................................... 33

APPENDIX I: TABLE ALL-DEFINITIONS OF THE VARIABLES SELECTED FOR THE MDA .................................................................................................................................................... 37

APPENDIX II: LIST OF DEFAULT AND NON-DEFAULT VIETNAMESE LISTED ENTERPRISES FROM 2005 TO 2010 ........................................................................................ 39

APPENDIX III: TABLE OF THE SAMPLE SIZES ....................................................................... 40

APPENDIX IV: TABLE OF DESCRIPTIVE STATISTICS ......................................................... 45

APPENDIX V: GROUP STATISTICS TABLES ............................................................................ 47

APPENDIX VI: LOG DETERMINANTS AND BOX’S M TABLES ............................................ 54

APPENDIX VII: STEPWISE STATISTICS TABLES .................................................................. 55

APPENDIX VIII: WILKS’ LAMBDA TABLE ............................................................................... 56

APPENDIX IX: TABLE OF EIGENVALUES ................................................................................. 56

APPENDIX X: THE STANDARDIZED CANONICAL DISCRIMINANT FUNCTION COEFFICIENTS TABLE ................................................................................................................... 57

APPENDIX XI: THE STRUCTURE MATRIX TABLE ................................................................ 58

APPENDIX XII: THE CANONCIAL DISCRIMINANT FUNCTION COEFFICIENT TABLE 60

APPENDIX XIII: THE GROUP CENTROIDS TABLE ................................................................ 61

APPENDIX XIV: CLASSIFICATION TABLE ............................................................................... 62

APPENDIX XV: TABLE OF Z-SCORES FOR ENTERPRISES .................................................. 63

APPENDIX XVI: ENTERPRISES CLASSIFICATION DEPEND ON Z-SCORES .................... 69

APPENDIX XVII: DETERMINE RISKY SECTORS BASED ON Z-SCORES ........................... 70

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List of tables

Table 1: Independent variables selected for the MDA ............................................................ 37 Table 2: List of default and non-default Vietnamese listed enterprises from 2005 to 2010..... 39

Table 3: “non-default” enterprises .......................................................................................... 40 Table 4: “default” enterprises ................................................................................................. 42 Table 5: Descriptive Statistics .................................................................................................. 45 Table 6: Group Statistics .......................................................................................................... 47 Table 7: Tests of Equality of Group Means .............................................................................. 52

Table 8: Log Determinants ....................................................................................................... 54

Table 9: Box’s M Test Results .................................................................................................. 54

Table 10: Variables Entered/Removeda,b,c,d .......................................................................... 55 Table 11: Wilks' Lambda .......................................................................................................... 56 Table 12: Wilks' Lambda .......................................................................................................... 56 Table 13: Eigenvalues .............................................................................................................. 56 Table 14: Standardized Canonical Discriminant Function Coefficients ................................. 57

Table 15: Structure Matrix ....................................................................................................... 58

Table 16: Canonical Discriminant Function Coefficients ....................................................... 60 Table 17: Coefificient correlation matrix ................................................................................. 60 Table 18: Functions at Group Centroids ................................................................................. 61

Table 19: Classification Resultsb,c .......................................................................................... 62 Table 20: Z-Scores for enterprises ........................................................................................... 63

Table 21: Discriminant point ................................................................................................... 69 Table 22: Sectors’ Average Z-Scores ....................................................................................... 70

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List of figures

Figure 1: Ratio of Sectors in the research sample ................................................................. 14

Figure 2: Histograms showing the distribution of discriminant scores for non-default and

default enterprises .................................................................................................................... 23

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Chapter 1: Introduction

1.1 Background and Relevance of the Thesis

Vietnam's economy is in the conversion mechanism operating under market-oriented socialist

and gradual industrialization - modernization. In the market mechanism of economic relations

took place mixed under the influence of market forces in the economic rules. In the market

economy, government, banks and enterprises always need an objective assessment of the

operations, prospects for future growth, the credit status to investment decisions, mergers and

acquisitions, credit financing, co-operation, or supply goods.

In the process of production and business operations, businesses often face with the risks and

potentially lead to default. Multiple Discriminant Analysis points out the limitations and

factors affecting the ability of business default.

On the other hand, in the banking business, banks face credit risk is unavoidable. The problem

is how to limit risks for banks at the acceptable rate.

Thus, in this study, we want to apply Multiple Discriminant Analysis to identify default for

Vietnamese listed enterprises. The results of this research are the basis for proposals to reduce

default of enterprises and help banks to give the right credit decisions for these enterprises.

The paper makes several contributions to the default prediction and financial analysis.

1.2 Scope, limitations and Research Questions

This study is only focus on evaluating default for Vietnamese listed enterprises from 2005 to

2010. Enterprises belonging in the financial sector (banks, investment enterprises) were

excluded.

We identified default for Vietnamese listed enterprises using MDA model to answer the

question: Which factors determine default for Vietnamese listed enterprises? Moreover,

some sub questions are also focused on in this thesis as follow:

Sub – questions:

1. How to classify enterprises depend on high risk or less risk?

2. Which sectors are more risky?

3. How the scale of the business affect to risk?

1.3 Structure of the Thesis

This thesis includes five chapters:

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Chapter I: Introduction. In this chapter, we introduce the topic of thesis work with

background, relevance, scope, limitations and research questions.

Chapter II: Literature review and current situation of Vietnam stock market from 2005

to 2010. In this chapter, we briefly summarize some popular methods to estimate default with

their advantages and disadvantages. We also review some literatures about Multiple

Discriminant Analysis with focusing on Vietnam and summarize situation of Vietnam stock

market form 2005 to 2010 with some main points.

Chapter III: Data and Methodology. In this chapter, we introduce our method to estimate

default for Vietnamese listed enterprises and Data collection.

Chapter IV: Estimation results. We estimate result and specify the regression model with

independent variables that is considered as the determinants of Multiple Discriminant

Analysis.

Chapter V: Summary, limitations and suggestions for further research. In this chapter, we

summarize the main findings in previous chapters. We also analyse the limitations of this

study and recommend some idea for further research.

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Chapter 2: Literature review and current situation of

Vietnam stock market from 2005 to 2010

2.1 Literature review

Detect problems in the operations of enterprises based on financial ratios is a particularly

vulnerable topic. Before developing the quantitative measure of enterprises’ performance,

many organizations have been established to provide the type of information to evaluate the

reliability of the repayment capacity of merchants. For example, Dun & Bradstreet

Corporation1 provided independent information on businesses and corporations for credit

decisions.

In 1930, studies of forecasting business failures began to appear. In studies focused on

forecasting the possibility of default and bankruptcy, there are some significant post relatively

important contributions. Ramser and Foster (1931), Fitzpatrick (1932), Winakor and Smith

(1935), and Mervin identified problems associated with the value of financial ratios of

bankruptcy and non-bankruptcy companies are different. These studies have established the

foundation for studies of bankruptcy prediction later.

A change in traditional research occurs when the Beaver (1966) has introduced a univariate

analysis. Through separate test of every financial ratio calculated from financial statements of

79 bankrupt enterprises and 79 non-bankrupt enterprises, he found that the ratio “cash flows /

total debts” is the best variable for bankruptcy prediction in all ratios was examined. The

results of his research show that the rate of error prediction for one year before the bankruptcy

event is 13% and for two year before the bankruptcy event is only 9%.

Soon after, Altman (1968) was pioneer used multivariate approach in bankruptcy prediction.

He developed a basis model of discrimination. Discrimination analysis found a linear function

of variables and financial markets that can best distinguish between the two layers of

enterprise: non-bankruptcy and bankruptcy. Similarly, the logit analysis using financial

variables to predict probability of bankruptcy with an assumption that probability of

bankruptcy is Logistic distribution, this function is known as logistic function. Therefore, its

value is in the range (0, 1). Altman using model discrimination analysis, based on data of

bankrupt firms from 1946-1965 in the U.S. and obtained discrimination function (Z) as

follows:

Z = 1.2 X1 + 1.4 X2 + 3.3 X3 + 0.6 X4 + 1.0 X5

Where: X1,…,X5 are defined as bellow:

1 Dun & Bradstreet (NYSE:DNB) – a US company established in 1849 in Cincinnati, Ohio

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Variables Symbol

working capital / Total assets; X1

Retained earnings / Total assets;

X2

Earnings before interest and tax (EBIT) / Total assets; X3

Market value of equity / Book value of total liabilities; X4

Sales / Total assets X5

• If Z > 2.99: The enterprise is located in the safety zone, there is no risk of bankruptcy;

• If 1.81 < Z <2.99: The enterprise is located in the alert zone, there may be at risk of

bankruptcy;

• If Z < 1.81: The enterprise is located in the danger zone, a high risk of bankruptcy.

Discrimination function Z can be used for most industries and most types of businesses.

However, because of significant differences between sectors of the variables X5, the X5 was

launched from the Altman model and proposed model the following adjustments:

Z’ = 6.56X1 + 3.26X2 + 6.72X3 +1.05X4

• If Z' > 2.6 The enterprise is located in the safety zone, not risk bankruptcy;

• If 1.2 < Z'< 2.6 The enterprise is located in the alert zone, there may be at risk of

bankruptcy;

• If Z' < 1.2 The enterprise is located in the danger zone, high risk of bankruptcy.

Based on 700 research firms rated by S&P, Altman found similarities between the Z' and

credit rating by S&P, and he offers Z'' with the following function:

Z’’ = 3,25 + 6,56X1 + 3,26X2 + 6,72X3 + 1,05X4

According to Altman, the degree of similarity is quite high, but by no means absolute.

Altman (1997) has used discrimination analysis model with seven variables (including X1:

ratio of earnings before interest and tax / total assets; X2: coefficient Retained Earnings /

Total assets; X3: Ratio of Equity / Total capital; X4: current ratio; X5: Standard deviation of

X1 between 5 - 10 years; X6: Ratio of Earnings before interest and tax / Interest payments;

X7: Total assets). Many own versions of the model were launched. In general, the model

includes seven variables is often called the "Zeta". These models are considered as extension

of the model includes five variables of Altman (1968).

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Scott (1981) has used these variables in their experimental model. He concluded that the Zeta

models are in close relationship with his theory of bankruptcy due to construction.

Similarly, Ling Zhang, Shou Chen and Jerome Yen applied MDA models use 32 financial

ratios of 164 enterprises in China and obtained the following results:

Z=-8.751+6.3X1+0.761X6+1.29X21+0.41X23+0.015X24+0.105X31 -21.164X32

Of which: X1: Net income / total assets; X6: Cash flow from operations / Total number of

treasury shares; X21: Ln(fixed assets); X23: Growth from business activities; X24: Retained

earnings / Net income; X31: Market value of equity / Total liabilities; X32: Book value of

equity / Market value of equity.

• If Z > 0.71: The enterprise is located in the safety zone, without risk of bankruptcy;

• If -0.5 < Z <0.71: The enterprise is located in the alert zone, could risk bankruptcy;

• If Z < 0.71: The enterprise is located in the danger zone, high risk of bankruptcy.

Model in China has obtained an independent variable in the function of distinguishing more

and different models of Altman found in the U.S., this difference might be due to differences

in the characteristics of the economy in two countries.

Martin (1977) use logit model and discrimination analysis to predict bankruptcy of banks in

the 1975-1976 periods. Meanwhile, there were 25 banks were insolvent; both models gave

results consistent with real class.

Ohlson (1980) calculated financial ratios from 105 bankrupt enterprises and 2.058 non-

bankrupt enterprises from 1970 to 1976. He used logit model with nine independent variables

to predict bankruptcy. He obtained overall O-Score function as bellow:

O-Score = -1.32 – 0.407 log(MKT ASSET/CPI) + 6.03TLTA – 1.43WCta + 0.076Clca –

1.72OENEG – 2.37NITA – 1.83FUTL + 0.285INTWO– 0.521CHIN

Of which: MKT ASSET = Total liabilities + market equity; CPI: consumer price index;

TLTA = Book value of debt / MKT ASSET; WCTA = working capital / MKT ASSET;

CLCA = current liability / current assets; OENEG = 1 if total liabilities > total assets, else =0;

NITA = net income / MKT ASSET; FUTL = fund provided by operations / liability; INTWO

= 1 if net income <0 for the last two years, else = 0; Chin = (Net incomet – Net incomet-

1)/(|Net incomet – Net incomet-1|). Results of his research indicate that these factors can

predict bankruptcy for one and / or two years before bankruptcy and accuracy of bankruptcy

prediction is better in one year before bankruptcy.

West (1985) used the logit model combined with factors analysis to measure the financial

conditions of financial institutions and give the probability of bank insolvency. Specifically,

factors used in this model similar to Camel model used for ranking banks. Platt (1991) used

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the logit model of the testing and selecting financial variables and found that using sector’s

financial variables are better than financial variables of a single enterprise for predicting

bankruptcy of enterprises. Smith and Lawrence (1995) used logit model to select the best

variable to predict insolvency of the country. They said that debt data used in the past is

important in forecasting default.

Casey and Bartczak (1985) use financial data of 60 bankrupt and 230 non-bankrupt

enterprises in the period of five years before bankruptcy with variables related to cash flow

(Operational cash flows / Total debts, Operational cash flows / Current debts) and financial

ratios (Net benefit of the working capital / Total assets, Net benefit / Total assets, Debts /

Total assets …) to predict bankruptcy. The results of his research suggest that variables

related to cash flow have more power in predicting bankruptcy than financial ratios.

Gahlon and Vigeland (1988) compare the ratios related to cash flows (The operational cash

flows resulting from sale) to the financial ratios (cash coverage ratio, current assets / current

debts, EBIT / total assets...) for 60 bankrupt and 204 non-bankrupt enterprises in a period of

five year before bankruptcy and found that ratios related to cash flows and financial ratios are

all useful to predict the bankruptcy.

Gilbert et al. (1990) used financial data of 52 bankrupt and 208 non-bankrupt enterprises with

variables (Operational cash flows / current debts, operational cash flows / total debts, cash

flows / total debts, EBIT / total assets, sale / total assets ,…) to predict bankruptcy. Using

stepwise method, his study show that financial ratios from accounting data is significance for

bankruptcy prediction and variables form cash flows also useful.

Research by Robbins and Pearce (1992) show that Z-score is not only used to predict the risk

of bankruptcy but also can be used as a benchmark to measure changes in the business

recovery process. Research shows that Z-score of enterprises can be improved following a

restructure process more than 12 months through improving financial ratios.

Also use discriminant analysis method, Gardiner (1996) has accurately predicted hospital

failed. His research find out a discriminant function combine of financial variables and non-

financial variables to predict bankruptcy for all public hospitals and private hospitals.

Through data analysis of financial statements published by the Greek business using

traditional Z-scores added nine ratios, which are all included in a logistic regression model,

Spathis (2002) have found that the potential causes of bankruptcy is a financial fraud such as

inflated profits, overstated revenues, understated expenses and overstated assets. However, he

did not provide a specific level of impact.

In Japan, Cindy Yoshiko Shirata (1998) collected financial data of 686 bankrupt and 300 non-

bankrupt Japanese enterprises from Teikoku Data Bank’s Cosmos1Data Base from 1986 to

1996. He obtained a linear MDA model as bellow:

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Z= 0.7416 + 0.014X2 –0.003X10 – 0.058X24 – 0.062X36

Of which: X2: Retained earnings / total assets; X10: Current gross capital / Previous gross

capital -1; X24: Interest and discount expense / Borrowings + corporate bond + note

receivable discounted; X36: (Note payable + accounts payable) / Sale.

• If Z > 0.38: The enterprise is located in the safety zone, without risk of bankruptcy;

• If Z < 0.38: The enterprise is located in the danger zone, high risk of bankruptcy (86,14%

probability go bankrupt).

Besides, the research also found that industry and scale is not affect to bankruptcy probability

and this model is better in predicting bankruptcy for Japanese enterprises.

In India, Arindam Bandyopadhyay (2006) collected financial data of 104 listed enterprises

from Credit Rating and Information Services of India Ltd. (CRISIL), used multiple

discriminant analysis to develop a discriminant function (Z-Score) for Indian enterprises

apply in predicting bond default. The results of his study showed that the discriminant model

can highly detect bad enterprises and can early predict bankruptcy for enterprises with an

accuracy rate of 97% (for one year) and 96.3% (for two year). This model can help the banks

and investors to identify high-risk bonds in the bond investment activities.

In Iran, using financial data of companies listed on Tehran Stock Exchange (TES) from 1995

to 2007, the regression model of Mahdi Salehi, Bizhan Abedini (2009) used 22 primary

financial ratios of 60 enterprises (30 enterprises faced financial distress and 30 enterprises do

not have any financial distress) indicated that the accounting data have a high-strength

predictions in predicting the financial distress, which may lead to bankruptcy. By predicting

financial distress, enterprises can adopt necessary policies to strengthen financial ratios to

avoid financial distress and bankruptcy.

In Ghana, Bright Kpodoh (2009) used financial data of mobile telecommunication industry

from National Communication Authority - Ghana to test Altman’s Z-score prediction model.

His research confirmed that Z-score model have a high ability in predicting businesses failure

for telecommunication enterprises.

In the United Kingdom, Khorasgani, Amir (2011) used more than 30.000 SMEs in the period

2000-2008 in 22 United Kingdom manufacturing industries to find suitable predictive model

for enterprise’s default. This study examines default predicting models of both Altman and

Ohlson and found that Altman’s model is more efficiency compare to Ohlson’s model. The

results of his research also found that default prediction for SMEs differences across

industries and segments (private and public). This study found that corporate governance and

controlling market share have significantly effect to default prediction models and it is useful

for banks and SMEs in making financing decision.

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In Vietnam, Lam Minh Chanh (2009) used Z-Score model of E.I. Altman to detect the risk of

bankruptcy and ranked credit ratings base on Z-score in Vietnam. He has also given some

methods to increase Z-score by affect each X-indicator in the model. He also found a high

similarity between adjusted Z-score (Z'') and S&P’s credit rating, through which, the author

assessed the reliability of Z'' and can rely on Z'' to rank credit rating. These results help

enterprises and investors can easily identify the basic financial situation and solvency of the

company's debt.

Nguyen Thanh Cuong, Pham The Anh (2010) have applied the model of E.I.Altman Z scores

to assess the risk of bankruptcy of enterprises processing aquatic products are listed on the

stock market in Vietnam. Based on research results, the authors found Z targets depends

primarily on total assets and make recommendations to help businesses increase Z score to

reduce the risk of bankruptcy, such as management and use effectiveness of the business

assets, financial restructuring towards increasing equity and improving the efficiency of

production and business activities. Research results also help banks with the right decision

before granting credit to the fishery enterprises, thus contributing to improve the effectiveness

of risk management in banking activities.

In recent years, there have been many different methods without using the model parameters

in the development process. Including classification tree models and neural networks. Altman,

Marco & Varetto (1994) and Yang (1999) used neural network models and analysis results

better than the classic layer model.

Through summarizing the results of previous studies showed that there were many methods or

models have been proposed, applied and obtained good results in practice. However,

statistical models have been used by most experts because the models based on proven

scientific principles with highly accuracy and efficiency when applied while the neural

network model with more complex functions require powerful soft-wares with missing in

Vietnam. These are important lessons and a prerequisite for me to use the MDA approach in

this thesis.

2.2 Financial situation of listed enterprises from 2005 to 2010

Vietnam stock market was formed in 2000 with transactions not very exciting until 2005. In

2005, stock market trading was exciting and the real evolution of the market is always

associated with Vietnam’s economic fluctuations. The market trading value of Vietnam stock

market growth up 64% over 2004, the largest growth since its formation. Market value of

listed securities increased 1.6 times, equivalent to 6.5% of GDP in 2005. In 2005 also marked

the launch of Hanoi Securities Trading Center (HASTC) on 08/03/2005, more active

supported for the equalization process, created the binding between the equalization and stock

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markets, facilitated the expanding stock markets with high organization, protected investors

and increased the accessibility of public, enterprises to the stock markets. Overall in 2005,

though always active in terms of price volatility in raw materials and natural disasters affected

the disease, but with effort and predict the difficult situation in 2005, Listed enterprises have

obtained business results relatively optimistic. Many enterprises have growth in revenue and

profit than in 2004, in which SAM (SACOM Cables and Telecom Materials) is most

impressive: increased revenue and profit rose 72.13% and 41.19%; DHA (Hoa An Stones and

Materials): increased revenue 35.09% and 63.73% profit increase; NKD (North Kinh Do

Food): revenue increased 28.99% and 49.28% profit increase. Regarding the financial

situation, enterprises wishing to raise capital to finance its projects by issuing additional

shares, most listed companies are not much changed in financial position compared to 2004,

the ratio at a safe level, and the debt on the property without many changes compared to 2004.

The indicators on the profitability of listed companies will have no more changes than in

2004.

Stage 2006 - 2007 marked a period of booming stock market. In 2006, trading activity of

Vietnam stock markets was very exciting, growth up to 60% from early to mid-2006.

Vietnam's stock market stock market became No. 2 rapid growth of the worlds, just after the

Zimbabwe’s stock market. The strong growth of Vietnam's stock market has attracted capital

flows from domestic and foreign investors. The VN-Index (the total index of Ho Chi Minh

Stock Exchange - HoSE) increased 144% in 2006 and HASTC-Index (the total index of

Hanoi Securities Trading Center – HASTC) increased 152.4%. Total capitalized value

reached 13.8 billion at the end of 2006 (22.7% GDP). Foreign investors held approximately 4

billion USD stock values, accounting for 16.4% of the total market capitalization. Market

capitalization in 2006 has increased by 15 times the previous year; this is a very impressive

figure. Number of listed enterprises increased nearly five times from 41 enterprises in 2005

amounted to 193 enterprises, trading account number over 10 thousand, increasing three times

the previous year and 30 times over six years ago. Within a year, only the VN-Index up more

than 500 points, from more than 300 at the end of 2005 to 800 at the end of 2006.

Securities Law effective from 01.01.2007 has contributed to promoting market development

and enhanced integration into international financial markets. Openness and transparency of

the listed enterprises are strengthened. VN-Index peak at 1170.67 points and HASTC-Index

hit 459.36 points milestone. Overall evolution of the market and stock prices in transactions is

more volatile, Indexes of the two exchanges have strong fluctuation. At the end of 2007, VN-

Index downed to 927.02 points and HASTC-Index downed to 323.55 points. Therefore, after

a year, VN-Index achieved growth of 23.3%, HASTC-Index 33.2% from set point at the end

of 2006. In 2007, Ho Chi Minh Stock Exchange (HoSE) made 248 day of transactions with

total transaction volume of over 2.3 billion of securities equal to the total transaction value of

10

the market reached 224,000 billion VND with volume increase 2 times and value increase 2.8

times compared to 2006. Hanoi Securities Trading Centre (HASTC) also made 248 successful

day of transactions, with total trading volume reached 616.3 million securities equivalent to

the total transaction value of the market reached 63,859 billion with volume increase 6 times

and value increase 15.8 times compared to 2006.

Period 2006-2007 is the most successful stage of listed enterprises, particularly financial

sector. Profit of listed enterprises exceeded high. One of the sources provides superior

business results from activities such as investment securities. From financial statements of 50

enterprises with the largest profit on each trading floor in 2006 - 2007, there were more than

90% of enterprises with revenues from financial investments. Of these, over 50% of

enterprises with financial revenues accounted for more than 10% of profit after tax.

2006 - 2007 is also a stage that listed companies issue stocks to increase equity dramatically.

The issuing took place in almost all listed companies to increase capital ratios averaged about

17% / year. Although stocks are diluted by the issuance, revenue growth, profit growth and

profit margin in this period is still very satisfactory. Average growth rate of revenue growth

and profits are over in this period are 56% and 26.2%. Enterprises in the banking sector (STB

– Sacombank and ACB – Asia commercial bank) and securities (SSI – Saigon Securities Inc.

and BVS – Baoviet securities) growth with revenue growth respectively of 96.3%, 118.24%,

202.1% and 247%. Some enterprises tend to diversify participation in the business of real

estate and finance; two of the industries are growing and most profitable of the Vietnam

economy in this period. FPT Group (FPT) has obtained permission to set up securities firm

and invest in a new establishment bank; Refrigeration Electrical Engineering (REE) expanded

into real estate and finance; Kinh Do confectioneries (KDC) buy 6.42% shares in Eximbank

and start real estate investment in Ho Chi Minh.

In the 2008 period, with the general downtrend of the economy, Vietnam's stock market

closed in 2008 with the sharp decline. Looking back on the market after 01 years of trading,

the highlights of the market was: Indexes decline, price of stocks fell sharply (several stocks

fell below par value), less liquidity, the degradation of foreign capital, the intervention of the

government and gloom in the psychology of investors.

In this period, commodity price increases in all goods from necessities, building materials ...

to the essential commodities of the country such as gasoline, electricity ... Lack of capital

leads to pressure the mortgage operations and mortgage securities, interest rate increases and

limited lending capacity of banks affect the production and business activities of listed

enterprises, especially enterprises in financial and real estate sectors. Most enterprises have

profits decline significantly in 2007, of which, many companies have huge losses from

financial investments, in which, Refrigeration Electrical Engineering (REE) with 100 billion

loss because of provisioning by financial investment in the quarter I/2008 and continues to be

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127 billion after five months in 2008. In this year, many listed enterprises fall into losses with

over 40 companies reported losses.

In 2009, Vietnam's stock market continues to decline. The VN-Index from the beginning of

the year down 22%, the worst lower than other Asian markets. Value of shares traded per day

in February / 2009 in Ho Chi Minh City Stock Exchange and the Securities Exchange Hanoi

is 13 million. The total market value falls to $ 10 billion. Profit of listed enterprises in 2009 is

not very positive. Sales growth of 40% over the same period last year but operating profits

rose 8%. Losses in real estate investments and securities pushed net profit down 25%. Of the

329 listed enterprises, 23 enterprises lost.

Vietnam's stock market in 2010 suffered from the impact of macroeconomic factors of world

economy and domestic economy. Some of the direct causes little to no impact on the stock

market in 2010, namely the continuation of the economic stimulus package in 2009 to the end

of first quarter 2010, the fever "bubble" real estate market production in first quarter 2010,

gold prices rose to record highs as the cause of a strong impact on the evolution of the stock

market in 2010.

Through the monitoring of changes in the stock market in 2010, can be assessed in common:

the stock market in 2010 but showed signs of better than the situation in 2009, reflecting the

recovery of the Vietnamese economy from the impact of world financial crisis, but growth is

steady, continuously fluctuated in a narrow margin to more trading with very low value. By

the end of 2010, the two stock exchanges (HoSE & HASTC) have 647 listed enterprises, up to

42%, or 192 newly listed enterprises, a highest annual growth in 10 years. Total market

capitalization of reach 717.2 trillion (equivalent to about 36% of GDP). In terms of absolute

value, the capitalization of this year nearly 100 trillion VND, higher than in 2009 (in 2009,

stock market capitalization reached 620.5 trillion). With the growth of new listed enterprises,

the number of investor accounts opened at securities enterprises has increased sharply and

reached over 1 million accounts by the end of 2010, up 33.5% compared to investor's

accounts at the end of 2009. There were 105 securities enterprises, 47 fund management

enterprises, 382 foreign investment funds. By the end of 2010 there were more than 1,400

accounts of organizations and more than 13,000 individual accounts of foreign investors in

Vietnam's stock market opened. General Results from the Ho Chi Minh Stock Exchange and

Hanoi Stock Exchange Centre that, from the beginning of the year, foreign investors have

maintained the trend of net buying on the Vietnam’s stock. Total net buying was estimated at

nearly 20,000 billion - about $ 1 billion (more than 5 times the figure in 2009 - in 2009, net

buying by foreigners only reached 3,500 billion). This is the only net buyers ranked second

after the record of more than 24,000 billion in 2007.

12

Chapter 3: Data and Methodology

When using a complete list of financial ratios to assess the potential default of an enterprise, it

is truly that some measure will be the collinear or highly correlated with other metrics. While

this aspect requires careful selection of variables (the ratio), it can find a model with a few

measurements have the potential to be selected in large part information. This information

may indicate very clearly the difference between the groups, but the difference this makes

sense or not is an important aspect of this analysis. It is truly that there are differences

between default enterprises and non-default enterprises, but these differences can be critical to

be able to build a predictive model correct?

The main advantage of multiple discriminant analysis (MDA) is that research objects are

classified by analysis of the entire set of variables of the object at a time rather than turn to

consider its own specific characteristics. MDA approach for ratio analysis techniques has

traditionally shown the potential for this problem correctly. In particular, the combination of

scores can be analyzed together to eliminate confusion and misclassification may be that we

found in previous studies.

Based on the qualities described above, we choose MDA to study the default of Vietnamese

listed enterprises.

3.1 Default definition

Businesses can fall into default as an objective economic phenomenon in the market economy

that it is the result of conflicts of interest of all stakeholders involved in economic relations. It

is not only a conflict between the interests of debtors unable to pay its creditors, but also led

to conflict with the collective interests of the employees working in establishments of the

debtor, to benefit common good of society, to public order and security situation at a local,

national and world economies.

As such, businesses can fall into default as a normal phenomenon and is the inevitable

consequence of market economy, correct and complete understanding of this concept is an

indispensable requirement of all stakeholders in the economy. This concept is the basis for the

State may intervene in the sense of this phenomenon to minimize the negative consequences

and exploit the positive aspects, the identification of risk owners able in the economy when

engaging in economic activities, as input data in risk management especially in credit rating.

So, according to Article 2 of bankruptcy laws in 1993 and Clause 1, Article 3 of Decree 189

of Vietnam has given this concept: The enterprise, cooperatives are likely to fall into default

if:

- Difficulty or loss of business;

13

- Losses in two consecutive years is not enough to repay the debts, not paying adequate wages

for workers under labor agreements and labor contracts in three consecutive months;

- Has applied the necessary financial measures but unable to pay debts.

In practical implementation shows that conditions can determine the condition can fall into

default of such enterprises is very complicated, causing difficulties for the opening of

bankruptcy procedures. In order to overcome this limitation, the Bankruptcy Law in 2004 has

defined the direction of simplifying the criteria for bankruptcy. According to Article 3 of the

"enterprise, cooperatives are not able to pay these debts when the creditors require it

considered default." Thus, this criterion has been defined simply than before, easily

implemented because not based on time loss, the cause of loss. Although this concept is the

more complete than Bankruptcy Law in 1993 but is still limited in its properties thoroughly.

Article 3 Bankruptcy Law in 2004 does not clearly define debt and overdue time not makes

the payment obligations of the debtor. So in form, the debtor simply as a debt the amount of

overdue payments and a day after creditors petition debt may also be considered default. This

can lead to abuse the right to file petition for bankruptcy procedures from creditors.

Experience of some countries in building concepts capable of default by quantitative often

have quantitative regulations on specific debt, the time delay debt payments from the debtor

after creditors when debt requirements. For example:

Bankruptcy Law of the Russian Federation debt provisions not less than 100,000 rubles to the

creditor is 10,000 rubles for legal entities and individual creditors.

Under the Australian Corporations Act, the creditors may request the court to decide to start

the procedures for payment of assets of a company due to a likely fall into default if company

has a debt to term of at least AUD $ 2,000 and the company cannot prove ability to pay such

debts.

According to Basel II, default is seen as events or incidents relating to the borrowing entity,

when at least one of the following possibilities occurs:

- Inability to pay debt obligations as to repayment or loss of ability to pay debts, including

loans and loan interest rates.

- Inability to perform duties as credit overdue for more than 90 days.

- The value of assets less debt

- Require open bankruptcy procedures or similar protection from creditors.

In the opinion of many experts, the definition of default "is probably the most difficult for

lawmakers." Therefore, in this thesis refers only to the business identification signs are likely

to fall into default as follows: The business is likely to fall into default as "Difficulty or losses

in business activity"

14

3.2 Data description

In this study, we used audited annual financial statements of enterprises listed on Ho Chi

Minh City Stock Exchange and Hanoi Stock Exchange in the period 2005 - 2010. Source data

is provided by Stockplus Financial Media Corporation and the Website: http://www.hsx.vn

and http://www.hnx.vn.

At the industry level, we have statistical proportion of the sample (we used sector class – level

2 in this thesis) with percentage as follows:

Figure 1: Ratio of Sectors in the research sample

From the figure above, we can see that “Foods & Beverages” and “Constructions &

Materials” are two sectors which largest ratio.

As per default definition above, we gathered financial data of all Vietnamese listed enterprises

from the year 2005 to 2010 with separate to two groups “default” and “non-default”2.

3.3 Selection of the sample

From initial data above, after excluded enterprises operation in financial sector that is not

representative, we randomly select a sample of two equal groups: forty-one “default”

enterprises and forty-one “non-default” enterprises to create a balanced sample of eighty-two

enterprises - forty-one enterprises in each group. The names of sampled enterprises are

2 See Appendix II

3%

7%

20%

24% 20%

10%

7%

2% 7%

Chemicals

Information Technology

Industrial Goods & Services

Foods & Beverages

Constructions & Materials

Basic Resources

Property

Tourism & Leisures

Electricity, Water, Gas & Oils

15

provided in Appendix III3. “Default” Group (1) consists of enterprises fall into a loss in the

period 2005-2010. These enterprises have average asset size is 966.34 billion VND, with the

range varied between 17.80 and 10,797.31 billion VND. “Non-default” Group (0) consists of

enterprises selected based on stratified random (by industry and by size), with average asset

size of these enterprises is 1,067.59 billion VND and asset size range between 45.90 and

12,304.54 billion VND. Enterprises in the “non-default” group (0) are the enterprises

continuously profitable operations for the period 2005-2010.

In this sample, there are 10 default enterprises from “Food & Beverages” sector; the same 8

form “Industrial Goods & Services” & “Constructions” sectors; 4 from “Basic resources”

sector; the same 3 from “Information Technology”, “Property” & “Electricity, Water, Gas &

Oils” sectors and the same one from “Chemicals” & “Tourism & Leisures” sectors.

In this thesis, we used 39 ratios calculated from financial statements of enterprises in this

sample. We classified these variables in five ratios categories: leverage, liquidity, operating,

profitability and efficiency.4 Statistical summary for these variables are shown in Table 5.

5

Quality of input data is good because all annual financial statements are audited.

3.4 Selection of variables

To apply the analysis to distinguish, in the process of building a model to identify which

variable is the independent variable and dependent variable.

Dependent variable

Dependent variable has many categories, each category represents a group and makes this

able to distinguish the best and only on the basis set of independent variables was selected,

and in other words, each observation must be arranged into a single group. In this study the

dependent variable (Y) are selected as follows:

Independent variables

After choosing the dependent variable, the next step to identify independent variables will be

used in the analysis. The selection of independent variables is usually carried out in two ways.

3 See Appendix III

4 See Appendix I – Table 1

5 See Appendix IV – Table 5

If businesses default

If businesses not default

0

1

i Y

16

The first approach is based on theoretical grounds and from previous studies. The second

approach is based on intuitive knowledge of experts and intuitive selection of variables is the

first study and a reasonable theoretical basis. In both, the independent variables were selected

as the variables that affect the ability to distinguish between groups of dependent variables.

We set out a list of twenty-two variables (ratios) are potentially useful for evaluation. These

variables are categorized into five groups the ratio of the standard, including the leverage

ratio, liquidity, operating, profitability, profitability and Efficiency. The ratio is selected based

on: (a) the popularity of literature and (2) the potential relevance of this study.

Definitions and formulas for variable calculations can be found in Appendix I.

3.5 Methodology

In practice, discriminant analysis was widely applied to evaluating default for enterprises.

However, if the data is qualitative, it is not realizable to apply discriminant analysis. This

model is only suitable for the analysis of financial criteria, because the annual financial

statements of enterprises, data about the business activities are quantitative data. One

advantage of using discriminant analysis model to other classification procedures is that

discriminant function is linear and individual coefficients can be expressed in economic

terms. Those are the reasons why we use a multiple discriminant analysis (MDA) to estimate

Z-scores as the basis for the classification of enterprises of different industries with different

scales are more or less risk to answer main research question and sub-question 1 and 2 above.

Multiple discriminant analysis model is built based on discriminant analysis (DA). The

overall objective of discriminant analysis in evaluating default is to distinguish between

default enterprises and non-default enterprises in an objective and accurate way using the

discriminant function, variables are factors calculated from the annual financial statements.

The main objective is to find a system of linear combinations of variables to best distinguish

groups. Individuals in each group were closest and groups are best distinguishing (most

apart). The main content can be summarized as follows:

Suppose there is a set of n observations (enterprises) divided into two groups: default and

non-default group. Group Di have ni individuals, i = 1,2 on each individual we measured the

value of variable X1,…, Xp. yijk denoted to values of individual j of Di; i = 1,2; j = 1, ..,ni; k =

1, ..,p. Assume that ni> p; n1 + n2 = n.

Group (Di) Individual

Variable

X1 X2 ……… Xp

17

1 (non-default) 1

2

.

.

n1

y111

y121

y1n1p

y112

y122

y1n12

y11p

y12p

y1n1p

2 (default) 1

2

.

.

n2

y211

y221

y2n2p

y212

y222

y2n22

y21p

y22p

y2n2p

There are two groups so we have two point-clouds, Cloud Di have ni point.

We denoted gi is the center of Di cloud: p

ipiii Rgggg ),...,,( 21

In which: pkyn

gin

j

ijk

i

ik ,1;1

1

We denoted G is the center of the whole cloud, so: ),...,,( 21 pGGGG

In which: pkgnn

Gi

ikik ,1;1 2

1

Assume that all clouds are focus into the center, so we have kijkijk Gyx

Then, we denoted Xn,p is a matrix with n rows and p columns set up with a table data set of all

variables focused into the center, the inertia matrix of all the original clouds is defined as

follows:

phkXXhknk hkXX

nXX

nT

,1

'' 11

In which: phxx

n

xxxxxX

i

k

j

ijhijkXX

knkknkkk

hk,1;

1

),...,,,..,,(

2

1 1

22111211

'

21

We have inertia matrix of each group Di:

18

2

1

i ))((1

Wi

ikijhikijk

i

gxgxn

And inertia matrix between the Di groups (external inertia) is determined:

phki

iniki ggnn

B,1,

2

1

1

We can calculate the internal inertia matrix groups (internal inertia) as follows:

2

1

1

i

iiWnn

W

Since the inertia matrix of the entire cloud equal to the sum of the internal and external inertia

matrix, we have: T = W + B

For each individual j of Di groups, we set a linear combination of variables focused into the

center X1, ..., Xp; we denoted: i

p

k

ijkkij njixaa ,1;2,1;1

a denoted to p-dimensional vector in which its components is a = (a1, ..., ap), the variance it is:

Baan

Waan

Taan

a

'''2 111

The variance of a is a linear combination of variables X1, .., Xp, equal to total internal and

external variances of those variables

In the linear combination of variables X1, .., Xp we find any combination which have

maximum external inertia and minimum internal inertia. In other words, we looking for a

combinations for max'

'

Waa

Baa (or max

'

'

Taa

Baa) with conditions a'Ta = 1. This linear

combination is called a discriminant function. This function is defined with the aim to

distinguish enterprises between default and non-default as accurate as possible.

This problem is equivalent to the maximum problem of Lagrange function:

L = a’Ba- (a’Ta-1) Max

There are only two groups, so we can use the Mahalanobis method to solve the problem:

Since n1 + n2 = n so we have: ))((1

21221

2

1

kkjij

i

ikiji ggggn

nnggn

n, then external inertia

matrix is rewritten as follows:

19

2

21211121

212111

2

2111

21

)(..........))((

.

.

))((.......)(

pppp

pp

gggggg

gggggg

n

nnB

Let denote:

)(

.

.

)(

21

21

2111

21

1

pp ggn

nn

ggn

nn

B

Then '

11BBB

According to Mahalanobis, eigenvalues (called the total distance between two groups

known as Mahalanobis distance) is determined as follows:

1

1'

1 BTB

and this value corresponds to the largest a'Ba.

Separate vector a with the largest eigenvalue : a= T-1

B1 is the only discriminant function.

If a new individual with a value: y '= (y1, y2, ..., yp), we will arrange this individual to group i

as bellow:

21);2min(2 1'1'1'1' tyTggTgyTggTg tttiii

Multiple discriminant analysis determine linear combinations of two or more independent

variables that are best able to distinguish between groups known before, in our study is the

default enterprises and non-default enterprises. This is done by a rule of decision is to

maximize the statistical variance between groups compared with the variance within the

group. This relationship is expressed as the ratio between the variance of variance between

groups with internal groups. In this case, we have two groups, thus, a linear discriminant

function is constructed to differ two groups. Analysis of the distinction drawn from a

combination of linear equations has the following format:

Z = α + β1X1 + β2X2 + … + βnXn (1)

Where: Z: Overall index

X1, X2, …, Xn are independent variables as defined in Appendix I.

β1, β2, …, βn are regression coefficients.

20

Therefore, each enterprise received a combination of unique distinction, then it will be

compared with the threshold value to determine which companies belong to any groups.

In fact, the experiments show that the default enterprises especially frequent violations of the

standard conditions. Besides, the group condition of equal variances is violated. In addition,

the nature multicollinearity between the independent variables is also a serious problem,

especially when we use step-by-step procedures. However, experimental studies have shown

that the problems associated with the standard assumptions only little effect on the ability to

classify as well as the predictability of this approach.

The two methods most commonly used to draw the discriminant model is the “simultaneous

method (enter independents together)” and “stepwise method”. The first method based on

modeling as theoretical background, so the model can be determined from the beginning and

then use the analytical distinction. When applying the stepwise method, this procedure will

select a subset of variables to derive a good discriminant model using the selection first,

remove back, or select each step. Stepwise method that we use the built-in SAS programs.

Selection step begins with the number zero in the model variables. At each step, if the

variable contributing the least to the distinguish ability of the model, as measured by Wilks's

lambda coefficient, it will not meet the criteria to stay and will be removed. Variables that

contribute most to the ability to distinguish of the model will be added. The stepwise process

stop when could not find any way to add or remove variables may increase the ability to

distinguish of the model.

With two groups: “default enterprises” and “non-default enterprises”, this analysis is only one

dimension (simplest form of discriminant function). Discriminant function change variable

values into a separate unit or we can use Z-value to classify objects. MDA calculated

discriminant coefficients, βj, while the independent variable Xj is the real value. here, j = 1, 2,

... n.

To answer the sub-question number 3, we use a simple regression model in which, Z-Score

(calculated from MDA model above) are dependent variables and “TotalAssets” and

“TotalDebts” are independent variables. The regression model has the form as bellow:

Z-score = α + β1TotalAssets + β2TotalDebts + ε (2)

21

Chapter 4: Estimation results

4.1 Estimation results

We use stepwise discriminant analysis to find the most significant variables to predict default

and find the best prediction set of the variables. With this method, the independent variable

has the highest correlation was taken first, then, from step 2 onwards, the addition or removal

of a variable based on the level of significance based on the level for “F to remove”. This

process continues until no longer find a plan of adding or removing a variable that can

significantly increase the coefficient is measured in "canonical R squared".

4.1.1. Group statistics tables

Discriminant analysis is only meaningful if between the two groups: “default” and “non-

default” have a significant difference. Therefore, we used analysis of variance (ANOVA) to

test if there is significant difference between groups of each independent variable or not. If

there is no significant difference between the two groups, the results are not meaningful and

conducting any further analysis has no value. These information provided in table 6: The

Group Statistics6 and table 7: Tests of Equality of Group Means tables

7. Table 6 shows the

difference between the independent variables and Table 7 provide strong evidence of

statistical difference between the two groups: "default" and "non-default" with most of the

variables have high F values, in which, P3, E1 and P1 are variables with very high F-valued.

4.1.2. Log determinants and Box’s M tables

In discriminant analysis, the key assumption is the equivalent in covariance matrices. This

assumption can be tested by using Box’s M tables, which test the null hypothesis: “covariance

matrices between groups do not differ”. In our analysis, Box’s M is 169.4 and F-value =

16.024 and the result show that is significant at p <.000 (Tables 8 and 9)8. Log determinants

were quite different and Box’s M showed that the assumption of equivalent in covariance

matrices was not violated. With samples large enough, it is not too important to have a

significant result.

6 See Appendix V – Table 6

7 See Appendix V – Table 7

8 See Appendix VI – Table 8 and 9

22

4.1.3. Stepwise statistics tables

There are four steps were taken in this analysis (see Table 10)9. Each step included only one

variable, and finally, four variables: P3, O4, Li1 and Li3 were added to the discriminant

function to have the highest predictive power.

4.1.4. Wilks’ Lambda table

All four variable added to the discriminant function are significant with p-value <.000 (see

Table 11)10

. It means that predictive power of the discriminant function was increased when

adding these variables. Wilks’ lambda indicates the significance of the discriminant function.

Table 1211

shows a highly significant of discriminant function (p < .000). From this result, we

have 24.5% unexplained.

4.1.5. Table of eigenvalues

Table of eigenvalues showed that one canonical discriminant function was used in the

analysis because we used only two groups: Group (0): “non-default” and Group (1) -

“default”. The canonical correlation (which indicate multiple correlation between the

predictors and the discriminant function). In our research, canonical correlation = .86912

show

that the model explains 75.52% of the variation in classifying an enterprises to “default” or

“non-default” groups.

4.1.6. The Standardized canonical discriminant function coefficients table

Similar to multiple regression, the coefficients and the sign of each predictor were showed in

Table 1413

. P3 - score (.825) was the strongest predictor, Le4 (-.271) (– sign) was next. O4

and Li3 score were less successful as predictors and have strongly predicted allocation to the

“non-default” or “default” group.

9 See Appendix VII – Table 10

10 See Appendix VIII – Table 11

11 See Appendix VIII – Table 12

12 See Appendix IX – Table 13

13 See Appendix X – Table 14

23

4.1.7. The structure matrix table

Relative importances of predictors are shown in Table 15. Here we have P3 and Le4 are the

most important predictors to classify an enterprise to “non-default” and “default” group.

Generally, in this table, 0.35 is seen as the cut-off between important and less important

variables.

4.1.8. The canoncial discriminant function coefficient table

From (Table 16)14

with unstandardized coefficients, we have a discriminant function as

bellow:

Z = - 1.046 - 0.1438 Le4 + 1.631 Li3 + 0.35 O4 + 8.756 P3 (3)

The correlation matrix among these four independent variables15

shows that no couple of

variables has perfect multicollinearity.

4.1.9. Group centroids table

In this discriminant function, we have group centroids is projected onto the coordinate axes

with the value 1.735 (Group 0) and -1735 (Group 1). Group means of predictor variables

(called group centroids) can be found in Group Centroids table16

. In this research, mean of

“non-default” group is 1.735 while “default” group have a mean of -1.735. In the next step,

enterprises will be classified as follow: Enterprises have scores near to 1.735 are predicted

belong to “default” group and enterprises have scores near to -1.735 are predicted belong to

“non-default” group.

4.1.10. Classification table

The classification results of enterprises belong to “default” or “non-default” group in Table

1917

showed that 98.8% of enterprises were classified correctly into “non-default” or “default”

groups. We can conclude that the discriminant function is very good.

Figure 2: Histograms showing the distribution of discriminant scores for non-default and

default enterprises

14

See Appendix XII – Table 16 15

See Appendix XII – Table 17 16

See Appendix XIII – Table 18 17

See Appendix XIV – Table 19

24

4.1.11. Estimate results

All the predictors Le4 (Total debts / Total assets), Li3 (Current assets / Total assets), O4

(Accounts Receivable / EBIT) and P3 (EBT / Total assets) have significant mean differences.

Log determinants were quite different and Box’s M showed that the assumption of equivalent

in covariance matrices was not violated. The discriminant function explains 75.52% of the

variation in classifying an enterprise to “default” or “non-default” groups and results of

classification accuracy between the two groups was 98.8%. Group 0 (non-default) has the

accuracy rate is 100.0% and group 1 (default) have the accuracy rate is 97.6%

25

4.2 Discussion about Estimation results

4.2.1. Factors determine default for Vietnamese listed enterprises

We use multiple discriminant analysis (MDA) to distinguish “default” enterprises and “non-

default” enterprises in an objective and accurate way by using the discriminant function,

which variable is the financial criteria. The main objective is to find a system of linear

combinations of variables to distinguish enterprises to 02 groups: “default” and “non-default”.

Using this method, we estimated discriminant function with the results as follows:

Z = - 1.046 - 0.1438 Le4 + 1.631 Li3 + 0.35 O4 + 8.756 P3 (3)

Where:

Le4 = Total debts / Total assets

Li3 = Current assets / Total assets

O4 = Accounts Receivable / EBIT

P3 = EBT/ Total assets

Z = Overall index. The function Z is a combination of these indicators, the higher the

Z index indicates that companies have good financial situation, good business

performance, high profits and good liquidity.

The results indicate signs of the coefficients of independent variables in the discriminant

function are consistent with economic theory. By testing hypothesis H0: Discriminant

function is not significance. We can reject the hypothesis H0 and conclude that the

discriminant function is highly significant (p < .000) based on Wilks’ lambda (see Table 12).

With a distinguished function Z for the results of classification accuracy between the two

groups was 98.8%. Group 0 (non-default) has the accuracy rate is 100.0% and group 1

(default) have the accuracy rate is 97.6% (see Table 19). Thus, we can claim that the ability to

distinguish between the two groups regardless of the function obtained as well (greater than

25%). The results indicate signs of the coefficients of independent variables in the

discriminant function are consistent with the economic theories. Since:

Le3 (Total debts / Total assets) is a factor of the leverage group. This factor shows how much

the assets of the business are financed with debt. Through this ability to know the financial

autonomy of enterprises. This ratio that is too small, suggesting that businesses borrow less.

This may imply that business is capable of high financial autonomy. However, it can also

imply that enterprises in not good in use of financial leverage. In contrast, this ratio is too high

implies that the level of business risk is higher because of heavily rely on borrowed funds. If

this ratio is too high, businesses vulnerable to falling into the loss of liquidity and these

enterprises will be more difficult to raise loans for new projects.

26

Li3 (Current assets / Total assets) is a factor of the liquidity group. The positive sign of this

factor supports that enterprises with more current assets have lower probability of default. In

theory, enterprises with higher liquidity have lowered the ability of default. Current assets

includes all assets can be easily converted to cash in a short time (within one year). Current

assets include cash and cash equivalents, accounts receivable, investments in short-term

financial, inventory and other liquidity assets. Current assets are very important in business

operations because they are used for daily operations and pay for the daily costs. Thus, the

rate of current assets / total assets lower will cause enterprises to meet certain difficulties in

production and business activities, such as lack of working capital, lack of source to pay

incurred costs or lack of money to pay due debts.

O4 (Accounts Receivable / EBIT) is a factor the operating group. EBIT (Earning before

interest and tax) is often referred to as "operating income". Accounts receivable / EBIT

evaluate the customer's payment for goods and services that businesses provide. Besides, in

theory, accounts receivable can be converted into cash quickly; therefore, this factor has

negative relationship with default.

P3 (EBT / Total assets) is a factor of profitability group. EBT / Total assets is an important

indicator to show how effective exploitation of assets. Normally, we would expect this ratio

as high as possible and of course, the higher this factor indicate that the operation of

enterprise more efficiently, and therefore less risk of default.

Hence, four factors Le4 (Total debts / Total assets), Li3 (Current assets / Total assets), O4

(Accounts Receivable / EBIT) and P3 (EBT/ Total assets) determine default for Vietnamese

listed enterprises. The main research question was answered.

The function Z is a combination of these factors, higher Z-score indicates that enterprise has

demonstrated good financial situation, business performance, high profit and good liquidity.

4.2.2. Enterprises classification depend on Z-Scores

From estimated results, we have a table with Z-scores for enterprises.18

With a distinguished function Z for the results of classification accuracy between the two

groups was 98.8%. Group 0 (non-default) has the accuracy rate is 100.0% and group 1

(default) have the accuracy rate is 97.6% (see Table 19). Only one enterprise: Thang Long

Investment Group (TIG) with Z-score 0.42 (>0) was classification to default group. Thus, we

can claim that enterprise with Z-score from 0 to 0.42 may have probability of default.

Therefore, we can separate enterprises based on Z-scores to 03 groups: “Safety” group

(without probability of default for enterprises have Z-scores >0.42; “Alert” group (may have

18

See Appendix XV – Table 20

27

probability of default) for enterprises have Z-scores from 0 to 0.42 and “Danger” group (have

high probability of default) for enterprises have Z-scores < 0.19

4.2.3. Determine risky sectors based on Z-Scores

To answer the sub-question number two “Which sectors are more risky?”, we based on

average Z-Scores estimated (See table 20). In this thesis, we used sector class – level 2 and

calculated average Z-scores for all sectors.20

From Table 22, we can see that “Chemicals”, “Information Technology”, “Industrial Goods &

Services” are sectors with highest risk in nine sectors of the research sample with the average

Z-Scores are respectively -0.84, -0.47, and -0.41. These sectors suffered impact from raw

material price fluctuations on world markets and from increased exchange rate volatility

(VND/USD) in recent years. "Foods & Beverages" and "Constructions & Materials" are lower

risk sectors with the average Z-scores -0.09 and 0.09 respectively. These sectors least affected

by fluctuations in world market and exchange rate fluctuations because most products and

services of these sectors are served domestic market. "Electricity, Water, Gas & Oils",

"Tourism & Leisures", "Property" and "Basic Resources" are sectors with lowest risk in the

nine sectors of the research sample with the average Z-scores respectively 0.81, 0.46, 0.44 and

0.37. These sectors have certain natural advantages and have exploited these advantages in

recent years quite good.

4.2.4. How the scale of the businesses affect to Z-scores

We use a simple regression model:

Z-score = α + β1TotalAssets + β2TotalDebts + ε (2)

To answer for the sub-question number three “How the scale of the business affect to risk?”.

The results can be found as bellow:

.reg zscore Totalassets Totaldebts

Source SS df MS Number of obs= 82

F( 2, 79) = 3.01

Model 23.1610209 2 11.5805105 Prob > F = 0.0549

Residual 303.74977 79 3.8449338 R-squared = 0.0708

Adj R-squared = 0.0473

19

See Appendix XVI – Table 21 20

See Appendix XVII – Table 22

28

Total 326.910791 81 4.03593569 Root MSE = 1.9609

Z-score Coef. Std. Err. t P>t [95% Conf. Interval]

TotalAssets .0002879 .0001397 2.06 0.043 9.81e-06 .000566

TotalDebts -.0011973 .0005001 -2.39 0.019 -.0021927 -.0002019

_cons -.0010379 .2402357 -0.00 0.997 -.479215 .4771392

From these results, we can see that TotalAssets has a positive relationship with Z-scores that

implied that increase total assets would increase Z-scores. TotalDebts has a negative

relationship with Z-scores that implied that increase total debts would decrease Z-scores.

29

Chapter 5: Summary, limitations and suggestions for

further researches

5.1 Summary

This study used annual financial statements of over 600 enterprises listed on Vietnamese

stock markets from 2005 to 2010 to examine the reliability MDA approach to predict the

default for Vietnamese listed enterprises. The results of the study also help banks to better

control risk when lending out for Vietnamese listed enterprises.

The results of this study indicate that a combination of financial ratios together can help us

predict the likely default of enterprises in Vietnam and consistent with previous studies. In

this study, we found that four explanatory variables, Le4(Total debts / Total assets), Li3

Current assets / Total assets), O4 (Accounts Receivable / EBIT) and P3 (EBT/ Total assets)

were significantly in evaluating the probability of default. From this research results,

enterprises’ administrators can identify the factors that would affect their likelihood of default

to have appropriate policies to improve financial capability of their enterprises to avoid the

risk of default.

Besides, the results for this thesis suggested that based on Z-scores, we can separate

enterprises status to three groups: Without risk of default (safety) for Z-scores > 0.42; May

have risk of default (alert) for 0 < Z-scores < 0.42 and high risk of default (danger) for Z-

scores < 0.

In addition, the results for this thesis suggested that some sectors are less risky than other

sectors. “Chemicals”, “Information Technology” and “Industrial Goods & Services” are

sectors with highest risk while "Electricity, Water, Gas & Oils", "Tourism & Leisures",

"Property" and "Basic Resources" are sectors with lowest risk in the nine sectors of the

research sample.

Moreover, these results sound reasonable as scale of enterprises affect to risk in which higher

debts will bring to higher risk (decrease Z-score) while higher total assets will bring to lower

risk (higher Z-score). It is consistent with practices. When increase scale, enterprises will be

able to reduce input costs, improve competitiveness and open market shares. Moreover,

increase asset size through increase equity will help enterprises more strengthen, reduce the

pressure of interest payments. On the other hand, if such enterprises increase scale through

increase debts, the risk is increased because pressure to pay principal and interest on time

increase.

30

5.2 Policy implications

Z-Score is an important tool to assess the stability of the financial situation of enterprises.

Depending on the type of business, field production and business activities that evaluation

methods are slightly different. Elements sector management, business environment, marketing

strategies, management policies will be reviewed by the general index reflecting the financial

situation of enterprises. So Z-Score is used as an indicator reflects the trust of investors, banks

and enterprises’ managers of the financial situation of enterprises.

For investors, the choices of enterprises have a healthy financial situation for investment is a

top priority in making investment decisions. For the field of financial investment, investment

decision making requires a lot of information related to financial business. In Vietnam, the

majority of investors do not have a method to analysis the financial health of the business. A

method of evaluating the financial health based on combining the many indicators is the

problem that the majority of investor interest. My model has solved the problem of investors.

Based on scores Z-Score, investors can assess the business would have good financial health,

but any business is high risk and make investment decisions the right way to more

transparent. Advantages of this approach are that only use data of financial statements of

listed enterprises therefore helpful to investors in analyzing individual stocks of enterprises

they invest.

For banks, this research can help banks to classify businesses located in safe areas, not in

danger or alert of default. Necessary using this method to evaluating the capacity to business

credit situation. Z-Score is relatively simple but highly reliable, has been widely applied in the

U.S. and investors are gradually being used by the test results reliable. Thus, my model can

help banks can to decide the credit limit required for each business. Z-score can be regarded

as a powerful support tool for banking executives has the right decision before granting credit.

Therefore, when determining credit for businesses, banks need more careful scrutiny of the

financial status and ability of the enterprise default.

For enterprises’ managers, through the research results show that Z-Score depends primarily

on total assets. Therefore, managers can increase Z-score by:

- Financial restructuring towards increasing equity, decreasing debts by limit corporate

debt, issue additional shares. Implementing this solution will decrease Le4 (Total

debts/Total assets).

- Efficient use of enterprises’ assets through classified assets and liquidate all assets that

do not contribute directly or indirectly create revenue. Sale off inefficient assets will

increase current assets, increase Li3.

- Improve efficiency of production and business activities of enterprise will increase P3.

31

The implementation of these solutions to improve Z-score does not only mean immediate

action, but also can lead to improve Z-score in the future. Increase Z-score will improve

investor confidence, and therefore, enterprise will be easier in the issuance of shares, capital

restructuring. Additionally, when Z-score improves, customers will also look more

sympathetic to enterprise, thus, enterprise will be easily accessible business opportunities.

Therefore, enterprises should pay more attention to Z-score and should create a long-term

plan for improve Z-score.

5.3. Limitations and suggestion for further researches:

The concept of business fall into default (default) in Vietnam is lack of quantitative properties

that should generate many difficulties in defining and applying this concept in reality.

Therefore, the next time should have modified the concept of business fall into default in the

direction of quantifying these criteria.

The study also has limitations because the sample size is not big enough. This limitation has

two reasons: the first reason is the number of listed enterprises, the second reason is most

listed enterprises are profitability enterprises because listing conditions require two years

previous profitability (listed on the Ho Chi Minh Stock Exchange) and one year ago

profitability (listed on the Hanoi Stock Exchange). Therefore, the construction of balanced

sample for this study is restricted.

Although there are limitations, this study showed default prediction indicators to create a

discriminant function for Vietnamese listed enterprises. The results of the study are acceptable

and applicable in determining the likely default of enterprises listed on the Vietnam stock

market. Therefore, Z-score in this study can be used as a tool for the classification of

enterprises, thereby helping to investors, banks and enterprises’ managers decided more

rational policy.

Through the limitations of this study, researchers can discover new research directions in

order to complement the research on enterprises’ default in general and about the default of

Vietnamese enterprises in particular. Suggestions for future researches are follows:

There should be the study of signs of enterprises are in danger of bankruptcy based on

the actual amount in accordance with Vietnam.

Should have in-depth study estimates the probability of insolvency of enterprises in

each economic sector.

Continued modeling studies that the ability of insolvent enterprises under the new

approach suited to the conditions of asymmetric information in the current economic

relations.

32

Furthermore, derived from actual empirical studies of the thesis and the inadequacies of

research on default in Vietnam, the authorities should have some specific solutions. In our

point of view, we have solutions as bellows:

Firstly, lawmakers should create a legal system in accordance with actual conditions in

Vietnam, ensuring the operation of research on default as well as the regulations required the

relevant to the organization and operation of research on default. The legal system must

reflect the viewpoints of the State, showing the way the Party's policies. The organization and

operation of safe and efficient securities market, insurance market, and gradually expand the

scope of activities, including attracting foreign capital.

Secondly, Vietnam's stock market has been developing towards a stable process. The fact that

in many countries, evaluating default of listed enterprises is one of the factors that ensure the

stock market of that country stable development. Therefore, government should encourage the

formation of independent organizations research on default.

Thirdly, the authorities should have rules on disclosure and transparency of information to

progressively increasing levels of efficiency and perfection of the market. Thereby promoting

the development of activities to collect and publish information of Vietnam under the

direction of integration, establish common standards for research on default are more

favorable.

33

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37

APPENDIX I: Table all-definitions of the variables

selected for the MDA

Table 1: Independent variables selected for the MDA

No. Financial ratio risk factor Symbol

1 Liabilities/Total assets Leverage X1 Le1

2 Equity / Total assets

Leverage X2 Le2

3 Total long term debt / Total assets Leverage X3 Le3

4 Total debts / Total assets Leverage X4 Le4

5 Total debts / Total liabilities Leverage X5 Le5

6 Total debts / Equity Leverage X6 Le6

7 Working capital / Total assets Liquidity X7 Li1

8 Current liabilities / Total assets Liquidity X8 Li2

9 Current assets / Total assets Liquidity X9 Li3

10 Cash / Total assets Liquidity X10 Li4

11 Working Capital / Net Sales Liquidity X11 Li5

12 Cash / Net sales Liquidity X12 Li6

13 Current assets / Net sales Liquidity X13 Li7

14 (Current assets - Inventories) / Net sales Liquidity X14 Li8

15 Cash / Current liabilities Liquidity X15 Li9

16 Working Capital / Current liabilities Liquidity X16 Li10

17 (Curent assets - Inventories) / Curent liabilities Liquidity X17 Li11

28 Inventories / EBIT Operating X18 O1

38

19 Inventories / Net sales Operating X19 O2

20 Accounts Receivable / Net sales Operating X20 O3

21 Accounts Receivable / EBIT Operating X21 O4

22 Accounts Receivable /Liabilities Operating X22 O5

23 Accounts Payable / Net sales Operating X23 O6

24 Accounts Receivable /Inventories Operating X24 O7

25 Net sales / Current assets Operating X25 O8

26 EBIT / Total assets Profitability X26 P1

27 EBIT / Net sales

Profitability X27 P2

28 EBT / Total assets Profitability X28 P3

29 Net income / Net sales Profitability X29 P4

30 Retained earnings/ Total assets

Profitability X30 P5

31 Gross Profit / Net sales Profitability X31 P6

32 EBIT / Net sales Profitability X32 P7

33 EBT/ Net sales Profitability X33 P8

34 Retained Earnings / Equity Profitability X34 P9

35 ROA Efficiency X35 E1

36 ROE Efficiency X36 E2

37 ROCE Efficiency X37 E3

38 Net Sales / Market Cap Efficiency X38 E4

39 Net sales/ Number of Employees Efficiency X39 E5

39

APPENDIX II: List of default and non-default Vietnamese

listed enterprises from 2005 to 2010

Table 2: List of default and non-default Vietnamese listed enterprises from 2005 to 2010

Year Number of

enterprises Default

Non-

default

2005 238 7 231

2006 365 8 361

2007 495 4 491

2008 602 43 559

2009 613 16 597

2010 623 22 601

40

APPENDIX III: Table of the sample sizes

Table 3: “non-default” enterprises

No. SYM ENTERPRISE's NAME SECTOR Asset size

(Bil VND)

1 BMP Binh Minh Plastics Building Materials & Fixtures 982.15

2 DHA

Hoa An Stones and

Materials Building Materials & Fixtures 377.07

3 NHC Nhi Hiep Brick-Tile Building Materials & Fixtures 48.38

4 NTP Tien Phong Plastics Building Materials & Fixtures 1,402.00

5 VTS Viglacera Tu Son Ceramic Building Materials & Fixtures 76.27

6 SDA Song Da Manpower Supply

Business Training &

Employment Agencies 275.79

7 HRC Hoa Binh Rubber Commodity Chemicals 502.11

8 PAC

Dry Cell and Storage

Battery

Electrical Components &

Equipment 1,103.44

9 NBP Ninh Binh Thermal Power

Electricity Production &

Distribution 286.09

10 AAM Mekong Fisheries Farming & Fishing 331.34

11 ABT Ben Tre Aquaproduct Farming & Fishing 601.93

12 NSC National Seeds Farming & Fishing 302.99

13 SSC Southern Seed JSC Farming & Fishing 260.11

14 SJ1 Seafoods No 1 Farming & Fishing 111.29

15 VNM Vinamilk Food Products 10,773.03

16 HHC Haiha Confectionery Food Products 224.40

41

17 LSS Lam Son Sugar Food Products 1,549.88

18 SAF Safoco Foodstuff Food Products 105.96

19 PGD PV Gas Distribution Gas Distribution 977.57

20 SFC Saigon Fuel Co. Gas Distribution 230.15

21 CTD COTEC construction Heavy Construction 2,017.42

22 S99 Song Da 99 Heavy Construction 212.29

23 LCG LiCoGi 16 Heavy Construction 2,050.13

24 SRF SEAREFICO Industrial Machinery 591.04

25 VFC Vinafco Marine Transportation 565.65

26 SFI

Sea and Air Freight

International Marine Transportation 397.67

27 HGM Ha Giang Mineral Mining 144.24

28 GHA Hai Au Paper Paper 45.92

29 KHA Khanh Hoi Im-export Property 431.16

30 NTL

Tu Liem Urban

Development Property 2,135.01

31 RCL Cho Lon Real Estates Property 224.93

32 SCD Chuong Duong Beverages Soft Drinks 203.38

33 CMT

Information and Networking

Technology Software 192.27

34 FPT FPT Group Software 12,304.54

35 KKC

Metal Manufacturing and

Trading Steel 175.01

36 SSM VNECO Steel Structure Steel 186.51

42

37 CSG Sai Gon Cable

Telecommunications

Equipment 671.17

38 DXP Doan Xa Port Transportation Services 189.40

39 TCT

Tay Ninh Cable Car Tour

Co. Travel & Tourism 109.80

40 HTV Ha Tien Transport Trucking 247.35

41 VNL VINALINK Trucking 154.23

Table 4: “default” enterprises

NO. SYM ENTERPRISE's

NAME

Default

year SECTOR

Asset size

(Bil VND)

1 TTC

Thanh Thanh

Construction 2010

Building Materials &

Fixtures 152.64

2 CYC Chang Yih Ceramic 2009

Building Materials &

Fixtures 347.26

3 VTA Vitaly 2008

Building Materials &

Fixtures 288.17

4 TLT Thang Long Viglacera 2008

Building Materials &

Fixtures 355.40

5 TKU Tung Kuang Industrial 2008

Building Materials &

Fixtures 606.33

6 ILC

International Labour and

Services 2009

Business Training &

Employment Agencies 335.31

7 VKP Viky Plastic 2009 Commodity Chemicals 302.72

8 TYA

Taya (Vietnam) electric

wire & cable 2008

Electrical Components

& Equipment 660.61

43

9 PPC Pha Lai Thermal Power 2008

Electricity Production &

Distribution 10,797.31

10 FBT

Bentre Forestry

Aquaproduct 2009 Farming & Fishing 492.79

11 BAS Basaco 2009 Farming & Fishing 199.43

12 CAD Cadovimex Seafood 2009 Farming & Fishing 1,195.38

13 ANV Nam Viet 2009 Farming & Fishing 2,136.29

14 MPC Minh Phu Seafood Corp. 2008 Farming & Fishing 2,266.91

15 BLF Bac Lieu Fisheries 2008 Farming & Fishing 216.49

16 BHS Bien Hoa Sugar 2008 Food Products 598.53

17 HNM Hanoi Milk 2008 Food Products 214.54

18 AGC An Giang Coffee 2008 Food Products 441.05

19 VMG Vung Tau Petroleum 2010 Gas Distribution 212.27

20 MTG MT Gas 2008 Gas Distribution 182.83

21 SD8 Song Da 8 JSC 2009 Heavy Construction 285.29

22 FPC Full Power 2008 Heavy Construction 1,219.86

23 PVV

Vinaconex - PVC

Construction 2008 Heavy Construction 25.52

24 PTM

Precision Tools & CNC

Machine 2009 Industrial Machinery 21.42

25 VSP

Vinashin petroleum

transport 2009 Marine Transportation 3,464.71

26 SHC Sai Gon Maritime 2009 Marine Transportation 157.81

27 KTB

Tay Bac Minerals

Investment 2007 Mining 1,219.86

44

28 HAP Hai Phong Paper 2008 Paper 673.97

29 PVL PVPOWER LAND 2010 Property 832.90

30 SCR Sacomreal 2008 Property 5,426.62

31 TIG

ThangLong Investment

Group 2008 Property 17.85

32 TRI

Tribeco Sai Gon

beverages 2008 Soft Drinks 325.82

33 SRB SARA 2009 Software 66.73

34 SRA Sara Vietnam 2008 Software 26.59

35 NVC Nam Vang 2008 Steel 1,422.13

36 HLA Asia Huu Lien 2008 Steel 961.52

37 TLC Thang Long Telecom 2008

Telecommunications

Equipment 203.76

38 VSG

Southern Container

Shipping 2010 Transportation Services 498.52

39 PDC

Phuong Dong Petroleum

Tourism 2009 Travel & Tourism 312.70

40 MHC Hanoi Maritime 2009 Trucking 420.23

41 VCV Vinaconex Transport 2008 Trucking 33.90

45

APPENDIX IV: Table of descriptive statistics

Table 5: Descriptive Statistics

N Range

Minimu

m

Maximu

m Sum Mean

Std.

Deviation

Varianc

e Skewness Kurtosis

Stati

stic Statistic Statistic Statistic Statistic Statistic

Std.

Error Statistic Statistic Statistic

Std.

Error Statistic

Std.

Error

Le1 82 .9086 .0808 .9894 37.0056 .451288 .0256204 .2320030 .054 .349 .266 -.757 .526

Le2 82 .9086 .0106 .9192 44.9944 .548712 .0256204 .2320030 .054 -.349 .266 -.757 .526

Le3 82 .6353 .0000 .6353 4.7446 .057861 .0130193 .1178950 .014 3.017 .266 10.073 .526

Le4 82 .7821 .0000 .7821 18.4920 .225512 .0258788 .2343429 .055 .667 .266 -.864 .526

Le5 82 .9539 .0000 .9539 32.4745 .396031 .0361910 .3277230 .107 .106 .266 -1.517 .526

Le6 82 52.2628 -32.6122 19.6506 47.4649 .578840 .4873459 4.4131051 19.475 -4.313 .266 44.108 .526

Li1 82 28.7003 -.6489 28.0514 1.4055 1.71407 .4183450 3.7882749 14.351 4.736 .266 29.076 .526

Li2 82 .8474 .0550 .9024 30.6149 .373352 .0228719 .2071139 .043 .574 .266 -.369 .526

Li3 82 .9397 .0291 .9687 49.6016 .604898 .0241598 .2187760 .048 -.564 .266 -.225 .526

Li4 82 .6301 .0025 .6326 9.4233 .114919 .0150463 .1362502 .019 1.651 .266 2.312 .526

Li5 82 19.0992 -1.3656 17.7336 46.8066 .570813 .2286689 2.0706845 4.288 7.339 .266 60.011 .526

Li6 82 1.6659 .0038 1.6697 13.5468 .165205 .0301143 .2726962 .074 3.227 .266 12.555 .526

Li7 82 47.2886 .1073 47.3959 1.20272 1.46665 .5802815 5.2546728 27.612 8.482 .266 74.538 .526

Li8 82 27.6812 .0410 27.7222 77.1495 .940847 .3402552 3.0811421 9.493 8.335 .266 72.815 .526

Li9 82 7.7682 .0079 7.7761 41.6619 .508072 .1093707 .9903938 .981 5.284 .266 36.040 .526

Li10 82 9.2829 -.5953 8.6876 91.0913 1.11087 .1864366 1.6882548 2.850 2.384 .266 6.587 .526

Li11 82 9.4463 .0290 9.4753 1.18672 1.44723 .1890276 1.7117174 2.930 2.638 .266 8.218 .526

O1 82 1.88002 -1.80962 7.0462 -3.46202 -4.22196 2.376867 21.523449 463.259 -7.309 .266 58.270 .526

O2 82 19.6737 .0000 19.6737 43.1160 .525805 .2425084 2.1960071 4.822 8.430 .266 73.807 .526

O3 82 18.2309 .0148 18.2456 41.9687 .511813 .2225093 2.0149072 4.060 8.638 .266 76.679 .526

O4 82 1.30592 -1.18792 11.7958 -2.22922 -2.71859 1.532797 13.880070 192.656 -7.435 .266 62.031 .526

O5 82 2.8582 .0115 2.8697 51.2332 .624795 .0671201 .6077986 .369 2.134 .266 4.545 .526

O6 82 2.5059 .0022 2.5081 14.4392 .176088 .0375923 .3404132 .116 4.853 .266 28.472 .526

O7 82 6.08242 .0000 608.244 1.00953 1.23110 7.544186 68.315517 4.66733 8.443 .266 73.938 .526

O8 82 9.3006 .0211 9.3217 1.63772 1.99720 .1695399 1.5352491 2.357 2.130 .266 6.714 .526

P1 82 .7937 -.2871 .5066 8.3463 .101784 .0178318 .1614734 .026 .145 .266 -.086 .526

P2 82 5.7996 -.5037 5.2959 12.9018 .157339 .0677919 .6138820 .377 7.402 .266 62.237 .526

P3 82 .7628 -.3302 .4327 4.4737 .054558 .0184244 .1668404 .028 .257 .266 -.403 .526

46

P4 82 1.3595 -.7015 .6580 1.6375 .019970 .0239575 .2169447 .047 -.269 .266 2.365 .526

P5 82 .7650 -.4323 .3327 2.6729 .032596 .0158445 .1434783 .021 -.491 .266 1.022 .526

P6 82 1.4411 -.4801 .9610 13.8434 .168822 .0226617 .2052102 .042 .456 .266 2.953 .526

P7 82 3.1880 -2.3126 .8754 .2186 .002665 .0389235 .3524669 .124 -3.401 .266 22.941 .526

P8 82 1.5845 -.7076 .8770 3.3203 .040492 .0270088 .2445750 .060 .176 .266 2.304 .526

P9 82 34.8215 -8.0084 26.8131 20.6549 .251889 .3436613 3.1119857 9.684 7.628 .266 68.103 .526

E1 82 .8973 -.4450 .4523 4.9876 .060825 .0194225 .1758784 .031 .111 .266 .044 .526

E2 82 35.7384 -8.1349 27.6034 24.9943 .304809 .3531669 3.1980625 10.228 7.635 .266 68.115 .526

E3 82 1.7658 -.7115 1.0543 17.3365 .211421 .0304684 .2759032 .076 .057 .266 2.201 .526

E4 82 23.0328 .0234 23.0562 2.31292 2.82055 .3978990 3.6031291 12.983 3.311 .266 14.043 .526

Valid

N

(listwis

e)

82

47

APPENDIX V: Group statistics tables

Table 6: Group Statistics

Y Mean Std. Deviation

Valid N (listwise)

Unweighted Weighted

0 Le1 .320164 .1393833 41 41.000

Le2 .679836 .1393833 41 41.000

Le3 .012890 .0308251 41 41.000

Le4 .085819 .1245378 41 41.000

Le5 .214573 .2598566 41 41.000

Le6 .167406 .2795384 41 41.000

Li1 3.247555E0 4.8826498 41 41.000

Li2 .297276 .1286429 41 41.000

Li3 .714128 .1693933 41 41.000

Li4 .184144 .1352413 41 41.000

Li5 .454009 .4216966 41 41.000

Li6 .181454 .1783283 41 41.000

Li7 .766273 .5248931 41 41.000

Li8 .550935 .4622725 41 41.000

Li9 .734598 .6561252 41 41.000

Li10 1.875153E0 1.8809295 41 41.000

Li11 2.141748E0 1.9578473 41 41.000

O1 1.504323E0 1.4865789 41 41.000

48

O2 .215338 .2344184 41 41.000

O3 .207488 .1409590 41 41.000

O4 1.725372E0 2.0473516 41 41.000

O5 .833059 .7021887 41 41.000

O6 .063658 .0411115 41 41.000

O7 6.273574E0 20.1125951 41 41.000

O8 2.006781E0 1.5682032 41 41.000

P1 .227766 .1069386 41 41.000

P2 .235110 .1764485 41 41.000

P3 .191798 .1079434 41 41.000

P4 .173711 .1449146 41 41.000

P5 .138748 .0846013 41 41.000

P6 .271950 .1795259 41 41.000

P7 .199023 .1798630 41 41.000

P8 .214643 .1803671 41 41.000

P9 .215578 .1422828 41 41.000

E1 .202997 .1128094 41 41.000

E2 .311738 .1828674 41 41.000

E3 .375554 .2248005 41 41.000

E4 1.617856E0 1.2095476 41 41.000

1 Le1 .582412 .2330707 41 41.000

Le2 .417588 .2330707 41 41.000

49

Le3 .102831 .1518220 41 41.000

Le4 .365205 .2359920 41 41.000

Le5 .577488 .2871657 41 41.000

Le6 .990274 6.2460162 41 41.000

Li1 .180588 .6323273 41 41.000

Li2 .449429 .2417669 41 41.000

Li3 .495667 .2092104 41 41.000

Li4 .045694 .0973569 41 41.000

Li5 .687616 2.9115058 41 41.000

Li6 .148955 .3438652 41 41.000

Li7 2.167031E0 7.3913707 41 41.000

Li8 1.330760E0 4.3242165 41 41.000

Li9 .281546 1.2044034 41 41.000

Li10 .346585 1.0179891 41 41.000

Li11 .752711 1.0541383 41 41.000

O1 -9.948241E0 29.4731939 41 41.000

O2 .836271 3.0842997 41 41.000

O3 .816139 2.8304540 41 41.000

O4 -7.162559E0 18.5863481 41 41.000

O5 .416531 .4075395 41 41.000

O6 .288518 .4550341 41 41.000

O7 1.834850E1 94.7177546 41 41.000

O8 1.987609E0 1.5210046 41 41.000

50

P1 -.024198 .0939508 41 41.000

P2 .079569 .8482868 41 41.000

P3 -.082682 .0781294 41 41.000

P4 -.133772 .1607838 41 41.000

P5 -.073556 .1069090 41 41.000

P6 .065694 .1767568 41 41.000

P7 -.193692 .3744071 41 41.000

P8 -.133660 .1625512 41 41.000

P9 .288201 4.4258416 41 41.000

E1 -.081347 .0920732 41 41.000

E2 .297880 4.5472364 41 41.000

E3 .047288 .2199699 41 41.000

E4 4.023244E0 4.6756142 41 41.000

Total Le1 .451288 .2320030 82 82.000

Le2 .548712 .2320030 82 82.000

Le3 .057861 .1178950 82 82.000

Le4 .225512 .2343429 82 82.000

Le5 .396031 .3277230 82 82.000

Le6 .578840 4.4131051 82 82.000

Li1 1.714071E0 3.7882749 82 82.000

Li2 .373352 .2071139 82 82.000

Li3 .604898 .2187760 82 82.000

Li4 .114919 .1362502 82 82.000

51

Li5 .570813 2.0706845 82 82.000

Li6 .165205 .2726962 82 82.000

Li7 1.466652E0 5.2546728 82 82.000

Li8 .940847 3.0811421 82 82.000

Li9 .508072 .9903938 82 82.000

Li10 1.110869E0 1.6882548 82 82.000

Li11 1.447229E0 1.7117174 82 82.000

O1 -4.221959E0 21.5234491 82 82.000

O2 .525805 2.1960071 82 82.000

O3 .511813 2.0149072 82 82.000

O4 -2.718594E0 13.8800698 82 82.000

O5 .624795 .6077986 82 82.000

O6 .176088 .3404132 82 82.000

O7 1.231104E1 68.3155136 82 82.000

O8 1.997195E0 1.5352491 82 82.000

P1 .101784 .1614734 82 82.000

P2 .157339 .6138820 82 82.000

P3 .054558 .1668404 82 82.000

P4 .019970 .2169447 82 82.000

P5 .032596 .1434783 82 82.000

P6 .168822 .2052102 82 82.000

P7 .002665 .3524669 82 82.000

P8 .040492 .2445750 82 82.000

52

P9 .251889 3.1119857 82 82.000

E1 .060825 .1758784 82 82.000

E2 .304809 3.1980625 82 82.000

E3 .211421 .2759032 82 82.000

E4 2.820550E0 3.6031291 82 82.000

Table 7: Tests of Equality of Group Means

Wilks' Lambda F df1 df2 Sig.

Le1 .677 38.234 1 80 .000

Le2 .677 38.234 1 80 .000

Le3 .853 13.819 1 80 .000

Le4 .640 44.947 1 80 .000

Le5 .690 36.003 1 80 .000

Le6 .991 .710 1 80 .402

Li1 .834 15.910 1 80 .000

Li2 .863 12.655 1 80 .001

Li3 .748 27.003 1 80 .000

Li4 .739 28.302 1 80 .000

Li5 .997 .259 1 80 .613

Li6 .996 .289 1 80 .593

Li7 .982 1.465 1 80 .230

Li8 .984 1.318 1 80 .254

Li9 .947 4.474 1 80 .038

53

Li10 .793 20.943 1 80 .000

Li11 .833 15.999 1 80 .000

O1 .928 6.175 1 80 .015

O2 .980 1.652 1 80 .202

O3 .977 1.891 1 80 .173

O4 .896 9.263 1 80 .003

O5 .881 10.792 1 80 .002

O6 .890 9.931 1 80 .002

O7 .992 .638 1 80 .427

O8 1.000 .003 1 80 .955

P1 .384 128.459 1 80 .000

P2 .984 1.321 1 80 .254

P3 .315 173.964 1 80 .000

P4 .492 82.737 1 80 .000

P5 .446 99.424 1 80 .000

P6 .744 27.480 1 80 .000

P7 .686 36.650 1 80 .000

P8 .487 84.368 1 80 .000

P9 1.000 .011 1 80 .917

E1 .338 156.338 1 80 .000

E2 1.000 .000 1 80 .984

E3 .642 44.662 1 80 .000

E4 .887 10.171 1 80 .002

54

APPENDIX VI: Log determinants and Box’s M tables

Table 8: Log Determinants

Y Rank Log Determinant

0 4 -11.536

1 4 -5.466

Pooled within-groups 4 -6.383

The ranks and natural logarithms of determinants

printed are those of the group covariance matrices.

Table 9: Box’s M Test Results

Box's M 169.400

F Approx. 16.024

df1 10

df2 3.060E4

Sig. .000

Tests null hypothesis of equal population covariance matrices.

55

APPENDIX VII: Stepwise statistics tables

Table 10: Variables Entered/Removeda,b,c,d

Step Entered

Wilks' Lambda

Statistic df1 df2 df3

Exact F

Statistic df1 df2 Sig.

1 P3 .315 1 1 80.000 173.964 1 80.000 .000

2 O4 .276 2 1 80.000 103.605 2 79.000 .000

3 Li3 .258 3 1 80.000 74.725 3 78.000 .000

4 Le4 .245 4 1 80.000 59.381 4 77.000 .000

At each step, the variable that minimizes the overall Wilks' Lambda is entered.

a. Maximum number of steps is 76.

b. Minimum partial F to enter is 3.84.

c. Maximum partial F to remove is 2.71.

d. F level, tolerance, or VIN insufficient for further computation.

56

APPENDIX VIII: Wilks’ Lambda table

Table 11: Wilks' Lambda

Step Number of

Variables Lambda df1 df2 df3

Exact F

Statistic df1 df2 Sig.

1 1 .315 1 1 80 173.964 1 80.000 .000

2 2 .276 2 1 80 103.605 2 79.000 .000

3 3 .258 3 1 80 74.725 3 78.000 .000

4 4 .245 4 1 80 59.381 4 77.000 .000

Table 12: Wilks' Lambda

Test of

Function(s) Wilks' Lambda Chi-square df Sig.

1 .245 109.766 4 .000

APPENDIX IX: Table of eigenvalues

Table 13: Eigenvalues

Function Eigenvalue % of Variance Cumulative % Canonical

Correlation

1 3.085a 100.0 100.0 .869

a. First 1 canonical discriminant functions were used in the analysis.

57

APPENDIX X: The Standardized canonical discriminant

function coefficients table

Table 14: Standardized Canonical Discriminant Function Coefficients

Function

1

Le4 -.271

Li3 .310

O4 .458

P3 .825

58

APPENDIX XI: The structure matrix table

Table 15: Structure Matrix

Function

1

P3 .840

P1a .746

E1a .744

P5a .520

P8a .517

P4a .512

P6a .471

Le5a -.453

Le4 -.427

Li11a .424

Li1a .424

Li10a .411

Li9a .350

Le2a .335

Le1a -.335

E3a .334

Li3 .331

Li4a .314

Le3a -.302

59

O5a .251

P7a .222

O1a .213

P2a .201

O4 .194

Li6a .185

Li2a -.169

P9a -.149

E2a -.140

O8a -.114

Li8a .093

Li5a .090

Le6a .072

Li7a .070

E4a -.065

O3a .059

O2a .038

O6a .023

O7a -.016

Pooled within-groups correlations between discriminating variables

and standardized canonical discriminant functions

Variables ordered by absolute size of correlation within function.

a. This variable not used in the analysis.

60

APPENDIX XII: The canoncial discriminant function

coefficient table

Table 16: Canonical

Discriminant Function

Coefficients

Function

1

Le4 -1.438

Li3 1.631

O4 .035

P3 8.756

(Constant) -1.046

Unstandardized coefficients

Table 17: Coefificient correlation matrix

Le4 Li3 O4 P3

Correlation Le4 1.000 .020 .108 -.256

Li3 .020 1.000 -.192 .138

O4 .108 -.192 1.000 -.213

P3 -.256 .138 -.213 1.000

61

APPENDIX XIII: The Group Centroids table

Table 18:

Functions at

Group Centroids

Y

Function

1

0 1.735

1 -1.735

Unstandardized

canonical discriminant

functions evaluated at

group means

62

APPENDIX XIV: Classification table

Table 19: Classification Resultsb,c

Y

Predicted Group Membership

Total 0 1

Original Count 0 41 0 41

1 1 40 41

% 0 100.0 .0 100.0

1 2.4 97.6 100.0

Cross-validateda Count 0 41 0 41

1 1 40 41

% 0 100.0 .0 100.0

1 2.4 97.6 100.0

a. Cross validation is done only for those cases in the analysis. In cross validation,

each case is classified by the functions derived from all cases other than that case.

b. 98.8% of original grouped cases correctly classified.

c. 98.8% of cross-validated grouped cases correctly classified.

63

APPENDIX XV: Table of Z-scores for enterprises

Table 20: Z-Scores for enterprises

NON-DEFAULT GROUP

No. SYM ENTERPRISE's

NAME

SECTOR

LEVEL 2

SECTOR

LEVEL 4

Asset size

(Bil VND)

Z-

Score

1 BMP

Binh Minh

Plastics

Constructions &

Materials

Building Materials

& Fixtures

982.15

2.93

2 DHA

Hoa An Stones

and Materials

Constructions &

Materials

Building Materials

& Fixtures

377.07

1.01

3 NHC

Nhi Hiep Brick-

Tile

Constructions &

Materials

Building Materials

& Fixtures

48.38

2.69

4 NTP

Tien Phong

Plastics

Constructions &

Materials

Building Materials

& Fixtures

1,402.00

2.07

5 VTS

Viglacera Tu Son

Ceramic

Constructions &

Materials

Building Materials

& Fixtures

76.27

1.98

6 SDA

Song Da

Manpower Supply

Industrial Goods

& Services

Business Training

& Employment

Agencies

275.79

0.93

7 HRC Hoa Binh Rubber Chemicals

Commodity

Chemicals

502.11

0.81

8 PAC

Dry Cell and

Storage Battery

Industrial Goods

& Services

Electrical

Components &

Equipment

1,103.44

0.80

9 NBP

Ninh Binh

Thermal Power

Electricity,

Water, Gas &

Oils

Electricity

Production &

Distribution

286.09

3.36

10 AAM Mekong Fisheries

Foods &

Beverages Farming & Fishing

331.34

1.56

64

11 ABT

Ben Tre

Aquaproduct

Foods &

Beverages Farming & Fishing

601.93

1.51

12 NSC National Seeds

Foods &

Beverages Farming & Fishing

302.99

1.84

13 SSC

Southern Seed

JSC

Foods &

Beverages Farming & Fishing

260.11

2.30

14 SJ1 Seafoods No 1

Foods &

Beverages Farming & Fishing

111.29

0.74

15 VNM Vinamilk

Foods &

Beverages Food Products

10,773.03

2.75

16 HHC

Haiha

Confectionery

Foods &

Beverages Food Products

224.40

1.05

17 LSS Lam Son Sugar

Foods &

Beverages Food Products

1,549.88

2.41

18 SAF Safoco Foodstuff

Foods &

Beverages Food Products

105.96

1.72

19 PGD

PV Gas

Distribution

Electricity,

Water, Gas &

Oils Gas Distribution

977.57

3.06

20 SFC Saigon Fuel Co.

Electricity,

Water, Gas &

Oils Gas Distribution

230.15

2.73

21 CTD

COTEC

construction

Constructions &

Materials

Heavy

Construction

2,017.42

1.32

22 S99 Song Da 99

Constructions &

Materials

Heavy

Construction

212.29

0.72

23 LCG LiCoGi 16

Constructions &

Materials

Heavy

Construction

2,050.13

0.24

65

24 SRF SEAREFICO

Industrial Goods

& Services

Industrial

Machinery

591.04

0.81

25 VFC Vinafco

Industrial Goods

& Services

Marine

Transportation

565.65

0.14

26 SFI

Sea and Air

Freight

International

Industrial Goods

& Services

Marine

Transportation

397.67

0.39

27 HGM Ha Giang Mineral Basic Resources Mining

144.24

4.21

28 GHA Hai Au Paper Basic Resources Paper

45.92

2.19

29 KHA

Khanh Hoi Im-

export Property Property

431.16

1.31

30 NTL

Tu Liem Urban

Development Property Property

2,135.01

3.73

31 RCL

Cho Lon Real

Estates Property Property

224.93

2.33

32 SCD

Chuong Duong

Beverages

Foods &

Beverages Soft Drinks

203.38

1.42

33 CMT

Information and

Networking

Technology

Information

Technology Software

192.27

0.90

34 FPT FPT Group

Information

Technology Software

12,304.54

1.00

35 KKC

Metal

Manufacturing

and Trading Basic Resources Steel

175.01

0.77

36 SSM

VNECO Steel

Structure Basic Resources Steel

186.51

0.76

66

37 CSG Sai Gon Cable

Information

Technology

Telecommunicatio

ns Equipment

671.17

0.57

38 DXP Doan Xa Port

Industrial Goods

& Services

Transportation

Services

189.40

3.55

39 TCT

Tay Ninh Cable

Car Tour Co.

Tourism &

Leisures Travel & Tourism

109.80

3.78

40 HTV Ha Tien Transport

Industrial Goods

& Services Trucking

247.35

1.13

41 VNL VINALINK

Industrial Goods

& Services Trucking

154.23

1.61

DEFAULT GROUP

NO. SYM ENTERPRISE's

NAME

SECTOR

LEVEL 2 SECTOR

Asset size

(Bil VND)

Z-

Score

1 TTC

Thanh Thanh

Construction

Constructions &

Materials

Building Materials

& Fixtures

152.64

(1.35)

2 CYC

Chang Yih

Ceramic

Constructions &

Materials

Building Materials

& Fixtures

347.26

(1.18)

3 VTA Vitaly

Constructions &

Materials

Building Materials

& Fixtures

288.17

(1.70)

4 TLT

Thang Long

Viglacera

Constructions &

Materials

Building Materials

& Fixtures

355.40

(3.58)

5 TKU

Tung Kuang

Industrial

Constructions &

Materials

Building Materials

& Fixtures

606.33

(1.36)

6 ILC

International

Labour and

Services

Industrial Goods

& Services

Business Training

& Employment

Agencies

335.31

(2.07)

7 VKP Viky Plastic Chemicals

Commodity

Chemicals

302.72

(2.49)

67

8 TYA

Taya (Vietnam)

electric wire &

cable

Industrial Goods

& Services

Electrical

Components &

Equipment

660.61

(2.56)

9 PPC

Pha Lai Thermal

Power

Electricity,

Water, Gas &

Oils

Electricity

Production &

Distribution

10,797.31

(0.83)

10 FBT

Bentre Forestry

Aquaproduct

Foods &

Beverages Farming & Fishing

492.79

(2.45)

11 BAS Basaco

Foods &

Beverages Farming & Fishing

199.43

(1.44)

12 CAD

Cadovimex

Seafood

Foods &

Beverages Farming & Fishing

1,195.38

(1.91)

13 ANV Nam Viet

Foods &

Beverages Farming & Fishing

2,136.29

(1.34)

14 MPC

Minh Phu Seafood

Corp.

Foods &

Beverages Farming & Fishing

2,266.91

(1.14)

15 BLF Bac Lieu Fisheries

Foods &

Beverages Farming & Fishing

216.49

(1.55)

16 BHS Bien Hoa Sugar

Foods &

Beverages Food Products

598.53

(1.55)

17 HNM Hanoi Milk

Foods &

Beverages Food Products

214.54

(2.27)

18 AGC An Giang Coffee

Foods &

Beverages Food Products

441.05

(1.61)

19 VMG

Vung Tau

Petroleum

Electricity,

Water, Gas &

Oils Gas Distribution

212.27

(2.20)

20 MTG MT Gas Electricity,

Water, Gas & Gas Distribution

182.83

(1.26)

68

Oils

21 SD8 Song Da 8 JSC

Constructions &

Materials

Heavy

Construction

285.29

(0.89)

22 FPC Full Power

Constructions &

Materials

Heavy

Construction

1,219.86

(0.89)

23 PVV

Vinaconex - PVC

Construction

Constructions &

Materials

Heavy

Construction

25.52

(0.53)

24 PTM

Precision Tools &

CNC Machine

Industrial Goods

& Services

Industrial

Machinery

21.42

(1.24)

25 VSP

Vinashin

petroleum

transport

Industrial Goods

& Services

Marine

Transportation

3,464.71

(2.55)

26 SHC Sai Gon Maritime

Industrial Goods

& Services

Marine

Transportation

157.81

(2.02)

27 KTB

Tay Bac Minerals

Investment Basic Resources Mining

1,219.86

(1.01)

28 HAP Hai Phong Paper Basic Resources Paper

673.97

(1.60)

29 PVL

PVPOWER

LAND Property Property

832.90

(4.19)

30 SCR Sacomreal Property Property

5,426.62

(0.97)

31 TIG

ThangLong

Investment Group Property Property

17.85

0.42

32 TRI

Tribeco Sai Gon

beverages

Foods &

Beverages Soft Drinks

325.82

(3.84)

33 SRB SARA

Information

Technology Software

66.73

(1.18)

69

34 SRA Sara Vietnam

Information

Technology Software

26.59

(1.51)

35 NVC Nam Vang Basic Resources Steel

1,422.13

(0.96)

36 HLA Asia Huu Lien Basic Resources Steel

961.52

(1.37)

37 TLC

Thang Long

Telecom

Information

Technology

Telecommunicatio

ns Equipment

203.76

(2.62)

38 VSG

Southern

Container

Shipping

Industrial Goods

& Services

Transportation

Services

498.52

(2.85)

39 PDC

Phuong Dong

Petroleum

Tourism

Tourism &

Leisures Travel & Tourism

312.70

(2.86)

40 MHC Hanoi Maritime

Industrial Goods

& Services Trucking

420.23

(2.03)

41 VCV

Vinaconex

Transport

Industrial Goods

& Services Trucking

33.90

(0.62)

APPENDIX XVI: Enterprises classification depend on Z-

Scores

Table 21: Discriminant point

DISCRIMINANT POINT DESCRIPTION ENTERPRISE STATUS

Z> 0.42 Without probability of default Safety

0 < Z < 0.42 May have probability of default Alert

Z < 0 High probability risk of default Danger

70

APPENDIX XVII: Determine risky sectors based on Z-

Scores

Table 22: Sectors’ Average Z-Scores

STT SECTOR – Level 2 Number of

enterprises Average Z-Scores

1 Chemicals 2 -0.84

2 Information Technology 6 -0.47

3 Industrial Goods & Services 16 -0.41

4 Foods & Beverages 20 -0.09

5 Constructions & Materials 16 0.09

6 Basic Resources 8 0.37

7 Property 6 0.44

8 Tourism & Leisures 2 0.46

9 Electricity, Water, Gas & Oils 6 0.81