The application of altmans z model to measure default risk of the vietnamese commercial banks borrowers

Altman’s models have been used widely in developed countries as an efficient tool to predict the bankruptcy risk of companies.. However, in many theses relating to credit risk management

MINISTRY OF EDUCATION AND TRAINING UNIVERSITY OF ECONOMICS- HOCHIMINH CITY

Trần Thị Kim Dung

Pr.Dr Trần Ngọc Thơ

I would like to express my sincere gratitude and deep appreciation to my research Supervisor, Pro Dr Tran Ngoc Tho for his precious guidance, share of experience, ceaseless encouragement and highly valuable suggestions throughout the course of my thesis

I would like to thank many of my friends from The state bank of Dong nai branch, Vietcombank and Techcombank, who have helped me during the collection of data as well as support me during doing the research

Vietnam-I also would like to take this opportunity to express my appreciation to Professor Nguyen Dong Phong, UEH Board of Directors for creating MBA program in English and Dr Vo Xuan Vinh, Dr Tran Dinh Kien for his support during the course

Specially, my thanks go to Mr Quyet, Mr Trung for their valuable and enthusiastic support for this research study and for their comments of English from early draft of my thesis

Last but not least, the deepest and most sincere gratitude goes to my beloved parents, my brothers for their boundless support and encouragement throughout my period of study I, therefore, dedicate this work as a gift to them all

The bankruptcy of a series of large and long- standing banks in the United States has created an alarming signal to the Vietnamese commercial banks, especially in the context of existing too many banks combining with over- expanded credit growth in recent years

The objective of the thesis is to introduce an intenal model for banks which will improve their current predictive power of financial risk factors The thesis aims

at validating the efficiency of Altman z’ score model for credit risk evaluation through emphirical data Besides that, a simple and reliable defaulted prediction model for manufacturing and privately held firms is also developed on the basis of gathering variables from many models developed in developing countries Financial profiles of 48 defaulted and healthy companies are examined and a model is built using the Discriminant analysis technique The model can be used as a part of intenal credit rating in commercial banks It is also used to assist investors, creditors, auditors to predict business failure

TABLE OF CONTENTS

Acknowledgement……… i

Abstract……… ii

Table of contents………iii

List of figures……… v

CHAPTER ONE: INTRODUCTIONi 1.1 Introduction 1

1.2 Research background 4

1.3 Research problem 9

1.3.1 Research questions 11

1.3.2 Research objectives 11

1.4 Research method 12

1.5 Data analysis and findings 12

1.6 Significance and scope of the study 13

1.7 Structure of the study 13

CHAPTER TWO: LITERATURES REVIEW 15

2.1 Overview of the Vietnamese banking system 15

2.2 Credit risk 16

2.2.1 Causes 16

2.2.1.1 Macro factors 16

2.2.1.2 Customers 17

2.2.1.3 Banks 18

2.2.2 Credit risk management 18

2.3 Overview of Altman’s model 20

2.3.1 The Z’ model 24

2.3.2 Previous studies 27

CHAPTER 3: METHODOLOGY & DATA ANALYSIS 35

3.1 Research design 35

3.2 Item generation 36

3.3 Pilot test 37

3.4 Main study 37

3.4.1 Bank selection 37

3.4.2 Sampling 43

3.5 Description of sample 43

3.6 Results of discriminant analysis 47

3.7 New points and limitation 54

3.7.1 New points 54

3.7.2 Limitations 55

CHAPTER 4: CONCLUSIONS AND IMPLICATIONS 57

4.1 Conclusions related to the research question 57

4.2 Implications of the study 58

4.2.1 Theoretical implications 58

4.2.2 Implications for banks and practitioners 58

4.3 Limitations and recommendations for further research 59

LIST OF FIGURES

Figure 1.1- Export- Import during the period of 1986- 2008 5

Figure 1.2- GDP growth- Inflation during the period of 1986- 2008 5

Figure 1.3- Exchange rate USD/VND in the year of 2008 6

Figure 1.4- Research problem 11

Figure 3.1- Research process 36

Figure 3.2- The number of banks’ branches in Dong nai province 38

Figure 3.3- VCB DN’s outstanding structure in 2009 40

Figure 3.4- Loan portfolio……… 42

LIST OF TABLES Table 2.1- Vietnamese banking system 15

Table 2.2- The summary of Altman’s models 24

Table 3.1- The business result of VCB DN from 2002- 2009 40

Table 3.2- Bad debt (group 3- 5) of VCB DN from 2005 – 2009 41

Table 3.3- The summary of classification points of z’ score model 45

Table 3.4- Categorization of companies based on industry 46

Table 3.5- Categorization of companies based on total assets 46

Table 3.6- Group statistics……… 47

Table 3.7- Pooled within- groups matrices 48

Table 3.8- Eigenvalues 48

Table 3.9- Wilks’ Lamba 48

Table 3.10- Canonical Discriminant Function Coefficients 48

Table 3.11- Classification Results 49

Table 3.12- Tests of equality of group means 50

Table 3.13- Tests of equality of group means- a 51

Table 3.14- Pooled within- groups matrices 51

Table 3.15 – Eigenvalues 51

Table 3.16 – Wilks’ Lamba 52

Table 3.17- Standardized Canonical Discriminant Function Coefficients 52 Table 3.18- Canonical discriminant function coefficients ……… 52 Table 3.19 - Classification Results 53

CHAPTER ONE: INTRODUCTION

In Viet Nam, with bank account holders accounting for only 10% of the population, there has always been a promising market with large potential profits for the banking sector This characteristic creates attractiveness resulting in many banks being founded for only short periods of time Competition became fierce among banks

Due to a much deeper level of integration into the world’s economy, Vietnam has a diversified investment channels including the stock market, gold, and the real estate market

In the past, banks lent capital to businesses to support for their main operations Nowadays, investors can accept riskier investments to gain higher profits as the market grows There is an increasing risk of borrowers receiving multiple loans, specially as many big corporations expand their business into new sectors such as financial investment and real estate Over- dispersed investments

without overall strategic planning in macro perspective which is suitable for the development conditions in Vietnam is extremely risky Lessons in bankruptcy in the United States (too big to fail) has cautioned Vietnamese banks In Vietnam, more than 70% of commercial banks’ revenues are from credit activities So the State bank of Vietnam as well as banking managers always concentrate on credit risk management issues for the purpose of ensuring the safety and soundness of the banking system This thesis studies one of the tools that is used to predict the default/bankrupt risk of companies: Altman’s models Altman’s models have been used widely in developed countries as an efficient tool to predict the bankruptcy risk

of companies

The author has not found any research in Vietnam specific to only to this model However, in many theses relating to credit risk management in Vietnam, Altman score models are always cited as a popular method in developed countries

Nowadays there is numerous research in the world focusing on quantitative method formed models based on financial ratios as well as market- value formulas The Altman model may be relatively simple and although this model is world-famous in the world, it is not certain to be applicable in Vietnam In principal, any model is builted based on certain assumptions and they can not cover all conditions

in reality That is the reason why model analysis is always considered a part of the decision process In the research aspect, no single model helps users deliver their final decisions and Altman models are not exceptional in this respect Users can’t rely solely on this model to make a final judgement about bankrupt or non-bankrupt, default or non-default However, this thesis hopes to open up paths for further empirical study and to stress the importance and necessity of quantitative research before studying more complicated methods in credit risk management aspect Therefore, this thesis examines whether Altman’s models can be suitable to use as a credit risk management measurement for commercial banks in Vietnam

Altman has written score models for predicting bankruptcy risk including: the z score model for manufacturing and listed firms, the z’ score model for manufacturing and privately held firms, and the z’’ for non- manufacturers There is much empirical evidence in the application of Altman’s z model- used to measure the bankruptcy risk of listed manufacturing firms in many countries Almost all results favor Altman’s score as a good predictor for bankruptcy risk Therefore, this thesis focuses on testing another Altman model- the z’ score for privately held firms

in the manufacturing sector At the current time in Vietnam the research into methods for measuring default risk quantitatively are essential not only for bank directors but also for investors, shareholders, and state agencies This is the rationale behind the research: “The application of Altman’s z’ score model to measure default risk of the Vietnamese commercial banks’ borrowers”

The objectives of the thesis are (1) to apply Altman’s z’ model in measuring default risk of the sample companies including customers borrowing money from banks, (2) to contribute to the implications of Altman’s z’ model on the safety and soundness of credit operations in Vietnamese commercial banks

In term of structure, the thesis consists of four chapters The thesis begins by defining the research problem and questions, and providing a justification for the study Chapter one also reviews the research background, significance and scope of the study

Overview of the Vietnamese banking system and credit risk are presented in chapter two, previous researches and application of Altman’s model in the world are also involved in this chapter

Describing the methods used in the study is presented in chapter three The main objective of the study is to test whether Altman’s z’ model is suitable to apply

in Vietnam Surveying a sample of 48 companies is made to test Altman’s z’ model

in predicting default risk to banks’ customers

Finally, the thesis ends with chapter four This chapter will give conclusions and implications for commercial banks to improve its credit risk management system.

1.2 Research background

Being a developing country and like other emerging economies, Vietnam is constantly improving its policy environment to attract foreign investment Among the most attractive economies in Asia, Vietnam has excellent conditions for foreign investors such as: a stable political environment and a fast growing economy After being made an official member of the World Trade Organisation, Vietnam has begun a new macroeconomic reform program of deregulation, financial stability and trade liberalization in spite of controversial issues like whether or not the liberalization can kill many local infant industries, the increase of unemployment and social pressure

Vietnam has grown with average GDP during the past 10 years of around 7,2%, the year of 2009 with economic recession it was 5,2% and it is expected in

2010 to be 6% However, when the financial market becomes more and more developed, the effects of business cycles will be more profound Corporate distress and failure are part of that cycle There are many bankruptcy cases in the world: from the Asian financial crisis to the collapses of Enron and Worldcom, and the bankrupty declaration of hundreds of financial institutions in the US during the year

of 2008, 2009 due to the credit crunch The credit crunch that happened in the United States during the period of 2006 – 2008 has resulted in hundreds of large and long- established banks going bankrupt The fourth biggest investment banks in the United States, the 158 year- old, Lehman Brothers, was declared bankrupt in September 2009 The number of bankrupt banks in 2009 was 140 and this number

in 2010 is over 108 The Federal Deposit Insurance Corporation (FDIC) estimated a loss of 100 billion USD in 2013 (www.economynewJune 2010)

Vietnam spent the year of 2008 on significant movements to the economy in the sectors of high fluctuation of foreign exchange rates, low GDP growth, increasing trade deficit, and rocketing inflation.

Figure 1.1: Export- Import during the period of 1986-2008

(source: www.asset.vn)

Figure 1.2: GDP growth- Inflation during the period of 1990- 2008

(source: www.asset.vn)

Figure 1.3: Exchange rate USD/VND in the year of 2008

(Source: www.asset.vn)Banks play a vital role in the financial system, especially in Vietnam, where the security market has not yet fully developed Most of the functions performed by commercial banks can be divided into three broad areas: they provide a leading role

in the payments system, they intermediate between depositors and borrowers by offering deposit and loan products and they provide a variety of financial services such as fiduciary services, investment banking and off- balance sheet risk taking The banking industry has huge potential growth in the context of the number of people having banking accounts occupy is only 10% of the Vietnamese population,

a much smaller rate than other countries Moreover, banking products and services are rather simple, credit is the main source of income for banks Retail banking services have not yet developed and this area attracted the fast increase in newly founded banks

However, banks also are often found at the center of systemic financial

risk, operational risk, liquidity risk, compliance risk, foreign exchange risk, and reputation risk Unlike other businesses, bank failures can be contagious and stem from depositor panics or contractual links between banks If the problem of liquidity is serious then the whole banking system can be melt down.

Most banks in Vietnam operating as commercial banks are profit- seeking enterprises Banks’ primary source of revenue is interest income from their loan portfolios, and their primary risk is credit risk Credit risk can be understood as “the risk to earnings and capital that an obliger will fail to meet the terms of any contract with the bank, or otherwise fail to perform as agreed” (Benton E.Gup, James W.Kolari, 2005) The average bad debt ratio in Vietnamese commercial banks has been especially high in recent years

On the left hand of the balance sheet, most assets of banks are loans to customers Lending is a risky business and some of the loans will not be repaid Therefore, the state bank of Vietnam has promulgated regulation requiring banks to set aside a reserve for expected losses If the losses exceed the amount of reserve, bank capital will be deducted If the losses are large enough to eliminate most of the banks capital, the bank will fail unless additional capital is added

In term of capital structure, banks are considered as a highly leveraged organizations due to their special characteristics of operation To some extent, being leveraged allows them to enjoy the advantage of a tax shield from a firm point of view In addition, highly leveraged firms think that agency costs are reduced as lenders will try to increasingly supervise decisions of the board of management However, from the lender ‘s point of view, a firm with high leverage is limited in granting more credit and is risky

In term of agency costs, there is a difference between a firm and a bank While both want much highly leveraged, however, firms usually have limited scale and if they go bankrupt, lenders will be easy to take over them However, lenders of banks are numerous small depositors, no supervision are available to banks That is

the reason why the role of the state bank of Vietnam in promulgating banking regulation is extremely important.

The safety and soundness of the banking system is very important because of the crucial roles played by this sector in providing credit to nonfinancial firms, in transmitting the effects of monetary policy, and in providing stability to the

economy as a whole.

The credit risk management in Vietnam is a tough issue to banks’ managers Almost evaluation for making decision is subjective, emotional feeling for lacking quantitative analysis tools Moreover, asymmetric information is the main cause of adverse seclection and moral hazard (Huynh The Du, Nguyen Minh Kieu, Nguyen Trong Hoai, April 2005) Asymmetric information means the inequality of information between the bank and the borrower The borrowers usually have more information about themselves than is available to the bank That is the reason why banks tend to charge an interest rate that reflects the average rate of risk of all borrowers Adverse selection means that high-risk borrowers try to get loans from banks because they are willing to pay the average rate of interest, which is less than they would have to pay if their true condition were known to the bank Low-risk creditworthy investors borrow directly from the capital market at rates lower than those offered by banks

Contrary to adverse selection which occurs before the loan is made, moral hazard is the risk that the borrower might use the funds to engage in higher- risk investments in expectation of earning higher returns This would lead to increase the probability of default on the loan The moral hazard occurs especially when the lender is unable to monitor the borrower’s activities (Benton E.Gup, James

W.Kolari, “Commercial banking- the management of risk”, 3th edition, 2005).

The necessary conditions in credit activity includes specified guidance and clear legal regulation, accounting system and creditable financial statements

applicable ranking standards, independent credit evaluation organization, efficient asset registration system However, the current of credit information centre in Vietnam has not yet meet credit institutions’ need to evaluate creditworthiness, customers’ financial capacity, the efficiency of investment project That is the reason why banks make decision in granting loan base mainly on customers’ security assets, especially valuable and high liquid assets In principle, security asset

is just a reference in credit granting decisions instead of other evaluation such as the efficiency of investment projects, using loan for righ purpose This is considered as

a remedy to solve the moral hazard problem

However, facing with competition pressure is increasing nowadays, a customer can open transaction account in numerous banks and it is not easy for banks to negotiate to get full control of security assets For examples, a firm can use

a mortgaged/pleded assets to secure for loans in many credit institution so that it can get lending more exceeding their true need Therefore, there is an urgent need for the early detection of companies in financial distress, the urgency for such analyses also appears to follow the collapses of cyclical downturn It is important for lending institutions to examine whether any their customers are likely to face insolvency in both the short and longer term The thesis is also useful for financial institutions to make prudent decisions to minimize their risk in credit operation

1.3 Research problem

As stated above, the neccesary of credit risk management is very urgent Bank management need highly applicable, scientific processes of credit risk management However, the current credit risk measurement tools has not been developed in Vietnam Banks often evaluate credit risk of their customers due to their subjective judgement, their internal rating system and commonly financial analysis In term of financial analysis, it usually bases on individually financial ratios Altman- The professor of New York University has builted score model for predicting bankruptcy risk, in which z score model was developed in 1968 for

predicting bankruptcy risk to manufacturing companies listed in the stock exchange

He then continued developing z’ score model for manufacturing and privately held firm and z’’ score model for non- manufacturing firms Altman’s score modelsdescribe a method of measuring bankruptcy risk of a company based on a combination of specific financial ratios These models firstly have been applied in the United States However, Altman’s model has also been tested in many various countries such as India, Argentina, Israel, Sri Lanka, Malaysia,…and the useful of models has been confirmed in these countries

As bankruptcy problem in Vietnam has some differences compared with other countries as well as the difficulty of accessing bankrupt company database Therefore, the thesis aims to test Altman’s score model in term of default risk (bad debt) of companies A sample of 48 companies as borrowing customers of banks are tested to see whether Altman’s z’ models can predict how many percentage of accuracy default companies compared with reality

Therefore, the research problem for this thesis is the testing of Altman’s z’

score model of defaulted companies to see whether it is suitable to apply this method in the credit risk management of Vietnamese commercial banks.

This is a highly useful research problem in Vietnam at the current time Credit risk relates to the daily main operations of every banking organization This research brings many benefits because it can add a new method of credit risk management to banks as well as have implications for further research on credit risk measurement tools in future

Figure 1.4- Research problem

of Vietcombank and Techcombank banks doesn’t affect to the characteristics of the

Altman’s score model in

theory

The evidences of applying score model

How Altman’s z’ model

sample as well as representation for the population Then, selecting sample in a convenient way is acceptable to this model The study aims at validating the efficiency of Altman’s z’ Score model for credit risk evaluation through empirical data.

 To acknowledge Altman score model as an early sign in default prediction

 To apply Altman score model for privately held companies

 To give some recommendation to banks and suggestions for further research

1.4 Research method

This study was conducted in the Dong Nai province and a quantitative approach was used Data was collected by interviewing bank’s credit officers The purpose was to gather all necessary data for processing Discriminant analysis was employed to test the research model SPSS software version 16 was used for data analysis Chapter 3 will discuss the methodology of this study in detail

1.5 Data analysis and findings

This research is designed to test Altman’s z’score model for measuring default risk to customers of commercial banks As mentioned in the research objectives, first of all this thesis surveys the empirical evidence of using Altman score model in many research Next, the thesis will test Altman’s z’ score model of banks’ customers

The thesis tested Altman’s z’ model for a group of companies that were granted loans by commercial banks The objective was to see if the model could predict bankruptcy as well as default risk as they did in emerging economies and the United States of America The thesis tested 48 companies for the 2 years before they defaulted The sample was composed of 24 bad debt companies and 24 healthy companies As these companies have not been listed on the stock exchange, the research used Altman z’ score model for privately held companies

The secondary data method was used as literature reviews to summarize empirical evidence of previous research All over the world, this method is used to systematize all research results related to this thesis On that basis, it is useful to understand clearly how models were used in the past, to support this research problem

The primary data method was also used to collect and analyze information related to the current situation of the bank’s customers To get primary data to support this thesis, a collection of financial statements of customers will be conducted The results of the above survey can be used to describe and make some recommendations about the application of Altman models, as presented in chapter four

1.6 Significance and scope of the study

This thesis has a significant meaning in the current situation in Vietnam where quantitive analysis tools have not been developed yet Facing numerous investment opportunities, evaluating creditworthiness is confusing for banks as well

as for investors evaluating profitable investments Moreover, successful research will contribute to improving the efficiency of credit risk management in Vietnamese banks At the same time, it will help to cease the period where managers make decisions subjectively, lacking useful risk measurement tools

1.7 Structure of the study

Chapter 1: Introduction

Research background is described in this chapter Beside that, research problem, research objectives, significance and scope of the study are also included

in this chapter

Chapter 2: Literatures review

This chapter describes Altman models and overviews previous studies related to testing Altman models in many countries in the world

Chapter 3: Methodology & data analysis.

Discriminant analysis, method of sampling as well as the research results of z’ score model are presented in this chapter

Chapter 4: Conclusions and implications

The thesis ends with chapter 4 This chapter gives conclusions to the research problems in term of theory and practice, limitations of the study and usefulimplications for further research

CHAPTER TWO: LITERATURES REVIEW

2.1 Overview of the Vietnamese banking system

Table 2.1- The Vietnamese banking system in term of ownership

State-owned Commercial Banks (SOCBs)/ State- owned joint- stock

Commercial banks:

- Bank for Foreign Trade of Vietnam (VIETCOMBANK);

- Industrial and Commercial Bank of Vietnam (Vietinbank);

- Bank for Investment and Development of Vietnam (BIDV);

- Vietnam Bank for Agriculture and Rural Development (VBARD);

- Mekong Housing Bank (MHB)

05

The high profit margins of the banking industry together with the development of the stock market has attracted a growing number of new joint- stock commercial banks However, banking is a special business sector which requires high management skills especially capital management and risk management To face fierce competition and achieve credit growth targets, banks loosened loan policies The credit growth rate in 2007 was 54% As a result of this, high increasing inflation indicator leaded to significant fluctuation to the economy: The State bank of Vietnam adjusted prime rate four times, the highest number was 14%, the highest lending rate was 21%, the credit growth rate was restrainted to 30% in

2008 (Asia commercial bank, The analysis report of the Vietnamese banking

industry, June 2009, page 1) Banks faced severe liquidity problems and high bad

debt ratios

2.2 Credit risk

“Credit risk is the risk to earnings and capital that an obliger may fail to meet the terms of any contract with the bank” (Benton E.Gup and James W.Kolari (2005), commercial banking- the management of risk, page 12) Credit risk is usually associated with loans and investments, but nowadays it also arises in connection with other extensions of bank credit such as trade finance, guarantees, and commitments There are many causes relating to credit risk

2.2.1 Causes:

2.2.1.1 Macro factors:

- Natural disasters such as droughts, hurricanes, fires, earthquakes,

- Economic environment: inflation, unemployment and exchange rate These outside environments affect customer’s operations directly and therefore affect their repayment capacity

- The State’s macroeconomic policies such as investment, taxation, exchange rate, interest rate and international trade policy The unstable policies of the

- Customers are in trouble as output market is tightened, reduced orders, affected by difficulties of mother company operations in overseas countries

- Weak management capacity of customers, do not implement projects successfully

- Rocketing inflation, strongly fluctuating exchange rate, tightened monetary policy This creates business environment with unstable finance and many enterprises get in trouble

- The global economic crisis in the world occurred and Vietnam’s export market is strongly hindered Enterprises exporting almost their goods are at risk

- Changes in the government’s policies such as taxation,…also affect potential development of some industries

- Some FDI enterprises try to take advantage of policy preferences and cheap human resources, goods are exported to mother companies for consumption When mother companies are in trouble, FDI enterprises also suffer

- When a firm having credit relations with many banks defaults, main creditors are in disadvantaged position to choose debt management solutions

- Firms try to implement large investment projects but their financial capacity

is not strong enough and mainly dependent on outside finance

- Companies have interest conflicts in internal management

- Non- transparent debt between overseas mother company and local subsidiary is difficult to control and inherently risky

- Companies export 100% of their goods or the country where the mother company is operating has unstable politics

- Firms can declare booming values of security assets when they contributes to establishing businesses while the true values of these assets are not really high, so banks can make mistakes and see them as a basis of calculations for granting loans

2.2.1.3 Banks:

- Unreasonable credit policies, emphasis on profits and too high credit growth targets due to competition pressure among banks trying to increase their market share

- Credit officers do not act in accordance with credit policies and lending procedure leading to moral business violation

- Evaluation of security assets are inaccurate or necessary legal procedures are not completed

- A long- term business strategy and an efficient credit risk management strategy have not been built

- Highly concentrated on credit growth targets under the circumstance of fierce competition leads to loosened credit conditions and loan control

- Focusing loans on a group of customers creates, portfolios that are not diverse The credit policy of banks changes rapidly and when the economy adversely fluctuates, customers get in trouble

Each commercial bank has a different credit policy, depending on some of the following factors: mobilized capital, the monetary policy of the State bank, interest rate, inflation, stable or unstable economy and customers’ need The credit policy of commercial banks is performed by main policies such as interest rate, security policy, credit growth target, lending standard, preference policy to target customers Through these policies, banks can control their credit risk managementpolicy of their banks

Almost all previous research relating to credit risk management has been qualitative The issue of credit risk management is usually analyzed based on a specific bank by describing a lending process, the lending policy of the bank The solutions and recommendations given to the bank such as completing credit evaluation procedures, and improving credit officers abilities Beside that, some suggestions are also given to the State bank of Vietnam, the related ministries, and the government Besides that, there are some research are quantitative in some aspects of credit activity such as follows:

- Thuy Mai Trinh, Nguyen (May 2010) has studied “The application of Binary Logistic regression models to analyze factors affecting to the deliver lending decisions of Vietcombank- Dong Nai branch” The author built a model with four variables: debt per equity, return on equity, a clean repayment history (no repayment defaults), and providing information fully and timely under the bank’s request as a base for making lending decision

- Dinh Thuy Ngan, Le (April 2010) has studied “Bank credit appraisal criteria for borrowing firms in Vietnam” The author studied factors affecting the Ability to repay loans and the descriptive statistics and regression results have shown that the factors include corporate income tax, relations with banks, cash flow, total asset turnover and debt per equity ratio

This thesis will specify a financial aspect of credit risk evaluation: default risk predicting model

2.3 Overview of Altman’s model

Financial statements include balance sheets, profit and loss accounts and cash flows The absolute numbers on financial statements do not say too much about the current financial position of a firm Moreover, each industry has its own characteristic financial situation due to differences in business operations For instance, to trading firms, their revenue can be very big compared to their total assets and the inventory turnover is much higher than manufacturing firms Therefore, the analysis of financial ratios is widely used as a mean of evaluating a firm’s financial performance as well as making comparisions amongst firms From financial statements, many financial ratios can be calculated and each ratio has its own meaning

Forecasting a debtor’s ability to repay their financial obligations has been a crucial endeavor for lenders and investors for ages Answering the question, “how likely is it that my loan will be repaid on time?” is central to the valuation and asset allocation of debt portfolios

Many studies related to bankruptcy/ financial distress models have been conducted around the world William Beaver’s work (1966, 1968) applied statistical methods to predict bankruptcy for a pair- matched sample of firms He evaluated the importance of each of several accounting ratios based on univariate analysis

Inheriting Beaver’s research, Altman- a Professor of Finance at New York University’s Stern School of Business applied the statistical method of discriminant analysis to a dataset of publicly held manufacturers

In the study “Predicting financial distress of companies: revisiting the score and zeta models”, Edward I Altman (July 2000) has described his empirical study: The original data sample consisted of 66 firms, half of which had filed for bankruptcy All these firms were manufacturers Small firms with assets of <$1

z-asset size range restricted to between $1 and $25 million Two groups are made with the same bankrupt and existing firms The data collected are from the same years as those compiled for the bankrupt firms For the initial sample test, the data are derived from financial statements dated one annual reporting period prior to bankruptcy.

Because of the large number of variables found to be significant indicators of corporate problems in past studies, a list of 22 potentially helpful variables (ratios) was complied for evaluation The variables are classified into five standard ratio categories, including liquidity, profitability, leverage, solvency, and activity The ratios are chosen on the basis of their popularity in the literature and their potential relevancy to the study Altman used regression method to select the most five significant financial ratios which affect the bankrupt risk of a firm

X1= Working Capital/ Total Assets

 Working capital is defined as the difference between current assets and current liabilities This ratio measures liquid assets in relation to the firm's size In case of operating losses, current assets tend to shrink in relation to total assets Liquidity can also be measured by the current ratio, which is the ratio of current assets per current liabilities, or the quick ratio, which is the ratio of current assets minus inventory per current liabilities However, Altman did not see the current and quick ratios as good predictors as this measure

X2= Retained Earnings/ Total Assets

 Retained earnings is the account which reports the total amount of reinvested earnings and/or losses of a firm cumulated over time This ratio can be a signal that reflects the firm's age as well as earning power Many studies have shown failure rates to be closely related to the age of the business The age of a firm should be considered in this ratio The probability of failure is much higher in a firm’s earlier years In 1993, approximately 50% of all firms that failed did so in the first five

years of their existence (Dun & Bradstreet, 1994) In addition, the leverage of a firmcan be recognized by this ratio Those firms with high RE, relative to TA, have financed their assets through retention of profits and have not utilized as much debt.

X3= Earnings Before Interest and Taxes/ Total Assets

 Earnings Before Interest and Taxes means operating earnings that earning from business activities have not excluded tax and leveraging factors Since a firm’s ultimate existence is based on the earning power of its assets, this ratio is particularly appropriate for studies dealing with corporate failure

X4= Market Value of Equity / Book Value of Total Liabilities

 Equity is measured by the combined market value of all shares of stock, preferred and common, while liabilities include both current and long term The measure shows how much the firm’s assets can decline in value (measured by market value of equity plus debt) before the liabilities exceed the assets and the firm becomes insolvent For example, a company with a market value of its equity of

$1,000 and debt of $500 could experience a two-thirds drop in asset value before insolvency However, the same firm with $250 equity will be insolvent if assets drop only one-third in value

X5= Sales/ Total Assets

 This ratio is considered as standard measure for sales turnover which varies greatly from industry to industry The capital-turnover ratio is a standard financial ratio illustrating the sales generating ability of the firm’s assets It is one measure of management’s capacity in dealing with competitive conditions

(Edward I.Altman, July 2000, page 10- 12).

Altman’s has conducted the following procedures:

(1) Observation of the statistical significance of various alternative functions, including determination of the relative contributions of each independent variable;

(2) Evaluation of intercorrelations among the relevant variables;

(3) Observation of the predictive accuracy of the various profiles; and

(4) Judgement of the analyst

The final discriminant function is as follows:

Z = 1.2X1+ 1.4X2+ 3.3X3+ 6X4+ 999X5

Altman defined a “grey area” which is between 1.81 and 2.99 Firms, with scores within this range, are considered uncertain about credit risk and considered marginal cases to be watched with attention Firms with Z scores below 1.81 indicate failed firms Although, the cut-off point was set at 2.675, Altman advocates using the lower bound of the zone-of-ignorance (1.81) as a more realistic cutoff Z-Score So if Z < 1.81, then the company has a high probability of default On the other hand, the company is solvent, meaning that it is financial healthy

z-Using a Linear Discriminant Analysis model (LDA), Altman’s correctly classified 94% of bankrupt companies and 97% of non-bankrupt companies with overall 95% prediction accuracy from one year before failure From two years before failure, the LDA model predicted failure with 83% accuracy The study was considered a great improvement compared with previous studies at the time of Altman

The Z-Score is considered the most widely known and used model for predicting financial distress (Bemmann 2005) The probability of default is a crucial problem for banks International agreements as Basel Accord and the following Basel 2, have incentived the banks to adopt objectives systems of evaluating and monitoring risk of default in order to predict probability of default for new loans based on borrower’s characteristics The Altman’s model has proven to be a reliable tool for bankruptcy forecasting in a wide variety of contexts and markets (Gregory

J Eidleman, CPA, Feb 1995, “Z scores- a guide to failure prediction”, the CPA Journal online).

2.3.1 The Z’ model:

From about 1985 onwards, the Z-scores gained wide acceptance by auditors, management accountants, courts, and database systems used for loan evaluation (Eidleman) The model has been used in a variety of contexts and countries, although it was designed originally for publicly held manufacturing companies with assets of more than $1 million Besides z model, Altman’s has developed two other models to meet the requirement for measuring bankruptcy risk to privately held firm (the Altman Z'-Score), non-manufacturing companies (the Altman Z"-Score) and the Zeta Model

Table 2.2- The summary of Altman’s models

Year Discriminant function Decision criteria

Object: publicly trade manufacturing firms

1968 Z= 1.2X1 + 1.4X2 + 3.3X3

+ 0.6X4 + 1X5 Z<1.81 bankruptedZ>2.67 non- bankrupted

Z= 1.81 to 2.67 gray areaObject: privately held firms in manufacturing sector

1993 Z’’= 6.56X1 + 3.26X2 +

6.72X3 + 1.05X6

Z’’<1.1 bankruptedZ’’>2.6 non- bankruptedZ’’= 1.1 to 2.6 gray areaX’4: substituting the book values of equity for the market value in X4

In the emerging market model, Altman, Hatzell and Peck (1995) have added

a constant term of + 3.25 so as to standardize the scores with a score of zero equated

to a D (default) rated bond

Chapter 3 of the Ross, Westerfield, and Jordan (2006) corporate finance: X1, X5 are identified specifically as a liquidity ratio and as an asset utilization ratio respectively X2, X3 are reasonably close approximations of the equity ratio and the return on assets respectively

If the firm is a private manufacturing firm

- The financially sound firms have a z-score above 2,9

- Firms with a deteriorating financial future have a z-score between 1,23 and 2,9

- Potentially insolvent firms (high probability of bank in the near future) have

a z-score below 1,23

The z’-score is more than simply an assessment of bankruptcy potential When viewing the component ratios, one can reveal many of the critical issues/risks faced by the corporate that are assessed by traditional ratio analysis

- Liquidity issues are measured in X1

- Shareholder claims against assets are measured in X2 (low for a highly levered firm)

- Profitability are measured in X3

- Shareholder confidence (indicated by stock price) relative to debt are measured in X4 (low for an overly levered firm)

- Asset utilization are measured in X5

Each one of these issues/risks can potentially create significant problems (liquidity problems, operational problem, leverage, ) for the firm and individually can be analyzed much more extensively In essense, the z-score is a summary statistic of these risks that can be decomposed in a manner similar to Dupont

analysis However, the z-score examines much more than Dupont analysis and is used in one form or another in practice to assess bankruptcy risk (see Altman 2000)

or can be used as a predictor for a change in a firm’s bond rating (Altman and Rijken 2004)

The models are used both to classify and to predict business failure The classification and predictive power of a model is evaluated by determining the correct classification and incorrect classification There are two types of misses: type I error and type II error Type I error represents an actually bankrupt firm classified as non- bankrupt Type II error represents an actually non-bankrupt firm classified as bankrupt For investors, banks and the government the most serious and expensive mistake is to consider a firm as healthy when actually it will be in bankrupt soon Therefore, type I error represent real losses for shareholders, banks and other stakeholders On the other hand, type II error can be seen as an opportunity cost, an investor can lose the opportunity to make a good investment, a bank can lose the opportunity to lend money to a good customer or a supplier can lose the opportunity to make an additional sell There is time long enough for investors, banks, suppliers to take preventive measures to protect their investment

Different studies have been done using statistical models to analyze business performance and distress In 1977, Edvard I.Altman and Haldeman & Narayanan developed Zeta model It is now a proprietary model for subscribers to zeta services, Inc (Hoboken, NJ), therefore discriminant coefficients of a 7- variable model is not publized The variables includes return on assets, stability of earnings, debt service, cumulative profitability, liquidity, capitalization and size To develop this model, a sample of 111 firms including manufacturers and retailers has been tested with size

at least $20 million in assets In term of efficiency, zeta model especially can predict 2- 5 years prior to the distress date In Vietnam, financial ratio analysis has been based primarily on conventional and individual belief about the important of particular ratios The model of Altman attempt to be objective and to add empirical

evidence of the usefulness of ratios Since the 1960’s, many bankrupt studies have been done, especially for highly developed countries

2.3.2 Previous studies

Prediction of corporate financial distress has long been the object of numerous studies of corporate finance literature Since the seminal work of Altman (1968), numerous researchers have attempted to improve upon and replicate such studies in capital markets worldwide (see for example; Altman (1968), Deakin

(1972), Taffler (1984), Zavgren (1985), Theodossiou (1993), Ginoglou et al (2002)

One of the best-known models for predicting corporate financial distress is the Altman’s Z-Score model (Altman, 1968, 1983, 1993) Altman’s work has shown that the Z-score and its variants have a very high degree of accuracy in predicting corporate financial distress in the U.S as well as in the emerging markets (Altman, Hatzell and Peck, 1995)

There are numerous studies focusing on the methods of credit risk measurement These developments have been reflected in papers that have been published in the Journal of Banking and Finance over a period of time Some models such as multivariate accounting- based credit scoring models have been shown to perform quite well over many different time periods and across many different countries (Altman, Edward I & Anthony Saunders (1996))

The z score model has been tested in many different countries around the world Nikolaos Gerantonis, Konstantinos Vergos and Apostolos G.Christopoulos(2009) has examined whether Altman Z-score models can predict correctly company failures in Greece The empirical analysis has been made to examine all listed in the Athens Exchange companies, during the period of 2002-2008 and discontinuations of operation for these companies during the same period The study shows that Altman model performs well in predicting bankruptcies for a period up to three years earlier

Shilo Lifschutz, Arie Jacobi (2010) has conducted an empirical investigation

of whether it is possible to rely on two versions of the Altman Model (1968) to predict financial failure of publicly traded companies in Israel between 2000 and

2007 The findings of the study indicated that the model is able to predict bankruptcy of companies with a 95% accuracy rate one year prior to bankruptcy and with an 85% accuracy rate two years prior to bankruptcy

Lalith P Samarakoon (University of St Thomas, St Paul, MN ) and Tanweer Hasan (Roosevelt University, Chicago, IL) has studied the application of

Altman’s Z-Score Models in predicting corporate distress in the emerging Sri Lankan stock market This study investigates the ability of three versions of Altman’s Z-Score model (Z, Z’, and Z”) of distress prediction developed in the U.S

to predict the corporate distress in the emerging market of Sri Lanka The results show that these models have a remarkable degree of accuracy in predicting distress using financial ratios computed from financial statements in the year prior to distress The overall success rate of 81% is observed using the Z”-Score The out-of-sample evidence provided in this paper means that the Z-Score models seem to have a very good potential in evaluating the risk of corporate distress in smaller emerging markets as well

The purpose of this study is to provide an out-of-sample test of the Z-score model and its variants by applying them to a sample of firms in the emerging SriLankan stock market This study contributes to the literature by applying the well-known Z-Score distress prediction model to an emerging market The results provide us with evidence of the validity of a set of financial ratios, identified with reference to the U.S companies, in predicting financial distress in an emerging market

Ariel R.Sandin, Marcela Porporato conducted a research “Corporate bankruptcy prediction models applied to emerging economies: evidence from

Argentina and specifies a prediction model In this paper, the authors applied some models developed by Altman to a sample of Argentina companies One of the models showned to have predictive power in Argentina, even though they were developed in a much earlier time and for private companies in the United Stated.

The objective was to see if these models could predict bankruptcy as well as they did in the US Although that these models were developed for companies in another countries, in different business times and economical condition, it has been proved these models are relevant for Argentinean companies

The results reported have two main implications for investors First, they can use the z’ score model of Altman (1993) because it also has good prediction ability since it pays attention to solvency indicators, and secondly, in a rapid changing environment, they will also need to consider profitability ratios Most importantly, ithas been showned that this kind of models has an important role to play in helping investors and banks in decision making

The study has been demonstrated that the information available in the financial statements of companies quoted in the Buenos Aires stock exchange is useful to predict which companies are likely to go into bankrupt The ratios built based on financial statements are useful to predict bankruptcy in a period of stability

of an emerging economy, such as the case of Argentina in the 1990’s

Warren Miller, Morningstar Inc (July 2009) has compared models of corporate bankruptcy prediction: distance to default vs z score

The study confirms that credit scoring is the model utilised by the banks There is a comparison among the performance of two alternative formulations of market-based models for the prediction of corporate bankruptcy with a well-established UK-based z score model The results show that in terms of predictive accuracy, there is little difference between the market-based and accounting models(Vineet Agarwal and Richard Taffler, 2006)

There are economically large differences in profitability for credit risk model users with employment of the z-score model generating much higher risk-adjusted revenues, profits, and return on capital employed Although the accounting-ratio based approach is criticized for lack of theoretical grounding, it has three things in its favor: corporate failure is generally not a sudden event, it is rare that firms with good profitability and strong balance sheets file for bankruptcy because of a sudden change in the economic environment Usually, corporate failure is the culmination

of several years of adverse performance and, hence, will be largely captured by the firm’s accounting statements Second, the double entry system of accounting ensures that window dressing the accounts or change in accounting policies will have minimal effect on a measure that combines different facets of accounting information simultaneously Finally, loan covenants are generally based on accounting numbers and this information is more likely to be reflected in accounting-ratio based models

Despite extensive criticism of traditional accounting-ratio based credit risk assessment approaches, and the theoretically appealing contingent claims framework, in practice such conventional approaches are robust and not dominated empirically by KMV-type option-based models KMV Model is developed by the United States KMV Corporation, named by the three company founder, Kealhofer, MeQuow and Vasieek (Feixue Huang, Yue Sheng, Zhijce Li, page 73 -74) KMV model is based on Merton (1974) option pricing theory, through the enterprise's financial reports and the market value of equity and debt data such as the possibility

of likely future default KMV model's basic idea is to use stock to show the options nature, through the stock market and its volatility as well as the value of corporate debt data to value corporate assets and their volatility, and in the coming years in order to estimate the likelihood of corporate defaults by measuring the expected probability of default EDF

In fact, the accounting-based approach produces significant economic