Project name a study on application of altmans z score model in assessing the performance and the possibility of bankrupty of listed enterprises in viet nam

VIETNAM NATIONAL UNIVERSITY, HANOI INTERNATIONAL SCHOOL GRADUATION PROJECT PROJECT NAME: A STUDY ON APPLICATION OF ALTMAN’S Z-SCORE MODEL IN ASSESSING THE PERFORMANCE AND THE POSSIBILI

VIETNAM NATIONAL UNIVERSITY, HANOI

INTERNATIONAL SCHOOL

GRADUATION PROJECT

PROJECT NAME: A STUDY ON APPLICATION OF ALTMAN’S Z-SCORE MODEL IN ASSESSING THE PERFORMANCE AND THE POSSIBILITY OF BANKRUPTY

OF LISTED ENTERPRISES IN VIETNAM

STUDENT: PHAM THI THANH THAO

Hanoi - Year 2020

VIETNAM NATIONAL UNIVERSITY, HANOI

INTERNATIONAL SCHOOL

GRADUATION PROJECT

PROJECT NAME: A STUDY ON APPLICATION OF ALTMAN’S Z-SCORE MODEL IN ASSESSING THE PERFORMANCE AND THE POSSIBILITY OF BANKRUPTY

OF LISTED ENTERPRISES IN VIETNAM

SUPERVISOR: DR.DO PHUONG HUYEN STUDENT: PHAM THI THANH THAO STUDENT ID: 16071111 COHORT: QH-2016-Q

MAJOR: INTERNATIONAL BUSINESS

Hanoi - Year 2020

LETTER OF DECLARATION

I hereby declare that the Graduation Project “A STUDY ON APPLICATION

OF ALTMAN’S Z-SCORE MODEL IN ASSESSING THE PERFORMANCE AND THE POSSIBILITY OF BANKRUPTY OF LISTED ENTERPRISES IN VIETNAM” is the results of my own research and has never been published in any

work of others During the implementation process of this project, I have seriously taken research ethics; all findings of this project are results of my own research and surveys; all references in this project are clearly cited according to regulations

I take full responsibility for the fidelity of the number and data and other contents of my graduation project

Hanoi, 27/05/2020

Student (Signature and Full name)

ACKNOWLEDGEMENT

I would like to sincerely thank Dr Do Phuong Huyen for her support and guidance, without which this project would not be possible I would also like to thank the teachers in International School for imparting their knowledges and sharing their experiences

TABLE OF CONTENT

LETTER OF DECLARATION i

ACKNOWLEDGEMENT ii

TABLE OF CONTENT iii

TABLE OF NOTATIONS AND ABBREVIATIONS v

LIST OF TABLES vi

LIST OF CHARTS AND FIGURE vii

ABSTRACT 1

CHAPTER 1: INTRODUCTION 2

1.1 NECESSITY 2

1.2 OBJECTIVE OF STUDY 4

1.3 CONTRIBUTION OF STUDY 4

CHAPTER 2: LITERATURE REVIEW 5

2.1 INTERNATIONAL LITERATURE REVIEW 5

2.2 LITERATURE REVIEW IN VIETNAM 10

2.3 MUTIVARITE ANALYSIS METHOD- THE BASIS OF FORMING Z - SCORE MODEL 12

2.3.1 Z-SCORE INDICATOR 12

2.3.2 DESCRIPTIVE VARIABLES 13

2.3.3 Z- SCORE MODEL FOR PUBLIC MANUFACTURING COMPANIES 15

2.3.4 Z- SCORE MODEL FOR PRIVATE FIRM 16

2.3.5 Z- SCORE MODEL FOR FIRMS (NON- MANUFACTURING AND MANUFACTURING FIRMS) 17

2.3.6 Z- SCORE MODEL FOR FIRMS IN EMERGING MARKET 18

CHAPTER 3: DATA AND METHODOLOGY 20

3.1 DATA 20

3.1.1 SOURCE OF DATA 20

3.1.2 SCOPE OF DATA 20

3.1.3 RESEARCH QUESTION AND HYPOTHIES 20

3.2 METHODOLOGY 21

CHAPTER 4: EMPIRICAL RESULTS 22

4.1 RESULTS OF APPLYING THE Z-SCORE MODEL FOR 165 ENTERPRISES IN VIETNAM 22

4.1.1 STATISTICS ANALYSIS 22

4.1.2 RESULTS OF RESEARCH Z- SCORE MODEL 26

4.1.3 EVALUATE THE RESEARCH RESULTS 32

4.2 APPLICATION OF Z-SCORE MODEL FOR SAIGON PLASTIC PACKAGING COMPANY 34

4.2.1 HISTORY OF SAIGON PLASTIC PACKAGING COMPANY 34

4.2.2 ANALYSIS FINANCIAL SITUATION ANALYSIS FINANCIAL SITUATION OF SAIGON PLASTIC PACKAGING COMPANY 35

4.3.3 APPLICATION OF Z-SCORE MODEL FOR SAIGON PLASTIC PACKAGING COMPANY 43

CHAPTER 5: CONCLUSION AND RECOMMENDATIONS 48

5.1 CONCLUSION 48

5.2 LIMITATIONS OF RESEARCH 49

5.3 CONDITIONS TO APPLY Z- SCORE MODEL IN VIETNAM 51

5.4 RECOMMENDATIONS 52

5.4.1 IMPROVING INFORMATION TRANSPARENCY- FAIRNESS- MONITORING 52

5.4.2 DEVELOPING A SYSTEM OF VIETNAM CREDIT RATING ACCORDING TO INTERNATIONAL STANDARDS 54

5.4.3 COMPLETING BANKRUPTCY LAW AND RELATED DOCUMENTS 55

REFERENCES 56

APPENDIX 62

APPENDIX 1: AUDITED FINANCIAL STATEMENT OF SAIGON PLASTIC PACKAGING 2014-2019 62 APPENDIX 2: STATISTICS TABLE 165 ENTERPRISES IN VIETNAM 74

TABLE OF NOTATIONS AND ABBREVIATIONS

EBIT Earnings Before interest and taxes

S&P rating Standard and Poor Rating

LIST OF TABLES

Table 1: Correspondence between Z’’- score and Standard & Poor Rating Table 2: Statistics variables: X1, X2, X3, X4

Table 3: Percentage of firm – X1

Table 4: Percentage of firms- X2

Table 5: Percentage of firms- X3

Table 6: Percentage of firms- X4

Table 7: Z’’-score Results

Table 8: Asset Situation of Saigon Plastic Packaging Company 2012- 2019 Table 9: Asset Investment Rate of Saigon Plastic Packaging Company 2012- 2019 Table 10: Growth of Net Revenue of Saigon Plastic Packaging Company 2012- 2019

Table 11: Growth of Net Profit of Saigon Plastic Packaging Company 2012- 2019 Table 12: Expenses Situation of Saigon Plastic Packaging Company 2012- 2019 Table 13: Financial Ratios

Table 14: Working capital/ Total Assets Ratio (X1)

Table 15: Retained Earnings/ Total Assets (X2)

Table 16: EBIT/Total Assets

Table 17: Book value of Equity / Total Liabilities

Table 18: Z’’- score = 3.25+6.56*X1+3.26*X2+6.72*X3+1.05*X4

LIST OF CHARTS AND FIGURE

Figure 1: Percentage of firm – X1

Figure 2: Percentage of firms- X2

Figure 3: Percentage of firms- X3

Figure 4: Percentage of firms- X4

Figure 5: Z’’-score Trend for Enterprises in Vietnam

Figure 6: Net revenue of Saigon Plastic Packaging Company 2012- 2019

Figure 7: Net profit of Saigon Plastic Packaging Company 2012- 2019

Figure 8: Expenses Situation of Saigon Plastic Packaging Company 2012- 2019

ABSTRACT

The business environment always contains many volatile factors affecting the investment financing process as well as the business performance of enterprises, especially in the context of strong international economic integration which brings many opportunities and challenges for Vietnamese businesses Therefore, businesses always face positive periods alternating with the negative stages as well as the stage

of success and failure in financial trends which always contain unpredictable potential When a negative period shifts from temporary to structural and chronic (and thus continues over time), the company is often destined to cease This affects creditors and other stakeholders a lot because businesses are insolvent and unable to pay the amount they owe as originally committed

Therefore, early prediction about the bankruptcy of the company are of prime importance to the various stakeholders of the company as well as the whole society Bankruptcy prediction is an important task The first phase of determining the solvency can avoid evils shortly and protect the company from bankruptcy The bankruptcy of businesses can be predicted using many different research models However, this study applies the most famous model among the models, Z scores through the application of the sample of manufacturing and non-manufacturing companies of Vietnam from 2015 to 2019 to evaluate the performance of enterprises and predict bankruptcy and case about Saigon Plastic Packaging The study has found that the Z score model is appropriate and feasible to apply to analyze the performance

of Vietnamese enterprises by industries, sectors, and forecast bankruptcy risk of enterprises

Key word: Z-score model, bankruptcy, performance

CHAPTER 1: INTRODUCTION

1.1 NECESSITY

Most companies grow with the goal of maximizing returns To achieve the goal

of maximizing returns, the company needs strong support from internal and external factors The failure to manage internal systems such as efficient use of capital, labor, raw materials, etc and the influence of external factors such as economic, political, socio-culture and epidemics leading to the bankruptcy of companies Bankruptcy is a state of insolvency in which a company or person in financial difficulties, a loss or liquidation of an enterprise does not guarantee sufficient payment of the total amount

of due debts, which means no ability to pay creditors the amount of debt in which the company's total liabilities exceed the total assets Therefore, the net real value of the company is negative According to Stephen A Ross, Randolph W Westerfield, Bradford D Jordan, bankruptcy is a legal procedure to liquidate or reorganize a business (Chapter 16, Financial Leverage and Capital Structure Policy, page 562, Fundamentals of Corporate Finance, 11th edition) According to the Bankruptcy Law

in Vietnam (2014), bankruptcy is a condition of an enterprise or cooperative that is insolvent and declared bankrupt by a People's Court In particular, enterprises and cooperatives that are insolvent are enterprises and cooperatives that fail to perform the debt payment within 03 months from the date of maturity

Recent economic events have led many companies to apply for bankruptcy and

to study about risk and bankruptcy becoming the main concerns of the various equity holders in the business

Therefore, in today's global economic crisis, early forecasts about bankruptcy are of prime importance to the various stakeholders of the company as well as the whole society

There are many efforts to find the best way to measure the performance of a business and predict the bankruptcy of the business There are many researchers who have come up with the definition of bankruptcy in the past decades and tried

to build models based on financial exhaustion criteria, or based on the likelihood

of cash flow loss, loan default, capital restructuring, government support, renegotiation of loans with banks Some scientists have focused on assessing corporate performance and predicting bankruptcy using statistics and financial indicators Pioneers began in the 1930s when building models to help banks make decisions about whether to approve credit requirements In the late 1960s, the application of univariate and multivariate statistical analysis was developed by many researchers and focused on economic-financial indicators These studies have also been conducted by other researchers because of the simplicity of their application Although there is a great deal of research on assessing corporate performance and predicting bankruptcy worldwide, the Z-score model was built

by Altman (1968) with its adjustments (1983; 1993; 1995; 2005) have become prototypes applied worldwide Z-score is a model that can help investors foresee the bankruptcy of a company Altman analyzed 33 publicly produced US bankruptcy companies and their respective matches Moreover, he was based on his research over the year and by performing data segregation analysis, he could develop a model that enhances performance evaluation and bankruptcy prediction for companies publicly produced by the US Therefore, although the Z-score model has existed and developed for more than 45 years, it is still applied both in research and practice as the main tool or to assist in forecasting financial suffering, especially in the Italian context The scale of the model in question still ap plies, despite the heterogeneity of businesses and time, as it focuses on the core elements, allowing businesses to pursue their operations over time: financial stability and profitability

This study focuses on the application of the Z-score model in assessing the performance and predicting bankruptcy of 165 listed enterprises in Vietnam, especially the application of the model for Saigon Plastic Packaging Joint Stock Company which declared bankruptcy in the first quarter of 2020 Through it evaluates the effectiveness of the model in applying the model in Vietnam, and at the same time

points out the advantages and limitations of the model application in the analysis of

the case of Vietnam and making recommendations for solutions, policy implications

1.2 OBJECTIVE OF STUDY

Firstly, this study tends to summarize theoretical basis for the model of financial

health assessment and predicting bankruptcy risk; conditions for applying the Z-score

model in Vietnamese enterprises

Secondly, this study also considers the possibility of applying the Z-score model

to evaluate the performance and predict bankruptcy for 165 manufacturing and

non-manufacturing enterprises in Vietnam listed on stock exchanges, especially for Sai

Gon Plastic Packaging Joint Stock Company which replaces the commonly used

traditional method (separate analysis of financial indicators), in order to serve many

relevant purposes and objects such as main planning, determining the value of

businesses for investment, mergers, acquisitions, loans of commercial banks

Thirdly, it builds feasible solutions to improve the ability to assess the

performance of enterprises as well as predict the financial distress and likely future

bankruptcy for enterprises by using Z- score model in Vietnam

1.3 CONTRIBUTION OF STUDY

Firstly, the study presents a relatively complete system of the building process

in prediction model - Z-score which is quite famous in the world but rarely used in

Vietnam

Secondly, the study applies Z-score model in assessing the performance and

predicting bankruptcy of 165 listed enterprises in Vietnam and Saigon Plastic

Packaging Company

Finally, the study also points out the limitations when applying the Z-score

model and the policy implications of using the model in Vietnam

CHAPTER 2: LITERATURE REVIEW

2.1 INTERNATIONAL LITERATURE REVIEW

As more and more companies defaulted and filed for bankruptcy in the recent recession due to the severe impact of the COVID pandemic, investors became increasingly interested in evaluating company performance such as understanding how companies deal with financial pain and the risk of bankruptcy Studies in the world have shown that assessing the performance of enterprises (focusing on performance evaluation) mainly through the results of analyzing financial indicators (Fitzpatrick (1932), Smith & Winakor (1935), Merwin (1942), Chudson (1945), Jackendoff (1965), Altman (1968), Green (1978) ) in univariate analysis methods, Dupont (Beaver, 1967) and differential integrating many variables through econometric models to evaluate such as: Logit regression model, neural network model, Black-Scholes-Merton model, Z-score Although there are many studies have been done by various measures to evaluate the performance of the business as well as manage financial difficulties, forecast bankruptcy risks and their impact on the company through public restructuring or private, bankruptcy prediction studies are still limited to certain areas

One of the earliest research conducted in the field of bankruptcy ratio analysis and classification was developed and conducted by Beaver (1967) In a practical sense, his univariate analysis of several bankruptcy predictors paves the way for multivariate technical efforts for other authors to follow Beaver found that some financial ratios can differentiate between corporate and bankrupt companies' combined patterns for the period up to five years before bankruptcy This study implies a potential identification of ratios as a predictor of bankruptcy Beaver identifies a business failure as a failure to pay interest on its debt, withdraw a bank account or declare bankruptcy He studied large-scale companies that went bankrupt during 1954-1964 and classified successful companies using discriminatory analytical models Beaver chose to examine its debt / total assets, after-tax income /

total assets, and cash flow / total debt, and concluded that cash flow / total debt was the best ratio forecasting tool In general, the prevailing measures of profitability, liquidity, and solvency are the most important indicators However, the weight of each type is not clear because most of his studies are focused and univariate instead

of multivariate analysis He questioned the use of a multivariate analysis model, although a study suggested trying this procedure

James A.O (1980) studied about financial indicators and the ability to

predict bankruptcy The paper presents the results of quantitative prediction studies The company's failure report is proof of bankruptcy events The main findings of the paper can be briefly summarized as follows: first, the ability to identify four groups of statistically significant factors that affect the probability of business failure (within a year), that is: (1) firm size, (2) financial structure, (3) efficiency and (4) liquidity Second, previous studies that exaggerated the power

of its bankruptcy prediction models and tests Another problem is that the forecasting factors (financial indicators) are taken from the financial statements published after the bankruptcy date, after which evidence indicates that these factors will predict bankruptcy

E.I Altman (1968) researched at New York University in the late 1960‟ s Altman first introduced a method of statistical multivariate analysis in predicting corporate failures by using a linear discriminatory method to measure credit risk and developing a model called a model Z-score This model was developed based on the traditional method of Beaver (1967), from the analysis of 22 financial ratios through

a statistical filter based on a sample of 33 distressed manufacturing companies and

33 public companies The company does not suffer of the 22 variables initially selected, Altman discovered five ratios that were incorporated into the capital differentiation function: Working capital / Total assets, EBIT / total assets, market value of equity / book value of total debt, revenue / total assets The ratios for each variable and the whole equation were calculated and found that they make sense for all but the ratio of sales to total assets Altman studied 66 companies and the result

was 79% accurate a year before going bankrupt However, none of the companies studied by Altman were construction companies

The original Altman model was modified to fit several other industries made according to the coefficients including small companies, banks, insurance companies, construction companies, etc

Following this groundbreaking work, the multivariate approach to predict failure has spread among researchers worldwide in economics, banking, and credit risk The Z-Score model has become a standard for several of those internal-rate based models, Altman (1983, 1993) proposed that the management of distressed firms would be able to use the Z-Score model as a guide to a financial turnaround

Altman and McGough (1974) were the first to propose the utility of

bankruptcy prediction models to determine the existing status of concerns In a paper published in 1974, they carried out a study aimed at establishing criteria to assist auditors in recognizing circumstances where a company’s status as a matter of concern is in question by examining the relationship between bankrupt firms and auditors’ report in a pre-bankruptcy The study concluded that the auditor's judgment must be a determining factor on the appropriate going concern decision and that the Z-Score model could be an important assistance to the auditor in the context of his judgment

Grice and Ingram (2001) analyzed the generality of applying Z-score The

research found negative results in applying Z-score in recent periods and to manufacturing companies, but positive results for forecasting distress other than bankruptcy as it was originally designed for bankruptcy purposes It showed that the accuracy of applying this model to assess the performance of the firm is 57.6% compared to 83.5% as demonstrated by Altman (1968)

Anjum’s (2012) research paper talked about business failures, the frequent

changes made in the Altman Z score model in period 1968 -1993, and the comparison between different models built in bankruptcy terms It proved that the model is commonly regarded as a "failure predictor." It notes that the Altman Z score model

can be safety implemented to the modern economy to forecast bankruptcy two to three years before the bankruptcy case is announced

Manoj Kumar and Madhu Anand (2013): Based on their research

implemented on Kingfisher Airlines Limited (KAL), they determined that KAL's financial health performance analysis (and distress) when using Altman's Z score was satisfactory They discovered that the company’s financial health was reliably low during the study period, i.e from 2005 to 2012 In addition, reported prediction about financial distress in a corporation does not automatically mean bankruptcy This is just a possibility and an indicator of a possible future loss but could also be reversed

if proper action is taken

Bal and Raja (2013) research earnings control and strategies for forecasting

solvency positions Their analysis uses Z-score to predict the financial distress of IOCL and concludes that, as per the original Z-score, the financial situation of the company is not that good Although many researches have been conducted in this context, there may still be far fewer studies in the Indian context, especially in the case of FMCG companies The present study uses Z-score to evaluate the probability

of bankruptcy in selected firms

Vandana Guptal (2014) major research studies related to the present work have

been reviewed in the broad categories vis studies of accounting models Beaver (1966, 1968) and Altman (1968) developed the first set of accounting models to determine corporate distress risks Altman and Narayanan (1997) conducted researches in 22 countries where the key conclusion of the research was that models based on accounting ratios (MDA, logistic regression, and profit models) could effectively predict default risk

Bal, (2015): The purpose of the research paper described above was to evaluate

the accuracy of the Altman Z score model for the five FMCG enterprises which were selected from 2011 to 2015 The research offers a detailed explanation of the liquidity analysis It concludes that the Z score model is useful in predicting the bankruptcy of FMCG firms and recommends that the same be used by financial investors The study

also indicates that businesses should periodically estimate Z-score for approaches to boost their financial position

June Li (2012), when applying to the research of US manufacturing enterprises,

concluded that the Z-score model is not only highly effective in evaluating manufacturing enterprises but also effective for enterprises non-production

Fawad Hussain (2014) assessed 21 textile enterprises in Pakistan and

concluded that the use of the Z-score model in assessing and predicting the performance of textile enterprises in particular and other fields in general is: Very good, giving accurate forecasts results within 4 years

Nikolaos G (2009) also applied the Z-score model to evaluate 373 construction

enterprises in Greece and concluded that this is a useful tool in operating, managing,

or re-conducting, corporate structuring the merger of the company when it can improve the financial situation but only in a short time

Ahmad Khaliq (2014), with the case of application in Malaysia, concludes that

it is necessary to consider the relationship between the ratio in the model according

to the inputs in the studied industries

Appiah (2011) of enterprise research in Ghana concluded that the results of the

Z-score model depend on the accounting principles that each country applies

Pranee Leksrisakul and Michael Evans (2005) in the study of the business

bankruptcy model in Thailand provided new evidence that the use of multivariate discriminatory analysis (MDA) could be chosen as a predictive tool for the failure of listed businesses in Thailand Data sources used are businesses listed on the Thailand Stock Exchange (SET) during 1997-2002 The financial variables are taken from the bankruptcy prediction model of Altman (1968) The results show that the variables

of profitability, financial leverage, asset quality, and liquidity have an impact on the likelihood of corporate bankruptcy prediction, and they are all statistically significant

In addition, the test results show that the financial ratios of bankrupt enterprises have significant differences compared to non-bankrupt enterprises, the financial ratio of profits and liquidity The number and quality of assets of bankrupt firms are lower

than those of non-bankrupt enterprises, but the leverage tends to be in the opposition between these two groups of enterprises

Ming Xu and Chu Zhang (2008) researched on the case of Japanese listed

enterprises forecasting bankruptcy of listed companies in Japan during this period 1992-2005 The authors found that traditional measures such as Altman's Z-score (1968), Ohlson's Oscarore (1980), and previously developed option prices for the US market, were also useful for the Japanese market Moreover, predictive power is significantly stronger when these measures are combined The results show that bankruptcy prediction is based on a more successful option pricing method than accounting variables

2.2 LITERATURE REVIEW IN VIETNAM

In Vietnam, there are also many studies related to this topic

Research by Nguyen Minh Ha and Nguyen Ba Huong (2016) analyzes the

factors affecting bank bankruptcy risk by the Z-Score method The objective of the study is to identify factors affecting the risk of bankruptcy of Vietnam banks by the Z-score method, thereby suggesting appropriate policies to enhance the stability and soundness in operations of joint-stock commercial banks in Vietnam The study uses data including 23 Vietnamese joint-stock commercial banks with 115 observations from 2009-2013 The study found factors that are negatively associated with the risk

of bank bankruptcy: Credit growth, the ratio of provision for bad debts, the ratio of net interest income, equity to total assets, income diversification, state ownership, years of operation of a bank, and a listed bank Factors that have a positive relationship with the risk of bank bankruptcy including Cost management effectiveness and scale

Research by Pham Tuong Van (2016) investigates the applicability of the

Z-score model to assess the performance of Vietnamese enterprises in lieu of the commonly used communication method The objective of the study is to examine the similarity of research results among the methods used (method of individual analysis

of financial indicator groups, method of polynomial analysis through the Z-score

model and compare the Z score with the S&P ranking index and point out the advantages and limitations of applying the Z-score model in the analysis for the case

of Vietnam and draw key implications Research on using data in the analysis by economic sector and enterprise size, divided into 3 large economic sectors, which have a large number of businesses but not specific as the Finance sector, including: Processing-manufacturing (focusing on the analysis of pharmaceuticals, healthcare, food, fisheries, construction materials, plastics-packaging), construction and tourism-services Data used in the study Research is a set of data compiled from financial statements for the period of 2010-2014 of enterprises listed on both HOSE and HNX,

in which the manufacturing and processing industry has 201 enterprises; the construction industry consists of 105 enterprises and tourism - service industry includes 12 enterprises The research results show that the results between the two methods and with S&P ranking show quite similarities The assessment and ranking

of enterprises at the level of difficulty completely coincide, particularly for the number of businesses in safety and warnings, there are certain deviations due to the limit between the safety level and the warning level report S&P ratings differently from Z” (possibly due to a type II error) This shows that, basically, the Z-score model

is suitable and feasible in applying to analyze the performance of Vietnamese enterprises by industry and field

Based on the synthesis of empirical studies, the Altman Z-core model is applied

in many countries (from the US to some European countries and currently Asian countries also apply more) analyzing and predicting the operating situation of companies, showing the preeminence in classifying risk areas of companies in many different fields

Although there are still certain drawbacks due to the nature and characteristics

of businesses in each country are different and in different industries and sectors, however, the above studies show that, the Z model -score can be applied in analyzing, assessing and forecasting the situation of enterprises not only in developed countries but also in developing countries; suitable for evaluation by industry, field; according

to the size of the business; number of businesses and business sector Therefore, it is feasible to apply the Z-score model in analyzing and assessing the performance of enterprises in Vietnam instead of the traditional method currently applied

2.3 MUTIVARITE ANALYSIS METHOD- THE BASIS OF FORMING Z - SCORE MODEL

One of the most well-known financial distress prediction models, due to its easy predictability and application was built by Altman in 1968 He introduced a method of statistical multivariate analysis of enterprise failure prediction and estimation of a model called a "Z-Score model" This model was developed based on the traditional method of Beaver (1967) This is a statistical tool used to classify an observation into one of the given groups depending on the specific characteristics of the observation the method to find the linear relationship of the best descriptive characteristics of groups The variables selected that are relevant and consider liquidity, profitability, leverage, and solvency which were based on two separate criteria: their popularity in literature and their potential relevance for research

In 1977, Altman et al (1977) adjusted the original Z-Score model - to add

to the model the financial reporting criteria - into a new, better model, called the

"Zeta analysis.”

Multivariate analysis method (MDA = Multiple Discriminant Analysis) The MDA model includes a linear relationship between variables, which helps classify bankrupt and non-bankrupt business groups

And Altman (1968) introduced a Z-Score model, as follows:

With the steps of the sample selection process, through the bankrupt (distress) and non-bankrupt companies, and select the variables for the polynomial equation (Altman, 2000) and (Altman, 1968, 1977, 2000) offer four steps to assemble the final numbers as follows:

(1) observe the statistical significance levels of the various alternative functions, including determining the relative contributions of the independent variables; (2) assess the correlation between the relevant variables; (3) observing predictive accuracy of variable sets; and (4) expert judgment

The final discriminant is shown as follows:

Z = 1.2 X1 + 1.4 X2 + 3.3 X3 + 0.6 X4 + 0.999 X5

In which:

X1 = Working capital/ Total Assets

X2 = Retained earnings/ Total Assets

X3 = EBIT/ Total Assets

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

X5 = Sales/ Total Assets

(Source: Altman, 1968,1977, 2000)

Note that the model does not have a constant (limit number) That's because specific software is used, and the result is that the corresponding limit score between the two groups is not zero Other software, like SAS and SPSS, have a constant, which standardizes the limit at 0 if the sample numbers of the two groups are equal

2.3.2 DESCRIPTIVE VARIABLES

X1 = Working Capital/ Total Assets

Altman (1968, 1977, 2000) argues that the X1, often found in studies of enterprise problems, is a measure of the net liquidity of firm assets relative to Total capital Working capital is defined as the difference between current assets and current liabilities Liquidity and size characteristics are clearly considered Typically, a company experiencing a period of extended operating losses will have its working assets shrink compared to its total assets Out of the three liquidity

indexes, this index proved to be the most valuable index The other two liquidity ratios tested are current payment index and instant payment index They appear to

be less useful and depend on the conservative tendencies of a few failed companies

X2 = Retained earnings/ Total Assets

Altman (1968, 1977, 2000) states that retained earnings represent the total amount of reinvested income or the loss of a business over its lifetime This index is also considered as a surplus earned from the operation process It is worth noting that this index depends on the movement through restructuring and dividend distribution, which is not the object of this study, can understand that a trend will be formed through reorganization, or dividend policy or the appropriate main things in the accounting account An interesting new aspect of retained earnings is the ability to measure accrued profits over time The short or long operating life of a company is fully considered in this index

Therefore, it can be argued that young firms are to some degree discriminated

in this analysis, and the likelihood that these firms are classified as bankruptcy is relatively higher than that of the analysis companies have more uptime In addition, the RE / TA index measures the leverage of a business Companies with a high RE, compared to TA, can finance assets through keeping profits and not using too much debt, which a healthy sign of growth most of the time

X3 = EBIT/ Total Assets

According to Altman (1968, 1977, 2000) the X3 ratio measures the true productivity of firm assets, independently of taxes and debt Because the ultimate survival of a business is based on its ability to generate money, this indicator appears great in research related to business failure Moreover, the insolvency in bankruptcy cases occurs when the total debt is greater than the correct value of the company's assets with the value determined based on the profitability of the assets This ratio has

a better indicator of other profitability indicators, including cash flow

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

According to Altman (1968, 1977, 2000), equity is measured by the market value of all stocks, preferred stocks, and common stocks, while debt includes both short-term and long-term debt This index measures the degree of possible decline in the value of company assets before the debt exceeds the assets and the company becomes insolvent This index adds dimensions of market value that most other bankruptcy studies do not mention

X5 = Sales/ Total Assets

According to Altman (1968, 1977, 2000), the ratio of turnover to total assets is a standard financial indicator that illustrates the ability of enterprises to generate income

It is a measure of governance ability in a competitive environment This last indicator

is quite important, but it is the least important one on an individual basis In fact, based

on significance level tests using univariate statistics, it should not appear However, because of its unique relationship with the other variables of the model, the sales / total assets index ranks second in contributing to the model's overall ability to differentiate However, there is a big difference in revenue across industries, and Altman will develop an alternative model (Z '') without the X5 target later

2.3.3 Z- SCORE MODEL FOR PUBLIC MANUFACTURING

X1 = Working capital/ Total Assets

X2 = Retained earnings/ Total Assets

X3 = EBIT/ Total Assets

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

X5 = Sales/ Total Assets

(Source: Altman, 1968,1977, 2000)

To assess the bankruptcy of companies, their Z index is determined as follows:

Zone of Discrimination

Z <1.81: “Distress Zone” – High risk of Bankruptcy

1.81 <Z <2.99: “Grey Zone” Uncertain Results

2.99 <Z: “Safe Zone”- Healthy

The first application of the model involved a group of 66 US manufacturing companies (33 companies with healthy financial status and 33 bankruptcy companies) listed on the stock exchange Results showed that companies with a Z-score of less than 1.81 are at higher risk and are likely to go bankrupt; companies with a healthy financial position if the Z-score is greater than 2.99 and companies with uncertain results will be in the range of 1.81-2.99 in the grey area This model produced extremely accurate results with 95% correct prediction rate, and it received

a lot of positive feedback

2.3.4 Z- SCORE MODEL FOR PRIVATE FIRM

Altman has repeatedly expanded and modified its model to fit all types of businesses and fields of activity, different groups of companies other than manufacturing enterprises since the original model

The first amendment was for private companies because credit analysts, private agents, auditors, accountants, and businesses themselves were concerned that the original model would only apply to listed and public transaction Altman advocated reassessing the whole model instead of simply replacing a variable that is book value and market value in X4

According to Altman (1968, 1977, 2000), the Z-Score model is applied to private companies, as follows:

Z’= 0.717 X1 + 0.847 X2 + 3.107 X3 + 0.420 X4 + 0.998 X5

(Source: Altman 1983, pp.122)

In which:

X1 = Working Capital / Total Assets

X2 = Retained Earnings / Total Assets

X3 = Earnings Before Interest and Taxes / Total Assets

X4 = Book Value of Equity / Total Liabilities

X5 = Sales/ Total Assets

Zones of Discrimination:

Z > 2.90 - “Safe” Zones- Healthy

1.23 < Z < 2.90 - “Grey” Zones- Uncertain Results

Z < 1.23 - “Distress” Zones – High Risk of Bankruptcy

The X4 index became less influential on the Z-score with this modification This results in a wider gray area

2.3.5 Z- SCORE MODEL FOR FIRMS (NON- MANUFACTURING

AND MANUFACTURING FIRMS)

In the following years, the parameters and coefficients continue to be adjusted for different situations The Z-score model was introduced (Altman, Hartzell and Peck,

1995 and Altman and Hotchkiss, 2006, p 314) with 4 variables instead of 5 variables

as previous models with exclusion of the sales/ total assets (X5) for manufacturing companies as well as manufacturing sectors or companies operating

non-in the US and non-in developnon-ing markets (the 1995 research conducted a sample of

X1 = Working Capital / Total Assets

X2 = Retained Earnings / Total Assets

X3 = Earnings Before Interest and Taxes / Total Assets

X4 = Book Value of Equity / Total Liabilities

The threshold for this model is as follows:

Z” <1.1 – Distress Zone- High risk of Bankruptcy:

1.1 <Z '' <2.6- “Grey” Zones- Uncertain Results

2.6 <Z ''- “Safe” Zones- Healthy

2.3.6 Z- SCORE MODEL FOR FIRMS IN EMERGING MARKET

According to Altman (1968, 1977, 2000), credit in emerging economies can be analyzed in the same manner as used for traditional analysis by US companies Whenever a quantitative risk assessment process arises, an analyst can then use a qualitative assessment process to further adjust such factors as currency and industry risk, industry characteristics, and position competition of companies in that industry

It is often not possible to build a specific model for an emerging economic zone country based on data samples from that country because of a lack of credit evaluation experience there

To solve this problem, Altman (1968, 1977, 2000) revised the original Z-Score model to create an index model for emerging economies (EMS = emerging market scoring) The similarity keeps the Z '' adjusted and S&P rating of the company, written by Professor Altman in "The use of Credit scoring Models and The Important

of a Credit Culture" and presented in the following table One more thing we need to note is that the adjusted Z '' parameter, although used quite well in decent markets, should also be studied to adapt to the environment in Vietnam

In calculating the Z’’ score for emerging market, Altman, Hartzell and Peck (1995) suggested adding a constant +3.25 to standardize the research results so that scores equal or less than 0 might be equivalent to the default situation (Altman, Danovi and Falini, 2013)

New model Z '' - Score is:

Z '= 3.25 +6.56 X1 + 3.26 X2 + 6.72 X3 + 1.05 X4

In which:

X1 = Working Capital / Total Assets

X2 = Retained Earnings / Total Assets

X3 = Earnings Before Interest and Taxes / Total Assets

X4 = Book Value of Equity / Total Liabilities

Altman and Hotchkiss (2006) drew a correspondence between the score and the ratings assigned by Standard & Poor’s, as shown in table 1:

Table 1: Correspondence between Z’’- score and Standard & Poor Rating

Z’’- Score Threshold S&P Rating

CHAPTER 3: DATA AND METHODOLOGY

Based on the balance sheet, cash flow statement and income statement to calculate the value of working capital, total assets, retained earnings, earnings before taxes, book value of equity and debt Since then, using this spending method to calculate 4 financial indicators X1, X2, X3, X4 is used to calculate in

Regarding the scope of time: This topic mainly focuses on collecting aggregated data from audited financial statements and is published in the period 2015-2019 of

165 enterprises listed on two stock exchanges in Hanoi and Ho Chi Minh and in the period 2012-2019 of Saigon Plastic Packaging Company

3.1.3 RESEARCH QUESTION AND HYPOTHIES

How to assess the performance of a company and predict a bankruptcy without spending too much effort analyzing a large volume of qualitative and quantitative information?

3.2 METHODOLOGY

For the polynomial analysis method in applying the Z ”-score model (Altman, Hartzell & Peck (1995), The research uses Z”- score (has been adjusted to apply analysis of sectors and field, sector) with 4 variables as Altman's research, including:

X1 = Working Capital / Total Assets

X2 = Retained Earnings / Total Assets

X3 = Earnings Before Interest and Taxes / Total Assets

X4 = Book Value of Equity / Total Liabilities

Reality, Vietnam is frontier- emerging market but Vietnam could be upgraded to emerging-market status in 2022 Vietnam has already met most of MSCI’s quantitative requirements for emerging market (EM) index inclusion, including market size, a sufficient number of large-cap stocks with sufficient daily trading liquidity, and other metrics Therefore, the author has chosen the model to consider the applicability in the Vietnamese market

So the data is collected from 165 manufacturing and non-manufacturing companies, the Z’’- score model for emerging market is used when considering the similarities between the results of the Z -score model and the rating of S&P

Z” = 3.25 +6.56 X1 + 3.26 X2 + 6.72 X3 + 1.05 X4

The author chose this model because of some reasons Firstly, in Vietnam, there have been several previous studies that conducted model studies to evaluate the applicability of the model in Vietnam and gave some positive results when applied paradigm Secondly, the author chose the model because the author realized that it is suitable for some characteristics and properties of the Vietnam market Thirdly, the model has been applied in many countries, currently, Asian countries are also applying a lot in predictive analysis, proving the superiority in classifying risk areas

of businesses in many countries in different fields

X1 X2 X3 X4

Mean 0.255281 Mean 0.114671 Mean 0.093253 Mean 2.245229 Standard Error 0.008228 Standard Error 0.044587 Standard Error 0.004045 Standard Error 0.294102 Median 0.249629 Median 0.062011 Median 0.076041 Median 0.939028 Standard Deviation 0.236333 Standard Deviation 1.28065 Standard Deviation 0.116198 Standard Deviation 8.447426 Sample Variance 0.055853 Sample Variance 1.640065 Sample Variance 0.013502 Sample Variance 71.359 Range 2.368087 Range 38.23859 Range 2.635878 Range 269.7214 Minimum -1.4536 Minimum -1.62282 Minimum -1.63885 Minimum -179.731 Maximum 0.91449 Maximum 36.61577 Maximum 0.997023 Maximum 89.99049 Sum 210.6072 Sum 94.6037 Sum 76.93368 Sum 1852.314 Count 825 Count 825 Count 825 Count 825

CHAPTER 4: EMPIRICAL RESULTS 4.1 RESULTS OF APPLYING THE Z-SCORE MODEL FOR 165 ENTERPRISES IN VIETNAM

4.1.1 STATISTICS ANALYSIS

Table 2: Statistics variables: X1, X2, X3, X4

(Source: Calculated by Author)

Firstly, the X1 data shows that the value of the X1 variable of 165 manufacturing and non-manufacturing enterprises is very diverse and quite different, but about 89 enterprises have their current assets / total assets index which is equal or higher than average level, about 62 enterprises with the ratio of current assets / total assets are often quite low, especially, 14 businesses with negative ratio Enterprises with low or negative ratios tend to be predicted to go bankrupt, which is a quite important ratio, showing the ability of the enterprises to guarantee their short-term debts

Table 3: Percentage of firm – X1

Figure 1: Percentage of firm – X1

(Source: Calculated by Author)

This is followed by the X2 variable, which is the lowest of the four variables in the model, since retained earnings are a relatively small number compared to the total assets According to statistics, most businesses retain after-tax income to reinvest, but the retention rate is different for each enterprise For companies with negative EBIT, the profit is also zero, but for some businesses, the value of this variable X2 is less than zero, indicating that the company is paying a lot of dividends to existing shareholders, this may be consistent with the theory of the development stages of the business, during the recession the business pays very high dividends to compensate shareholders

Table 4: Percentage of firms- X2

(Source: Calculated by Author)

The variable X3 is the variable that has the coefficient before the highest variable of all 4 variables of the model, this proves that this is one of the most important variables in the model, the EBIT / Total assets index explains that over 100 VND of capital can generate how many VND of EBIT Due to differences in business size, business market as well as the ability to manage and run businesses EBIT of businesses is very different Because this variable has a great influence on the model results, businesses with high X3 value will be considered safer than businesses with low X3 variable value

Table 5: Percentage of firms- X3

(Source: Calculated by Author)

The variable X4 is the variable before the smallest coefficient in the four variables (1.05), the variable X4 represents the debt to equity ratio in the capital structure of the business, also known as the financial leverage ratio For variables X4 with a value greater than 1, that is, the ratio of financing by equity in the capital structure is higher than financing by debt and vice versa Through calculating and observing statistics, there are 80 enterprises with X4 ratio greater than or equal to

1, meaning that debt financing is less, and the remaining 85 enterprises have this ratio less than 1, meaning that more debt support This is also a very important indicator in predicting bankruptcy of businesses, the high debt will lead to higher interest rates for creditors, increase financial risks, as well as increase financial distress costs when businesses fall into recession However, the high debt also increases the financial leverage, which helps amplify shareholder income and create

a tax shield for businesses

Table 6: Percentage of firms- X4

(Source: Calculated by Author) 4.1.2 RESULTS OF RESEARCH Z- SCORE MODEL

After analyzing data of 165 listed enterprises of HOSE, HNX, with results obtained from the bankruptcy forecast model according to Z-Score and this is the data statistics:

48.5%

51.5%

Percentage of firms- X4

X4>=1 X4<1

Table 7: Z’’-score Results

(Source: Calculated by Author)

ss Zo ne

The Z- score was applied for 165 manufacturing and non-manufacturing firms from 2015-2019 by examining the Z’’- Score Bond Rating Equivalent (BREs) following S&P rating, on average, 13.5% were classified in the distress zone During the period under study nearly 65% of companies were classified in safe areas This rate remained quite stable; However, it is interesting to note that in the 2017-2018 period, the proportion of firms in the security group decreased, the proportion of companies in the warning zone, may be in danger of bankruptcy and enterprises in danger zones, There is a high risk of bankruptcy due to the influence

of the economy The average rate of companies classified in the three regions remains relatively stable: 1-2 out of 10 companies are classified in high risk of bankruptcy each year, 6-7 companies in a safe area This means that the classification within the grey area is up to about 22%, meaning that only one of the

10 companies may be like distress or healthy companies in the following year In general, the assessment and ranking of companies at levels with S&P ratings show

a high similarity

It is a fact that Vietnam's market has information asymmetry The information

on the audited financial statements has many points to note Many businesses operate poorly, face financial difficulties but a lot of their information is not disclosed and transparent in the market, thus making it difficult for managers and investors to receive accurate information This is a limitation and the condition for applying the Z-score model to assess the business situation stated by the author in section 5 of the study

As the research results have shown, in the warning zone, there are about 22 enterprises in 2015, 15 enterprises in 2016, 21 enterprises in 2017, 28 enterprises in

2018 and 25 enterprises in 2019 In fact, from the calculations and observations, the author found that some businesses that fell into the warning zone over the next one

or two years later improved their financial position in the following years But there are also some businesses that are in a state of exhaustion for three to five consecutive years, such as Camimex group company (CMX) for 4 consecutive years from 2016-

2019 in a state of exhaustion according to the Z model -score prediction Specifically, the revenue of CMX plummeted, selling expenses, corporate management and financial expenses increased significantly, inventory and accounts receivable increased making working capital difficult, leading to the possibility of shortage of cash flow, plus huge short-term borrowings and finance lease liabilities Like the case of Hung Vuong Corporation (HVG), it has been in exhaustion for 5 consecutive years In fact, HVG is facing financial difficulties for many reasons, the first one can be mentioned is the use of short-term capital in long-term investment activities causing the imbalance of cash flow, followed by the Investing

in the pig industry continued to experience epidemics, after more than 5 years of investment is still ineffective making specific business losses in 2019 HVG recorded a loss of nearly 1 100 billion Currently, HVG also recorded two bank debts including VND 900 billion at BIDV and VND 300 billion at VCB Financial imbalance, auditing emphasizes the ability of HVG business at this time depends

on the ability to arrange cash flow and business in the future as well as the restructuring of bank debts row As of March 31, 2019, Hung Vuong has 8,827 billion assets with 6,991 billion short-term assets and 1,836 billion long-term assets Short-term assets are "accumulated" in receivables with 4,752.5 billion - equivalent

to 68% weight, along with 1,809 billion inventories Hung Vuong also continued to increase the provision for these two items, including 679 billion provision for short-term doubtful debts and over 12 billion provision for devaluation of inventories Regarding debt, the total current debt of the enterprise is recorded at VND 6,630 billion, of which short-term debt is VND 6,481 billion (accounting for 93%

of short-term assets) and long-term debt is VND 149 billion Currently, Hung Vuong is in a short-term bank debt of 2,969 billion dong, most borrowed at BIDV with over 1,935 billion dong, followed by Vietcombank with 602 billion dong, HDBank 169.5 billion and some short-term loans at other banks In particular, the due debt of the enterprise was more than VND 54.5 billion Equity of Hung Vuong

is 2,197 billion dong, accumulated loss is over 398 billion dongs

Similar to the business situation of Ngo Quyen Export Seafood Processing Joint Stock Company (NGC), NGC said that the company's financial situation is in extremely difficult situation, financial risk is very high , the capital imbalance that has existed for a few years but until now, 2019, has not been overcome, plus the business results in 2019 suffered heavy losses, the capital imbalance became more and more serious, currently now the company is discontinuing production

Dabaco (DBC) also faced financial difficulties when consecutive epidemics caused the price of pigs to plummet, but the situation improved in late 2019

Similarly, the trust of Nam Nam Rubber Joint Stock Company (CSM) has also continuously slipped in difficulties, the business results were not very effective due

to the sharp increase in raw material prices and interest pressure

Dong Nai Plastic Company (DNP), Tan Phu Plastic Company (TPP), Ninh Van Bay Tourism Real Estate Joint Stock Company (NVT), DAH, VCR, CLG, TCR, VIS, all meet Similar difficulties in the business situation, some businesses are put under special control and are at risk of delisting

Trends of companies in 3 areas in the period of 2015-2019 are similar In the period of 2015-2016, thanks to the economic recovery after the crisis, businesses started to recover, so the proportion of enterprises in the safe area increased with the proportion of businesses at risk high bankruptcy drops In the period of 2016-2017, the proportion of healthy businesses decreased, businesses located in warning areas and danger areas, which are at high risk of bankruptcy, increased significantly In the period of 2017-2018, the rate of enterprises in the safe area increased slightly while enterprises in the warning area plummeted, showing the movement of enterprises from the warning area to the danger zone of high bankruptcy In the period of 2018-

2019, the proportion of businesses in the safe area increased, showing that the policies

of enterprises to restructure and promote business performance are guaranteed to get businesses out of difficulties

Figure 5: Z’’-score Trend for Enterprises in Vietnam

(Source: Calculated by Author)

Most businesses are predicted to be in the "Distress" zone where the values of the X1 and X3 variables are too low or may be negative, followed by the X2 and X4 variables This can be easily explained by the fact that when the working capital on the total assets are low, resulting in the ability to guarantee for low current liabilities, the payment of due debts becomes more difficult for businesses

In addition, when the business is inefficient, low sales or high production costs result in low EBIT, the profit from business activities is insufficient or loss, resulting

in payment of interest rates for loans become more difficult

Businesses are forecast to be in the "Safe" zone, which results in high values of variables, resulting in a higher ability to guarantee the payment of short-term debt obligations, which will result in less risk At the same time, efficient production and business activities bring high revenue and EBIT, increasing the ability to pay fixed financial expenses such as interest Moreover, the abundant profits from production and business activities also increase the retained earnings of businesses, create capital

to reinvest in future projects, improve the growth prospects of the business

2015 2016 2017 2018 2019 Safe Zone 65.5% 67.9% 60.6% 61.8% 65.5%

Z''- score Trend for Enterprises in Vietnam

Safe Zone Grey Zone Distress Zone