Luận văn 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 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 POSSIB

‘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 LN VIETNAM

STUDENT: PIIAM TIM TIANII TIIAO

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 POSSIBELITY OF BANKRUPTY

OF LISTED ENTERPRISES LN VIETNAM

SUPERVISOR: DR.DO PHUORG HUYEN STUDENT: PIIAM TID TILANII TIIAG

STUDENT ID: 16071111

COHORT: QH-2016-Q MAJOR: INTERNATIONAL BUSINESS

Tlanoi - Year 2020

LETTER OF DECLARATION

Thereby declare thai the Graduation Project “A STUDY ON APPLICATION:

OF ALTMAN’S Z-SCORE MODEL IN ASSESSING THE PERFORMANCE AND THE POSSLBILITY 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, | have seriously taken research ethics; ail findings of this project are results of my own research and

surveys; all references im this project are clearly ented according to regulalioms

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)

KNOWLEDGEMEXT

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

experiences

CHAPTER 2: LITERATURE REVIEW

4.2 APPLICATION OF Z-SCORE MODEL FOR SAIGON PLASTIC

§.4.2 DEVELOPING A SYSTEM OF VIETNAM CREDIT RATING

5.4.3 COMPLETING BANKRUPTCY LAW AND RELATED

TABLE OF NOTATIONS AND ABBREVIATIONS

ROE Return on Equity ROS Return on sales S&P rating Standard and Poor Rating SPP Saigon Plastic Packaging

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: 7."'-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 af 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 Saigan 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: EBII7Lotal Assets

Table 17: Book value of Equity / Total Liabilities

Table 18: 7°?- scpre = 3.2546.56"X 143,26" 246,72°X3+1.05°X4

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ABSTRACT

The business environment always contains many volatile factors affecting the

investmer financing process as well as the business performances of enterprises,

especially in the context of strong international economic integration which brings many opportunities and challenges for Vietnamese businesses Therefore, businesses always lace positive periods allemating with [he negative stages as well as Ihe stage

of success and failure in fimancial 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 awe as originally committed

Therefore, early prediction about the bankruptey 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 proleel the company from bankruptcy The

bankruptcy of businesses can be predicted using many different research models

However, this siudy applies the most famous model among the models, 7, 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 predicl bankruptcy arm cas ¢ about Saigon Plastic Packaging The study has found that the Z score model is appropriate and feasible to apply to analyze the performance

of Viemamese 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 achicve the goal

of maximizing returns, the company needs strong support from intemal and external

factors The failure lo manage internal systems such as elFiciont 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 dilficultics, 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 Lolal 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 Iordan, bankruptey is a legal procedure to liquidate or reorganize a business (Chapler 16, Financial Loverage and Capital Siructure Policy, page $62, 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

insulverd and declared bankrupt by a People's Court In parlicular, enterprises and cooperatives that are insolvent are enterprises and cooperatives that fail to perform

the debt payment, wilhin 03 months from the date of maturity

Recent coonomic events have led many companics to apply for bankruptcy and

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

holders in the busmess

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 bankrupley in the past decades and Iried

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

of cash flow loss, loan default, capital restructuring, government support,

T118(8 hay

renegotiation of loans with banks Some focused on asses mg,

corporate performance and predicting bankruptcy using statistics and financial indicators Pioneers began in the 1930s when building models to help banks make

decisions aboul whether lo approve credit requirements Tn the late 1960s, ihe

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

bankrupicy comparics 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 thal enhances performance evaluation and bankrupley prediction for companics 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 lool or to assist in forceasting financial suffering,

espevially in the Italian context he scale of the model in question still applies, 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 bankruptey of 165 listed enlerprises im Vietnam,

especially the application of the model for Saigon Plastic Packaging Joint Stock Company which declared bankruptey in the first quarter of 2020 Through it evaluates the effectiveness of Ihe model in applying the model in Vietnam, andl al 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

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

health assessment and predicting hankruptey risk, conditions for applying the Z-score

tmodel 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-

tmanufacturing enterprises im Victnam listed on stack exchanges, cspecially for Sai

Gon Plastic Packaging Joint Stock Company wluch replaces the commonly used traditional methed (separate analysis of financial indicators), in order to serve many xelevant 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

‘bankruptoy for enterprises by using Z- score model in Vietnam

1.3 CONTRIBUTION OF STUDY

Firsily, tho study presenis a relatively complete system of the building process

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

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 cvalualing company performance such as understanding how companies deal with financial pain and the risk of bankruptcy Studies im the world have shown that assessing the performance of enterprises (focusing on performance evaluation) mainly through the results of analyzing financial indicators

(Witzpatrick (1932), Smith & Winakor (1935), Merwin (1942), Chudson (1945),

Jackendoff (1965), Altman (1968), Green (1978) ) in univariate analysis methods,

Dupont (Beaver, 1967) and differential inlegraling 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

ave been done by various measures to cvaluale 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, bankrupley prediction studies

are still limited to certain arcas

One of the earliest research conducted in the field of bankruptcy ratio analysis

and classification was developed and conducted by Beaver (1967) In # 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 differentiale between corporale and bankrupl 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 fathare to pay interest on its debl, withdraw a bank

account or declare bankruptey Lle sbudied large-scale companies that went bankrupt during 1954-1964 and classified successful companies using discriminatory

analytical models Beaver chose to examine ils debt / total assets, afler-tax income /

total asscts, 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 [le questioned the use of a multivariate analysis model,

although a sludy 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 bankrupicy 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 (1) liquidity Second, previous studies that exaggerated the power

of its bankruptey prediction models and tests Another problem is that the forecasting factors ([maneial indicators) ave taken from the financial statements

published after the bankruptoy date, after which evidence indicates that these

factors will predict bankrupicy

E.L Altman (1268) rescarched at New York University in the late 1960" s

Altman first introduced a method of statistical multivariate analysis in predicting

oomporate failures by using a linear diseriminalory method to measure orcdit 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 furiclion: Working capital / Total assets, ERTT / total assets, market

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

The orginal Aman model was modified to fil several other indusinies made

according to the coefficients including small companies, banks, insurance companies,

construction companies, etc

Following this groundbreaking work, ihe multivariate approach lo predict

failure has spread among researchers worldwide in economics, banking, and eredit

tisk The Z-Score model has become a standard for several of those internal-rate

‘based models, Altman (1 983, 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 ol’ concerns Tn 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

coneem is in question by examining ihe relationship belween bankrupt (rms and

auditors’ report ina pre-bankruptey ‘The study concluded that the auditor's judgment

must be a determining factor on the appropriate going concern decision and thal 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 7-score The

research found negative results in applying Z-score in revent 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 (ailures, he frequent

changes made in the Altman 4 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 prediclor." T notes that the Altman 7, soore model

can be safety implemented to the modem cconomy to forecast bankruptey two to three years before the bankruptcy case is announced

Manoj Kumar and Madhu Anand (2013): Based on thei rescarch

implemented on Kingfisher Aurlines Limited (KAL), they determined that KAL's financial health performance analysis (and distress) when using Altman's Z score was

satisfactory They discovered thal the company’s Financial Health was reliably tow

during the study period, ie 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 toss but could also be reversed

if proper action is taken

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

solvency positions Their analysis uses Z-score to predict Ihe Gnancial distross 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 Tndian corlext, 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 1t concludes that the Z score model is useful in predicting the bankruptey of

FMCG finns and recormends that the same be used by [maneiat investors The sludy

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

Jane 1.i (2012), whon applying to the research of US manufacturing enterprises,

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

Fawad Hussain (2014) assessed 21 textile enterpriscs in Pukisian 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 wilhin 4 years

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

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

or re-condusling, corporale structuring the merger of the company when if, can

improve the financial situation but only in a short time

Ahmad Khaliq (2014), with the case of application in Malaysia, concludes that itis necessary to consider Ihe relationship between the ratio in the model according

to the inputs in the studied industries

Appiah (2011) of enterprise research in Ghana concluded thal the results of the Z-seore model depend on the accounting principles that cach country applies

Prance Leksrisakal and Michael [vans (2005) in the study of the business

bankruptcy mods] in Thailand provided new evidence thal 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 bankruptey 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 profils and liquidity The number and quality of øssels of bankrupl (inns arc lower

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

Ming Xu anf Chu Zhang (2008) renearched on the vase 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), Ohison's Oscarore (1980), and previously developed option prices far 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 bankrupicy preshction is based on a more successful option pricy, method than accounting variables

2.2 LITERATURE REVIEW IN VIETNAM

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

Research by Nguyen Minh 1a and Nguyen Ba Huong (2016) analyzes the factors affecting bank bankruptey risk by the Z-Score method The objective of the study is to identify factors affecting the risk of bankruptoy 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 Viemam 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 bankruptey: Credit growth, the ratio of provision for bad debts, the ralio of

net uterest mecome, equity to total agyscts, 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 bankrupley including Cosl management

effectiveness and scale

Research by Pham Tuong Van (2016} investigates the applicability of the Z-

score model lo asacss the performance of Vietrennese enterprises in hicu 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

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

of Vielnam 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, heallhcare,

food, fisheries, construction materials, plastics-packaging), construction and tourism-

services Data used in the siudy Research is a set of data compiled from financial

statements for the period of 2010-201 4 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

imeludes 12 enterprises The research resulis show that the results between the lwo

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 safely and warnings, (here are cortain deviations duc to the

limit between the safety level and the waming level report S&P ratings differently

from 7.” (possibly due to a type TI error), This stows that, basically, the 7-score model

is suitable and feasible in applying to analyze the performance of Victnamese

enterprises by industry and field

Based on the synthesis of cmmpirical siudies, the Altman 7.-core model is applied

in many countries (from the US to some European countries and cimrently 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 arul 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, Geld; according

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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 7, -

SCORE MODEL

One of the most well-known financial distress prediction models, due to its easy predictability and application was built by Aliman in 1968 He introduced a method of statistical multivariate analysis of enterprise failure prediction and estimation of a

anodel called a '"Z-Score model” ‘his 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, Aitman et al (1977) adjusted the original Z-Score model - to add

to the model the financial reporting crileria - tite a new, belter model, called the

"Zeta analysis.”

Multivariate analysis method (MDA = Multiple Discrimmant Analysis) The

MDA model inchides a linear relationship between variables, which helps classify

bankrupt and non-bankrupt business groups

And Altman (1968) introduced a 7.-Score model, as follows

Z= VIXI + V2X2+ + VnXn

In which:

~ V1, V2, Vin = discriminant coefficients

~ XI, X2, Xn = independent variables

(Source Altman, 1968)

2.3.4 Z-SCORE INDICATOR

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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 vanables; (2)

assess the correlation between the relevant variables: (3) observing predictive accuracy of variable sets, and (4) expert judgment

The final discrimumart is shown as follows:

Z#=12X1+ 1.4X2 + 3.3 X3 +0.0 X4 + 0.999 X5

In which:

XI Working capital/ Total Assets

X2= Retained eamings/ Total Assets

X3 = EBIT’ Total Assets

X4— Market Valuc of Equity / Book Value of Total Liabilities

X5 = Sales/ Total Assets

(Source: Aleman, 1968,1977, 2000)

Note that the model does not have a constant (lunit 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, al 0if 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 studics of

enterprise problems, is a measure of the net liquidity of firm assets relative to Total capilal Working capital is defined as the difference between current assels and curent liabilities Liquidity and size characteristics are clearly considered Typically, a company experiencing a period of extended operating losses will have

ils working assels shrink compared to its lotal assets, Oul af the three liquidity

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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 Jess useful and depend on the conservative lendencies of a few failed

companies

X2= Retained carnings/ Total Asscts Altman (1968, 1977, 2000) slates thal retamed carmngs represent the total

amount of reinvested mcome or the loss of a business over its lifetime ‘I'his index is

also considered as a surplus eared from the operation process It is worth noting that

this index depends on (he movement [hrough 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 aspecl of relamed carnings is the abilily to

measure accrued profits over time The short or long operating life of a company is

fully considered in this index

Therefore, iLean be argued that yours firms are lo some degree chseriminated

in this analysis, and the likelihood that these firms are classified as bankruptcy is

relatively higher than that of the analysis compaties have more uptime Tn addition, the RE / TA index measures the leverage of a business Companics with a high RE,

compared to ‘CA, can finance assets through keeping profits and not using too much

debt, which a healthy sign of growl most of the tire

‘X3 = EBIT/ Tatal 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

reat in research related to business failure Moreover, the insolvency in bankruptcy

occurs when the lolal debt is greater than the correct value of the company’s

assets with the value determined based on the profitability of the assets ‘his ratio has

a better indicator of other profitability indicators, including, cash flow

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

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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 shorl-lerm and long-term debt This imdsx measures [he 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 bankruptey studies de nol mention

X5 = Sales/ Total Assets

According to Altman (1968, 1977, 2000), the ratio of turnover to total assets is a

standard financial indicator thal illustrates the ability of enterprises lo generale income

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

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

on significance level tests using univariate statistics, it should nol 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 revenne across industries, and Altman will

develop an alternative model (% ") without the X5 target later

2.3.3 Z- SCORE MODEL FOR PUBLIC MANUFACTURING

XI = Working capital’ Total Assets

X2 = Retained earnings/ ‘otal Assets

X3 = EBIT’ Total Assets

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

XS = Sales/ Total Assets

(Source: Alonan, 1968,1977, 2000)

To assess Ihe bankrupley of compenies, their 7, index is delormined as follows

Zone of Discrimination

Z<L.81; “Distress Zone” Ligh risk of Bankruptcy

1.81 <7, <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 wilh healthy financial status and 33 bankruptcy

companies) listed on the stock exchange Results showed that companies with a Z- score of less than 1.8] are at higher risk and are likely to go bankrupt; companies

with # healthy financial position if the Z-soore is grealer 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 resulis with 95% correct prediction rate, and it received

a lol of positive (ecdback

2.3.4 Z- SCORE MUDEL FOR PRIVATE FIRM

Altman has repeatedly expanded and modified its model to fir all types of

businesses and fields of activity, differenl groups of companics other than

smanufacturing enterprises since the original model

The first amendment was for private companies because credit analysts, privale agents, auditors, accountants, and businesses themselves were concerned that the

original model would only apply to listed and public transaction, Alman advocated

roassessing the whole modctinslead of simply replacing a variable that is book value

and market value in 4

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 X6

(Source: Altman 1983, pp.122)

Tn which

XI = Working Capital / Total Assets

X2= Retained Eamings / Total Assets

X3 Earnings Before Interes and Taxes / Total Asseis

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X4— Book Value of Equity / Total Liabilitics

XS = Sales/ Total Assets

Zones of Discrimination:

Z > 2.90 - “Safe” Zones- Healthy

1.23 < Z < 2.80 - “Grey” Zones- Uncertain Results

7, ©1233 - “Distr

Zones — High Risk of Bankruptey

‘The X4 index became less influential on the 4-score with this modification This

results in a wider gray area

2.3.5 7- SCORE MODEL FOR FIRMS (NON- MANUFACTURING

AND MANUFACTURING FIRMS)

In the following years, the parameters and coefficients continue to he adjusted for

diferent situaltons The Z-svore model was mtroduced (Aliinan, Hartzell and Peck,

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

as previous models with exclusion of the sales/ total assets (X5) for non-

amanufacturing companies as well as manufacluring scclurs or eumpanics operating

in the US and in developing markets {the 1995 research conducted a sample of Mexican companies)

According to Altman (1968, 1977, 2000), the Z-Score model applied to enterprises is

X1 = Working Capital / total Assets

X2 = Retained Lamings / Total Assets

3 — Rarnings Before Interest and Taxes / Total Assets

X4 = Book Value of Liquity / ‘Total Liabilities

The threshold for this model is as follows:

7” <1.) —Distress Zone- High tisk of Bankrupley

1.1 <Z " <2.0- “Grey” Zones- Uncertain Results

2.6 <2" “Safe” ⁄ones- Llealthy

2.3.6 7- SCORE MODEL FOR FIRMS IN EMERGIN

According to Altman (1968, 1977, 2000), credit in emerging economies can be

MARKET

avalyved in the same manner as used (or traditional analysis by US companies

Whenever a quantitative risk assessment provess arises, an analyst can then use a

qualitative assessment process to further adjust such factors as currency and industry

visk, industry characteristics, and posilion competition of companie:

To solve this problem, Alman (1968, 1977, 2000) revised the original Z-Score

model to create an index model for emerging economies (EMS = emerging market

scoring) The similarly keeps the 7" adjusted and S&P rating of the company,

written by Professor Altman in "he use of Credit scoring Models and Ihe Important

of a Credil Culture” and presented in the Collowing lable One more thing we need 10 note 1s that the adjusted Z " parameter, although used quite well in decent markets,

should also be studied to adapt to the environment im Vietnam

Tu calculating the 7? score for emerging market, Altman, Hartzell and Pack

(1995) suggested adding a constant +3.25 to standardize the research results so that scores equal or less than © might be equivalent to the default situation (Alaman,

Danovi and Falini, 2013)

New model 4" - Score is

#4 '=3.25 +0.56 X1 + 3.26 X2 + 0.72 X3 + 1.05 X4

Tn which:

XI = Working Capital / ‘Total Assets

X2= Retained Eamings / Total Assets

X3 Earnings Before Intoresl and Taxes / Tolal Assets

18

X4— Book Value of Equity / Total Liebilities

Altman and Llotchkiss (2006) drew a correspondence between the score and the yalings assigned by Standard & Poor’s, as shown in lable 7

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

CHAPTER 3: DATA AND METHODOLOGY

3.1 DATA

3.1.1 SOTIRCE OF DATA

Data used in the study are aggregated data from audited financial statements in the perind of 2015- 2019 ol 165 anerprises (especially collecting data in the period

of 2012-2019 of Saigon Plastic Packaging) listed on two stock exchanges in Ho Chi

Minh City and Ilanoi Audited financial statements of 165 enterprises listed (included Saigon Plastic Packaging) on HOSE and HNX collected through reliable financial

information is vietstock.vn

Based on the balance sheet, cash flow statement and income statement to

calculate the value of working capital, 1olal assels, relained earnings, earnings

before taxes, book value of equity and debt Since then, using this spending

method to calculate 4 financial indicators M1, X2, X3, X4 is used to calculate in

”

score model

3.12 SCOPE OF DATA

Regarding the scape of space: due to the limitation of the study time, the thesis

only sludies the case of 165 manufacturing and noremanufacturing enterprises listed

on the stock exchange across all business sectors, irrespective of size of business, industry, or sector in period 2015-2019 and the Saigon Plastic Packaging Joimt Stock Company Due to the characteristics and conditions of applying cach model, and to ensure the objectivity and the accuracy of the research, consider the diversity of

results, the author might nol divide firms into two types: manufacturing and nen-

manufacturing

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

20

How to assoss 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 METIIODOLOGY

For the polynomial analysis method in applying the Z ”-score model (Allman, Tlartzell & Peck (1995), The research uses Z.”- score (has been adjusted to apply analysis of sectors and Cield, scelor) wilh 4 variables as Aliman's rescarch, including

XI — Working Capital / Total Assets

X2 Relained Eamings / Tobsl Assets

X3 = Tamings 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 cmerging-market status in 2022 Vietnam has alrcady met most of MSCI’s quantitative requirements for emerging market (EM) index inclusion, inchading market size, a sufficient number of large-cap stocks with sufficient daily trading liquidity, and other motries Therefore, the author has chosen the model to consider the applicability in the Vietaamese market

So the data is collected from 165 manufacturing and non-mamufacturing companies, the 77+ score model for emerging market is used wher considering the similarities between the results of the % -score model and the rating of S&P

Z" = 3.28 +656 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 thal conducled model studies lo cvaluatc 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 suilable for some churacteristies ancl properties of the Vietnam markel Thirdly, the model has been applied in many countries, currently, Asian countries are alsa applying a lol in prediclive analysis, proving the superiorily in elassilying risk arcas

of businesses in many countries m different fields

21

CHAPTER 4: EMPIRICAL RESULTS

4.1 RESUL’

ENTERPRISES LN VIETNAM

4.1.1 STATISTICS ANALYSIS

SOF APPLYING THE Z-SCORE MODEL FOR 165

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

Mean 0.255281 Mean 0.114671 Mean 0.093253

Standard E 0.008228 StandardE 0.044587 Standard E 0.004045

Median 0.249629 Median ‘0.062011 Median 0.076041

Standard C 0.236333 Standard 1.28065 Standard 0.116198

Sample Va 0.055853 Sample Va 1.640065 Sample Va 0.013502

Range 2.368087 Range 38.23859 Range 2.635878

Minimum = -1.4536 Minimum -1.62282 Minimum -1.63885

Maximum 0.91449 Maximum 36.61577 Maximum 0.997023

xa

Mean 2.245229 Standard E 0.294102 Median 0.939028 Standard 8.447426 Sample Va 71.358 Range 269.7214 Minimum -179.731 Maximum 89,99049

(Source: Calculated by Author) Fustly, the X1 data shows that the value of the X1 variable of 165 manufacturing,

and now-tmannfacluring enterprises is very diverse and quile different, bul aboul 89

enterprises have their curent 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 negalive ratio Enterprises with low or agate:

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

Percentage of firms - X1

© X1>= 0.255281 80<X1<0255281 = X1<0

(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

(Source: Calculated by Author)

Figure 2: Percentage of firms- X2

Percentage of firms- X2

wX2»z0114671 = 0€X2<0.114671 «X2<0

(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)

Figure 3: Percentage of firms- X3

24

Percentage of firms - X3

3.6%

\

= X3>*0,093253 = 0<X3<0.093253 =X3<0

(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

Percentage of firms- X4

=X4»sl mX4<1

(Source: Calculated by Author)

4.12 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

26

Table 7: Z”’-score Results

The Z- score was applied for 165 manufacturing and non-manufacturing firms

from 2015-2019 by examining the Z*’- Score Bond Rating Liquivalent (3RUs)

follawing S&P rating, on average, 13.5% were classilied in the distress zone During the period under study nearly 65% of companies were classified in safe areas, This rate remained quite stable; Ilowever, 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 male of companies classified in the three regions

remains relatively stable: 1-2 out of 10 companies are classified in high risk of banlruptey each year, 6-7 companies in a safe area This means that the

classification within the grey arca is up lo 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

ahigh similarity

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

on the audited financial stalements has many poiis to note Many businesses operate poorly, face financial difficultics but a lot of their information is not

disclosed and transparent in the market, thus making it difficult for managers and

invesiors Lo receive accurate mformation This is a imitation and the condition [or

applying the Z-score model to assess the business situation stated by the author in section § 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 2027, 28 enterprises in

2018 and 25 emerprises in 2019 In fact, from the calculations and observations, the

author found that some businesses thal fell mio the warming 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 ina state of exhaustion for three to five consecutive

years, such as Camimex group company (CMX) lor 4 conscvulive years [rom 2016-

28

2019 in a state of exhaustion according to the Z model -score prediction Specifically, the revenue of CMX plummeted, selling expenses, corporate

numagement, and financial expenses imcreascd signifi

ily, inventory and accounls

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 Corporalion (HVG), il has been in exhaustiom 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 Tnvesiing

in the pig industry continued to experience epidemics, afer more than 5 years of

investment is still ineffective making specific business losses in 2019 HVG

recorded # loss of nearly 1100 bilhon 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 armange cash Mow and business in the fulure 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 assels

Short-tezm assets are “accumulated” in receivables with 4,752.5 billion - equivalent

to 68% weight, along with 1,802 billion inventories [lung Vuong also continued to

increase the provision [or these two items, including 679 billion provision for shorl-

tem 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 wilh over 1,935 billion dong, followed by Vietcombank with 602 billion dong, ILDank 169.5 billion and some short-term loans at other banks In particular, the

due debt of the enterprise was more than VND 51.5 billion Equity of Hung Vuong

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

29

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 thal

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 Nain Nam Rubber Joint Stock Company (CSM) lias 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 state Joint Stock Company (NVT), DAIL, VCR, CLG,

TCR, VIS, all meet Similar difficulties in the business situation, some businesses

are pul under special control and are al risk of delisting

‘Irends of companies in 3 areas in the period of 2015-2019 are similar In the

period of 2015-2016, thanks to the economic recovery afier the crisis, businesses started to recover, so the proportion of enterprises in the safe arca increased with the

proportion of businesses at risk high bankruptcy drops In the period of 2016-2017,

the proportion of healthy businesses docrcased, businesses located in waring 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 erferprises 1o restructure and promote lusiness performance are guaranteed to get

businesses out of difficulties

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

30

Z"- score Trend for Enterprises in Vietnam

Se Sale Zone —@—Grey/7one —@—Disuess7one

(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

31