a new approach to determining credit rating and its application

8/12/2019 A New Approach to Determining Credit Rating and Its Application

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A NEW APPROACH TO DETERMINING CREDIT RATING &

ITS APPLICATIONS TO VIETNAMS LISTED FIRMS

VO HONG DUCa b *

& NGUYEN DINH THIENb

aEconomic Regulation Authority, Perth, Australia;

bOpen University, Ho Chi Minh City, Vietnam.

Abstract1

Rating credit worthiness of a company has been hotly debated because of its

importance and the subjectivity of ratings assessments. International well-known rating

agencies such as Moodys, Standard and Poors and Fitch have formed a very pessimistic

view on developing nations such as Vietnam. As such, credit ratings for Vietnamese

companies are very low based on these international rating agencies. Moreover, these

international rating agencies also adopt a qualitative assessment in their rating process

which is impossible to replicate. This paper uses a pioneer approach to make a significant

contribution to address the current weaknesses in credit rating processes. This study

provides a theoretical foundation of rating credit worthiness based on a well-known

mathematical theory, fuzzy logic. This theoretical framework has been used to rate 643

businesses listed in the Vietnams stock exchangesusing their reported financial ratios.

JEL: G24; G32

Key words: fuzzy logic; quantitative assessment; credit rating; listed firms, Vietnam

April 2013

*Corresponding author. E-mail: [email protected]


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A NEW APPROACH TO DETERMINING CREDIT RATING &

ITS APPLICATIONS TO VIETNAMS LISTED FIRMS

Abstract

2

Rating credit worthiness of a company has been hotly debated because of its

importance and the subjectivity of ratings assessments. International well-known rating

agencies such as Moodys, Standard and Poors and Fitch have formed a very pessimistic

view on developing nations such as Vietnam. As such, credit ratings for Vietnamese

companies are very low based on these international rating agencies. Moreover, these

international rating agencies also adopt a qualitative assessment in their rating process

which is impossible to replicate. This paper uses a pioneer approach to make a significant

contribution to address the current weaknesses in credit rating processes. This study

provides a theoretical foundation of rating credit worthiness based on a well-known

mathematical theory, fuzzy logic. This theoretical framework has been used to rate 643

businesses listed in the Vietnams stock exchangesusing their reported financial ratios.

JEL: G24; G32

Key words: fuzzy logic; quantitative assessment; credit rating; listed firms, Vietnam


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II. Literature ReviewCredit Rating

Provision of rankings (or credit ratings) by rating agencies dates back to the 19th century

in the early days of the US railways where investors sought information to help them make

investment decisions. S&P; Moody's and Fitch became the first three companies to be

recognised as "international rating agencies". In addition, some countries have also

developed their own ratings for domestic businesses.

S&P considers that credit rating is an assessment of a credit risk in the future based on

the current conditions of the company with respect to its specific financial obligations. In

other words, credit ratings are considered as the indicator of safety when investing in

valuable assets of the companies such as bonds, stocks or other similar debt certificates. A

credit rating reflects a businesss capacity to pay its debts. Fitch considers that a credit

rating is an assessment of a companys ability to fulfill its obligations in relation to itsliabilities such as payments on interest, preferred dividends, insurance or other liabilities of

a business. Both methods adopted by S&P and Fitch include a combination of both

financial and non-financial factors.

In summary, a credit rating is an overall assessment of the level of risk when investing

in a business taking account of both internal and external factors. External factors include

politics, industry, and the macro-economic environment all of which have proved difficult

to quantify. Internal factors include financial and non financial elements of the business

being rated.

In Vietnam, the most typical methods for a credit rating can be traced back to the

methods applied by the Vietnam Joint Stock Commercial Bank for Industry and Trade

(Vietinbank) and the Bank for Investment and Development of Vietnam (BIDV). These

two approaches are summarised in Table 1 below.

Table 1. Credit Rating Comparison of BIDV and Vietinbank

Items BIDV Vietinbank

Financial indicators Non-financial indicators 14 41 11 60

The weights of financial indicators non financial

(audited reports)35% 65% 55% 45%

The weights of financial indicators non financial

(non-audited reports)30% 70% 40% 60%

Relations with the bank 40% 33%

Source: BIDV, Vietinbank

Table 1 shows that both BIDV and Vietinbank assess businesses for loans based on both

financial and non-financial information. However, there are significant differences in the

number of indicators utilized and the weight assigned to each group of indicators between

the two. In BIDVs ranking approach,there are 3 more financial indicators and 19 fewer

non-financial indicators used compared with Vietinbanks approach. The key similarity

between BIDV and Vietinbank relates to the group of non-financial indicators labeled as


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"relation with the bank" with a 40 per cent weight assigned by BIDV and 33 per cent by

Vietinbank. In reality, this non-financial factor significantly contributes to the overall

assessment of a credit rating of a business; however no one is satisfied with how this

component is rated by the banks staff.

Fuzzy Logic

In classical mathematical theory, an elementcan only take one of two states: "belonging

to" (assigned a value of 1) or "not belonging to" (assigned a value of 0) in a particular set.

Mathematical theory has since developed such that an element can now accept the values

of 2, or 3, or 4 or more to solve the real problems that arise. Even though the state of the

element has been assessed in a greater detail than before, it is still only in the form of

discrete values. The range is divided to consider the importance of each element belonging

to the range appears reasonable. However, this approach also considers all elements in therange to be equivalent.

Zadeh (1973) considered that the ability to assess the complexity of a problem

accurately is extremely difficult. He also argued that action and decision will turn out to

be completely incorrect in the context of lacking information for the decision making

process. In this circumstance, fuzzy logic is a better mechanism to describe the more

ambiguous or inaccurate concepts, such as about, "as though", almost, maybeor a

range of value.

Fuzzy logic is an extended logic system based on classical algebraic logic, and it is

described by the membership function. A state of an element becomes continuous due to

the existence of the membership function. As such, it is more accurate for the assessment

of any element. A fuzzy set is used to describe the set to which the members belong. A

member function is used to reflect the extent of dependence of each member to the set.

A fuzzy set is a collection in which each basic element xis assigned a real value (x)

in the range of [0;1] to indicate the dependence of that element xin the given set (Nguyen

Nhu Phong, 2005). A membership function is a function that evaluates a membership

degree in the set. A membership degree is used to reflect the degree of dependency of the

member in a set, depending on the characteristic of that member (Nguyen Nhu Phong,2005). The most common representation of fuzzy logic is as below:

A(x)= {(x, A(x)) | x X}

where:

o x: an element belonging to set A.o A(x): membership function.o A(x): is the degree of membership ofx.


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The most common membership functions include: (i) a continuous monotonous

increase/decrease function; and (ii) a continuous probability distribution function. Each of

these membership functions is explained in turn below.

A continuous monotonous increase/decrease function

Figure 1. A continuous monotonous increase function for the assessment of the

return on equity (ROE)

Figure 1 presents the membership function of the ROE in relation to the level of being a

"good" ROE. In this case, if the ROE is less than 3 per cent, then that company has a value

of a membership function 0. This means the company does not belong to the goodset.

In contrast, when a companys ROE is 22 per cent or higher, the value of the membership

function is 1, meaning the company is in the "good" set. The conclusion is that the higher

the ROE, the greater the companys membership degree.

A continuous probability distribution function

Figure 2 presents the membership function in relation to the level of "being good" based on

capital structure. In this example, the best value for the membership function only exists at

a single point at the sharp point of the membership function. A membership function

value in this case is in the range of [0;1], with 0 representing no member in the set of

"being good" and the value of 1 indicates that a member in the set of being goodfor a

capital structure.


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Figure 2. A membership function representing levels of "good" for capital

structure

The above two examples open the door in which a fuzzy logic can be applied in the

calculations of points for the financial indicators. A membership function as presented

in Figure 1 is a good function, for calculating the point of each financial indicator, being

applied to the financial indicators with the characteristics being "as large as possible". In

addition, Figure 2 is a good function being applied to the financial indicators with the

characteristics being "optimal" with the sharp point representing the optimal value.

Some previous academic studies

Bojadziev, G. and Bojadziev, M. (2007) concluded that most decisions experience multi-

dimensional criticism. This observation is particularly correct in economics. For example,

a decision to payout a dividend of 13 per cent cannot be judged as to whether this level of

payout is high, average or low. A dividend payout ratio of 13 per cent can be considered to

be high or low depending on the different views of shareholders and analysts.

Decisions in the financial sector are complex and difficult due to the large amount of

information which constantly changes in every hour, or even every minute. As such, the

act of making the decision is done in a state of incomplete information and therefore in a

fuzzy manner. Decision makers can estimate the probability of making a correct

decision but they are not able to confirm that the decision made is correct.

Vlachos, D. and Tolias, Y. A. (2003) applied fuzzy logic in forecasting the probability

of business failure. This study was aimed to compare their findings with those produced

using Altman scores. In this study, only 5 financial indicators adopted by Altman are

utilized. A sample of 129 companies for the period from 1975 1982 was used. During

this period, 64 companies went into bankruptcy. Financial data used for these 64

companies are those from the years before the bankruptcy. A finding is promising if it can

predict 100 per cent of the companies who went into bankruptcy. Even though there is an


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issue with the sample selection (on the 64 companies bankruptcy), the findings from this

study also demonstrates the strength of fuzzy logic in forecasting bankruptcy.

Malagoli, S. et al. (2007) assessed and rated credit ratings for the Camuzzi Italian gas

distribution companies using a combination of expert opinions and fuzzy logic. The

authors used the clause "if ... then ..." to calculate the total scores and the assessment of a

credit rating in this study based on both quantitative and qualitative criteria. With 21 input

indicators, research aggregated intermediate variables through the fuzzy rules. Enterprise

value variables will be variable defuzzification results in the range [0; 1] representing the

"financial health" of the business. While the study focused only on a specific company, the

model can also be used for other businesses in the same industry.

Khcherem and Bouri (2009) applied fuzzy logic in the decision to buy and sell

securities in the Turkish market using data from 2001-2008. The finding of this paper is

that the rate of being successful was up to 93.26 per cent when using this method for stocktrading.

Yildiz and Akkoc (2010) had adopted fuzzy logic to predict bankruptcy of Turkish

banks. In this study, the sample included 55 banks. The assessments of these bank

performances were based on the group of 24 financial indicators with the level of

significance of 5 per cent. This empirical study was conducted using two different

techniques: (i) linear regression; and (ii) non-linear regression on the basis of fuzzy logic.

The findings from this study indicated that the method using fuzzy logic correctly forecast

90.91 per cent whereas using linear regression correctly forecast 81.82 per cent.

Othman and Etienne (2010) used fuzzy logic together with artificial intelligence to

study the decision making process for securities transactions. In this study, the inputs to

the model included expert opinion, the return on each stock, and the desired rate of return.

The finding from this study is that the incorporation of artificial intelligence, particularly

fuzzy logic, to the stock market which is typically volatile and complex is a simple way to

bring profit to investors.

Korol and Korodian (2011) conducted research to evaluate the degree of effectiveness

of the fuzzy logic model in predicting corporate bankruptcy. In the course of research, the

authors used the financial statements of 132 listed companies on the stock market (25companies of which went bankrupt). The authors used both data (quantitative) and

uncertainty (qualitative) as input data to forecast the likelihood of bankruptcy of the

company in 1, 2 and 3 years. Results when using the quantitative data were not much

different than the model predicting bankruptcy risk such as the Z-score model. However,

the result when using qualitative data from the fuzzy logic model was significantly better.

All in all, the market changes quickly and with voluminous and complex information

in financial markets, recognising the early state of the market will help investors make

better decisions. In addition, due to asymmetric, unclear and incomplete information, and


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a lack of precision in practice, decision making for investors is riskier. Therefore, the use

of fuzzy logic in the finance sector is being more extensively researched and developed in

order to reduce the risks in investment decision making.

III.Research Data and HypothesesResearch Data

In this paper, data was collected from the annual financial reports (audited) of listed

companies in both stock exchanges of Vietnam: the Ho Chi Minh City Stock Exchange

(HOSE) and the Hanoi Stock Exchange (HNX). The sample of companies in this study

does not include credit and financial institutions. The assessment of credit ratings for

listed companies was conducted for 2 years 2010 and 2011. A total of 643 companies of

the 701 companies with stocks listed at the end of 2011 met the requirements; and as such,are in the sample.

Raw data from the audited financial reports were used to calculate financial ratios in

order to assess "financial health", risk, and growth prospects of the business. Companies

which did not have adequate data were excluded from the sample and as such, they were

not rated in that year. In the process of "fuzzy" (fuzzification), the outlier was processed

by statistical methods based on normal distribution. In theory, the statistical values in the

range of [ - 3 ; + 3], which accounts for 99.8 per cent of the entire data, are

considered.

Research Hypotheses

In this study, the assessment of credit rating is conducted in two different ways: (i) within-

industry assessment (when a company is compared with others in the same industry); and

(ii) within-market assessment (when a company is compared with others in the entire

market regardless of an industry). As such, two different averages will be used: (i) the

industry average; and (ii) the market average. As such, for those financial indicators

associated with the optimallevel, the averages of the industry and of the entire market are

the optimal values for the within-industry assessment and for the within-market assessment

respectively.

In all economic environments, a particular company may excel because they can: (i)

take advantage of the economy to move ahead the others in the economy; and/or (ii) have a

good risk management practice, and are in a good financial situation where they can stand

against the difficult economic conditions. On the other hand, there are companies which

incur: (i) slower growth (or no growth) in favorable economic conditions compared to

other companies, or (ii) large losses in periods of crisis compared with others. The


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appropriate distribution of the economic conditions follows a normal distribution. As such,

the risk of bankruptcy of businesses may also be normally distributed.

IV. MethodologyA selection of cr iteri a and weighti ng

The financial criteria used to evaluate the group include: (i) liquidity; (ii) profitability; (iii)

performance; (iv) Capital structure, leverage and solvency; and (iv) cost structure. The

selection criteria of these financial areas in the assessment of a credit rating were intended

to avoid duplication of the indicators. These selected financial indicators must be able to

reflect the full "financial health" and level of risk of the business. In addition, some

indicators are not only expressed quantitatively, but also contain qualitative factors.

Therefore, among many, 34 financial ratios are selected to determine credit rating using

fuzzy theory.An important step in the assessment of a credit rating is to consider the relative

importance of each financial indicator to the other indicators from the group of 24. It is

argued that there is no clear foundation to consider one sub-group of financial indicators is

more important than the other groups. Consequently, each financial indicator has been

assigned an equal weight of 1/34.

Fuzzification

Fuzzification is the stage of constructing a membership function for each selected

financial indicator. A membership function was developed based on a sample of 643 listed

companies for each financial indicator. A membership function was only accepted for a

particular financial indicator if it fits the statistical test of Chi-square and the Komogorov-

Sminov with a significance level of 1%.


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Table 2. Membership functions for 34 selected financial indicators in 2010 and 2011

Source:Authors calculations

No Financial Ratio Year 2011 Year 2010

Li quidity ratios

1 Current ratio LOGN(0.368; 1.23) LOGN(0.613; 1.76)

2 Quick ratio LOGN(1.14; 1.15) LOGN(1.23; 1.14)3 Cash ratio LOGN(1.85; 1.3) LOGN(1.92; 1.28)

Effi ciency Ratios

4 Accounts Receivable Turnover WEIB(9.59; 0.869) EXPO(15.5)

5 Inventory Turnover WEIB(7.64; 0.725) EXPO(12.7)

6 Accounts Payable Turnover EXPO(13.8) EXPO(19.9)

7 Fixed Assets Turnover WEIB(6.87; 0.819) EXPO(9.6)

8 Asset Turnover GAMM(0.973; 1.24) GAMM(0.632; 1.9)

9 Equity Turnover WEIB(3.22; 1.06) GAMM(2.34; 1.38)

Capi tal str ucture, leverage and solvency

10 Long-term Debt / Total Assets WEIB(6.84; 0.547) WEIB(6.61; 0.54)

11 Debt ratio NORM(42.1; 20.8) NORM(40.6; 20.4)

12 Long-term Debt / Equity GAMM(129; 0.336) WEIB(17.4; 0.478)

13 Total Assets / Equity NORM(3.06; 2.5) NORM(2.8; 1.82)

14 Short-term Debt / Total Debt NORM(81.5; 21.6) NORM(79.8; 23.3)

15 Interest Coverage Ratio WEIB(5.79; 0.542) WEIB(16.4; 0.491)

Profitabi li ty analysis

16 Return on Average Assets (ROAA) NORM(10.1; 10.2) NORM(12.6; 9.73)

17 Return on Equity (ROE) NORM(10.7; 14.3) NORM(16; 12.5)

18 Gross margin GAMM(5.53; 6.26) GAMM(6.07; 4.8)

19 Earning Per Share (EPS) NORM(2,120; 2,870) NORM(3,143; 3,003)

20 Return on Sales (ROS) NORM(11.5; 15.8) GAMM(5.1; 6.9)

21 EBITDA/ Revenue NORM(14.2; 19.3) GAMM(6.77; 4.77)

22 EBITDA / Total Assets NORM(11.9; 10.2) GAMM(5.65; 2.99)

23 EBITDA / Equity NORM(30.3; 24.9) NORM(34.4; 21.4)

Cost structrur e

24 Cost of goods sold / Revenue NORM(80.9; 14.9) NORM(78.9; 14)

25 Cost of Sales / Revenue EXPO(3.48) GAMM(9.94; 0.326)

26 Administrative Expense / Revenue WEIB(7.75; 1.2) GAMM(3.47; 1.71)

Assets str uctr ure

27 Short-term Assets / Total Assets NORM(61.3; 22.9) NORM(61.7; 22.6)

28 Short-term Accounts Receivable / Short-term Assets NORM(39.5; 20.2) NORM(39.7; 20.3)

29 Inventory / Short-term Assets NORM(36.6; 23.1) NORM(34.1; 21.4)

30 Fixed Assets / Total Assets GAMM(22.7; 1.25) WEIB(30.7; 1.3)

31 Tangible Assets / Fixed Assets NORM(70.4; 29.1) NORM(69.5; 30)

Growth rate

32 Revenue NORM(8.68; 41) NORM(33; 58.3)

33 Earning After Tax NORM(-28.6; 87.3) NORM(37.1; 110)

34 EPS NORM(-43.8; 174) NORM(30.8; 181)


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As presented in Table 2, a membership function (as represented by a probability

distribution function) for the same financial indicator may be different in 2010 and 2011.

The ability to incorporate this change is a real advantage of using fuzzy logic in assessing

the credit rating for businesses. For example, let us consider the mean and standard

deviation values for the financial indicator ofprofit after tax(as shown in row 33 of Table2 above). In 2010, the average of profit after tax for the entire market is 37.1 per cent,

representing the advantage of the Vietnamese macroeconomic environment. In this year,

Vietnam enjoyed a 6.78 per cent economic growth rate. However, the dispersion of this

indicator across a sample of 643 companies is very significant, being 110 per cent. In

contrast, Vietnams economic growth rate in 2010 was only 5.89 per cent. This reduction

in the economic growth rate was clearly reflected in the economic performances of listed

firms in Vietnam in 2011, with average profit after taxof 28.6 per cent and dispersion of

87.3 per cent, a significant reduction in both average and standard deviation values

compared with those in 2010.

As such, a company with a profit after tax of 0 per cent in 2010 can be considered

weak because other companies had taken the advantage of a better economic growth rate

to develop. However, the same company with a profit after tax of 0 per cent in 2011 was

not necessarily in a weak position because most companies incurred a loss in 2011. A

change in a membership function across years overcomes the static nature adopted by

other methods. For example, other rating approaches consider that the level of credit rating

will stay unchanged across years if a company achieves the same level of outcome.

Fuzzy Rules

Fuzzy rules are developed based on two different representations of continuous

distributions: (i) the probability density; or (ii) the cumulative distribution. Depending on

the nature of the financial indicators being considered from the group of 34, a membership

function, as represented by a probability distribution, can be different. However, as

previously discussed, a membership function can only be one of the two following

representations: (i) the probability density is to be used in relation to any financial

indicators associated with the optimal level (for example: capital structure); and (ii) acumulative distribution function / reverse cumulative distribution function is to be used in

relation to any financial indicators associated with the characteristic of "as high as

possible" / "as low as possible" of the financial indicators.


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(i) probability density (ii) cumulative distribution

Figure 3. Two expressions of continuous function

As shown in the above Figure 3(i), the maximum score for each financial indicator

associated with this peak is the average of the industry or the average of the entire market.

Lower scores will be assigned to different companies in the sample based on their

performance in relation to this financial indicator. In relation to Figure 3(ii), the starting

point of zero is assigned to the company with the worst performance in relation to a

particular financial indicator. The company with the best performance in relation to a

particular financial indicator is assigned the value of 1 the maximum score for this

distribution.

Defuzzification

Defuzzification is the process by which assigned scores can be calculated using

mathematics. Based on the membership function which was developed in the so-called

fuzzification stage, fuzzy rules are developed based on the characteristics of each

financial indicator. Defuzzification is used to determine a specific score for each

company for each financial indicator. Figure 4 below presents an example in which scores

for each company can be calculated based on their representations: optimal value versus

as high as possible value.


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(a) probability density (b) cumulative distribution

Figure 4. Specifying the location of the 2 representations of a membership

function

Scores will be determined based on the value of each financial indicator for each

company as previously calculated from its raw data. For example, in Figure 4 (a) which

shows a financial indicator associated with as high as possible, a value of 33 for a

particular financial indicator calculated from a raw data will be equivalent to a score of

0.72 using the related membership function for this indicator. Whereas, in Figure 4(b)

which shows a financial indicator associated with an optimal value, a value of 63 (or

81.5) calculated from raw data will be equivalent with the score of 0.85 (or 1) using the

related membership function for this financial indicator.

Scores and Rati ngs

The overall scores for each company is the sum of scores assigned for each of the 34

financial indicators:

oints n

i

where:on: a number of financial indicators, which is 34 in this studyodi: scores assigned to each company from a financial indicator i

It is noted that the total score for each company in the sample will vary within the

range of [0;1]. It is initially assumed that the probability of a company bankruptcy follows

the normal distribution. Consequently, this study recommended parameters adopted for

this assumed normal distribution are: (i) the median = 0,5; and (ii) the standard deviation

= 0,166 for the purpose of calculating total scores for each company in the sample. This

proposal is based on the grounds that, from statistical theory, the value range of [- 3;


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+ 3] will cover 99.8 per cent of the dataset. As such, the normal distribution has a mean

of 0.5 and standard deviation and 0.166 will ensure that total scores for each company

from all 34 financial indicators to vary within the range of [0;1].

V. ResultsAs previously discussed, there are two different methods in which a credit rating for a

particular company can be assigned. First, each company can be rated within the industry

to which it belongs. An adjustment factor is then required to convert this rating within the

industry into a rating applied to the entire market. Second, each company can be rated

directly within the entire market. Each of these two methods has its own advantages and

disadvantages. For the first approach, theaverage of the industrymay serve as a better

proxy for the company being rated because its industry is directly and closely related to the

financial strength of the business. However, the development of an adjustment factor isproblematic. For the second approach, it is more convenient to rate all companies at once

with the average of the market serving as a proxy to consider where the business ranks

among all businesses in the market. It is argued that, as a company operates within the

entire economys environment, it is more appropriate to rate each company with reference

to the average value of the market, not the industry in which a business operates.

In this study, each company in the following two industries: (i) real estates and (ii)

food and beverage industry has been rated twice: first, at the industry level; andsecond, at

the entire market level. Ratings at the industry level will provide some convincing

evidence the movement in terms of credit rating for a particular company when it is rated

within the industry; and then within the entire market. For example, a company is rated at

a very high level when the assessment of its financial strength is conducted within the

industry the company operates (say, IndustryX). In this case, an average of the industry is

adopted. However, when the entire market is considered, the rating of this company

deteriorates, this movement [from a higher credit rating into a lower credit rating]

presents the key finding that Industry X is not well placed in comparison with all other

industries in the market. The opposite case holds as well. It is noted that the focus of the

study is to rate each company once using the entire market approach which overcomes thedifficulty of an adjustment factor. Ratings at the industry level are conducted for

information purposes only.

Rakings of businesses in the Real estatesindustry in 2011

Real estate is the second tiered group (8600 - Real Estates) which belongs to the first tiered

group of Financials (8000 Financials) based on the Industry Classification Benchmark

(ICB) launched by Dow Jones and FTSE in 2005 and now owned solely by FTSE

International. In Vietnam, a company may operate in various industries. As such, there


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should be the criteria to classify companies in the key industry. The criteria adopted in this

study are that a company will be classified under the real estate group if its revenue from

real estate over the last three years is the largest in total companys revenue. For example,

Hoang Anh Gia Lai (HAG) operates in various industries including real estates,

agriculture, commodity, services and financials. Figure 5 shows that HAGs revenue fromreal estates is the largest component; as such, this company is classified in the real estates

industry for the purpose of this study.

Figure 5. Net Revenue of HAG

Source: Financial Statement of HAG

47 companies operating in this industry were rated in 2011. As such, the credit rating for

each company is a comparison of the companys financial strengths and weaknesses to

those in the same industry. It is important to note that any particular company in this group

considered good, simple represents the relative concept that this company is relatively

good in comparison with the other companies in the same industry. The results of rating

for this industry in 2011 can be summarized below.

1,760

481339 334

132 101

1,226

2,395

888

-

501161

22

1,262

3,374

538319

133-

194

-

500

1,000

1,500

2,000

2,500

3,000

3,500

4,000

Apartment Goods Mining ContructionContract

Rendering ofService

Electricity FinacialActivities

VND Billion

Year 2011 Year 2010 Year 2009


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financial indicators. As such, giving up a businesss own characteristics is not expected to

cause any major distortion of the outcome. However, businesses within the same industry

have also been grouped to see the divergence of ratings between industries.

It is observed that a normal distribution is achieved for the rating of all businesses in

the entire market. This is consistent with our initial hypothesis that a probability of

defaults of Vietnams listed businesses should be normally distributed.

Figure 9. Cumulative Distribution of Credit Ratings in 2011

In Vietnam, there are two separate stock exchanges: one in Ho Chi Minh City and the other

in Ha Noi. The number of companies with quality credit ratings (say, from AA- and

better) in the HCMC stock exchange is higher in comparison with the Ha Noi Stock

Exchange, 13,48% in HOSE versus 6,65% in HNX. In addition, it is argued that the

quality of stocks in HOSE is higher than in HNX due to its stricter regulations for listing.

Figure 9 above presents the cumulative credit ratings for businesses listed in both

exchanges. It is clear that the cumulative credit rating for the HOSE consistently lies

above the line represented for HNX.

0

10

20

30

40

50

60

70

80

90

100%

HOSE HNX


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Figure 10. Credit Ratings of the businesses listed in HOSE in 2011

Figure 11. Credit Ratings of the businesses listed in HNX in 2011As previously discussed, it is expected that a credit rating for a business will be different

when the two approaches are employed: (i) a within-industry approach; and (ii) a between-

industry (or entire market) approach. It is due to the fact that ratings assigned under the

first approach refer to the average of the industry whereas it refers to the average of the

entire market under the second approach. For example, for KDC, the company is rated

BB- under the first approach but it is rated A- when the second approach is employed.

Another example is applied to VNM when its credit rating is improved from A+ (under the

first approach) to AA+ (under the second approach); or a credit rating for Ha Noi Milk

Joint Stock Company (HNM) is improved from B- (under the first approach) to BBB-

0

7

17

15

25

22

26

19

23

1917

22

16

12 12

98

6 6

1

0

5

10

15

20

25

30

0

35

17

29 29 30

35

3230

25

33

21

16 17

8 9

13

7

2

0

5

10

15

20

25

30

35

40


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(under the second approach). Overall, 51 out of 55 companies in the food and beverage

industry have had their credit ratings improved when the second approach is adopted. The

overall conclusion is that food and beverages companies are on average in a better

financial condition in comparison with other companies operating in other industries in the

entire market. This observation is confirmed by an improvement of credit ratings for allcompanies in this industry when the entire market (or the second approach) is considered.

In contrast, when credit ratings of businesses classified under the real estate industry

are considered, the majority of businesses are assigned lower credit ratings when the

second approach is adopted in comparison with the first approach. 81.6 per cent of

companies in this industry are rated with a lowercredit rating when the entire market is

considered. The other companies, which have not been rated at a lower grade when the

second approach is used, have maintained their credit rating and there is no improvement

for any business in this industry when the second approach is considered. The overall

conclusion is that real estates industry is, on average, in weaker financial conditions in

comparison with other companies operating in other industries in the entire market.


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Table 3. A summary of credit ratings for 643 listed businesses in Vietnam in 2011

Note:

1: Electronic & Electrical Equipment;

General Industrials.

2: Industrial Transportation.

3: Construction & Materials.

4: Food & Beverage.

5: Automobiles & Parts; Personal &

Household Goods.

6: Technology; Telecommunications.

7: Utilities.

8: Mining.

9: Health Care.

10: Real Estate.

11: Others

From Table 3, the food and beverage industry (labeled as 4) and health industry (labeled

as 9) are the best performing businesses, the most stable and have very high growth prospects.

The number of companies achieving a credit rating of AA- and better account for 30.4 per

cent and 27.8 per cent, respectively. In contrast, the telecommunications and information

technology industry (labeled as 6) and the real estate industry (labeled as 10) are evident to be

affected strongly by the economy. Of the number of companies located in the danger zone

(rating from CCC and below), the telecommunications and information technology industry

accounts for 22.2 percent, and the real estate industry accounts for 18.8 per cent. As an

HOSE HNX 1 2 3 4 5 6 7 8 9 10 11

AAA 0 0 0 0 0 0 0 0 0 0 0 0 0 0

AA+ 10 7 3 0 0 2 4 0 0 0 1 0 0 3AA 22 17 5 2 2 2 7 2 2 0 2 2 0 1

AA- 32 15 17 4 3 4 6 3 0 4 2 3 0 3

A+ 54 25 29 6 6 9 7 6 0 10 5 1 1 3

A 51 22 29 6 5 13 8 2 0 6 2 3 1 5

A- 56 26 30 9 4 17 3 2 1 7 5 2 2 4

BBB+ 54 19 35 5 5 17 4 1 1 13 4 1 1 2

BBB 55 23 32 4 5 17 4 4 1 6 5 0 5 4

BBB- 49 19 30 3 6 17 4 3 2 6 2 1 4 1

BB+ 42 17 25 4 6 18 1 1 0 3 4 1 2 2

BB 55 22 33 5 5 22 2 1 2 6 6 1 4 1

BB- 37 16 21 4 3 10 2 1 2 2 5 2 6 0

B+ 28 12 16 1 5 11 0 2 0 3 0 0 4 2

B 29 12 17 0 2 10 0 2 2 3 2 0 7 1

B- 17 9 8 1 4 5 0 1 1 1 2 0 2 0

CCC+ 17 8 9 3 1 9 1 0 0 0 2 0 1 0

CCC 19 6 13 4 1 4 2 0 1 0 2 1 3 1

CCC- 13 6 7 1 1 2 0 0 3 0 1 0 5 0

D 3 1 2 0 0 1 1 0 0 1 0 0 0 0

Total 643 282 361 62 64 190 56 31 18 71 52 18 48 33

Rating Entire

market

Stock Exchange Industry Classification


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overall level for the market, 64 companies are rated with AA-or higher (accounting for 10 per

cent of all companies in the sample) and 52 companies fall into the dangerous zone with the

ratings of CCC and below (accounting for 8.1 per cent).

Compare the resul ts rank i n 2010 and 2011

The same exercise on the assessment of credit ratings for listed companies in Vietnam is also

conducted. In 2010, the sample includes only 601 listed compared with 643 companies in

2011. In Vietnam, 2010 was a much better year for listed businesses. The return on equity

(ROE), on average, was 16 per cent compared to 10.7 per cent in 2011. In addition, a growth

in the after-tax profit for listed companies was 37.1 per cent in comparison with -28.6 per cent

in 2011.

When the assessment of credit rating is implemented on a "static" basis, there is no doubt

many listed businesses are rated with a high credit rating because they performed very well in

2010. These companies performed well in 2010 in the environment when almost all

companies were performing well. There will be too many good companies in the market.

This is a key concern for the banks leading them to introduce non-quantitative factors such as

relations with the bank into the assessment of the credit rating for their lending decisions.

An approach to determining the credit rating for businesses using fuzzy logic will correct

this static nature. As previously discussed, a company with a growth which is well below

the growth level from other companies will not be considered as an improvement in the credit

rating. As such, the overall outcome of the credit rating in 2010 also reflects a distribution

and it does not skew the ratings towards better ratings. It is however noted that, due to afavourable economic climate in Vietnam in 2010, a number of listed companies with the

credit rating of A- and above increased significantly, 52.2 per cent compared with 34.2 per

cent in 2011. In addition, only 10 companies (1.7 per cent) were rated in the danger zone

(rated from CCC and below) compared to 50 companies (or 7.8 per cent) in 2011.


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VI. Conclusion

The assessment of credit ratings is a difficult and complex process. The credit ranking of a

company will reflect its reputation. In addition, deterioration in the credit rating of a company

will result in an increase in its cost of capital and make it is more difficult for the company to

access the international capital market. As a result, an approach to assess credit ratings must

be objective (not to be easily influenced by the raters by using qualitative factors in the

assessment); transparent(the rating is able to be replicated); and dynamic(able to reflect the

prevailing conditions in the market). The approach proposed in this paper using fuzzy logic

has achieved these important characteristics. And, this is the first and the key contribution of

this paper.

A selection of 34 financial indicators, which represents almost all key financial indicators

one can find in any finance textbook, ensures that various aspects of business operation is

covered. A sample of 643 companies is large enough to represent the entire market inVietnam. Listed companies are only excluded when, and only when, required data is not

publicly available. This is clearly the second contribution of the paper in relation to all

companies are rated.

This paper has achieved meaningful results for credit ratings for listed companies in

Vietnam. This paper also provides a complete guide on the set of financial indicators which

can be used in the assessment of the credit rating. All subjective adjustments currently

included in assessments of credit rating are no longer relevant under the fuzzy logic approach.

An assessment of credit rating using fuzzy logic is entirely based on the objective grounds

using mathematics and statistics.

The results from this study can be replicated for any year in Vietnam and the approach

can be adopted by any country in the world, particularly in developing and transitional

economies. The outcomes from this study, and any future study using the same approach, can

be referenced to by banks in making their lending decisions; investment funds in terms of

selecting investment portfolio in the market; general investors before investing in the

company. Corporate executives will be well aware of the current conditions of a

corporations financial health. Above all, the greatest significance of the study is the

foundation for the research of fuzzy logic applications in the field of general economics,

banking and finance, in particular in Vietnam and other developing and transitional

economies.


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a new approach to determining credit rating and its application

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