A NEW APPROACH TO DETERMINING CREDIT RATING & ITS APPLICATIONS TO VIETNAM’S LISTED FIRMS
VO HONG DUC a b * & NGUYEN DINH THIEN b a
Economic Regulation Authority, Perth, Australia; b
Open University, Ho Chi Minh City, Vietnam.
Abstract 1
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 Moody’s, Standard and Poor’s 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 Vietnam’s stock exchanges using 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 VIETNAM’S 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 Moody’s, Standard and Poor’s 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 Vietnam’s stock exchanges using their reported financial ratios.
JEL: G24; G32 Key words: fuzzy logic; quantitative assessment; credit rating; listed firms, Vietnam
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I.
Introduction
A credit rating of a particular company plays a very important role to investors, the business itself, stakeholders, suppliers, and debtors. A company which is in a good financial position with growth prospects will be assigned a good credit rating. With a good credit rating, shareholders will be more confident with their investment and business partners will place more confidence on the firm. In addition, with a good credit rating, a company may receive preferential terms when borrowing from creditors including banks. There is no doubt that the approaches for assessing credit ratings for Vietnamese companies in Vietnam remain very limited, both in terms of quantity and quality. The main cause of this limitation is the fact that credit institutions, such as banks, do not have confidence in the approaches adopted for rating credit worthiness of businesses. In their approaches, the focus of a credit rating is heavily influenced by quantitative factors. In addition, the methodology and the results of credit ratings have not been widely published, reviewed or referenced. This study presents a significant contribution to the application of the well-known mathematical theory, fuzzy logic (FL) in the assessment of credit ratings of companies within the same country using their reported financial ratios. Fuzzy logic was first mentioned in the study of the fuzzy set (Zadeh, 1965) which was published in the journal of Information Technology and Control. Since then, the fuzzy logic theory has been widely applied in most technical fields. Towards the end of the 20th century, the application of fuzzy theory gradually moved into the economics and finance sectors and the findings are promising. In Vietnam, application of fuzzy logic has been very limited in the context of economics and finance studies. This study shows that incorporating fuzzy logic in the methodology for assessing credit rating of listed businesses would solve a number of current problems to achieve the following outcomes: (i) provision of a set of rating criteria; (ii) a proposed method of quantitative evaluation based on probability and mathematical statistics; and (iii) a classification of "financial health" and the growth prospects among all listed companies in Vietnam. This study involves a review of the financial data of all listed companies in Vietnam, excluding credit institutions. 643 listed companies had the required data available in 2011 when this study was conducted. Among various indicators related to firms’ operation, 34 financial indicators are selected for the purpose of assessing credit rating of these listed companies on a ground of theoretical consideration.
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II. Literature Review Credit 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 business’s capacity to pay its debts. Fitch considers that a credit rating is an assessment of a company’s ability to fulfill its obligations in relation to its liabilities 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 Financial indicators Non-financial indicators The weights of financial indicators non financial (audited reports) The weights of financial indicators non financial (non-audited reports) Relations with the bank
BIDV 14 41
Vietinbank 11 60
35% 65%
55% 45%
30% 70%
40% 60%
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 BIDV’s ranking approach, there are 3 more financial indicators and 19 fewer non-financial indicators used compared with Vietinbank’s approach. The key similarity between BIDV and Vietinbank relates to the group of non-financial indicators labeled as 4|Page
"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 “element” can 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 the range 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”, “maybe” or 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 x is assigned a real value (x) in the range of [0;1] to indicate the dependence of that element x in 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: µA(x) = {(x, µA(x)) | x X} where: o x: an element belonging to set A. o µA(x): membership function. o µA(x): is the degree of membership of x.
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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 “good” set. In contrast, when a company’s 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 company’s 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 good” for 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 multidimensional 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 stock trading. 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 (25 companies 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 8|Page
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 Hypotheses Research 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) withinindustry 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 optimal level, 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 9|Page
appropriate distribution of the economic conditions follows a normal distribution. As such, the risk of bankruptcy of businesses may also be normally distributed. IV. Methodology A selection of criteria and weighting 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 KomogorovSminov with a significance level of 1%.
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Table 2. Membership functions for 34 selected financial indicators in 2010 and 2011 Financial Ratio
No
Year 2011
Year 2010
LOGN(0.368; 1.23)
LOGN(0.613; 1.76)
LOGN(1.14; 1.15)
LOGN(1.23; 1.14)
LOGN(1.85; 1.3)
LOGN(1.92; 1.28)
WEIB(9.59; 0.869)
EXPO(15.5)
WEIB(7.64; 0.725)
EXPO(12.7)
Liquidity ratios 1 Current ratio 2 Quick ratio 3 Cash ratio Efficiency Ratios 4 Accounts Receivable Turnover 5 Inventory Turnover 6 Accounts Payable Turnover 7 Fixed Assets Turnover
EXPO(13.8)
EXPO(19.9)
WEIB(6.87; 0.819)
EXPO(9.6)
GAMM(0.973; 1.24)
GAMM(0.632; 1.9)
WEIB(3.22; 1.06)
GAMM(2.34; 1.38)
10 Long-term Debt / Total Assets 11 Debt ratio
WEIB(6.84; 0.547)
WEIB(6.61; 0.54)
NORM(42.1; 20.8)
NORM(40.6; 20.4)
12 Long-term Debt / Equity 13 Total Assets / Equity
GAMM(129; 0.336)
WEIB(17.4; 0.478)
NORM(3.06; 2.5)
NORM(2.8; 1.82)
14 Short-term Debt / Total Debt 15 Interest Coverage Ratio
NORM(81.5; 21.6)
NORM(79.8; 23.3)
WEIB(5.79; 0.542)
WEIB(16.4; 0.491)
NORM(10.1; 10.2)
NORM(12.6; 9.73)
NORM(10.7; 14.3)
NORM(16; 12.5)
GAMM(5.53; 6.26)
GAMM(6.07; 4.8)
NORM(2,120; 2,870)
NORM(3,143; 3,003)
20 Return on Sales (ROS) 21 EBITDA/ Revenue
NORM(11.5; 15.8)
GAMM(5.1; 6.9)
NORM(14.2; 19.3)
GAMM(6.77; 4.77)
22 EBITDA / Total Assets 23 EBITDA / Equity
NORM(11.9; 10.2)
GAMM(5.65; 2.99)
NORM(30.3; 24.9)
NORM(34.4; 21.4)
NORM(80.9; 14.9)
NORM(78.9; 14)
8 Asset Turnover 9 Equity Turnover Capital structure, leverage and solvency
Profitability analysis 16 Return on Average Assets (ROAA) 17 Return on Equity (ROE) 18 Gross margin 19 Earning Per Share (EPS)
Cost structrure 24 Cost of goods sold / Revenue 25 Cost of Sales / Revenue
EXPO(3.48)
GAMM(9.94; 0.326)
WEIB(7.75; 1.2)
GAMM(3.47; 1.71)
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)
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)
26 Administrative Expense / Revenue Assets structrure
Growth rate 32 Revenue
Source: Authors’ calculations
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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 of profit after tax (as shown in row 33 of Table 2 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, Vietnam’s 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 tax of 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) a cumulative 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 Ratings The overall scores for each company is the sum of scores assigned for each of the 34 financial indicators: n
oints i
where: o n: a number of financial indicators, which is 34 in this study o di: 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 µ = 0,5; and (ii) the standard deviation σ = 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 ; 14 | P a g e
+ 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. Results As 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, the average of the industry may 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 is problematic. 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 economy’s 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; and second, 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, Industry X). 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 the difficulty of an adjustment factor. Ratings at the industry level are conducted for information purposes only. Rakings of businesses in the Real estate’s industry 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 15 | P a g e
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 company’s revenue. For example, Hoang Anh Gia Lai (HAG) operates in various industries including real estates, agriculture, commodity, services and financials. Figure 5 shows that HAG’s revenue from real estates is the largest component; as such, this company is classified in the real estates industry for the purpose of this study. VND Billion 4,000 3,374
3,500 3,000
2,395
2,500 2,000
1,760 1,262 1,226
1,500 888
1,000
481
538
500
339
334
501 319
-
161 132 133
101 22 -
194
Apartment
Goods
Mining
Year 2011
Contruction Contract
Year 2010
Rendering of Service
Electricity
Finacial Activities
Year 2009
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 company’s 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.
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7 6 5 4 3 2 1 0
Entire market
HOSE
HNX
Figure 6. Ranking of businesses for real estates businesses in 2011 When the ratings within the real estate industry are considered, some companies are rated relatively high. Of the 47 companies rated, 29 companies are rated with investment grades (BBB- and better). In addition, 8.2 per cent of total companies are rated with a high probability of default. Ratings of businesses in the Food and Beverage industry in 2011 In comparing the differences between ratings within the industry and within the entire market for the same business, the second industry is selected. This is the food and beverage industry. Within this industry, the 1st tiered group is on consumer goods (3000 Consumer Goods). Food and beverage is one out of three 2nd tiered groups belonging to this industry. 55 companies were classified in this industry in 2011. 64.38 per cent of the total companies in the food and beverage industry fall in the range of the credit rating between BBB+ and B-, in comparison with 57.14 per cent in the real estate industry. This outcome presents a more stable operation from the companies operating in the food and beverage industry compared to those in the real estate industry.
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10 9 8 7 6 5 4 3 2 1 0
Entire market
HOSE
HNX
Figure 7. Rankings of businesses for the food and beverage industry in 2011 Ratings of the entire market in 2011 60
54
56 51
50
54 55
55 49 42 37
40 32
28 29
30 22
17 17
20
19 13
10 10
3
0 0 AAA AA+ AA
AA-
A+
A
A- BBB+ BBB BBB- BB+
BB
BB-
B+
B
B- CCC+ CCC CCC-
D
Figure 8. Credit ratings of all Vietnam’s listed companies in 2011 When all companies in the entire market are rated simultaneously using the average of the entire market as a proxy, a specific industry’s classification and characteristics are no longer considered. In this approach, a rating for a particular company is based on a comparison of this business’s financial strengths and weaknesses to all other companies in the entire market regardless of the industry. It is however noted that the characteristics of each industry should have been reflected in the financial strengths of the business via its 18 | P a g e
financial indicators. As such, giving up a business’s 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 Vietnam’s listed businesses should be normally distributed. 100 90 80 70 60 50 40 30 20 10 0
%
HOSE
HNX
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.
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30 26
25 25
23
22 19
20
22 19
17
17
16
15 15
12 12 9
10
7
8 6
6
5 1
0 0
Figure 10. Credit Ratings of the businesses listed in HOSE in 2011 40 35 35 29 29 30
30
32
33 30 25
25
21
20
17
16 17 13
15 8
10 3
5
9
5
0
7 2
0
Figure 11. Credit Ratings of the businesses listed in HNX in 2011 As 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 betweenindustry (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 BBB20 | P a g e
(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 all companies 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 lower credit 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. Rating AAA AA+ AA AAA+ A ABBB+ BBB BBBBB+ BB BBB+ B BCCC+ CCC CCCD Total
A summary of credit ratings for 643 listed businesses in Vietnam in 2011 Entire Stock Exchange market HOSE HNX 1 0 0 0 0 10 7 3 0 22 17 5 2 32 15 17 4 54 25 29 6 51 22 29 6 56 26 30 9 54 19 35 5 55 23 32 4 49 19 30 3 42 17 25 4 55 22 33 5 37 16 21 4 28 12 16 1 29 12 17 0 17 9 8 1 17 8 9 3 19 6 13 4 13 6 7 1 3 1 2 0 643 282 361 62
Industry Classification 2 3 4 5 6 7 8 0 0 0 0 0 0 0 0 2 4 0 0 0 1 2 2 7 2 2 0 2 3 4 6 3 0 4 2 6 9 7 6 0 10 5 5 13 8 2 0 6 2 4 17 3 2 1 7 5 5 17 4 1 1 13 4 5 17 4 4 1 6 5 6 17 4 3 2 6 2 6 18 1 1 0 3 4 5 22 2 1 2 6 6 3 10 2 1 2 2 5 5 11 0 2 0 3 0 2 10 0 2 2 3 2 4 5 0 1 1 1 2 1 9 1 0 0 0 2 1 4 2 0 1 0 2 1 2 0 0 3 0 1 0 1 1 0 0 1 0 64 190 56 31 18 71 52
9 0 0 2 3 1 3 2 1 0 1 1 1 2 0 0 0 0 1 0 0 18
10 0 0 0 0 1 1 2 1 5 4 2 4 6 4 7 2 1 3 5 0 48
11 0 3 1 3 3 5 4 2 4 1 2 1 0 2 1 0 0 1 0 0 33
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 22 | P a g e
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 results rank in 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 a favourable 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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Table 4. A comparison of credit ratings: 2010 versus 2011 2011
2010
AAA AA+ AA
AA- A+
A
A-
BBB+ BBB BBB- BB+ BB
BB-
B+
B
B-
CCC+ CCC CCC- D
AAA
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
AA+
0
0
0
1
0
0
0
0
0
0
1
2
2
0
0
0
0
0
0
0
AA
0
1
3
8
2
5
2
1
5
0
1
2
1
0
0
0
0
1
0
0
AA-
0
1
4
7
8
7
4
5
6
0
2
1
3
2
1
0
2
1
3
0
A+
0
0
3
7
8
12 12
9
9
3
7
3
2
2
1
3
0
1
0
0
A
0
0
1
8
9
8
6
11
2
5
5
3
3
0
5
1
1
1
1
0
A-
0
0
1
4
6
4
8
7
7
7
3
7
3
0
3
1
3
1
1
0
BBB+
0
0
0
1
4
4
6
7
9
5
3
3
3
4
1
1
0
0
0
0
BBB
0
0
0
1
2
4
9
1
7
6
5
10
4
4
4
0
3
3
0
0
BBB-
0
0
0
0
0
2
2
2
7
6
5
4
4
4
2
0
0
2
0
0
BB+
0
0
1
0
2
1
4
0
0
8
3
7
2
2
4
1
1
1
0
0
BB
0
0
0
0
0
0
2
1
1
5
2
4
3
3
0
0
2
1
1
0
BB-
0
0
0
0
0
0
1
1
2
1
0
2
2
3
2
3
1
0
2
0
B+
0
0
0
0
1
0
0
0
0
1
2
2
4
2
2
1
0
1
2
1
B
0
0
0
0
0
0
1
3
0
2
1
1
0
1
0
1
2
1
0
0
B-
0
0
0
0
0
0
0
0
0
0
0
0
2
1
1
1
1
2
0
1
CCC+
0
0
0
0
0
0
0
0
0
0
1
1
0
0
1
0
1
0
0
0
CCC
0
0
0
0
0
0
0
0
0
1
0
0
0
1
1
1
0
1
0
0
CCC-
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
D
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Note:
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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 in Vietnam. 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 corporation’s 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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References Bojadziev, G. and Bojadziev, M. (2007), Fuzzy logic for business, finance and management, World Scientific Publishing, USA. Gil-Lafuente, A.M. (2005), Fuzzy Logic in Financial Analysis, Springer, New York. Khcherem, F. and Bouri, A. (2009), “Fuzzy Logic and Investment Strategy”, Global Economy & Finance Journal, Vol (2), pp 22-37. Korol, T. and Korodian, A. (2011), “Evaluation of effectiveness of fuzzy logic model in predicting the business bankruptcy”, Romanian Journal of Economic Forecasting, pp 92 – 107. Othman, S. and Etienne, S. (2010), “Decision making using fuzzy logic for stock trading”, Institute of Electrical and Electronics Engineers (IEEE), Information Technology (ITSim), International Symposium Publications, Vol (2), pp 880 - 884. Malagoli, S. et al (2009), “Rating and Ranking Firms with Fuzzy Expert Systems: The Case of Camuzzi”, IUP Journal of Applied Finance, Vol (15), October 2009. Nguyen, P. (2005), Fuzzy Logic and Its Applications, The Science and Technology Publisher. Vlachos, D. và Tolias, Y. A. (2003), “Neuro-fuzzy modeling in bankruptcy prediction”, Yugoslav Journal of Operations Research, Vol (13), Issue (2), pp 165-174. Warren, C.S., Reeve, J.M. và Duchac,J.E (2012), Financial Accounting, 12th Edition, SouthWestern College Pub, pp 773 – 794. Yildiz, B. and Akkoc, S. (2010), “Bankruptcy Prediction Using Neuro Fuzzy: An Application in Turkish Banks”, International Research Journal of Finance and Economics, Issue (60).
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