Title Science and Technology Indonesia e-ISSN:2580-4391 p-ISSN:2580-4405 Vol. 6, No. 3, July 2021 Research Paper Determining the Credit Score and Credit Rating of Firms using the Combination of KMV-Merton Model and Financial Ratios Norliza Muhamad Yusof1*, Iman Qamilia Alias1, Ainee Jahirah Md Kassim1, Farah Liyana Natasha Mohd Zaidi1 1Faculty of Computer and Mathematical Sciences, UiTM, Cawangan Negeri Sembilan, Kampus Seremban, 70300 Seremban, Negeri Sembilan, Malaysia *Corresponding author: norliza3111@uitm.edu.my Abstract Credit risk management has become a must in this era due to the increase in the number of businesses defaulting. Building upon the legacy of Kealhofer, Mc�own, and Vasicek (KMV), a mathematical model is introduced based on Merton model called KMV-Merton model to predict the credit risk of firms. The KMV-Merton model is commonly used in previous default studies but is said to be lacking in necessary detail. Hence, this study aims to combine the KMV-Merton model with the financial ratios to determine the firms’ credit scores and ratings. Based on the sample data of four firms, the KMV-Merton model is used to estimate the default probabilities. The data is also used to estimate the firms’ liquidity, solvency, indebtedness, return on asset (ROA), and interest coverage. According to the weightages established in this analysis, scores were assigned based on those estimates to calculate the total credit score. The firms were then given a rating based on their respective credit score. The credit ratings are compared to the real credit ratings rated by Malaysian Rating Corporation Berhad (MARC). According to the comparison, three of the four companies have credit scores that are comparable to MARC’s. Two A-rated firms and one D-rated firm have the same ratings. The other receives a C instead of a B. This shows that the credit scoring technique used can grade the low and the high credit risk firms, but not strictly for a firm with a medium level of credit risk. Although research on credit scoring have been done previously, the combination of KMV-Merton model and financial ratios in one credit scoring model based on the calculated weightages gives new branch to the current studies. In practice, this study aids risk managers, bankers, and investors in making wise decisions through a smooth and persuasive process of monitoring firms’ credit risk. Keywords Credit Score, KMV-Merton Model, Financial Ratios, Credit Rating, Default, Credit Risk Received: 9 February 2021, Accepted: 10 June 2021 https://doi.org/10.26554/sti.2021.6.3.105-112 1. INTRODUCTION Recent statistics by Kraemer (2020) revealed that 2019 is the most challenging year compared to the previous years as the number of �rms bankruptcies rose. A rare default case occurred after the investment-grade (high) rated �rms were reported to default. The �rms’ failure to pay debts will a�ect the organi- zation in the �rms themselves and lenders and the economy. Therefore, practical credit risk assessment is a must to curb the risk transmission. Credit scoring is a perfect example of one of the methods to grade �rms’ credit risk. The literature of credit scoring is very limited and only starts to be widely used from the 21st century especially for consumer lending (Abdou and Pointon, 2011). Credit scoring contains elements that are quanti- tative and qualitative (Haralambie et al., 2016). The qualitative part can be done based on judgemental, but the essential factor is the quantitative part where empirical criterion can be obtained statistically or mathematically (Chijoriga, 2011). In this research, credit scoring is done quantitatively based on the KMV-Merton model and �nancial ratios. Commonly, KMV-Merton Model is used to predict the prob- ability of default of �rms. This includes studies from (Crosbie and Bohn, 2019; Vassalou and Xing, 2004; Zhang et al., 2010; Bharath and Shumway, 2004; Kollár and Gondžárová, 2015). Meanwhile, the �nancial ratios are needed to evaluate �rms’ liquidity, solvency, leverage, and pro�tability as done by (Alt- man, 1967; Beaver, 1966; Zorn et al., 2018). However, (Bharath and Shumway, 2004) demonstrated that the use of the KMV- Merton model is inadequate as a default forecaster. Thus, some �nancial ratios were recommended to improve the KMV-Merton model’s performance (Liang, 2012). A few researchers such as Benos and Papanastasopoulos, 2007; Liang, 2012; Andrikopoulos and Khorasgani, 2018 focused on incorporating the �nancial ratios with the KMV-Merton model to improve the accuracy of their default prediction. Most https://crossmark.crossref.org/dialog/?doi=10.26554/sti.2021.6.3.105-112&domain=pdf https://doi.org/10.26554/sti.2021.6.3.105-112 Yusof et. al. Science and Technology Indonesia, 6 (2021) 105-112 Figure 1. The Research Process of the Study of the researchers used both the KMV-Merton model and �nan- cial ratios to produce a hybrid default model. Contradicted to this research where the KMV-Merton model is combined with the �nancial ratios into one credit scoring formula to determine the credit ratings of �rms. In this credit scoring model, the weightage of each �nancial ratio and KMV-Merton model are calculated based on certain criteria. Therefore, this research aims to determine the credit score and credit ratings of the selected �rms using the combination of the KMV-Merton model and �- nancial ratios. This research presents the other view to credit scoring that employs the KMV-Merton model and �nancial ra- tios to assess a �rm’s credit risk. In addition, comparisons are made between the credit ratings measured in this research and the MARC credit ratings. The following is how the rest of the paper is organized: The second section explains the research methods, including data setting, default probability estimations, calculating weightages and credit scores, and assessing �rm credit ratings. The �ndings of this study were discussed in the third part. Lastly is the conclusion. 2. METHODOLOGY The process of this study is presented in term of �owchart as in Figure 1. All the process are explained in detail in the following sub-sections. 2.1 Data Setting Samples of four �rms’ �nancial data: Sime Darby Plantation, Tenaga Nasional Berhad (TNB), Alam Maritim Resources Berhad, and Press Metal Berhad are used in this study. Based on MARC’s credit ratings, these four �rms were chosen to represent the strong, medium, and bad rated �rms. All data was gathered according to the year in which the credit rating was published from 2014 to 2019. The data is obtained from the �rms’ annual report, including current assets, current liabilities, total equity, total liabilities, total assets, net pro�t, earning before net interest, and interest expenses. Table 1 describes the descriptive statistics of data obtained from the �rms’ annual report. These data are used to calculate the �nancial ratios of the �rms, as presented in Table 3. Table 3 shows the selected �nancial ratios and their formula based on (Caracota et al., 2010). Liquidity refers to a �rm’s ability to repay its short-term debt (Yameen et al., 2019). Next, when dealing with the banking sector, solvency is used to assess a �rm’s viability (Zorn et al., 2018). The term "indebtedness" refers to a �rm’s willingness to carry debt and ful�l its obligations (Gibson, 1987). Meanwhile, return on asset (ROA) is a metric that calculates how pro�table a �rm is as a result of its assets (Rosikah et al., 2018). Finally, interest coverage is used to assess a �rm’s ability to pay interest (Nwanna and Ivie, 2017). There are �ve ratios involved, and they are liquidity, solvency, indebtedness, return on assets (ROA), and interest coverage (time interest earned). The data from the �rms’ quarter report that includes the short-term and long-term borrowings are used to de�ne the book value of liabilities. This study also utilized the outstanding shares obtained from the quarterly report and the historical daily price obtained from Finance, 2020; Investing, 2020. The short- term borrowings, long-term borrowings and the outstanding share are assumed �xed according to the quarter reports. Table 2 describes the descriptive statistics of data obtained from the �rms’ quarterly report. This data was set up as a process to estimate �rms’ default probabilities. 2.2 Estimating the Default Probabilities of Firms using the KMV-Merton Model KMV-Merton model is the extended model of the Merton (1974) model where a new parameter called distance to default (DD) is introduced in this model. Default occurs when the �rm’s market value of the asset falls below the default point, de�ned as the �rms’ book value of liabilities (Crosbie and Bohn, 2019). There are �ve steps involved to estimate the probability of default of �rms using the KMV-Merton model. The �rst step is to calculate the daily market value of �rms’ equity by multiply- ing each of the daily prices with the outstanding shares. The second step is to calculate the daily book value of liabilities, D by de�ning it as a total borrowing of the short term plus half of the long-term borrowings. One-half of the liabilities are used, as the default point usually lies between total liabilities and current liabilities (Crosbie and Bohn, 2019). The third step is to add together the �rms’ market value of equity and the book value of liabilities to get the daily asset’s market value, Vt . The fourth step is to generate the daily natural log of the assets’ market values returns, ln(Vt /Vt−1) . Here, the average return, � and the standard deviation, � are calculated as the �rms’ expected © 2021 The Authors. Page 106 of 112 Yusof et. al. Science and Technology Indonesia, 6 (2021) 105-112 Table 1. The Descriptive Statistics of Data Obtained from the Firms’ Annual Report Item (RM) Descriptive Statistics N Minimum Maximum Mean Std. Deviation Current Asset 4 1,669,956,000 278,540,906,000 77,601,969,750 134,390,656,391 Current Liabilities 4 2,072,044,000 252,381,385,000 70,628,376,750 121,561,848,422 Equity 4 597,127,000 59,282,100,000 19,480,180,500 27,405,184,942 Total Liabilities 4 151,297,000 45,411,700,000 13,914,587,250 21,239,042,871 Total Assets 4 870,890,000 178,847,200,000 53,420,926,250 84,487,428,574 Net Pro�t 4 -145,380,000 4,529,200,000 1,192,855,000 2,230,747,917 Earning Before Net 4 -138,897,441 8,206,800,000 2,226,447,64 3,995,582,703 Interest Expenses 4 6,654,090 1,487,700,000 389,511,523 732,403,723 Table 2. The Descriptive Statistics of Data Obtained from the Firms’ Quarterly Report Item (RM) Descriptive Statistics N Minimum Maximum Mean Std. Deviation Outstanding Share 16 257,869,000 6,885,000,000 3,458,423,438 2,961,295,203 Short Term Borrowing (RM) 16 89,363,000 6,061,000,000 2,209,695,813 1,891,270,258 Long Term Borrowing (RM) 16 15,016,000 43,737,900,000 11,921,773,688 18,723,901,636 returns and daily volatility, respectively. Since asset returns follows the random walk properties and probability of default is estimated annually, thus the daily volatility is annualized by multiplying it by the square root of trading days, which is 252 days in a typical year (Glenn, 2018). The �fth step is calculating the distance to default, d using the following equation: d = ln(VtD ) + (� − �2 2 )t t√� (1) The parameter d is de�ned as the number of standard devia- tions away from default (Crosbie and Bohn, 2019) where Vt is the market value of the asset at any time t, D is the book value of liabilities, � is the expected asset returns, � is the asset volatility and t =1 year. Finally is to estimate the annual default probability of the �rms. Merton, 1974 assumed that the asset returns’ random com- ponent is normally distributed. Thus, the probability of default, Pt , is written in term of standard cumulative normal distribution function and it is de�ned as the inverse of d expressed as follows: Pt = 1 − P(Z < d) = P(Z < −d) = ∫ −d −∞ e− 1 2 z 2 dz (2) A �rm is said to have a higher default probability as the value approaching one, and a lower default probability as the value is approaching 0. The larger the distance to default, the lesser the �rm’s probability to default. 2.3 Calculating the Weightage of Default Probability and Financial Ratios Calculating weightage is essential to determine the weightage of each �nancial ratio and default probability according to spe- ci�c criteria in credit scoring. All the �ve �nancial rat ios (X1,X2,X3,X4,X5) used in Table 3 and the default probability (Pt) are denoted as the credit risk indicators i = Pt ,X1,X2,X3,X4,X5. The formula used to calculate the weightage of each credit risk indicators i, Wi, is presented as: Wi = Si ∑6i=1 Si (3) Given Si is the score of each credit risk indicator i, and it is calculated based on the approach of O’Loughlin (2009) as expressed below: Si = 8 ∑ j=1 WjSj (4) where j is the eight criteria shown in Table 4. Hence, Wj is the weight to criteria and Sj is the criteria score that is determined in this study. There are eight criteria de�ned by O’Loughlin (2009) in the weighted scoring model, which are value, risk, urgency, stake- holder, success, di�culty, relationship, and compliance. Each criterion has been given its own percentage, Wj, as shown in Ta- ble 4. The value and risk represent the accuracy of the credit risk indicators and the ability to measure risk, respectively. Urgency shows the ability to alert the �rms on taking immediate action in any case of a default event. A stakeholder is where there is any © 2021 The Authors. Page 107 of 112 Yusof et. al. Science and Technology Indonesia, 6 (2021) 105-112 Table 3. The Financial Ratios (Caracota et al., 2010) No. Financial ratios Formula 1 Liquidity, X1 current assetscurrent liabilities 2 Solvency, X2 equitytotal liabilities 3 Indebtedness, X3 total liabilitiesequity 4 Return on asset (ROA), X4 net pro�ttotal assets 5 Interest coverage (Time interest earned), X4 earning before net interest, costs, and taxinterest expense Table 4. The Score Weightage Criteria, j Weight to criteria Wj Criteria Score, Sj Pt X1 X2 X3 X4 X5 Value 20% 9 4 7 9 6 6 Risk 20% 9 5 8 8 7 8 Urgency 15% 9 7 8 8 7 7 Success 10% 6 6 7 6 8 8 Compliance 5% 7 2 6 6 2 2 Relationships 5% 9 4 9 9 4 8 Stakeholder 15% 4 5 4 8 9 7 Di�culty 10% 7 4 4 4 4 4 Score, Si 7.65 4.90 6.65 7.55 6.50 6.60 Weightage, Wi (%) 100 19 12 17 19 16 17 Table 5. The Scoring of Probability of Default (Credit, 2014) Default Probability, Pt (%) Score, k 0.00-0.12 10 0.12 – 0.27 9 0.27-0.34 8 0.34 – 0.55 7 0.55 – 0.87 6 0.87 – 1.40 5 1.40 – 2.10 4 2.10 – 4.00 3 4.00 – 9.99 2 9.99 – 50.00 1 50.00 – 100 0 involvement between the credit risk indicators and stakeholders. Success describes the success of credit risk indicators to measure the �rm’s �nancial performance. Di�culty relates to how the model acquires its needed parameters. The relationship criterion shows how the models have any relation to credit risk. Lastly, the compliance measures the level of the credit risk indicators in conforming to any related law. The criteria scores, Sj is set up in this study based on the im- portance and relevancy of the credit risk indicators i in ful�lling the criteria. Its score can be in the ranges extremely important (9 to 10), averagely important (6 to 8), and least important (0 to Table 6. The Scoring of Financial Ratios (Caracota et al., 2010) Financial ratios Ratio Score, k Liquidity X1 ≥ 1.3 7 1.1 ≤ X1 < 1.3 5 1 ≤ X1 < 1.1 3 0 ≤ X1 < 1 1 Solvency X2 ≥ 0.1 9 0.07 ≤ X2 < 0.1 6 0.05 ≤ X2 < 0.07 3 X2 < 0.05 0 Indebtedness 0 ≤ X3 < 2 6 2 ≤ X3 < 4 5 4 ≤ X3 < 6 3 X3> 6 0 ROA X4 ≥ 0.05 2 0 < X4 < 0.05 0 Interest coverage X5 ≥ 0.03 10 0.02 ≤ X5 < 0.03 8 0.01 ≤ X5 < 0.02 5 X5 < 0.01 0 © 2021 The Authors. Page 108 of 112 Yusof et. al. Science and Technology Indonesia, 6 (2021) 105-112 5). For example, as shown in Table 4, the probability of default is scored as 9 for the value criterion. It is scored as extremely important because the probability of default value is considered a signi�cant value that could predict �rms’ default (Liang, 2012). The probability of default is shown to score the highest in almost all the criteria, corresponding to its importance for this research. There is an exception in the stakeholder criterion. Usually, stake- holders are concerned about the �rm’s pro�t and its consistency with the revenue stream. The ROA is the most preferred by the stakeholders as they measure the �rm’s pro�tability. Thus ROA scored the highest. The results of implementing the equations (3) and (4) are given in Table 4. Table 4 presents the score weightage of the credit risk indicators i. 2.4 Calculating the Credit Score of Firms This part is where the combination of the KMV-Merton model and �nancial ratios took place, as all the scores were added into one formula to determine the credit score of the selected �rms. The credit score of the �rms, f is determined based on the following equation expressed as (Chikomba et al., 2013): f = 6 ∑ i=1 Wi( ki max ki ) (5) where Wi is the weightage of the credit risk indicators i calculated using equation (3). Meanwhile ki is the score assigned as the default probability and �nancial ratios were estimated and then mapped into Tables 5 and 6. Tables 5 and 6 show the score given for the default probability and �nancial ratios. The max ki is the maximum score that can be obtained for the default probability and �nancial ratios as given in Tables 5 and 6. The scores given in Tables 5 and 6 were assigned by (Credit, 2014; Caracota et al., 2010) to indicate the strength of �rms based on a certain level of credit risk. In this case, the worst score given is zero, while the excellent score can be varied from two to ten. Table 7. The Credit Rating Maps to the Credit Score (Chikomba et al., 2013) Credit Score, f (%) Credit rating Level of credit risk 75 – 100 A Low 60 – 74 B Medium 50 – 59 C High 25 – 0 D Default 2.5 Determining the Credit Rating of the Firms The �rms’ credit rating can be determined by comparing the calculated �rms’ credit scores with Table 7. Table 7 presents the credit rating maps to the credit score. The last step is com- paring the credit ratings determined with the ratings given by the MARC. Beforehand, some adjustment is made to standardize MARC’s credit rating as presented in Table 8. Table 8. Credit Ratings Equivalent to MARC Ratings Credit Rating MARC Rating A AAA,AA,A B BBB,BB,B C C D D 3. RESULTS AND DISCUSSION 3.1 The Default Probabilities and Financial Ratios In this study, the default probabilities of the four �rms are calcu- lated using equation (2) of the KMV-Merton model, as described in section 2.2. In the meantime, the �nancial ratios are deter- mined using the formula in Table 3. Tables 9 and 10 show the results of implementing the equation and the formula. Table 9 presents the results of estimating the �rms’ default probabilities using the KMV-Merton model. Based on the asset and liabilities values given in Table 9, the leverage ratio (book value of liabilities / market value of asset) is calculated to measure �rm’s �nancial leverage. Sime Darby Plantation has the lowest leverage ratio of 0.12, which is followed by TNB (0.24), Alam Maritim (0.45), and Press Metal (0.77). Leverage ratio indicates how much of a �rm’s capital is funded by debt. The Press Metal borrowed 77 percent of its money, while Alam Maritim borrowed nearly half. Sime Darby Plantation and TNB, on the other hand, only used 12 percent and 24 percent of their resources in the form of debt, respectively. The amount of permissible leverage, on the other hand, is determined by the sector in which the �rm work. Some businesses are prone to taking on a lot of debt. As a result, other factors such as the anticipated return must be considered. Based on Table 9 only the Sime Darby Plantation is expected to have a positive return while others have negative returns. In terms of volatility, the asset of Press Metal is the most volatilized, and next is the Alam Maritim, Sime Darby Plantation, and TNB. These are parallel where �rms with higher asset volatility tend to have a lesser amount of leverage ratio (Patel and Pereira, 2007). Considering all these, Sime Darby Plantation is predicted to have the highest DD, while the Press Metal is expected to have the lowest DD. Therefore, the PD of the Sime Darby is the lowest, followed by TNB. Still, both have approximately 0% of PD. Then, the value of PD goes higher to 4% for Alam Maritim and even higher than 42% for Press Metal. Table 10 shows the �nancial ratios estimated for the selected �rms. Alam Maritim is found to have the highest liquidity and solvency among all the �rms and the lowest indebtedness, ROA, and interest coverage. This contradicted the Press Metal, where it has the lowest liquidity and solvency but the highest indebt- edness, ROA, and interest coverage. Instability in these �nancial ratios of both �rms showing a sign of poor �nancial performance. Although Alam Maritim has the highest liquidity and solvency, it has problems paying debt and gaining pro�t. Meanwhile, Press Metal has problems countering its assets over its liability and has the lowest viability even if it can pay its debt. Unlike the © 2021 The Authors. Page 109 of 112 Yusof et. al. Science and Technology Indonesia, 6 (2021) 105-112 Table 9. The Result of Estimating the �rms’ default probabilities using the KMV-Merton Model Sime Darby Tenaga Nasional Alam Maritim Press Metal Plantation Berhad (TNB) Resources Berhad Berhad Market value of asset (RM’000) 42640750 99853648 304671 2214991 Book Value of Liabilities (RM’000) 5117500 24445500 138268 1703075 Expected return 0.0006711 -0.0000698 -0.000712 -0.000034 Asset volatility 0.2016 0.1493 0.4041475 0.5659 Distance to default (DD) 10.4169 9.3502 1.751 0.1814 Probability of default (PD) 1.04E-25 4.37E-21 4.00E-02 4.28E-01 Table 10. The Financial Ratios of Firms Sime Darby Tenaga Nasional Alam Maritim Press Metal Plantation Berhad (TNB) Resources Berhad Berhad Liquidity 1.0435 1.0820 1.1037 0.8059 Solvency 2.0479 1.3054 3.9467 0.9278 Indebtedness 0.4883 0.7660 0.2534 1.0778 Return on asset 0.0043 0.0253 -0.1669 0.0487 Interest coverage 7.7993 5.5164 -20.8740 36.5553 Table 11. The Credit Score of the Firms Credit Risk Weightage, Wi (%) Max ki Score, ki Sime Darby Tenaga Nasional Alam Maritim Press Metal Plantation Berhad Resources Berhad Berhad Pt 19 10 10 10 2 1 X1 12 7 3 3 5 1 X2 17 9 9 9 9 9 X3 19 6 6 6 6 6 X4 16 2 0 0 0 0 X5 17 10 10 10 0 10 Credit score, f (%) 100 - 77 77 48 57 Table 12. The Comparison of the Credit rating and MARC Rating Firm Credit Score, f (%) Credit rating MARC Rating Sime Darby Plantation 77 A A Tenaga Nasional Berhad 77 A A Press Metal Berhad 57 C B Alam Maritim Resources Berhad 48 D D © 2021 The Authors. Page 110 of 112 Yusof et. al. Science and Technology Indonesia, 6 (2021) 105-112 Sime Darby Plantation and TNB, where their �nancial ratios are more stable. 3.2 Credit Score and Credit Rating The credit score of the �rms is calculated using equation (5), where the weightage of the default probability and �nancial ratios are determined beforehand using equations (3) and (4). Then, the credit ratings of the �rms are determined according to the score obtained using Table 7. Tables 11 and 12 presents the results of calculating the credit score and the determining the credit ratings, respectively. Table 11 shows the credit score of �rms. A larger score means the �rms have better �nancial performances. Based on Tables 9 and 10, we found that Sime Darby and TNB can be categorized as �rms with low default risk and stable �rms, and thus, both �rms were given maximum scores in PD and three out of �ve �nancial ratios. This is contradicted to the Alam Maritim, where it only scored maximum in solvency and indebtedness. The same goes for Press Metal with the addition of maximum score in interest coverage. None of the �rms obtained the maximum score in liquidity and ROA. As a result, the �nal credit score for Sime Darby Plantation and TNB (77%) are the highest, followed by Press Metal (57%) and lastly Alam Maritim (48%). This can also be seen clearly in Table 12. Table 12 presents the comparison of the credit rating and MARC Rating. Sime Darby Plantation and TNB were rated A, while Alam Maritim was rated D. Only Press Metal rating does not match with the MARC ratings as Press Metal was rated C instead of B. 4. CONCLUSIONS In this research, a method for determining a �rm’s credit score is presented to grade the credit risk of the �rms, which uses a combination of the KMV-Merton model and �nancial ratios corresponding to the certain weightage. Four �rms have been selected: Sime Darby Plantation, Tenaga Nasional Berhad, Alam Maritim Resources Berhad, and Press metal Berhad. These �rms’ �nancial data was utilized to estimate the �rms’ PD, liquidity, solvency, indebtedness, pro�tability, and interest coverage. We found that higher asset to debt ratio, higher returns, and lower volatility estimates higher DD and, thus, lower PD. Meanwhile, higher liquidity, solvency, pro�tability, interest coverage, and lower indebtedness estimate better �nancial performance. Based on these results, Sime Darby Plantation and TNB are found to have a low default risk and secure �nancial account compared to the Alam Maritim and Press Metal. This is seen as the credit score determined for both Sime Darby Plantation and TNB is 77%, followed by Press Metal 57% and Alam Maritim 48%. Those scores bring Sime Darby Plantation and TNB as A-rated �rms and Alam Maritim as a D-rated �rm, while ratings for Press Metal are between B-rated and C-rated �rms. 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