scholarly journals Determining the Credit Score and Credit Rating of Firms using the Combination of KMV-Merton Model and Financial Ratios

2021 ◽  
Vol 6 (3) ◽  
pp. 105-112
Author(s):  
Norliza Muhamad Yusof ◽  
Iman Qamalia Alias ◽  
Ainee Jahirah Md Kassim ◽  
Farah Liyana Natasha Mohd Zaidi
Keyword(s):  
Credit Risk ◽  
Credit Rating ◽  
Credit Ratings ◽  
Credit Scoring ◽  
Financial Ratios ◽  
Credit Score ◽  
Merton Model ◽  
Medium Level ◽  
Credit Scores ◽  
Risk Managers

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, McQuown, 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.

scholarly journals Credit Rating Model of Small Enterprises Based on Optimal Discriminant Ability and Its Empirical Study

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Zhanjiang Li ◽  
Lin Guo
Keyword(s):  
Loss Rate ◽  
Credit Rating ◽  
Recursive Algorithm ◽  
Credit Ratings ◽  
Small Enterprise ◽  
Small Enterprises ◽  
Credit Score ◽  
Discriminant Ability ◽  
Credit Scores ◽  
Rating Model

As an important part of the national economy, small enterprises are now facing the problem of financing difficulties, so a scientific and reasonable credit rating method for small enterprises is very important. This paper proposes a credit rating model of small enterprises based on optimal discriminant ability; the credit score gap of small enterprises within the same credit rating is the smallest, and the credit score gap of small enterprises between different credit ratings is the largest, which is the dividing principle of credit rating of small enterprises based on the optimal discriminant ability. Based on this principle, a nonlinear optimization model for credit rating division of small enterprises is built, and the approximate solution of the model is solved by a recursive algorithm with strong reproducibility and clear structure. The small enterprise credit rating division not only satisfies the principle that the higher the credit grade, the lower the default loss rate, but also satisfies the principle that the credit group of small enterprises matches the credit grade, with credit data of 3111 small enterprises from a commercial bank for empirical analysis. The innovation of this study is the maximum ratio of the sum of the dispersions of credit scores between different credit ratings and the sum of the dispersions of credit scores within the same credit rating as the objective function, as well as the default loss rate of the next credit grade strictly larger than the default loss rate of the previous credit grade as the inequality constraint; a nonlinear credit rating optimal partition model is constructed. It ensures that the small enterprises with small credit score gap are of the same credit grade, while the small enterprises with large credit score gap are of different credit grades, overcoming the disadvantages of the existing research that only considers the small enterprises with large credit score gap and ignores the small enterprises with small credit score gap. The empirical results show that the credit rating of small enterprises in this study not only matches the reasonable default loss rate but also matches the credit status of small enterprises. The test and comparative analysis with the existing research based on customer number distribution, K-means clustering, and default pyramid division show that the credit rating model in this study is reasonable and the distribution of credit score interval is more stable.

scholarly journals Dynamic Mapping of Probability of Default and Credit Ratings of Russian Banks

2020 ◽  
Vol 14 (4) ◽  
pp. 31-46
Author(s):  
Ella Khromova
Keyword(s):  
Credit Risk ◽  
Credit Rating ◽  
Credit Ratings ◽  
Quantitative Measure ◽  
Optimal Time ◽  
Investment Horizon ◽  
Investment Strategies ◽  
Probability Of Default ◽  
Rating Agencies ◽  
Dynamic Mapping

Investors are interested in a quantitative measure of banks’ credit risk. This paper maps the credit ratings of Russian banks to default probabilities for different time horizons by constructing an empirical dynamic calibration scale. As such, we construct a dynamic scale of credit risk calibration to the probability of default (PD).Our study is based on a random sample of 395 Russian banks (86 of which defaulted) for the period of 2007-2017. The scale proposed by this paper has three features which distinguish it from existing scales: dynamic nature (quarterly probability of default estimates), compatibility with all rating agencies (base scale credit ratings), and a focus on Russian banks.Our results indicate that banks with high ratings are more stable just after the rating assignment, while a speculative bank’s probability of default decreases over time. Hence, we conclude that investors should account for not only the current rating grade of a bank, but also how long ago it was assigned. As a result, a rising capital strategy was formulated: the better a bank’s credit rating, the shorter the investment horizon should be and the closer the date of investment should be to the rating assignment date in order to minimise credit risk.The scientific novelty of this paper arises from the process of calibration of a rating grade to dynamic PD in order to evaluate the optimal time horizon of investments into a bank in each rating class. In practical terms, investors may use this scale not only to obtain a desired credit rating, but also to identify quantitative measure of credit risk, which will help to plan investment strategies and to calculate expected losses.

Credit risk signals in CDS market vs agency ratings

2016 ◽  
Vol 17 (2) ◽  
pp. 194-217 ◽  
Author(s):  
Michael Jacobs Jr ◽  
Ahmet K. Karagozoglu ◽  
Dina Naples Layish
Keyword(s):  
Credit Risk ◽  
Credit Rating ◽  
Credit Ratings ◽  
Credit Default Swap ◽  
Risk Assessments ◽  
Content Type ◽  
Rating Agencies ◽  
Market Participants ◽  
Credit Default ◽  
The Relationship

Purpose This research aims to model the relationship between the credit risk signals in the credit default swap (CDS) market and agency credit ratings, and determines the factors that help explain the variation in such signals. Design/methodology/approach A comprehensive analysis of the differences in the relative credit risk assessments of CDS-based risk signals and agency ratings is provided. It is shown that the divergence between credit risk signals in the CDS market and agency ratings is explained by factors which the rating agencies may consider differently than credit market participants. Findings The results suggest that agency credit ratings of relative riskiness of a reference entity do not always correspond with assessments by CDS spreads, as the price of risk is a function of additional macro and micro factors that can be explained using statistical analysis. Originality/value This research is unique in modeling the relationship between the credit risk assessments of the CDS market and the agency ratings, which to the best of the authors' knowledge has not been analyzed before in terms of their agreement and the level of discrepancy between them. This model can be used by investors in debt instruments that are not explicitly CDSs or which have illiquid CDS contracts, to replicate market-based, point-in-time credit risk signals. Based on both market-based and firm-specific factors in this model, the results can be used to augment through-the-cycle credit risk assessments, analyze issues surrounding the pricing of CDSs and examine the policies of credit rating agencies.

The impact of market sectors and rating agencies on credit ratings: global evidence

2019 ◽  
Vol 20 (5) ◽  
pp. 389-410
Author(s):  
Kerstin Lopatta ◽  
Magdalena Tchikov ◽  
Finn Marten Körner
Keyword(s):  
Credit Risk ◽  
Credit Rating ◽  
Credit Ratings ◽  
Content Type ◽  
Rating Agencies ◽  
Credit Rating Agency ◽  
Rating Agency ◽  
Investment Grade ◽  
The Impact ◽  
Rating Downgrades

Purpose A credit rating, as a single indicator on one consistent scale, is designed as an objective and comparable measure within a credit rating agency (CRA). While research focuses mainly on the comparability of ratings between agencies, this paper additionally questions empirically how CRAs meet their promise of providing a consistent assessment of credit risk for issuers within and between market segments of the same agency. Design/methodology/approach Exhaustive and robust regression analyses are run to assess the impact of market sectors and rating agencies on credit ratings. The examinations consider the rating level, as well as rating downgrades as a further measure of empirical credit risk. Data stems from a large global sample of Bloomberg ratings from 11 market sectors for the period 2010-2018. Findings The analyses show differing effects of sectors and agencies on issuer ratings and downgrade probabilities. Empirical results on credit ratings and rating downgrades can then be attributed to investment grade and non-investment grade ratings. Originality/value The paper contributes to current finance research and practice by examining the credit rating differences between sectors and agencies and providing assistance to investors and other stakeholders, as well as researchers, how issuers’ sector and rating agency affiliations act as relative metrics.

Bad Credit

2014 ◽  
Vol 126 (1) ◽  
pp. 31-57 ◽  
Author(s):  
Annie McClanahan
Keyword(s):  
Credit Rating ◽  
Credit Scoring ◽  
First Century ◽  
The Novel ◽  
Qualitative Model ◽  
Credit Score ◽  
Love Story ◽  
The Social ◽  
Literary Character ◽  
True Love

This essay reads twenty-first-century credit scoring against eighteenth- and nineteenth-century forms of credit evaluation. While the latter famously draws its qualitative model of credibility from the novel, and the former predictably describes itself as quantitative and impersonal, in fact the credit score, the social person, and literary character remain significantly entangled. Through a reading of Gary Shteyngart’s Super Sad True Love Story, this essay shows what kinds of persons the practice of credit rating produces.

scholarly journals NEW INTERNAL RATING APPROACH FOR CREDIT RISK ASSESSMENT / NAUJAS VIDAUS REITINGU MODELIS KREDITO RIZIKOS VERTINIMUI

2011 ◽  
Vol 17 (2) ◽  
pp. 369-381 ◽  
Author(s):  
Vytautas Boguslauskas ◽  
Ričardas Mileris ◽  
Rūta Adlytė
Keyword(s):  
Risk Assessment ◽  
Credit Risk ◽  
Financial Risk ◽  
Credit Ratings ◽  
Assessment Model ◽  
Financial Ratios ◽  
Mahalanobis Distances ◽  
Credit Risk Assessment ◽  
Internal Rating ◽  
Kolmogorov Smirnov

The assessment and modeling of the credit risk is one of the most important topics in the field of financial risk management. In this investigation the credit risk assessment model was developed and tested for Lithuanian companies. 20 financial ratios of the companies were calculated for each year of the 3 year period of interest. The analysis of variance (ANOVA) and Kolmogorov-Smirnov test were applied and the set of variables reduced from 60 to 25. Logistic regression was used for the classification of the companies into reliable and not reliable ones. Financial ratios, having the highest correlation to the possibility of default were selected for further investigation and several credit ratings were attributed to the companies according to these variables’ values. The average values of Mahalanobis Distances calculated for the most reliable companies were the lowest and these values increased with a decreased reliability of the company. The differences between Mahalanobis Distances of the companies having different credit ratings confirmed the reliability of the model results. Santrauka Kredito rizikos vertinimas ir modeliavimas – viena iš aktualiausiu temų, kalbant apie finansinės rizikos valdymą. Atlikto tyrimo metu buvo sukurtas kredito rizikos modelis. šis modelis išbandytas 198 Įmonių aibėje, skaičiuojant po 20 finansinių rodiklių 3 analizuojamų metu laikotarpiu. Panaudojus ANOVA metodą ir Kolmogorovo – Smirnovo statistiką, kintamųjų kiekis buvo sumažintas nuo 60 iki 25 rodiklių. Įmonįu klasifikavimui į 2 grupes: patikimus ir nepatikimus banko klientus, atsižvelgiant į jų įsipareigojimų nevykdymo tikimybę, buvo naudojama logistinė regresija. 97 proc. patikimų (nebankrutavusių) ir 82 proc. nepatikimų (bankrutavusių) įmonių suklasifikuotos teisingai. Tolimesniam tyrimui atrinkti 7 finansiniai rodikliai, kurių koreliacinis ryšys su įsipareigojimų nevykdymo tikimybe buvo didžiausias. Atsižvelgiant į šių kintamųjų reikšmės, įmonėms buvo priskirti 9 kredito reitingai. Vidutines Mahalanobio atstumu reikšmes, apskaiČiuotos patikimiausioms kompanijoms buvo mažiausios; šios reikšmės didėjo, mažejantįmonių patikimumui. Skirtingį reitingį įmonėms apskaiČiuoti Mahalanobio atstumų skirtumai, pagrindė modelio rezultatų patikimumą.

scholarly journals The Effect of Reputation Shocks to Rating Agencies on Corporate Disclosures

2018 ◽  
Vol 94 (1) ◽  
pp. 299-326 ◽  
Author(s):  
Mani Sethuraman
Keyword(s):  
Credit Risk ◽  
Voluntary Disclosure ◽  
Credit Rating ◽  
Credit Ratings ◽  
Earnings Forecasts ◽  
Corporate Bond ◽  
Management Earnings Forecasts ◽  
Rating Agencies ◽  
Theoretical Predictions ◽  
Bond Issuers

ABSTRACT This paper explores the effect of a credit rating agency's (CRA) reputation on the voluntary disclosures of corporate bond issuers. Academics, practitioners, and regulators disagree on the informational role played by major CRAs and the usefulness of credit ratings in influencing investors' perception of the credit risk of bond issuers. Using management earnings forecasts as a measure of voluntary disclosure, I find that investors demand more (less) disclosure from corporate bond issuers when the ratings become less (more) credible. In addition, using content analytics, I find that bond issuers disclose more qualitative information during periods of low CRA reputation to aid investors in assessing credit risk. My findings are consistent with credit ratings providing incremental information to investors and reducing adverse selection in lending markets. Further, consistent with theoretical predictions, my findings suggest that managers rely on voluntary disclosure as a credible mechanism to reduce information asymmetry in bond markets.

Do asset sales affect firm credit risk? – Evidence from credit rating assignments

2014 ◽  
Vol 40 (9) ◽  
pp. 903-927 ◽  
Author(s):  
Vinod Venkiteshwaran
Keyword(s):  
Credit Risk ◽  
Credit Rating ◽  
Credit Ratings ◽  
Ordered Probit ◽  
Asset Sales ◽  
Content Type ◽  
Credit Quality ◽  
The One ◽  
Opposing Effects ◽  
The Impact

Purpose – Asset sales can have opposing effects on firm credit quality. On the one hand asset sales could signal increased credit risk resulting from distress or on the other hand they could improve internal liquidity and hence credit quality. Therefore the impact potential asset sales can have on credit quality is an empirical question and one that has previously not been examined in the literature. The paper aims to discuss these issues. Design/methodology/approach – Using credit ratings as a measure of firm credit quality, in ordered probit regressions, this study finds evidence consistent with the internal liquidity view of the asset sales-credit risk relationship. Findings – Results from ordered probit regressions of credit ratings show that the likelihood of higher credit ratings is increasing in industry-level turnover of real assets Originality/value – Credit-rating agencies often cite the impact of asset sales on firm credit quality as a motivation for their rating assignments. Distress-driven asset sales could reduce firm credit quality whereas other asset sales could result in increased internal firm liquidity and hence improve firm credit quality. This bi-directional expectation leaves the question of how asset sales affect credit quality to be answered empirically and has not been previously tested in the literature.

scholarly journals On the Nonlinearity of the Financial Ratios-Credit Ratings Relationship

2016 ◽  
Vol 2 (2) ◽  
pp. 65
Author(s):  
Afef Feki Krichene ◽  
Walid Khoufi
Keyword(s):  
Credit Rating ◽  
Credit Ratings ◽  
Pronounced Effect ◽  
Financial Ratios ◽  
Coverage Ratio

In this paper, we study the specificity of financial ratios in determining credit ratings. Specifically, we examine the nonlinearity of the financial ratios-credit ratings relationship. Among financial ratios, the interest coverage and debt coverage ratios have the most pronounced effect on credit ratings. To determine the form of the nonlinearity, the interest and debt coverage ratios are divided to four sub-variables with different weights associated to each increment. We find that different coefficients are associated to different increments of the interest coverage and debt coverage ratios. An interest coverage ratio loses all significance when it is less than zero and when it exceeds 20. Similarly, a debt coverage ratio loses all significance when it less than negative one and when it exceeds one. Our results confirm the nonlinearity of the financial ratios-credit rating relationship.

scholarly journals Relationship Between Audit Opinion And Credit Rating: Evidence From Korea

2016 ◽  
Vol 32 (2) ◽  
pp. 621 ◽  
Author(s):  
Myungki Cha ◽  
Kookjae Hwang ◽  
Youngjun Yeo
Keyword(s):  
Credit Rating ◽  
Empirical Test ◽  
Credit Ratings ◽  
Audit Opinion ◽  
Credit Score ◽  
Audit System ◽  
High Profile ◽  
Predictive Function ◽  
Audit Opinions ◽  
The Relationship

In this study, we investigate the relationship between credit ratings and audit opinions of financially distressed companies impending bankruptcy. Using Korean publicly-held firms for the years 2007 through 2014, we analyze 97 bankrupt companies with credit rating available before they file bankruptcy. Following prior research (Geiger et al., 2005), we find that the propensity to issue a going concern audit opinion is associated with the credit score issued by NICE immediately prior to the audit opinion date. We also compare credit ratings to audit opinions to investigate which of the two is more conservative and provides the earlier signal of bankruptcy. Through empirical test, we can conclude that audit system has more successfully predictive function in signaling preceding bankruptcy than CRAs' system with overly optimism. We argue that after a string of high-profile corporate failures such as Enron and Arthur Anderson’s bankruptcies, legislators portrayed auditors negatively and ultimately led to the enactment and more forced liabilities and thus auditors become more conservative. To remedy CRAs' failure by providing overly optimism, we suggest that like as auditors, CRAs' regulations should be more strengthened on their liability about issuing credit ratings.

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