Credit ratings, financial ratios, and equity risk: A decomposition analysis based on Moody’s, Standard & Poor’s and Fitch’s ratings

2021 ◽  
pp. 102512
Author(s):  
Yixiao Jiang
Keyword(s):  
Credit Ratings ◽  
Decomposition Analysis ◽  
Financial Ratios ◽  
Equity Risk

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 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.

The influence of pension plan risk on equity risk and credit ratings: a study of FTSE100 companies

2009 ◽  
Vol 8 (4) ◽  
pp. 405-428 ◽  
Author(s):  
DONAL MCKILLOP ◽  
MICHAEL POGUE
Keyword(s):  
Direct Relationship ◽  
Risk Measures ◽  
Credit Ratings ◽  
Pension Plan ◽  
Pension Plans ◽  
Defined Benefit ◽  
Rating Agency ◽  
Equity Risk ◽  
Funding Risk ◽  
The Relationship

AbstractThis paper examines the relationship between funding risk of defined benefit pension plans and both corporate debt ratings and equity risk measures for FTSE100 companies. Panel based models highlighted a direct relationship between pension plan risk and equity risk. Pension risk was also demonstrated to be factored into credit ratings with the analysis highlighting that the greater the pension risk, the greater the probability of obtaining a lower debt rating. From a rating agency viewpoint, this is positive news, particularly at present when agencies are being criticized for a perceived failure to reflect sub-prime mortgage problems in firm-specific ratings.

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.

ANALISIS PREDIKSI POTENSI KEBANGKRUTAN PADA PT BANK MUAMALAT INDONESIA Tbk PERIODE 2012-2016 DENGAN MENGGUNAKAN METODE ALTMAN MODIFIKASI

2017 ◽  
Vol 5 (2) ◽  
pp. 287-324
Author(s):  
Dewi Laela Hilyatin
Keyword(s):  
Financial Performance ◽  
Financial Structure ◽  
Financial Statements ◽  
Financial Ratios ◽  
Z Score ◽  
Altman Analysis ◽  
Gray Area ◽  
Modification Method ◽  
A Company

Abstract Bankruptcy is a very essential issue that every company should be aware of. Bankruptcy of a company can be minimized by advanced prediction; such as analyzing the financial statements. This study discusses the financial performance of PT Bank Muamalat Indonesia Tbk, which indicates that there is a degression in some number of financial ratios, the closing of offices and firing of employees in 2012-2016, causing he fact that BMI must pay attention and improve its financial performance and anticipate the existence of a bankruptcy in the company. Based on Altman analysis modification for financial performance of PT Bank Muamalat Indonesia Tbk in 2012-2016, it found Z-Score value of 0,825, 0,659, 1,243, 0,982 and 0,892. Based on Z-Score criteria, PT Bank Muamalat Indonesia Tbk is predicted to experience problems in management and financial structure and also in potentially bankruptcy due to Z-Score value <1,1 while the highest Z-Score value is in 2014, which shows the value of Z-Score>1,1 and <2,6, which means the company is in the gray area, meaning the company’s category is not said to be bankrupt and also not healthy. Keywords: Bankruptcy, Altman Modification Method

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