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 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.

A credit rating model based on a customer number bell-shaped distribution

2018 ◽  
Vol 56 (5) ◽  
pp. 987-1007 ◽  
Author(s):  
Yajing Zhang ◽  
Guotai Chi
Keyword(s):  
Objective Function ◽  
Loss Rate ◽  
Credit Rating ◽  
Credit Ratings ◽  
Absolute Difference ◽  
Loan Pricing ◽  
Content Type ◽  
Rating Model ◽  
Number Of Customers ◽  
Loss Rates

Purpose The purpose of this paper is to split loan customers to different credit ratings to ensure the results that show that customers with lower credit ratings have higher loss rates, and the number of customers that satisfies the bell-shaped distribution. Hence, the number of credit ratings, the distribution of the rated obligors among ratings can achieve a meaningful differentiation of risk, which can avoid the loan pricing confusion. Design/methodology/approach The authors introduce a multi-objective programming to establish the credit rating model. Objective function 1 minimizes the absolute difference between the obligor number proportion and perfect client proportion, following a standard normal distribution. Objective function 2 minimizes the total difference of the deviation between two adjacent credit ratings’ loss rates. This study combines the two objective functions to ensure the obligor number distribution and the monotonicity of the loss rate, and applies genetic algorithm to solve the model. Findings This study’s analysis is based on data from 6,155 enterprises, provided by a Chinese bank and Prosper P2P loan data. The empirical results reveal that the proposed approach can ensure the balance between both criteria and avoid undue concentration of obligors in particular grades. Originality/value The proposed credit model could help building a reasonable credit rating system, which is the prerequisite of loan pricing; thus, inaccurate credit rating can cause incorrect loss rate estimates and loan pricing.

A Novel Credit Rating Model: Empirical Analysis from Chinese Small Enterprises

2021 ◽  
pp. 1-20
Author(s):  
Bin Meng ◽  
Haibo Kuang ◽  
Liang Lv ◽  
Lidong Fan ◽  
Hongyu Chen
Keyword(s):  
Empirical Analysis ◽  
Credit Rating ◽  
Small Enterprises ◽  
Rating Model

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.

scholarly journals Determination of Sovereign Credit Rating Model for European Countries

2021 ◽  
Vol 5 (3) ◽  
Author(s):  
Isik Akin
Keyword(s):  
Financial Crisis ◽  
Credit Rating ◽  
Global Financial Crisis ◽  
Credit Ratings ◽  
Developed Countries ◽  
Credit Rating Agencies ◽  
Rating Agencies ◽  
Sovereign Credit ◽  
Rating Model ◽  
Sovereign Credit Rating

Credit rating agencies play a key role in financial markets, as they help to reduce asymmetric information among market participants via credit ratings. The credit ratings determined by the credit rating agencies reflect the opinion of whether a country can fulfil the liability or its credit reliability at a particular time. Therefore, credit ratings are a very valuable tool, especially for investors. In addition, the issue that credit rating agencies are generally criticised is that they are unsuccessful in times of financial crisis. Credit rating methodologies of credit rating agencies have been subject to intense criticism, especially after the 2007/08 Global Financial Crisis. Some of the criticised issues are that credit rating agencies’ methodologies are not transparent; they are unable to make ratings on time, and they make incorrect ratings. In order to create a more reliable credit rating methodology, the credit rating industry and the ratings determined by rating agencies need to be critically examined and further investigated in this area. For this reason, in this study credit rating model has been developed for countries. Supervisory and regulatory variables, political indicators and macroeconomic factors were used as independent variables for the sovereign credit rating model. As a result of the study, the new sovereign credit rating calculates exactly the same credit rating with Fitch Rating Agency for developed countries, but there are 1 or 2 points differences for developing countries. In order to better understand the reason for these differences, credit rating agencies need to make their methodologies more transparent and disclose them to the public.

Evaluating credit rating prediction by using the KMV model and random forest

Kybernetes ◽  
2016 ◽  
Vol 45 (10) ◽  
pp. 1637-1651 ◽  
Author(s):  
Hsu-Che Wu ◽  
Yu-Ting Wu
Keyword(s):  
Feature Selection ◽  
Credit Rating ◽  
Credit Ratings ◽  
Financial Data ◽  
Content Type ◽  
Financial Variables ◽  
Kmv Model ◽  
Rating Prediction ◽  
Rating Model ◽  
Accuracy Rates

Purpose An increasing number of investors have begun using financial data to develop optimal investment portfolios; therefore, the public financial data shared in the capital market plays a critical role in credit ratings. These data enable investors to understand the credit levels of debtors from a bank perspective; this facilitates predicting the debtor default rate to efficiently evaluate investment risks. The paper aims to discuss these issues. Design/methodology/approach A credit rating model can be developed to reduce the risk of adverse selection and moral hazard caused by information asymmetry in the loan market. In this study, a random forest (RF) was used to evaluate financial variables and construct credit rating prediction models. Data-mining techniques, including an RF, decision tree, neural networks, and support vector machine, were used to search for suitable credit rating forecasting methods. The distance to default from the KMV model was then incorporated into the credit rating model as a research variable to increase predictive power of various data-mining techniques. In addition, four-level and nine-level classification were set to investigate the accuracy rates of various models. Findings The experimental results indicated that applying the RF in the variable feature selection process and developing a forecasting model was the most effective method of predicting credit ratings; the four-level and nine-level feature-selection settings achieved 95.5 and 87.8 percent accuracy rates, respectively, indicating that RF demonstrated outstanding feature selection and forecasting capacity. Research limitations/implications The experimental cases were based on financial data from public companies in North America. Practical implications Practical implication of this study indicates the most effective financial variables were dividends common/ordinary, cash dividends, volatility assumption, and risk-free rate assumption. Originality/value The RF model can be used to perform feature selection and efficiently filter numerous financial variables to obtain crediting rating information instantly.

Rating Agency Adjustments to GAAP Financial Statements and Their Effect on Ratings and Credit Spreads

2014 ◽  
Vol 90 (2) ◽  
pp. 641-674 ◽  
Author(s):  
Pepa Kraft
Keyword(s):  
Default Risk ◽  
Credit Rating ◽  
Credit Ratings ◽  
Balance Sheet ◽  
Financial Statements ◽  
Financial Statement ◽  
Credit Spread ◽  
Credit Spreads ◽  
Term Structures ◽  
Interest Expense

ABSTRACT I examine a dataset of both quantitative (hard) adjustments to firms' reported U.S. GAAP financial statement numbers and qualitative (soft) adjustments to firms' credit ratings that Moody's develops and uses in its credit rating process. I first document differences between firms' reported and Moody's adjusted numbers that are both large and frequent across firms. For example, primarily because of upward adjustments to interest expense and debt attributable to firms' off-balance sheet debt, on average, adjusted coverage (cash flow-to-debt) ratios are 27 percent (8 percent) lower and adjusted leverage ratios are 70 percent higher than the corresponding U.S. GAAP ratios. I then find that Moody's hard and soft rating adjustments are associated with significantly higher credit spreads and flatter credit spread term structures. Overall, the results indicate that Moody's quantitative adjustments to financial statement numbers and qualitative adjustments to credit ratings enable it to better capture default risk, consistent with it effectively processing both hard and soft information.

Uncertain Meanings of Risk

2018 ◽  
Author(s):  
Natalia Besedovsky
Keyword(s):  
Financial Markets ◽  
Credit Rating ◽  
Traditional Approach ◽  
Credit Ratings ◽  
Assessment Practices ◽  
Structured Finance ◽  
Rating Agencies ◽  
Measures Of Risk ◽  
Methodological Approaches ◽  
Ontological Uncertainty

This chapter studies calculative risk-assessment practices in credit rating agencies. It identifies two fundamentally different methodological approaches for producing ratings, which in turn shape the respective conceptions of credit risk. The traditional approach sees ‘risk’ as an only partially calculable and predictable set of hazards that should be avoided or minimized. This approach is particularly evident in the production of country credit ratings and gives rise to ordinal rankings of risk. By contrast, structured finance rating practices conceive of ‘risk’ as both fully calculable and controllable; they construct cardinal measures of risk by assuming that ontological uncertainty does not exist and that models can capture all possible events in a probabilistic manner. This assumption—that uncertainty can be turned into measurable risk—is a necessary precondition for structured finance securities and has become an influential imaginary in financial markets.

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