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.

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.

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.

A comparative study of economic growth as a key determinant of sovereign credit ratings in Africa

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Misheck Mutize ◽  
McBride Peter Nkhalamba
Keyword(s):  
Economic Growth ◽  
Credit Rating ◽  
Credit Ratings ◽  
Credit Rating Agencies ◽  
African Union ◽  
African Countries ◽  
Content Type ◽  
Rating Agencies ◽  
Sovereign Credit ◽  
Sovereign Credit Ratings

PurposeThis study is a comparative analysis of the magnitude of economic growth as a key determinant of long-term foreign currency sovereign credit ratings in 30 countries in Africa, Europe, Asia and Latin America from 2010 to 2018.Design/methodology/approachThe analysis applies the fixed effects (FE) and random effects (RE) panel least squares (PLS) models.FindingsThe authors find that the magnitude economic coefficients are marginally small for African countries compared to other developing countries in Asia, Europe and Latin America. Results of the probit and logit binary estimation models show positive coefficients for economic growth sub-factors for non-African countries (developing and developed) compared to negative coefficients for African countries.Practical implicationsThese findings mean that, an increase in economic growth in Africa does not significantly increase the likelihood that sovereign credit ratings will be upgraded. This implies that there is lack of uniformity in the application of the economic growth determinant despite the claims of a consistent framework by rating agencies. Thus, macroeconomic factors are relatively less important in determining country's risk profile in Africa than in other developing and developed countries.Originality/valueFirst, studies that investigate the accuracy of sovereign credit rating indicators and risk factors in Africa are rare. This study is a key literature at the time when the majority of African countries are exploring the window of sovereign bonds as an alternative funding model to the traditional concessionary borrowings from multilateral institutions. On the other hand, the persistent poor rating is driving the cost of sovereign bonds to unreasonably high levels, invariably threatening their hopes of diversifying funding options. Second, there is criticism that the rating assessments of the credit rating agencies are biased in favour of developed countries and there is a gap in literature on studies that explore the whether the credit rating agencies are biased against African countries. This paper thus explores the rationale behind the African Union Decision Assembly/AU/Dec.631 (XXVIII) adopted by the 28th Ordinary Session of the African Union held in Addis Ababa, Ethiopia in January 2017 (African Union, 2017), directing its specialized governance agency, the African Peer Review Mechanism (APRM), to provide support to its Member States in the field of international credit rating agencies. The Assembly of African Heads of State and Government highlight that African countries are facing the challenges of credit downgrades despite an average positive economic growth. Lastly, the paper makes contribution to the argument that the majority of African countries are unfairly rated by international credit rating agencies, raising a discussion of the possibility of establishing a Pan-African credit rating institution.

US rating agency reform to rest on enforcement

2016 ◽  
Keyword(s):  
Business Model ◽  
Global Economy ◽  
Annual Report ◽  
Credit Rating ◽  
Credit Ratings ◽  
Securities And Exchange Commission ◽  
Political Support ◽  
Political Debate ◽  
Content Type ◽  
Rating Agencies

Subject The latest annual report of the Securities and Exchange Commission (SEC) on credit ratings agencies (CRAs). Significance The latest annual report of the Securities and Exchange Commission (SEC) on credit rating agencies (CRAs) suggests that practices that contributed to the 2007-08 financial crisis persist, and that the prevailing CRA business model continues to incentivise high credit ratings rather than accurate ones. The underlying conflict of interest inherent in the prevailing CRA business model is well-recognised, but there is a lack of broad political support to address the problem. Impacts The report will increase pressure on the SEC to strengthen its CRA enforcement policy. The report is shaping the terms of political debate and providing fodder, especially for Democratic presidential candidate Bernie Sanders. Renewed financial market turbulence and strains in the global economy could provide fresh tests for CRAs.

Goodwill impairment loss and bond credit rating

2017 ◽  
Vol 25 (1) ◽  
pp. 2-20 ◽  
Author(s):  
Li Sun ◽  
Joseph H. Zhang
Keyword(s):  
Regression Analysis ◽  
Credit Rating ◽  
Credit Ratings ◽  
Negative Relationship ◽  
Least Squares Regression ◽  
Firm Level ◽  
Content Type ◽  
Goodwill Impairment ◽  
The Impact ◽  
The Relationship

Purpose The purpose of this study is to examine the impact of goodwill impairment losses on bond credit ratings. Design/methodology/approach The authors use regression analysis to examine the relationship between goodwill impairment losses and bond credit ratings. Findings The empirical results show a negative relationship between the amount of goodwill impairment losses and bond credit ratings, suggesting that firms with goodwill impairment losses receive lower credit ratings. The authors perform various additional tests, including subsamples in good or bad market time, changes analysis, first time goodwill impairment firms vs subsequent impairment and the two-stage least squares regression analysis to address potential endogeneity issues. The main results persist. Originality/value This paper links and contributes to two streams of literature: goodwill impairment in accounting literature and bond credit ratings in finance literature. Whether a firm’s goodwill impairment losses affect the firm’s bond credit rating remains an interesting question that has not been examined previously. To the best of the authors’ knowledge, this is the first study that directly examines the relationship between goodwill impairment losses and bond ratings at the firm level.

The geographical and income differences in the determinants of African sovereign credit ratings

2020 ◽  
Vol 11 (4) ◽  
pp. 609-624
Author(s):  
Ilse Botha ◽  
Marinda Pretorius
Keyword(s):  
Credit Rating ◽  
Credit Ratings ◽  
African Countries ◽  
Content Type ◽  
Rating Agencies ◽  
Sovereign Credit ◽  
Additional Information ◽  
Income Groups ◽  
Sovereign Credit Ratings ◽  
Developmental Indicators

PurposeThe importance of obtaining a sovereign credit rating from an agency is still underrated in Africa. Literature on the determinants of sovereign credit ratings in Africa is scarce. The purpose of this research is to determine what the determinants are for sovereign credit ratings in Africa and whether these determinants differ between regions and income groups.Design/methodology/approachA sample of 19 African countries' determinants of sovereign credit ratings are compared between 2007 and 2014 using a panel-ordered probit approach.FindingsThe findings indicated that the determinants of sovereign credit ratings differ between African regions and income groups. The developmental indicators were the most significant determinants across all income groups and regions. The results affirm that the identified determinants in the literature are not as applicable to African sovereigns, and that developmental variables and different income groups and regions are important determinants to consider for sovereign credit ratings in Africa.Originality/valueThe results affirm that the identified determinants in the literature are not as applicable to African sovereigns, and that developmental variables and different income groups and regions are important determinants to consider for sovereign credit ratings in Africa. Rating agencies follow the same rating assignment process for developed and developing countries, which means investors will have to supplement the allocated credit rating with additional information. Africa can attract more investment if African countries obtain formal, accurate sovereign credit ratings, which take the characteristics of the continent into consideration.

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.

The relevance of credit ratings over the business cycle

2016 ◽  
Vol 17 (2) ◽  
pp. 152-168
Author(s):  
Christian Fieberg ◽  
Richard Lennart Mertens ◽  
Thorsten Poddig
Keyword(s):  
Business Cycle ◽  
Agency Costs ◽  
Credit Rating ◽  
Credit Ratings ◽  
Credit Rating Agencies ◽  
Cross Sectional ◽  
Content Type ◽  
Rating Agencies ◽  
The Business Cycle ◽  
Rating Changes

Purpose Credit market models and the microstructure theory of the ratings market suggest that information provided by credit rating agencies becomes more relevant in recessions when agency costs are high and less relevant in expansions when agency costs are low. The purpose of this paper is to empirically test these hypotheses with regard to equity markets. Design/methodology/approach The authors use business cycle identification algorithms to map rating events (credit rating changes and watchlist inclusions) to business cycle phases and apply the event study methodology. The results are backed by cross-sectional regressions using a variety of control variables. Findings The authors find that the relevance of information provided by credit rating agencies for equity prices heavily depends on the level of agency costs. Furthermore, the authors detect a “flight-to-quality” during recessions in the speculative grade segment and a weakened relevance of rating events in expansions in the investment grade segment. Originality/value This paper is the first to empirically analyse how equity investors perceive credit rating changes and watchlist inclusions over the business cycle. In the empirical analysis, the authors use a large sample of about 25,000 rating events in all Organisation for Economic Co-operation and Development markets. The presented results underline that credit ratings address the agency problem in financial markets and can thus be regarded as useful for risk management or regulation.

Credit ratings, relationship lending and loan market efficiency

2017 ◽  
Vol 34 (1) ◽  
pp. 122-142 ◽  
Author(s):  
Keldon Bauer ◽  
Omar A. Esqueda
Keyword(s):  
Small Business ◽  
Market Efficiency ◽  
Credit Rating ◽  
Credit Ratings ◽  
Bank Lending ◽  
Relationship Lending ◽  
Content Type ◽  
Lending Relationships ◽  
Loan Market ◽  
Business Loan

Purpose Using the small-business loan market, this paper aims to test whether a structural shift in access to borrowers’ financial information (i.e. credit ratings) improves market efficiency, thereby improving entrepreneurs’ access to external capital. Design/methodology/approach This research uses the National Survey of Small Business Finance in a conditional logistic regression framework to tease out the marginal propensity to grant lines of credit given the firm’s credit rating – treating both of the events, namely, line of credit and credit ratings, as endogenous variables. This methodology overcomes potential reverse causality issues. Findings The results show that information brokers have allowed small firms to break away from long-term monopolistic lending relationships, thus contributing to more informationally efficient markets. Small businesses benefit from better-informed lenders by having better access to capital. Also, women appear less likely to receive a line of credit even after adjusting for credit ratings. Practical implications This research highlights the importance of credit report awareness/monitoring by entrepreneurs, as the small-business credit rating grows rapidly. Relationship lending is not enough to reach optimal financing costs. These papers call for more regulated credit ratings industry to reduce potential moral hazards. Originality/value This paper tests whether bank lending relationships (soft information) still matter after accounting for credit ratings (hard information). Additionally, this study measures the extent to which information sharing by data services bureaus, a proxy for informational efficiency, has increased allocation efficiency in the small-business loan market.

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