scholarly journals An Analysis of Bank Financial Strength Ratings and Credit Rating Data

Risks ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 155
Author(s):  
John A. Ruddy
Keyword(s):  
Credit Rating ◽  
Credit Ratings ◽  
Study Data ◽  
Rating Data ◽  
Rating Agencies ◽  
Rating Agency ◽  
Financial Strength ◽  
Banking Institutions ◽  
Research Findings ◽  
Highly Correlated

In this study, data from two credit rating agencies are analyzed to consider how different Bank Financial Strength Ratings and Credit Ratings from two rating agencies compare. To my knowledge, prior research has not analyzed Bank Financial Strength Ratings from different rating agencies, nor has it compared Bank Financial Strength Ratings to general credit ratings. These facts make this research unique. Univariate analyses are utilized to show relationships in the ratings data, along with parametric and non-parametric tests to make statistical inferences about the ratings data. There are five findings. First, ratings from different rating agencies are highly correlated. Second, different types of ratings from the same rating agency are highly correlated. Third, bank financial strength ratings are more conservative than credit ratings. Fourth, bank financial strength ratings declined in rating more quickly at the start of the financial crisis. Fifth, bank financial strength ratings from the Kroll Bond Rating Agency were more conservative than ratings from Moody’s Investors Service. The research findings and results are important for investors who consider ratings agency data to determine the risk of banking institutions. The results are also important to businesses that rely on bank credit rating data and policy makers who regulate banking institutions.

scholarly journals Forecasting Credit Ratings of EU Banks

2020 ◽  
Vol 8 (3) ◽  
pp. 49
Author(s):  
Vasilios Plakandaras ◽  
Periklis Gogas ◽  
Theophilos Papadimitriou ◽  
Efterpi Doumpa ◽  
Maria Stefanidou
Keyword(s):  
Credit Rating ◽  
Credit Ratings ◽  
Support Vector ◽  
Credit Rating Agencies ◽  
Selection Scheme ◽  
Explanatory Variables ◽  
Rating Agencies ◽  
Financial Variables ◽  
Forecasting Models ◽  
Banking Institutions

The aim of this study is to forecast credit ratings of E.U. banking institutions, as dictated by Credit Rating Agencies (CRAs). To do so, we developed alternative forecasting models that determine the non-disclosed criteria used in rating. We compiled a sample of 112 E.U. banking institutions, including their Fitch assigned ratings for 2017 and the publicly available information from their corresponding financial statements spanning the period 2013 to 2016, that lead to the corresponding ratings. Our assessment is based on identifying the financial variables that are relevant to forecasting the ratings and the rating methodology used. In the empirical section, we employed a vigorous variable selection scheme prior to training both Probit and Support Vector Machines (SVM) models, given that the latter originates from the area of machine learning and is gaining popularity among economists and CRAs. Our results show that the most accurate, in terms of in-sample forecasting, is an SVM model coupled with the nonlinear RBF kernel that identifies correctly 91.07% of the banks’ ratings, using only 8 explanatory variables. Our findings suggest that a forecasting model based solely on publicly available financial information can adhere closely to the official ratings produced by Fitch. This provides evidence that the actual assessment procedures of the Credit Rating Agencies can be fairly accurately proxied by forecasting models based on freely available data and information on undisclosed information is of lower importance.

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.

Credit Ratings Accuracy and Analyst Incentives

2011 ◽  
Vol 101 (3) ◽  
pp. 120-124 ◽  
Author(s):  
Heski Bar-Isaac ◽  
Joel Shapiro
Keyword(s):  
Financial Crisis ◽  
Credit Rating ◽  
Credit Ratings ◽  
Incentive Contracts ◽  
Revolving Door ◽  
Credit Rating Agencies ◽  
Bank Profitability ◽  
Rating Agencies ◽  
Rating Agency

The financial crisis has brought a new focus on the accuracy of credit rating agencies (CRAs). In this paper, we highlight the incentives of analysts at the CRAs to provide accurate ratings. We construct a model in which analysts initially work at a CRA and can then either remain or move to a bank. The CRA uses incentive contracts to motivate analysts, but does not capture the benefits if the analyst moves. We find that rating agency accuracy increases with CRA monitoring, bank profitability (a positive “revolving door” effect), and can be non-monotonic in the probability of an analyst leaving.

Evaluating Proposed Remedies for Credit Rating Agency Failures

2014 ◽  
Vol 89 (4) ◽  
pp. 1399-1420 ◽  
Author(s):  
S. Jane Jollineau ◽  
Lloyd J. Tanlu ◽  
Amanda Winn
Keyword(s):  
Financial Crisis ◽  
Credit Rating ◽  
Credit Ratings ◽  
Quantitative Model ◽  
Securities And Exchange Commission ◽  
Data Availability ◽  
Rating Agencies ◽  
Credit Rating Agency ◽  
Rating Agency

ABSTRACT: Regulators and the financial press have criticized credit rating agencies (CRAs) for exacerbating the financial crisis by providing overly optimistic debt ratings. Allegedly, CRAs departed from their quantitative models in order to please security issuers with higher credit ratings. In response, the Dodd-Frank Act of 2010 required the Securities and Exchange Commission to conduct a study on alternative models for compensating CRAs. We conduct an experiment exploring how the credit ratings of M.B.A. students, who assume the role of credit rating analysts, are affected by two proposals for reform: (1) changing who pays the CRAs, and (2) requiring analysts to justify departures from a quantitative model. We find that credit ratings are highest when the borrower pays CRAs for ratings and a justification requirement is not in place. Implementing either proposed reform independently reduces credit ratings, but credit ratings are not further reduced when both reforms are implemented together. Data Availability: Data are available from the authors upon request.

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.

scholarly journals Three Letters that Move the Markets: Credit Ratings between Market Information and Legal Regulation

2017 ◽  
Author(s):  
Ulrich G. Schroeter
Keyword(s):  
Financial Markets ◽  
Applied Research ◽  
Credit Rating ◽  
Credit Ratings ◽  
Legal Regulation ◽  
Legal Regulations ◽  
Market Information ◽  
Market Reactions ◽  
Rating Agencies ◽  
Sovereign Credit Ratings

Journal of Applied Research in Accounting and Finance, Vol. 6, No. 1 (2011), pp. 14-30As demonstrated by the market reactions to downgrades of various sovereign credit ratings in 2011, the credit rating agencies occupy an important role in today’s globalized financial markets. This article provides an overview of the central characteristics of credit ratings and discusses risks arising from both their widespread use as market information and from the increasing references to credit ratings contained in laws, legal regulations and private contracts.

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