Credit Ratings Accuracy and Analyst Incentives

2011 ◽  
Vol 101 (3) ◽  
pp. 120-124 ◽  
Author(s):  
Heski Bar-Isaac ◽  
Joel Shapiro

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.

2019 ◽  
Vol 23 (3) ◽  
pp. 266-286 ◽  
Author(s):  
Giulia Mennillo ◽  
Timothy J Sinclair

Credit rating agencies such as Moody’s and Standard & Poor’s are key players in the governance of global financial markets. Given the very strong criticism the rating agencies faced in the wake of the global financial crisis 2008, how can we explain the puzzle of their survival? Market and regulatory reliance on ratings continues, despite the shift from a light-touch to a mandatory system of agency regulation and supervision. Drawing on the analysis of rating agency regulation in the US and the EU before and after the financial crisis, we argue that a pervasive, persistent and, in our view, erroneous understanding of rating has supported the never-ending story of rating agency authority. We show how treating ratings as metrics, private goods, and independent and neutral third-party opinions contributes to the ineffectiveness of rating agency regulation and supports the continuing authoritative standing of the credit rating agencies in market and regulatory practices.


2014 ◽  
Vol 89 (4) ◽  
pp. 1399-1420 ◽  
Author(s):  
S. Jane Jollineau ◽  
Lloyd J. Tanlu ◽  
Amanda Winn

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.


2021 ◽  
Vol 5 (3) ◽  
Author(s):  
Isik Akin

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.


2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Misheck Mutize ◽  
McBride Peter Nkhalamba

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.


2020 ◽  
Vol 8 (3) ◽  
pp. 49
Author(s):  
Vasilios Plakandaras ◽  
Periklis Gogas ◽  
Theophilos Papadimitriou ◽  
Efterpi Doumpa ◽  
Maria Stefanidou

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.


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