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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.

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.

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.

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 creditworthiness of corporates is most visible in credit ratings. This paper aims to present an alternative credit rating measure independently of credit rating agencies. The credit rating score (CRS) is based on the credit default swap (CDS) market trading.

A CRS is developed which is a linear function of logarithmized CDS spreads. This new CRS is the first one that is completely independent of the rating agency. The estimated ratings are compared with ratings provided by Fitch Ratings for 310 European and US non-financial corporates.

The empirical analysis shows that logarithmized CDS spreads and issuer credit ratings by agencies have a linear relationship. The new CRS provides market participants with an alternative risk assessment, which is solely based on market factors, and does not rely on credit rating analysts. The results indicate that our CRS is able to anticipate agency ratings in advance. Moreover, the analysis shows that the trading volume has only a limited influence in the anticipation of rating changes.

This study shows a new approach to measure the creditworthiness of firms by analyzing CDS spreads. This is highly relevant for regulation, firm monitoring and investors.

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.

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.

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.

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.

Both consumer and corporate credit ratings agencies played a major role in the US subprime mortgage crisis. Equifax, Experian, and TransUnion deployed a formalized scoring system to assess individuals in mortgage origination, mortgage pools then were assessed for securitization by Moody's, S&P, and Fitch relying on expert judgment aided by formal models. What can we learn about the limits of formalization from the crisis? We discuss five problems responsible for the rating failures – reactivity, endogeneity, learning, correlated outcomes, and conflict of interest – and compare the way consumer and corporate rating agencies tackled these difficulties. We conclude with some policy lessons.

The purpose of this paper is to examine the potential effectiveness of government reforms aimed at improving the accuracy of ratings issued by credit ratings agencies in US financial markets.

The paper identifies unconscious bias as a source of inaccuracy in the credit ratings process. It examines prior behavioral research on unconscious bias, and uses this research to identify structural issues within the credit ratings industry that give rise to biased judgments. Finally, it examines whether government reforms will be effective in improving the accuracy of credit ratings, and offers additional reforms aimed at combating unconscious bias.

Recent government reforms will be most effective in curbing intentional decisions to compromise the ratings process. However, the reforms will be less effective at mitigating unconscious biases in judgments underlying credit ratings, because they do not adequately address relevant structural issues. To combat unconscious bias, changes need to be made to ratings agencies' fee structures, business models, and risk management functions.

The analysis is of use to regulators who are contemplating the need for reforms aimed at improving the accuracy of credit ratings. While focusing on events in the USA, the analysis is relevant to any country in which credit ratings are influential in financial markets.

This is the first paper to examine the performance of credit ratings agencies through the lens of behavioral psychology, and to introduce the concept of unconscious bias as a determining factor in the accuracy of credit ratings.

The purpose of the article is to explain the significance of key features of the SEC’s new rules for credit rating agencies. Those rules include three key items: they prohibit the influence of sales or marketing considerations on criteria development; they include guidance that preserves the ability of ratings to serve as relative rather than absolute measures of credit risk; and they require cross-sector consistency of rating symbols. When they were released the significance of the rules was under-appreciated because of other simultaneous regulatory announcements.

The approach is to consider how effectively the rules address their target issues. In doing so the article explores how the final rules evolved from their original proposed form and from the statutory specifications in the 2010 Dodd-Frank Act.

The new rules should promote the integrity of credit ratings in the future. They should be effective in reducing the influence of sales and marketing considerations on the development of rating criteria. In addition they should enhance rating integrity through superior cross-sector consistency in the meanings of rating symbols while allowing rating agencies to maintain their traditional emphasis on relative risk.

The authors are not aware of any similar work assessing the selected provisions of the new SEC rules for credit rating agencies.

This 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.

The analysis applies the fixed effects (FE) and random effects (RE) panel least squares (PLS) models.

The 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.

These 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.

First, 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.

This paper aims to provide a theoretical explanation of why credit rating agencies typically disclose credit risk of issuers in classes rather than publishing the qualitative ranking those classes are based upon. Thus, its goal is to develop a better understanding of what determines the number and size of rating classes.

Investors expect ratings to be sufficiently accurate in estimating credit risk. In a theoretical model framework, it is therefore assumed that credit rating agencies, which observe credit risk with limited accuracy, are careful in not misclassifying an issuer with a lower credit quality to a higher rating class. This situation is analyzed as a Bayesian inference setting for the credit rating agencies.

A disclosure in intervals, typically used by credit rating agencies results from their objective of keeping misclassification errors sufficiently low in conjunction with the limited accuracy with which they observe credit risk. The number and size of the rating intervals depend in the model on how much accuracy the credit rating agencies can supply.

The paper uses Bayesian hypothesis testing to illustrate the link between limited accuracy of a credit rating agency and its disclosure of issuers’ credit risk in intervals. The findings that accuracy and the objective of avoiding misclassification determine the rating scale in this theoretical setting can lead to a better understanding of what influences the interval disclosure of major rating agencies observed in practice.

This study examines rating agency explanations that accompany changes in credit ratings for nonprofit hospitals. A national sample of Standard & Poor’s revision announcements is used to identify hospital characteristics that purportedly motivate credit rating changes. Significant differences in agency-cited performance dimensions, such as profitability, liquidity, service-mix, capital structure and market share, are observed across upward and downward revisions. The relative usefulness of these citations for explaining and classifying credit changes is also evaluated. The results suggest that agency explanations provide limited value relative to conventional, multivariate information sets.

Purpose – The purpose of this chapter is to explore the voluntary corporate governance role played by credit rating agencies, closing the ‘trust at a distance’ gap which might otherwise hinder fundraising in debt capital markets.

Methodology/approach – The chapter draws on Giddens’ system trust theory and Foucauldian perspectives of knowledge/power to unpack trust production as a discursive process in financial markets. Foucauldian discourse analytic techniques are used to examine texts deployed by or about Standard & Poor's, the global credit rating agency, leading up to the 2007 credit crunch.

Findings – The texts analysed illustrate the influence of rating agencies in producing trust as well as mistrust in debt instruments.

Research limitations/implications – Rating agencies produce trust by aligning with state regulatory systems, simplifying complex debt instruments with ‘AAA’ and other well-known mnemonics, as well as offering apparent transparency and guarantees.

Practical implications – While influential, rating agencies can only produce trust by proxy. Their contribution to the actual protection of investments is minimal.

Social implications – The analysis highlights the flawed nature of trust relations in debt capital markets as rating agencies’ primary customers are the arrangers and issuers of debt rather than the investors who seek protection from risk.

Originality/value – The chapter sheds light on the deliberate nature of trust production in financial markets. Five trust/mistrust production practices are introduced – protecting, guaranteeing, aligning, making visible and simplifying. Strategic trust production is established as part of corporate governance ideology in financial markets.