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This paper aims to examine the informational value of credit rating changes for investors. The article analyses whether credit rating changes indicate the future financial performance of a firm.

The study employs pooled time-series cross-section regression technique and two-sample t-test for analysis. The paper utilizes a firm's operating profit as a proxy of its future financial performance to understand what inference can be drawn about future financial performance from a change in a firm's credit rating.

The paper finds that a firm operating profit declines in the year after a credit rating downgrade. However, no such significant relationship is evident in the case of a rating upgrade. The results are consistent across rating categories and individual years of the sample period.

The study uses non-financial corporate rating data; hence, the findings may not apply to credit rating changes in financial corporates and structured finance.

Investors and analysts can incorporate credit rating downgrade by CRAs as a key input in a firm's future financial forecast. Analysts and investment managers can also look at credit rating changes of firms in the same industry and draw a definite conclusion about which firm is likely to see a higher deterioration in performance.

The author has not come across any literature that directly investigates credit rating changes from the perspective of information content about future financial performance.

The purpose of this study is to investigate the effectiveness of qualitative information extracted from firm’s annual report in predicting corporate credit rating. Qualitative information represented by published reports or management interview has been known as an important source in addition to quantitative information represented by financial values in assigning corporate credit rating in practice. Nevertheless, prior studies have room for further research in that they rarely employed qualitative information in developing prediction model of corporate credit rating.

This study adopted three document vectorization methods, Bag-Of-Words (BOW), Word to Vector (Word2Vec) and Document to Vector (Doc2Vec), to transform an unstructured textual data into a numeric vector, so that Machine Learning (ML) algorithms accept it as an input. For the experiments, we used the corpus of Management’s Discussion and Analysis (MD&A) section in 10-K financial reports as well as financial variables and corporate credit rating data.

Experimental results from a series of multi-class classification experiments show the predictive models trained by both financial variables and vectors extracted from MD&A data outperform the benchmark models trained only by traditional financial variables.

This study proposed a new approach for corporate credit rating prediction by using qualitative information extracted from MD&A documents as an input to ML-based prediction models. Also, this research adopted and compared three textual vectorization methods in the domain of corporate credit rating prediction and showed that BOW mostly outperformed Word2Vec and Doc2Vec.

This study aims to examine the information transfer effects of customers’ credit rating downgrades on supplier firms.

In this study, the authors use suppliers’ cumulative abnormal returns around customers’ credit rating downgrade events to identify how shocks to customer credit impact supplier equity prices. The authors also incorporate ordinary least squares and weighted least squares regressions regression analysis of the determinants of supplier market response to customer downgrades.

The authors find that customer credit rating downgrades present significant negative shocks to the stock prices of supplier firms. Moreover, the authors show that the information transfer effects are determined by both firm- and industry-level factors, including the market anticipation of downgrades, the strength of the customer–supplier linkage, the industry rivals’ reactions to the downgrades and investor attention. The authors also find that the likelihood that a supplier will receive a rating downgrade is significantly higher following its primary customer firm’s downgrade.

To the best of the authors’ knowledge, this paper is the first to explore the information transfer effects of credit rating downgrades on primary stakeholders within the supply chain. The authors document that customer–supplier networks have valuable implications for the spillover effect across debt and equity holders. Information about customers’ financial stress is incorporated into suppliers’ equity prices outside of the context of customer bankruptcy.

The purpose of this paper is to examine, in the context of movement towards a fair‐value based pension accounting standard, the value relevance of both recognized and disclosed pension accounting information.

Using hand‐collected data from Fortune 200 firms, this study includes both recognized and disclosed pension accounting measures (aggregated and disaggregated) in multivariate regression models. The investigation employs tests of relative and incremental value relevance in both equity and credit rating evaluation contexts.

Findings indicate that pension information recognized under a fair‐value‐based accounting model is no more or less value relevant than pension information recognized under the SFAS 87 model. Also, the disclosed off‐balance sheet pension amount is incrementally value relevant for determining share prices. However, it is not value relevant for the credit rating decision.

This study tests the relevance and reliability of accounting information jointly. Theoretically, however, relevance and reliability affect information usefulness and, thus, valuation decisions independently.

This paper yields a number of significant implications for standard setters. The unique evidence that investors apply off‐balance sheet pension amounts in the equity valuation context implies that required recognition under a fair‐value standard may not provide a significant incremental benefit over DB plan disclosures. However, such a standard may yield potential improvements in the credit rating decision context and may be much more likely to impact credit rating decisions going forward. Considering the continued shift towards fair‐value‐based pension accounting standards internationally, recognizing transitory elements of fair‐value pension cost separately from operating income is essential for mitigating any potential loss in value relevance.

Although the study of credit ratings has focused on traditional credit bureau resources, scholars have recently emphasized the importance of big data. The purpose of this paper is to examine both how these data affect the credit evaluations of small businesses and how financial managers use them to stabilize their risks.

Using data from 97,889 data points for normal guarantees and 1,678 data points for accidents in public funds, the authors explore the effects of trade area grades as well as the superiority of the use of big data when evaluating credit ratings for small businesses.

The results indicate that the grade information of trade areas is useful in predicting accident rates, particularly for small businesses with high credit scores (AAA-A). On the other hand, the accident rates of small businesses with low credit scores increased from 3.15-16.67 to 3.20-33.3 percent. These findings demonstrate that accident rates for the businesses with high credit scores decrease, but accident rates for businesses with low credit scores increase when using the grades of trade areas.

The authors contribute to the literature in two ways. First, this study provides one of the first investigations on information on trade areas through public financial perspectives, thereby extending the financial risk and retail literature. Second, the current study extends the research on the credit evaluation of small businesses through the big data application of real transaction-based trade areas, answering the call of Park et al. (2012), who recommended an exploration of the relationship between business start-ups and financial risk.

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.

The purpose of this paper is to investigate the role and responsibility of credit rating agencies in promoting soundness and integrity, especially in the course of their business activities.

The paper describes, and uses, the framework for the activities of credit rating agencies introduced by the International Organization of Securities Commissions (IOSCO), in order to give effect to this investigation.

Credit rating agencies have implemented the provisions of the Code of Conduct Fundamentals for Credit Rating Agencies of the IOSCO on the quality and integrity of the rating process, to the extent of the resources available to them.

The main source of data is the information collected by the IOSCO from nine credit rating agencies, including the main three, on the quality and integrity of their rating processes. The absence of triangulation of research methods limits the robustness of the findings.

The paper addresses a specific aspect of the credit ratings story since the financial crisis on which there is currently little in the literature. It also focuses upon the actions of credit rating agencies, rather than on how these organisations are, or should be, regulated.

The purpose of this paper is to examine whether credit ratings issued by Fitch predict auditor’s opinion for the Tunisian financial companies. It studies the association between Fitch’s credit rating and the audit opinion.

The whole population was analyzed. It is composed of 35 banks, leasing companies and factoring companies in Tunisia. The hand-collected data over 11 years (2005–2015) were used and a multiple-ordered logistic regression was performed.

The findings show that firms with a high short-term grade, a high long-term grade or a positive outlook are more likely to receive an unqualified audit opinion. In addition, companies with a stable outlook are more likely to receive an explanatory paragraph, a qualification or a going-concern opinion.

Studies examining the relationship between credit ratings and audit opinion are rare. This piece of research adds to knowledge about the relationship between different components of agency ratings and the auditor’s opinion in a developing country. Previous studies have investigated the case of developed countries and have been interested in the only impact of the long-term credit rating. This study analyzes three components of credit rating, namely long-term credit rating, short-term credit rating and rating outlook. In addition, it sheds light on the effect of various rating grades issued by rating agencies on the audit opinion. It gives a broader view of the relationship between credit ratings and audit opinion.

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.

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.

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.

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.

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.

The purpose of this paper is to examine the emerging Chinese credit rating agencies (CRAs), and their development, regulatory regime and challenges. The Chinese financial system has made many improvements; in particular, the regulatory regime has reached a more effective level. However, it should be admitted that some aspects still require further development. Compared with other developed markets, the Chinese credit rating industry is still young. Under these circumstances, questions are raised about the performance of the CRAs in China. Whether the legal framework is effective enough? A further point, in terms of the development, is what are the major obstacles lying ahead for the Chinese CRAs?

This paper will concentrate on the study of Chinese CRAs. Starting with a brief introduction and analysis on the Chinese CRAs, it will further examine the rating methodologies of the Chinese CRAs. Following this, the regulatory regimes will be analyzed in detail, from the perspectives of the securities, banking and insurance market. Moreover, the paper will identify the key problems under the current regulatory regime. Last but not least, a conclusion and some future suggestions for the development of the regulatory regime will also be made based on the earlier observations and study.

The current development stage and future reform requirement of the Chinese credit rating industry.

Provide a full dimension and in depth analysis on the Chinese credit rating industry.