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The purpose of this paper is to examine how credit ratings affect corporate financial behavior from the perspective of merger and acquisition (M&A) decisions. The goal is to test the financing and supervisory effects of credit ratings and study the economic consequences of credit ratings in the context of China.

Using a sample of Chinese A-share listed companies over the 2008–2017 period, this paper empirically examines the effect of credit ratings on firms’ M&A decisions. The authors used a probit model for regression when they tested the effect of credit rating on M&A likelihood and a tobit model when they tested the effect of credit rating on M&A intensity.

First, rated enterprises tend to make more acquisitions compared with non-rated enterprises, consistent with the hypothesis that credit ratings alleviate financing constraints. Second, high-rated enterprises are more cautious toward M&As due to concerns about preserving their ratings, which indicates that credit ratings also play a supervisory role in the M&A process. Additional tests show that enterprises reduce M&A activity after a rating downgrade to avoid further deterioration in their ratings; this further supports the supervisory role of credit ratings.

This paper adds incremental evidence to the literature on the impact of credit ratings on corporate financial behavior and extends the literature on the factors influencing M&As. The authors provided empirical evidence from emerging capital markets for the financing and supervisory effects of credit ratings and provided theoretical guidance for promoting the stable, long-term development of China’s credit rating industry.

The purpose of this paper is to propose a system with the highest discriminatory power by selecting an indicator system based on the K–S test according to the unique circumstances of small enterprises.

The proposed method relies on calculating the K–S test statistical magnitude of D iteratively to reach a system with the maximum discriminatory power.

The empirical results, demonstrated using 3,045 small businesses from a Chinese bank, show that credit rating system should focus on the indicator system’s discriminatory power rather than a single indicator’s discriminatory power, because the interaction between indicators affects the discriminatory power of the system.

The proposed method creates a credit rating system with the highest discriminatory power, rather than its indicators, which is a more reasonable and novel approach to credit rating.

The approach is unique because the final system will have high discriminatory power and has excellent potential for decision support. The authors believe that this contribution is theoretically and practically relevant because credit rating for small business is especially difficult and complicated.

The purpose of this paper is to empirically investigate the effect of real credit ratings change on capital structure decisions.

The study uses three models to examine the impact of credit rating on capital structure decisions within the framework of credit rating-capital structure hypotheses (broad rating, notch rating and investment or speculative grade). These hypotheses are tested by multiple linear regression models.

The results demonstrate that firms issue less net debt relative to equity post a change in the broad credit ratings level (e.g. a change from A- to BBB+). The findings also show that firms are less concerned by notch ratings change as long the firms remain the same broad credit rating level. Moreover, the paper indicates that firms issue less net debt relative to equity after an upgrade to investment grade.

The study covers the periods of 2009 to 2016; therefore, the research result may be affected by the period specific events such as the European debt crisis. Moreover, studying listed non-financial firms only in the Tadawul Stock Exchange has resulted in small sample which may not be adequate enough to reach concrete generalization. Despite the close proximity between the GCC countries, there could be jurisdictional difference due to country specific regulations, policies or financial development. Therefore, it will be interesting to conduct a cross country study on the GCC to see if the conclusions can be generalized to the region.

The paper contributes to the literature by testing previous researches on new context (Kingdom of Saudi Arabia, KSA) which lack sophisticated comparable studies to the one conducted on other regions of the world. The results highlight the importance of credit ratings for the decision makers who are required to make essential decisions in areas such as financing, structuring or operating firms and regulating markets. To the best of the authors’ knowledge, this is the first study of its kind that has been applied on the GCC region.

The study analyzes significant parameters defining the credit worthiness, economic viability and managerial efficiency of special purpose vehicles (SPVs) of infrastructure development firms engaged in the execution of road projects under PPP model in India.

The study is based on a comprehensive review of credit rating reports of major rating agencies. In particular, 18 special purpose vehicles (13 BOT-toll–based and 5 BOT-annuity–based road projects) during the period 2010–2019 were considered to conduct a comparative analysis of their rating progression. Considering both financial as well as nonfinancial parameters, their segregation was done on the basis of strengths, constraints and key rating sensitivities influencing the ratings of SPVs involved in road projects under PPP model.

Promoters' credibility emerged as an important factor affecting PPP credit ratings. Other prominent factors included nature of stretch and regulatory terms and conditions and the project's potential to generate cash flows. Inability of PPP projects to generate the projected levels of toll collections was a major constraint and hampered ratings over time. Growth in traffic was a key sensitive area in a toll-based project. Interestingly, despite the fixed nature of revenues, BOT (annuity) projects were impacted by rating changes.

Fewer sample projects (for which the data were available) was a constraint. Future research could consider larger data sets to provide deeper insights. An examination of credit rating parameters using rating reports of projects in other developing nations could provide meaningful implications. The findings of this research however cannot be undermined as the study bridges a gap in existing literature pertaining to the examination of PPP model from a credit rating perspective.

This study would guide project developers, government agencies and awarding agencies of PPP road projects to anticipate the challenges and take adequate steps to mitigate them.

Research in the area of PPP projects is skewed toward risk assessment with respect to financial parameters. The present study emphasizes the rating framework of SPVs. Comprehensive examination of factors affecting project ratings in the form of projects' strengths, constraints and sensitivities would provide inputs to academics and researchers.

The purpose of this paper is to build a neural network system to predict corporate credit rating in Jordanian non-financial firms, using 19 different financial characteristics such as profitability, leverage ratios, liquidity, bankruptcy, and sales performance.

The study adopts two neural network techniques namely, Kohonen network and Back Propagation Neural Network (BPNN). Our sample includes the manufacturing firms that have provided the required financial information for the period from 2000 to 2007.

BPNN has successfully predicted firms with high performance gaining A rating and the bankrupted firms with D rating for the period from 2005 to 2007.

This study is the first study to investigate credit rating in Jordan using Neural Network technique.

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.

Bank creditworthiness refers to the evaluation of a bank’s ability to meet its financial obligations. It is an assessment of the bank’s financial health, stability and capacity to manage risks. This paper aims to investigate the credit rating patterns that are crucial for assessing creditworthiness of the Islamic banks, thereby evaluating the stability of their industry.

Three distinct machine learning algorithms are exploited and evaluated for the desired objective. This research initially uses the decision tree machine learning algorithm as a base learner conducting an in-depth comparison with the ensemble decision tree and Random Forest. Subsequently, the Apriori algorithm is deployed to uncover the most significant attributes impacting a bank’s credit rating. To appraise the previously elucidated models, a ten-fold cross-validation method is applied. This method involves segmenting the data sets into ten folds, with nine used for training and one for testing alternatively ten times changeable. This approach aims to mitigate any potential biases that could arise during the learning and training phases. Following this process, the accuracy is assessed and depicted in a confusion matrix as outlined in the methodology section.

The findings of this investigation reveal that the Random Forest machine learning algorithm superperforms others, achieving an impressive 90.5% accuracy in predicting credit ratings. Notably, our research sheds light on the significance of the loan-to-deposit ratio as a primary attribute affecting credit rating predictions. Moreover, this study uncovers additional pivotal banking features that intensely impact the measurements under study. This paper’s findings provide evidence that the loan-to-deposit ratio looks to be the purest bank attribute that affects credit rating prediction. In addition, deposit-to-assets ratio and profit sharing investment account ratio criteria are found to be effective in credit rating prediction and the ownership structure criterion came to be viewed as one of the essential bank attributes in credit rating prediction.

These findings contribute significant evidence to the understanding of attributes that strongly influence credit rating predictions within the banking sector. This study uniquely contributes by uncovering patterns that have not been previously documented in the literature, broadening our understanding in this field.

In July 2008 the U.S. Securities and Exchange Commission (SEC) published three proposals relating to the use of credit ratings in its rules and forms. The proposals were designed to address concerns that the misuse of credit ratings may have contributed to the current crisis. The SEC sought market feedback regarding the effect the removal of credit rating references may produce on the markets.

This article examines the use of ratings by various market constituents, analyzes the details of the SEC proposals, and reviews the provided feedback. The main finding is that the majority of the market participants opposed the SEC proposals. Fiduciaries and regulated entities are looking to regulators to offer a common measure of risk, stable, accurate and free of conflict of interests.

We study the information content of issuer credit rating changes announced by a group of six Chinese credit rating agencies. We conduct an event study, and we use multivariate regression analyses to identify factors driving abnormal stock returns. Our results confirm prior findings for Western countries that downgrades are associated with significant negative abnormal returns. However, upgrades and positive or negative rating outlooks do not seem to have information content. While we cannot find differences in information content conditional on which rating agency issues the downgrade, we find that abnormal returns may vary with the target firm’s industry. In addition, the magnitude of stock price reactions to rating downgrades seems to be related to the business cycle to some extent. With respect to the role of the industry in explaining the information content of rating changes, our results may be biased due to small sample size. Nevertheless, they illustrate that the role the industry plays in explaining investor behavior may deserve special attention in future research. Our findings imply that new rating information seems to be processed quickly in the Chinese stock market, and that market reactions to rating signals are largely in line with what has been observed for Western stock markets. Both observations lend credibility to observable stock prices. The chapter sheds new light on the relevance of Chinese credit rating agencies from an equity investor’s perspective and confirms similarities between the Chinese and Western stock markets with respect to the way rating signals are processed by investors.

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.