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This study aims to examine the determinants of Sukuk credit ratings of issuing firms in Malaysia, and the rating changes from lower to higher rating and vice versa.

A total of 328 Sukuk issuances and 1,110 Sukuk rating announcements from 2009 to 2014 were analysed using generalized ordered logit regressions approach. Firm financial characteristics, corporate governance attributes, macroeconomic factors and Sukuk structures (debt or equity based) were among the important determinants used to explain the different Sukuk credit ratings.

The results indicate a positive association of Sukuk credit rating with issuing firm’s financial information, governance attributes and the Sukuk structure whilst the macroeconomic factors did not explain the changes in the Sukuk credit rating. Specifically, firm size, profitability and leverage characteristics had significant positive effect on Sukuk credit rating for listed firms whilst only firm’s profitability had a positive effect on Sukuk credit rating by unlisted firms. With regard to governance, the board structure which includes board size, board independence and CEO/Chairman non-duality is associated with positive Sukuk credit rating for listed firms. Only financial report audited by big four auditors is associated with positive Sukuk credit rating for unlisted firms. Equity-based Sukuk are associated with positive Sukuk credit rating for listed firms while for unlisted firms only the Ijarah Sukuk had a positive Sukuk credit rating.

Data on credit rating is scarce and had to be hand-collected from published reports. Furthermore, issues on the lack of standardisation of Islamic contracts in different geographical areas could constrain on the comparability of findings on determinants of ratings in different jurisdictions.

The findings provide some guide to the rating agencies to objectively assess the issuer’s creditworthiness that could mitigate default risk. Mitigating the default risk will boost investors’ confidence and credibility of credit rating agencies.

This study examines the determinants of Sukuk credit rating of issuing firms in Malaysia, which include not only the listed firms but also the unlisted firms.

Prior studies generally focus on income smoothing through discretionary accruals and document that managers have incentives to smooth earnings due to various reasons. This paper aims to focus on income smoothing through research and development (R&D) management and examine whether and how income smoothing through R&D management affects credit rating agencies’ perception of firm risk.

The authors use financial statement data from the CRSP/Compustat Merged data set universe for the period from 1992 to 2019 after excluding financial and utility industries. The authors follow the model for credit ratings used in previous literature to test the hypothesis. Specifically, the authors use an ordered probit model to express credit ratings as a function of income smoothing attributes.

The authors find that R&D-based income smoothing improves a firm’s credit rating. However, the positive effect of R&D-based income smoothing on credit ratings is less than that of accruals-based income smoothing. This study also shows that the positive effect of R&D-based income smoothing is more pronounced for firms less subject to opportunistic incentives, further strengthening the notion that managers smooth earnings through R&D management to provide more informative earnings.

This study contributes to the income smoothing literature in several ways. First, the authors contribute to the research by showing that managers’ income smoothing activity through R&D management positively affects firms’ credit rating. Second, the authors also document the relative benefits of the two different income smoothing techniques in terms of improving credit agencies’ perception of firms’ creditworthiness.

The objective of this paper is to study the impact of political connection and governance on credit rating and whether there is a substitution or complementary relationship between them.

In order to achieve the objective, a succession of eight ordered probit regressions has been carried out. Moderating variables between the political connection and governance characteristics were introduced. The whole population is taken as a sample, i.e., 27 Tunisian companies that are evaluated by FITSH NORTH AFRICA agencies over a period of 10 years (2009–2018).

The outcomes are mixed. They show that the political connection does not always influence credit rating; the size and board independence always improves credit rating; the duality between the functions affects credit rating; whereas the majorities’ proportion does not influence credit rating; and a substitution between the political connection and the governance characteristics is validated.

Like any other research, our results are factors of our measures and variable choice and depends heavily on the how these variables were conceived. Also, although our number of observations responds to the statistical result generalization requirements, our sample remains relatively narrow with 27 companies only.

In practice, the research will allow investors to have a better vision upon the future of their investments based on whether to develop their governance system or promote political networking. It will also prompt lenders to look beyond ratings and consider factors such as political connections to make a rational judgment on their future placements.

This study leads us to find various solutions: the establishment of credit agencies that take into consideration all the data of all the operators taken as a whole (bank, leasing company, and factoring). It encourages the reorganization of the Tunisian banking sector through mergers for example.

This study is a pioneer in the credit rating field in Tunisia, where the source of debt financing is the most used by all enterprises across all sectors. This study extends the literature of political connection effectiveness, independent directors, board size, in improving corporate performance and credit rating.

To determine the effect that covenants have on the credit ratings assigned by the two major agencies.

The authors examine 1,822 bond issues from 1991 to 2018, with a two-stage methodology to account for the endogeneity of the firms' choices and the ordinal nature of the ratings. The authors use Hendry's model selection method to find the best-fitting models from 37 control variables; the final models feature 20–24 orthogonalized variables, all significant at 5% and most at 1%.

The study’s results suggest that restrictive covenants positively affect ratings, particularly for bonds on the border of junk and investment grade. However, this effect appears to be decreasing with time, suggesting the financial crisis of 2008 has impacted ratings. Additionally, divergent covenant treatment leads to split ratings where the two agencies assign different levels of ratings on the same bonds. The study’s findings provide key insights into the factors that differentiate ratings given by each agency.

Managers must balance the perceived benefits of covenants against the costs, included lower credit ratings.

No other study has examined this issue controlling for both the ordinal nature of the ratings and the endogeneity in the decision to include specific covenants.

This study aims to provide further insights into the connection between corporate social responsibility (CSR) and companies’ credit ratings, while also exploring the role of corporate governance as a moderating factor. The hypotheses for this relationship are rooted in both legitimacy and stakeholder theories.

Using a sample of French non-financial listed firms from 2007 to 2020, this paper uses the ordered probit model introduced by Greene (2000). The issue of endogeneity has also been addressed.

The study reveals that CSR practices positively impact companies’ credit ratings by enhancing solvency and financial performance. Specifically, firms that prioritize CSR, particularly in the social and environmental dimensions (such as community relations, diversity, employee relations, environmental performance and product characteristics), tend to have higher credit ratings and a reduced risk of default. This suggests that credit rating agencies likely incorporate CSR performance when assigning credit ratings. Furthermore, the quality of corporate governance acts as a moderator, strengthening the relationship between CSR and credit ratings. The findings remain robust even after accounting for key firm attributes and addressing potential endogeneity between CSR and credit ratings.

This research provides valuable guidance for policymakers, corporate managers, investors and other stakeholders, as it offers insights into the influence of CSR activities on risk premiums and financing costs. For financial institutions, expanding credit decisions to encompass non-financial factors such as CSR can result in more accurate predictions of firm credit quality compared to relying solely on financial indicators.

To the best of the authors’ knowledge, this study stands out as the first to systematically examine the relationship between CSR and credit ratings within the French context. Moreover, it distinguishes itself by investigating the moderating influence of corporate governance on this relationship, setting it apart from prior research.

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.

Environmental, social and governance (ESG) factors have become increasingly important in investment decisions, leading to a surge in ESG investing and the rise of sustainable investment assets. Nevertheless, challenges in ESG disclosure, such as quantifying unstructured data, lack of guidelines and comparability, rampantly exist. ESG rating agencies play a crucial role in assessing corporate ESG performance, but concerns over their credibility and reliability persist. To address these issues, researchers are increasingly utilizing machine learning (ML) tools to enhance ESG reporting and evaluation. By leveraging ML, accounting practitioners and researchers gain deeper insights into the relationship between ESG practices and financial performance, offering a more data-driven understanding of ESG impacts on business communities.

The authors review the current research on ESG disclosure and ESG performance disagreement, followed by the review of current ESG research with ML tools in three areas: connecting ML with ESG disclosures, integrating ML with ESG rating disagreement and employing ML with ESG in other settings. By comparing different research's ML applications in ESG research, the authors conclude the positive and negative sides of those research studies.

The practice of ESG reporting and assurance is on the rise, but still in its technical infancy. ML methods offer advantages over traditional approaches in accounting, efficiently handling large, unstructured data and capturing complex patterns, contributing to their superiority. ML methods excel in prediction accuracy, making them ideal for tasks like fraud detection and financial forecasting. Their adaptability and feature interaction capabilities make them well-suited for addressing diverse and evolving accounting problems, surpassing traditional methods in accuracy and insight.

The authors broadly review the accounting research with the ML method in ESG-related issues. By emphasizing the advantages of ML compared to traditional methods, the authors offer suggestions for future research in ML applications in ESG-related fields.

This paper aims to assess the consequences of credit risk downgrades on credit default swaps (hereafter CDS) written on financial companies from two different perspectives, namely the overall stress level observed on the market and the rating agency performing the downgrade.

The authors' study relies on several wavelet analyses performed on different subsamples of data coming from the iTraxx index, the downgrade dates ranging between October 28, 2005 and February 3, 2015. This study highlights that both the overall stress level and the rating agency taking actions do have an influence on how market players will react.

The authors' study points out that market players will anticipate and react to downgrades in different ways depending on the level of stress. Feedback effects are observed after the downgrade only during periods of tension. From a rating agency point of view, the authors' study shows that the market share as well as the reputation of each agency have an influence on the aftermaths of a downgrade.

To the authors' knowledge, this paper is the first one relying on wavelet to analyse the consequences of a downgrade on CDS market. The use of this methodology allows to capture the multiple impacts of a downgrade through time and, therefore, to analyse the dynamics triggered on the market by a negative rating event. Moreover, the study of the downgrades' repercussions of each of the main rating agencies underlines a psychological dimension in the way market players react to a downgrade.

This paper aims to assess the effects of external governance mechanisms on the performance of microfinance institutions (MFIs) in Togo.

Using annual time series data from a sample of 30 MFIs during the period 2011–2015, the authors apply panel data econometrics in their estimations.

The results indicate that the notation by a rating agency positively and significantly affects the financial return of MFIs. The quality and the regularity of the audits negatively and significantly influence the financial performance (measured by return on assets and operating self-sufficiency) but favorably and significantly influence social performance (increased number of active borrowers (NAB) and reduced size of loans). Furthermore, supervision increases the amount of individual loans but decreases the NAB, which means deterioration in social performance. Overall, this paper shows that external governance mechanisms significantly affect the performance of Togolese MFIs, but with varying effects depending on the mechanism considered.

The sample size of 30 MFIs is small, and the geographic coverage of the study is restricted to MFIs operating in the city of Lomé, Togo. The authors did not have access to the information regarding the portfolio at risk at 30 days, even though it is a measure of financial performance. Likewise, we did not have access to the appendices to the financial statements for the calculation of prudential ratios. This method, which consists of asking the institutions using a questionnaire if they comply with prudential standards, may be biased because this study cannot verify the authenticity of the responses given that the standards are quantitative.

The study findings advocate that improving the financial and social performance of MFIs requires improving the quality of external governance mechanisms. MFIs should then pay close attention to well-functioning external governance mechanisms.

As MFIs are key social actors in a society, all mechanisms that contribute to their efficiency benefit society.

This study contributes to the corporate governance literature by showing that external governance mechanisms influence performance. These external mechanisms are complementary disciplinary measures to internal governance mechanisms and other tools.

This study seeks to identify the main determinants of the optimal capital structure by reexamining the interpretation of the conventional set of explanatory variables used as proxies for the costs and benefits of debt in the context of the dynamic tradeoff theory.

The authors isolate the variation in leverage due to different targets from that caused by deviations by aggregating the data across a dimension identifying firms with similar targets – credit rating category.

Contrary to theoretical priors, large and profitable rated firms have lower targets. The authors show that size and profitability proxy for non-financial risk and that, for rated firms, non-financial risk is positively correlated to the optimal leverage. The benefits of a better rating outweigh the costs of foregone tax shields for firms with relatively low non-financial risk. The authors find support for that theory in institutional trading – institutional investors do not punish highly rated firms when credit downgrades occur.

This paper contributes to the capital structure literature by developing a new approach based on data aggregation. This study is the first, to the authors’ knowledge, to find a positive effect of the firm's non-financial risk on target leverage among rated firms. The authors argue that the benefit of a better credit rating is an increasing function of the rating itself. The authors also contribute to the literature on the impact of credit ratings on the capital structure choices of the firm.