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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.

Jensen and Murphy (1990) and others have found a small but statistically significant relationship between firm performance (as measured by change in shareholder wealth or firm profits) and executive compensation. In this study we investigate the pay‐ performance relationship further by considering the relationship between an outside measure of firm performance (changes in the firm's bond rating) and the contemporaneous change in the compensation of the firm's CEO. We find that when a firm's bond rating is down‐graded, CEO total compensation declines by a relatively small amount ($165,500) and when a firm's bond rating is upgraded, CEO total compensation increases markedly ($3,202,900). Thus, while a positive pay‐performance relationship exists, the relationship is not symmetric. CEO compensation changes (increases) much more when firm performance improves than it changes (decreases) when firm performance declines. Further, most of the change in CEO compensation occurs in the stock gains (profits from the exercise of stock options) category for both firms experiencing bond rating upgrades and down‐grades.

The purpose of this paper is to analyse the quantitative determinants of individual ratings of commercial banks (as conducted by Fitch Ratings).

The ordered probit model is applied as an extension of the standard binary probit model. The model is estimated using a sample of 681 international banks.

Banks with a greater capitalisation, larger assets, and a higher return on assets have higher bank ratings. Further, the greater is a bank's liquidity, the larger is its net interest margin and the more is the ratio of its operating expenses to total operating income the lower is a bank's rating.

Modelling the determinants of international bank ratings spanning a sample of 90 countries. Applying a model with dynamics that considers whether the rating is determined by information up to four years prior to the rating date.

The effect of situational factors on the rating ability of 28 industrial analysts was determined through the use of rating films. The rating ability was evaluated in terms of rating accuracy and consistency. Significant differences in rating accuracy were found among the analysts from five different companies. The analysts who used time standards for planning functions surprisingly rated more consistently than those who employed time standards for a wage incentive programme. Shop labour organization, union or non‐union, had no significant impact on the analysts′ rating ability. The analysts′ rating consistency was significantly better for the medium (85‐120 per cent) and fast (125‐145 per cent) pace ranges than for the slow (60‐80 per cent) pace range. The rating consistency of the fast pace range was significantly better than the medium pace range. The familiar (machining) operations were rated more accurately and consistently than the unfamiliar (sheet metal) operations. The rating accuracy for the simple operations was significantly better than the moderate and complex operations. The simple and complex operations were rated significantly more consistently than the moderate operations.

Users often struggle to select choosing among similar online services. To help them make informed decisions, it is important to establish a service reputation measurement mechanism. User-provided feedback ratings serve as a primary source of information for this mechanism, and ensuring the credibility of user feedback is crucial for a reliable reputation measurement. Most of the previous studies use passive detection to identify false feedback without creating incentives for honest reporting. Therefore, this study aims to develop a reputation measure for online services that can provide incentives for users to report honestly.

In this paper, the authors present a method that uses a peer prediction mechanism to evaluate user credibility, which evaluates users’ credibility with their reports by applying the strictly proper scoring rule. Considering the heterogeneity among users, the authors measure user similarity, identify similar users as peers to assess credibility and calculate service reputation using an improved expectation-maximization algorithm based on user credibility.

Theoretical analysis and experimental results verify that the proposed method motivates truthful reporting, effectively identifies malicious users and achieves high service rating accuracy.

The proposed method has significant practical value in evaluating the authenticity of user feedback and promoting honest reporting.

The authors examine the role of CEO political ideology in the credit rating process.

This study adopts a quantitative method with panel data regressions using a sample of 5,211 observations from S&P 500 firms from 2001 to 2012.

The authors find that firms run by Republican-leaning CEOs, who tend to have conservative political ideologies, enjoy more favorable credit ratings than firms run by Democratic-leaning CEOs. In addition, the association between CEO political ideology and credit ratings is more pronounced for firms with high operating uncertainty, low capital intensity, high growth potential, weak corporate governance and low financial reporting quality. Finally, the authors find that CEO political ideology affects a firm's cost of debt incremental to credit ratings, consistent with debt investors incorporating CEO political ideology in their pricing decisions.

Leveraging CEO political ideology, the authors document that credit rating agencies incorporate managerial conservatism in their credit rating decisions. This finding suggests that CEO political ideology serves as a meaningful signal for managerial conservatism.

The study suggests that credit rating agencies incorporate CEO political ideology in their credit rating process. Other capital market participants such as auditors and retail investors can also use CEO political ideology as a proxy for managerial conservatism when evaluating firms.

The paper carries practical implications for practitioners, firm executives and regulators. The results on the association between CEO political ideology and credit ratings suggest that other financial institutions could also incorporate CEO political ideology in their evaluation in their evaluation of firms. For example, when evaluating audit risk and determining audit pricing, auditors may add CEO political ideology as a risk factor. For firms, especially those that have Democratic-leaning CEOs, the authors suggest that they could reduce the unfavorable effect of CEO political ideology on credit ratings by improving their corporate governance and financial reporting quality, as demonstrated in the cross-sectional analyses. Finally, this study shows that CEO political ideology, as measured by CEOs' political contributions, is closely related to a firm's credit ratings. This finding may inform regulators that greater transparency for CEOs' political contributions is needed as information on contributions could help capital market participants perform risk analyses for firms.

Credit rating agencies release their research methodologies for determining corporate credit ratings and identify managerial conservatism as an important factor that affects their risk assessments. The extant literature, however, has not empirically investigated the relation between credit ratings and managerial conservatism, which, according to behavioral consistency theory, can be proxied by CEO political ideology. This study provides novel empirical evidence that identifies CEO political ideology as an important input factor in the credit rating process.

The authors introduce a new construct, reputational self-awareness (RSA). RSA represents the congruence between how individuals think they are viewed by others (i.e. metaperceptions) versus how they are actually viewed (i.e. other ratings). The authors sought to demonstrate that RSA is a superior predictor of performance indices.

Personality self-ratings from 381 business students and their ratings by 966 others were collected via online surveys. Other raters rated self-raters' personalities as well as their task performance, organizational citizenship behaviors (OCBs) and counterproductive work behaviors (CWBs).

Results indicate that RSA predicts variance in performance above and beyond self-report ratings, and performance is highest when metaperceptions and other ratings of performance are aligned. These results support the use of a multi-perspective approach to personality assessment as a useful tool for coaching and career development.

The authors' results support the use of a multi-perspective approach to personality assessment as a useful tool for coaching and career development. A cross-sectional design was used in which personality and performance data were gathered from respondents, and the P 720 is a relatively new personality instrument.

RSA is a valuable tool for employee development, coaching and counseling because, as extant research and the authors' findings demonstrate, awareness of how others view and judge one, one's reputation is essential information to guide work behaviors and career success. Therefore, a key career-development goal for trainers and counselors should be to use a multi-perspective approach to maximize clients' RSA.

Use of other ratings as opposed to traditional self-rating of personality provides superior prediction of behavior and is more useful for career development.

This is the first study to demonstrate utility of RSA, i.e. that individuals who more accurately assess their personality are rated as performing better by others. The authors' results offer new insights for personality research and career development and support the use of personality assessment from multiple perspectives, thus enabling the exploration of potentially insightful research questions that cannot be examined by assessing personality from a single perspective.

This research aims to revisit Gul and Goodwin (2010), which focuses on exploring the relationship between debt maturity structure, credit ratings and audit fees. Furthermore, the authors investigate whether this association varies based on firm size, firm life cycle and financial reporting quality.

To investigate the research question, the authors use an extended sample period, 2004–2017, in comparison to the sample period, 2003–2006, used in Gul and Goodwin (2010). The authors use ordinary least squares regression as a baseline methodology along with two-stage least-squares regression and change analysis to control for endogeneity concerns.

According to Gul and Goodwin (2010), auditors charge lower audit fees for firms with higher short-maturity debt and better credit ratings, indicating a lower likelihood of financial misreporting. Further, Gul and Goodwin (2010) find that lower credit rated firms benefit more from short-term debt. Primarily, the findings are consistent with Gul and Goodwin (2010) and provide further evidence that the beneficial effects of short-maturity debt for firms with poor ratings are evident for small firms, firms in the growth stage of their life cycle and firms with poor earnings quality.

The findings imply that practitioners in the audit profession and investors should take a more nuanced and comprehensive approach to varied firm and financial factors, taking into consideration the intricate relationships between many elements impacting a firm’s financial health. As a result, audit professionals may give more accurate appraisals of a firm’s financial condition, and investors can make better investment decisions.

The authors reconfirm the findings of Gul and Goodwin (2010) using an extended sample. The findings are novel, which evidence that the lower audit fees for rated firms with short-maturity debt are moderated by firm size, life cycle and financial reporting quality.

The explosion of data due to the sophistication of information and communication technology makes it simple for prospective tourists to learn about previous hotel guests' experiences. They prioritize the rating score when selecting a hotel. However, rating scores are less reliable for suggesting a personalized preference for each aspect, especially when they are in a limited number. This study aims to recommend ratings and personalized preference hotels using cross-domain and aspect-based features.

We propose an aspect-based cross-domain personalized recommendation (AsCDPR), a novel framework for rating prediction and personalized customer preference recommendations. We incorporate a cross-domain personalized approach and aspect-based features of items from the review text. We extracted aspect-based feature vectors from two domains using bidirectional long short-term memory and then mapped them by a multilayer perceptron (MLP). The cross-domain recommendation module trains MLP to analyze sentiment and predict item ratings and the polarities of the aspect based on user preferences.

Expanded by its synonyms, aspect-based features significantly improve the performance of sentiment analysis on accuracy and the F1-score matrix. With relatively low mean absolute error and root mean square error values, AsCDPR outperforms matrix factorization, collaborative matrix factorization, EMCDPR and Personalized transfer of user preferences for cross-domain recommendation. These values are 1.3657 and 1.6682, respectively.

This study assists users in recommending hotels based on their priority preferences. Users do not need to read other people's reviews to capture the key aspects of items. This model could enhance system reliability in the hospitality industry by providing personalized recommendations.

This study introduces a new approach that embeds aspect-based features of items in a cross-domain personalized recommendation. AsCDPR predicts ratings and provides recommendations based on priority aspects of each user's preferences.

The purpose of this study is to develop a method to assess social performance. Traditionally, environment, social and governance (ESG) rating providers use subjectively weighted arithmetic averages to combine a set of social performance (SP) indicators into one single rating. To overcome this problem, this study investigates the preconditions for a new methodology for rating the SP component of the ESG by applying machine learning (ML) and artificial intelligence (AI) anchored to social controversies.

This study proposes the use of a data-driven rating methodology that derives the relative importance of SP features from their contribution to the prediction of social controversies. The authors use the proposed methodology to solve the weighting problem with overall ESG ratings and further investigate whether prediction is possible.

The authors find that ML models are able to predict controversies with high predictive performance and validity. The findings indicate that the weighting problem with the ESG ratings can be addressed with a data-driven approach. The decisive prerequisite, however, for the proposed rating methodology is that social controversies are predicted by a broad set of SP indicators. The results also suggest that predictively valid ratings can be developed with this ML-based AI method.

This study offers practical solutions to ESG rating problems that have implications for investors, ESG raters and socially responsible investments.

The proposed ML-based AI method can help to achieve better ESG ratings, which will in turn help to improve SP, which has implications for organizations and societies through sustainable development.

To the best of the authors’ knowledge, this research is one of the first studies that offers a unique method to address the ESG rating problem and improve sustainability by focusing on SP indicators.