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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.

This chapter differentiates the effect of solicited credit ratings (SCRs) and unsolicited credit ratings (UCRs) on bank leverage decision before and after the credit rating change. We find that banks with UCRs issue less debt relative to equity when the credit rating changes are approaching. Such findings are also prominent when bank credit rating moves from investment grade to speculative grade. After credit rating upgrades (downgrades), banks with unsolicited (solicited) credit ratings are inclined to issue more (less) debt relative to equity than those with solicited (unsolicited) credit ratings. We conclude that SCR and UCR changes lead to significantly different effects on bank leverage decision.

This paper aims to investigate the impact of sovereign rating signals on domestic banks’ stock returns in a European context.

The author uses an event study technique to measure short-term bank stock abnormal returns that result from domestic positive or negative sovereign rating events. Then, test results from the univariate event studies are further scrutinised with the bank- and sovereign-related factors related to cross-sectional variations in abnormal bank returns.

The univariate results show that positive sovereign rating events do not lead to significant bank stock price reactions, while negative events are associated with negative share price effects on domestic banks. The multivariate regression results for the subsample of negative rating events show that the degrees of contagion effects depend on which credit rating agency issues the signal, on whether the events are preceded by other negative sovereign rating signals, and in some cases on the sovereign’s initial rating level and on the bank’s liquidity ratio, profitability level and size.

The study improves the test procedures used by Caselli et al. (2016) and sheds light on the bank valuation effect induced by massive negative sovereign rating signals during the crisis period. The results highlight the share price effect of sovereign events and address political implications of introducing risk weights for sovereign debts.

The purpose of this study is to compare the classification learning ability of our algorithm based on boosted support vector machine (B-SVM), against other classification techniques in predicting the credit ratings of banks. The key feature of this study is the usage of an imbalanced dataset (in the response variable/rating) with a smaller number of observations (number of banks).

In general, datasets in banking sector are small and imbalanced too. In this study, 23 Scheduled Commercial Banks (SCBs) have been chosen (in India), and their corresponding corporate ratings have been collated from the Indian subsidiary of reputed global rating agency. The top management of the rating agency provided 12 input (quantitative) variables that are considered essential for rating a bank within India. In order to overcome the challenge of dataset being imbalanced and having small number of observations, this study uses an algorithm, namely “Modified Boosted Support Vector Machines” (MBSVMs) proposed by Punniyamoorthy Murugesan and Sundar Rengasamy. This study also compares the classification ability of the aforementioned algorithm against other classification techniques such as multi-class SVM, back propagation neural networks, multi-class linear discriminant analysis (LDA) and k-nearest neighbors (k-NN) classification, on the basis of geometric mean (GM).

The performances of each algorithm have been compared based on one metric—the geometric mean, also known as GMean (GM). This metric typically indicates the class-wise sensitivity by using the values of products. The findings of the study prove that the proposed MBSVM technique outperforms the other techniques.

This study provides an algorithm to predict ratings of banks where the dataset is small and imbalanced. One of the limitations of this research study is that subjective factors have not been included in our model; the sole focus is on the results generated by the models (driven by quantitative parameters). In future, studies may be conducted which may include subjective parameters (proxied by relevant and quantifiable variables).

Various stakeholders such as investors, regulators and central banks can predict the credit ratings of banks by themselves, by inputting appropriate data to the model.

In the process of rating banks, the usage of an imbalanced dataset can lessen the performance of the soft-computing techniques. In order to overcome this, the authors have come up with a novel classification approach based on “MBSVMs”, which can be used as a yardstick for such imbalanced datasets. For this purpose, through primary research, 12 features have been identified that are considered essential by the credit rating agencies.

The purpose of this paper is to study the information content of about 3,300 global bank rating changes before and after the Lehman bankruptcy in September 2008 to assess if differences in stock market reactions for small and big banks emerge.

The analysis of the stock market reactions of rating changes (upgrades and downgrades) and bank’s size (small and big) is conducted by an event study approach.

The authors find that while upgrades are not associated with significant abnormal bank stock returns, downgrades have a significantly negative effect. This result holds for both small and big banks, while negative abnormal returns are considerably stronger for the former. For small banks, the authors observe an increase in negative cumulative abnormal returns post-Lehman. The lack of a reaction to large banks’ rating downgrades in the narrow [−1,+1] event window indicates that their stock prices may, to some extent, be insulated from negative rating information even post-Lehman, which the authors attribute to an implicit “too big to fail” subsidy anticipated by equity investors.

This paper provides insights to the differences in the information content of changes in small and big banks’ credit rating on stock returns that is unrelated to the well-known size effect. Compared to small banks, big banks seem to some extent be insulated from negative rating changes even post-Lehman – contributing to the on-going too big to fail debate.

The purpose of this paper is to examine the information spillover of sovereign rating changes on the market valuation of bank stocks in Africa.

First, the authors apply event study methodology to evaluate the stock market reaction of African bank stocks on the announcement of sovereign rating changes. Second, the cross sections of the abnormal returns are examined by multivariate regression analyses. Third, the findings are proved for robustness.

The authors investigate how 37 African banks react to 203 African sovereign rating announcements from the three leading credit rating agencies over the period 2010-2016 and find that negative announcements trigger the significant positive stock reactions of African banks, especially contributed by banks in the non-reviewed African countries. These unusual reactions can be explained by the low integration and the severe information asymmetry of African capital markets. The authors further locate the influencing factors of banks’ reactions and show that rating downgrades magnify the abnormal effects while the membership of the African Free Trade Zone mildens the stock market reactions.

Limitations are given by the limited sample size. There are only limited numbers of publicly listed African banks with sufficient trading data.

The paper argues for a critical dependency of African bank equity valuation in the case of sovereign debt rating changes in neighbor countries. This observation is important for the risk assessment of African banking assets.

The paper is the first to examine stock market reactions on sovereign rating announcements for the evaluation of capital market integration in Africa. It thereby underlines the usefulness of this simply to apply approach as an instrument for ongoing examining the progress in capital market development in emerging countries.

This paper reviews the prevalence of the use of risk ratings by commercial banks that participated in the Federal Reserve’s Survey of Terms of Bank Lending to Farmers between 1997 and 2002. Adoption of risk rating procedures held about steady over the period, with a little less than half the banks on the panel either not using a risk rating system, or reporting the same rating for all their loans in the survey. However, most of these banks were small, and roughly four‐fifths of all sample loans carried an informative risk rating. After controlling for the size and performance of the bank and as many nonprice terms of the loan as possible, findings reveal that banks consistently charged higher rates of interest for the farm loans they characterized as riskier, with an average difference in rates between the most risky and least risky loans of about 1 and a half percentage points.

This paper seeks to examine whether the market values the monitoring activity undertaken by a quality bank in the presence of a credit rating agency. Specifically, the question is asked whether the quality of a lead lending bank influences a market reaction to adverse rating announcements concerning its borrowers.

The event study methodology and various bank quality proxies (size, growth rate in assets, profitability, capital ratio, bank's credit rating, and ownership) are used to examine the market reaction when a borrower's bank loan rating is placed with negative implication or is downgraded.

Firms which are certified and monitored by high‐quality banks are less susceptible to negative market reactions when adverse rating announcements are made.

The findings indicate high‐quality lending banks sustain investors' confidence in their borrowers in the face of deteriorating news. The paper argues that investors and borrowers value monitoring from a high‐quality bank, which is an implication of a bank having access to private information about its borrowers.

Customers of Islamic banking industry continue to be skeptical on Sharīʿah compliance of Islamic banks despite receiving fatwa from the competent authorities. The purpose of this paper is to quantify the Sharīʿah risk taken by Islamic banks, so that customers are better informed on the level of Sharīʿah compliance that will help in removing the persistent level of skepticism toward Sharīʿah compliance.

This research has used the scorecard based modeling approach to build the Sharīʿah risk rating model, which consists of 14 factors that capture Sharīʿah risk and are grouped in 5 major areas revolving around regulatory support, quality of Sharīʿah supervision, business structure, product mix and treatment of capital adequacy ratio. The score calculated by applying the model is grouped into 4 tiers reflecting the level Sharīʿah compliance at bank as non-compliant, weak compliance, satisfactory compliance and high level of Sharīʿah compliance. Three case studies were conducted by applying the model to Islamic banks from Malaysia, Pakistan and Saudi Arabia.

The final Sharīʿah risk scores calculated by the model clearly differentiate the 3 banks on basis of their Sharīʿah risk. The underlying scores also highlighted the areas where banks need to improve to reduce their Sharīʿah risk.

This model can be applied by customers of Islamic banks who are interested in understanding Sharīʿah-related aspects of Islamic banking industry. This model can be applied on standalone basis or as an extension to the conventional counter party risk rating models. This model can benefit management of Islamic banks toward allocation of capital against Sharīʿah risk under Basel III, and regulators can apply the model to measure industry wide risk of Sharīʿah non-compliance.

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.