Banking risk measurement and management remain one of many challenges for managers and policymakers. This study contributes to the banking literature and practice in two ways by (a) proposing a risk ranking index based on the Mahalanobis Distance (MD) between a multidimensional point representing a bank’s risk measures and the corresponding critical ratios set by the banking authorities and (b) determining the relative importance of a bank’s risk ratios in affecting its financial standing using an Adaptive Neuro-Fuzzy Inference System. In this study, ten financial ratios representing five risk areas were considered, namely: Capital Adequacy, Credit, Liquidity, Earning Quality, and Operational risk. Data from 45 Gulf banks for the period 2016–2020 was used to develop the model. Our findings indicate that a bank is in a sound risk position at the 99%, 95%, and 90% confidence level if its Mahalanobis distance exceeds 4.82, 4.28, and 4.0, respectively. The maximum distance computed for the banks in this study was 9.31; only five out of the forty-five banks were below the 4.82 and one below the 4.28 and 4.0 thresholds at 3.96. Sensitivity analysis of the risks indicated that the Net Interest Margin is the most significant factor in explaining variations in a bank’s risk position, followed by Capital Adequacy Ratio, Common Equity Tier1, and Tier1 Equity in order. The remaining financial ratios: Non-Performing Loans, Equity Leverage, Cost Income Ratio, Loans to Total Assets, and Loans to Deposits have the least influence in the order given; the Provisional Loans Ratio appears to have no influence.

The study aims to operationalize financial reporting quality in terms of the qualitative characteristics (QCs) as stated by the Accounting and Auditing Organization of Islamic Financial Institutions (AAOIFI) standards, as well as to investigate their association with earnings quality (EQ) and banking performance. The study uses secondary data extracted from DataStream to operationalize and measure the financial reporting quality in the annual reports of 25 out of the 27 Islamic banks in the Gulf Council Countries (GCC) for a 5-year period (2014–2018), meaning 125 annual reports were used. The study applies a manual content analysis to the annual reports to score all the items of QCs and operationalizes 25 measurement items that represent the six QCs. All items use 5-point Likert-type scales to compute the sub-score and the overall index through the Neural Network System. The findings of the model paths show a significant positive relationship between EQ and most of the QCs. The first hypothesis is partially accepted as there is a positive relationship between EQ and relevancy, reliability, prudence and general quality; however, there is no significant relationship between EQ and understandability and there is a significant negative relationship between EQ and comparability. Moreover, the study finds a significant positive relationship between EQ and ROA on one hand and EQ and ROE on the other hand (p-value = 0.00), meaning the second hypothesis is supported.

Summary: Predicting stock price remains one of the challenges for investors' investment strategies. This study helps with accurate prediction and the main factors affecting variations in stock prices. It applies an adaptive neuro‐fuzzy model on 58 listed firms from both the Abu Dhabi Securities Exchange and the Dubai Financial Market for the period 2014–2018 to estimate the predictive power of corporate performance measures and their significance. After examining four performance predictors—return on asset (ROA), return on equity (ROE), earning per share (EPS), and profit margin (PM)—the study finds that ROE is the most significant predictor and ROA is the least. EPS is the most influential profitability measure and PM the least.

The rapid advancement of Blockchain technology has significantly benefited banks with more efficiency, highly secured activities, compliance, fraud prevention, and risk control. All previous studies focused on stakeholders’ perceptions and ignored measuring the value of blockchain adoption. This study addresses this gap by quantifying and rating blockchain’s impact on reducing banking transaction costs. The data has been collected from 17 of 20 United Arab Emirates national banks over 2017–2023 and analyzed using the random forest method to assess the association between blockchain adoption and four transaction cost elements. The random forest technique accurately quantifies and classifies blockchain’s role in cost reduction. The findings indicate that blockchain adoption significantly reduces processing, transfer, and fraud costs. This study has a visible practical and theoretical contribution as it shifts focus to quantifying blockchain’s impact, providing useful insights for managers, and suggesting future research across different sectors and countries.