This research aims to examine the impact of green banking, digital transformation, asset quality, and operational efficiency on the financial performance of banks in Indonesia from 2021 to 2024. Using a quantitative approach, the study employs panel data regression with the Random Effects Model (REM) through EViews software. The sample consists of 30 banks listed on the Indonesia Stock Exchange, with secondary data from annual reports, financial statements, and sustainability reports. Green banking is measured through a disclosure index, while digital transformation is represented by the ratio of digital intangible assets to total intangible assets. Asset quality is assessed using the non-performing loan (NPL) ratio, and operational efficiency is evaluated by the BOPO ratio. Financial performance is represented by net income, with Return on Assets (ROA) as a control variable. The findings indicate that green banking has a positive impact on financial performance, while digital transformation has a negative effect. Asset quality and operational efficiency, although important, do not significantly influence financial performance. ROA as a control variable shows a positive and significant impact. These results emphasize the need for strengthening green banking practices to promote sustainable financial performance in the banking sector.

This study has examined the impact of covid-19 on the financial performance of listed commercial banks in Sri Lanka by analyzing the data of 23 listed commercial banks for 6 years from 2016 to 2021. The Return on Assets ratio and Return on Equity ratio are taken as the dependent variables and the impact on it is measured by comparing the above listed dependent variables before and during COVID-19 Pandemic Period. The paired sample t-test, descriptive analysis, Wilcoxon statistical test and linear regression analysis output of the SPSS Statistics 23 has noted that the mean ROA and ROE before and during COVID-19 shows a statistically significant difference and the null hypothesis is rejected. It can be concluded that there is a significant decrease in mean Return on Assets (ROA) and Return on Equity (ROE) before and during covid-19 of listed commercial banks in Sri Lanka.

Abstract Current research on corporate bond default identification in China faces several challenges, including imbalanced default samples, complex hyperparameter configurations, and limited interpretability of the identification model. To tackle these issues, this study employs an interpretable machine learning framework, leveraging its efficient data-processing capabilities. Using samples of Chinese defaulted bonds from 2014 to 2024, the framework first applies the Synthetic Minority Over-sampling Technique (SMOTE) to alleviate classification bias resulting from sample imbalance. Subsequently, the Light Gradient Boosting Machine (LightGBM) is employed for feature selection and default bond identification, while the multi-objective optimization algorithm Non-dominated Sorting Genetic Algorithm II (NSGA-II) is used to optimize the hyperparameters of the LightGBM, thereby improving the model’s generalization capability. Finally, the SHapley Additive exPlanations (SHAP) method is adopted to interpret the marginal contributions of default factors to the identification outcomes. Experimental results show that the proposed model achieves an average identification accuracy of over 82.88% across four different prediction windows, with an average efficiency metric of 93.54%. Moreover, SHAP analysis reveals that risk factors such as the cash asset ratio play a critical role in default identification within China’s bond market. These findings confirm that the proposed approach not only makes the decision-making process of key risk factors interpretable but also offers regulatory authorities a scientific basis for policymaking, thereby supporting the development of targeted regulatory frameworks and enabling proactive intervention in high-risk bonds.

Abstract The green asset ratio is designed to serve as a key indicator for comparing and evaluating the sustainability performance of European banks. However, we find that the GAR does not yet serve this purpose. Based on a sample of 97 ECB-supervised significant institutions, we find empirical evidence for multiple heterogeneity issues that hamper comparability of taxonomy reports and market transparency.

Indonesia’s digital economic transformation has produced 14 unicorn companies with unique financial characteristics that prioritize exponential growth over short-term profitability. This study aims to analyze the influence of finansial rasio determinants on the Altman Z-Score of Indonesian unicorn companies for the 2020-2025 period. Using a quantitative approach with a comparative causal design, this study analyzed panel data from 9 unicorn companies with a total of 54 observations through a fixed effects regression model. The results show that all five finansial rasio components have a significant positive effect on the Althman Z-Score, with the EBIT to total assets ratios as the most dominant determinant (coefficient 3.234), followed by working capital to total assets (2.145), retainded earnings to total assets (1.876), sales to total assets (0,934), and market value of equity to total liabilities (0,567). Simultaneously, the five ratios explain 78.45 percent of the variation in the Z-Score with an F-statistic of 35.426. A temporal analysis identified an increase in the proportion of companies in the safe zone from 22.2 percent to 44,4 percent, indicating the financial resilience of th unicorn ecosystem post-pandemic. This research contributtes to the literature on bankruptcy prediction in emerging markets and provides a framework form evaluating health for stakeholders in making investment decisions in the digital economy.

The study investigated the impact of credit risk management on the profitability of commercial banks in Ethiopia. The research adopted explanatory research design and a quantitative approach were employed. It used descriptive and inferential statistical analyses techniques. The population of this study includes 31 commercial banks that are working in Ethiopia, from which a purposive sample of ten leading banks were selected on the basis of their branch network and operational age. The study is panel from the period between 2013 and 2023. The Random effects Generalized Least Squares model was utilized as panel data regression analysis to test the impact of credit risk management factors on bank performance. The regression result shows that Non-Performing Loans were found to have a significant negative impact on bank profitability, which implies that a high level of non-performing loans decreases bank profitability, emphasizing the importance of effective credit risk management. Conversely, and contrary to conventional assumptions, both a higher Loan to Total Asset Ratio and Loan to Deposit Ratio were unexpectedly associated with decreased profitability, suggesting an optimal threshold for lending activity exists beyond which excessive loan expansion introduces detrimental liquidity and credit risks. In alignment with established financial principles, Capital Adequacy significantly enhanced bank performance, underscoring its crucial role in providing essential buffers against losses, maintaining stability, and fostering stakeholder confidence for sustainable returns. Bank size as measured by total assets is positively related to better performance but insignificance effect on it.

Correction: Estimated Sharpe ratio of asset returns with fat tails: theory and empirical evidence

This study investigates the relations among various characteristics of board members and financial performance of firms listed on Borsa Istanbul, taking into consideration the sizes of the firms. The relationships were examined using panel data analysis. The units of analysis were determined by examining the secondary data of 58 public firms with publicly traded shares and fully accessible data. In the analysis, those 58 firms’ annual data between 2012 and 2018 were used. Significant relationships were found between some variables representing the firms’ board structures and return on asset ratios. The study found that independent directors serving on multiple boards and academicians negatively affect return on assets for large firms. Similarly, independent directors serving on multiple boards and members who are experts in the sector also negatively affect return on assets for small firms.

Public sector asset disclosure systems face persistent challenges in converting compliance data into actionable oversight intelligence. This study introduces two preventive forensic accounting measures Asset Liquidity Concentration (ALC) and Asset Liquidity Structure (ALS) analyzed through percentile-based benchmarking to enable risk-stratified governance oversight. Examining the complete population of 992,081 asset disclosure reports from Indonesia’s LHKPN system during 2020–2022, the study documents extreme concentration of illiquid assets (median illiquid asset ratio: 95.9%), accompanied by substantial heterogeneity across government sectors and administrative area. Percentile analysis indicates that officials in the upper quartile (P75) hold 99.3% of their assets in illiquid forms, whereas those in the lower quartile (P25) maintain 84.8% illiquidity. Sectoral analysis reveals that executive branch officials exhibit the highest variability in asset liquidity (IQR: 0.174), while legislative officials display the most concentrated illiquid asset profiles (P75: 99.6%). The ALS complexity index (median: 0.614) suggests a level of portfolio diversification that substantially increases verification burdens. Analysis by administrative area further shows that central government officials maintain more complex asset structures (mean ALS: 0.627) compared to regional government officials (mean ALS: 0.626). These percentile-based thresholds facilitate systematic identification of statistical outliers warranting enhanced scrutiny, without presuming misconduct. Overall, the study advances preventive forensic accounting by demonstrating how structural asset characteristics, when assessed using distributional benchmarks, can transform passive compliance systems into active, risk-based governance tools under conditions of limited oversight resources.

This research to evaluate impact of Net Profit Margin (NPM), Operating Profit Margin (OPM), Debt to Total Asset Ratio (DAR), and Total Asset Turnover (TATO) on the stock value of coal mining sub-sector companies listed on the Indonesia Stock Exchange (IDX) during the period 2019–2023. The sample comprises six firms selected through a non-random procedure using predetermined criteria in accordance with the purposive sampling method. Data analysis was conducted using SPSS. The findings indicate that NPM, DAR, and TATO individually exert a significant effect on stock prices, whereas OPM does not display a statistically significant influence. Collectively, all independent variables demonstrate a significant impact on stock prices, as reflected by an F-statistic of 9.256, which surpasses the F-table value of 2.76. The coefficient of determination (R²) of 0.597 shows that 59.7% of the variation in stock prices is accounted for by the combined variables NPM, OPM, DAR, and TATO, while the remaining 40.3% is attributed to external factors.

India’s credit card market, exceeding 110 million active users in 2025, reflects a major leap in financial inclusion but also rising borrower vulnerability. Despite a 12% annual growth in card spending ₹1.93 lakh crore in July 2025 the Gross Non-Performing Asset (GNPA) ratio climbed from 1.84% in 2024 to 2.30% in 2025. Low financial literacy, affecting over 70% of adults, remains a key cause of repayment distress and poor credit awareness. This study adopts a mixed-method approach, combining a survey of 20 respondents from varied income and occupational backgrounds with secondary data from the Reserve Bank of India (RBI), CRIF High Mark, and the National Centre for Financial Education (NCFE). Quantitative and comparative analyses were used to explore the relationship between income, literacy, repayment behave or, and financial stress. Findings reveal that 60% of respondents own credit cards, with 55% using them mainly for online shopping and emergencies. Over 40% were unaware of their interest rates, and 30% reported payment-related stress. National data confirm high revolving rates (24%–55%) and increasing outstanding balances per card, aggravating borrower vulnerability. Credit cards improve liquidity but simultaneously expose financially unaware consumers to debt traps. Strengthening credit education, enforcing transparent interest disclosures, and limiting revolving interest rates are essential to sustain India’s financial health.

The purpose of this study is analyze the Financial Performance of the Balai Yasa Lahat Employee Cooperative in terms of the Financial Ratios of Liquidity, Solvency, and Profitability. The aim was to determine the financial position of the Balai Yasa Lahat Employee Cooperative. The research method used a quantitative descriptive approach in accordance with the standards of the Regulation of the Minister of Cooperatives and Small and Medium Enterprises of the Republic of Indonesia Number: 22/PER/M.KUKM/IV/2007 concerning Guidelines for Cooperative Rating. The results of the study show that the analysis of the financial report performance of the Liquidity Ratio at the Balai Yasa Lahat Employee Cooperative from 2021 - 2023 shows a figure with the criteria of "Very Not Ideal" because it is above 200%, in the Current Ratio analysis, namely 307% in 2021, 319% in 2022, and 527% in 2023. The Solvency Ratio analysis shows a figure with the criteria of "Very Not Ideal" because it is below 90% in the Debt Ratio analysis, namely 28% in 2021, 27% in 2022, and 16% in 2023. Meanwhile, in the Debt Equity Ratio (DER) analysis, namely 43% in 2021, 40% in 2022, and 20% in 2023. The Profitability Ratio analysis in the Return On Asset Ratio analysis in 2021 has The ratio is 6.04%, categorized as poor, falling between 4% and 7%. Meanwhile, in 2022 and 2023, the ratios were 2.28% and 2.39%, respectively, categorized as poor, falling below 4%. Meanwhile, in the Return on Equity analysis, the ratio for 2021 was 9.22%, categorized as quite good, falling between 8% and 11%. Meanwhile, in 2022 and 2023, the ratios were 3.26% and 2.96%, respectively, categorized as poor, falling below 4%.

The aim of this study to investigate whether bankruptcy probability effective on firm value for manufacturing firms, which are centre of economic development and sustainable growth. The bankruptcy probability is a key strategic factor in determining importance of firms' market value. In this context, this study provides original evidence that the Z-score can be used in firm valuation processes beyond merely being an indicator that measuring the bankruptcy probability. In the study, data obtained from 98 manufacturing firms on the Borsa Istanbul (BIST) between 2003: Q1–2024: Q3 were analysed with panel data analysis methods. To measure bankruptcy probability the Altman Z-Score were used and Tobin’s Q and the return on assets ratio used for firm value. Firm size and financial leverage ratios were used as control variables in the study. The results show that Z-score has a positive and statistically significant effect in both models and firm size has a negative effect on Tobin's Q and a positive effect on return on assets, while the effect of financial leverage ratio on firm value and profitability is not statistically significant. According to the results obtained, it is seen that effective risk management has a positive effect on the market value and firm’s profitability and that firms with high Z scores are considered reliable and stable by investors because they have a lower bankruptcy probability and therefore this situation is directly reflected in the market value of firms.

This research gauges the influence of the cash gap (CG) on firms’ performance and value for Jordanian industrial firms listed in the Amman Stock Exchange (ASE) during the period (2010–2019). By applying a panel data analysis on a sample of 39 firms, the study used the CG as the main independent variable. However, for dependent variables, the research focused on two measures of companies’ performance, which are operating profit margin (OPM) and return on assets (ROA). By employing Tobin’s Q (TQ) as a proxy for firm value (FV), the research reveals that there is a significant and inverse linkage between CG and both OPM and ROA. These results align with previous studies conducted by Kouaib and Bu Haya (2024) and Ruguru (2023). The results also revealed that reducing the CG and debt ratio, while increasing volume, financial assets ratio, and sales growth, improves company performance. Moreover, based on the Arellano-Bond estimation results, the research notes a significant and inverse nexus between the CG and FV. The results of this research suggest fruitful insights for decision-makers, executive managers, and debt holders, contributing to their financing and investing activities.

Abstract The study investigates the determinants of bank non-performing loans (NPL) in European and African countries, focusing on 32 European and African countries from 2010 to 2021. The results based on the two-stage least squares regression methodology show that the number of commercial bank branch, bank liquid reserves to bank assets ratio, inflation rate, exchange rate, real interest rate and the lending rate are significant determinants of bank NPL in the full sample. Size of domestic private credit, bank capital to asset ratio, bank liquid reserve to bank asset ratio, unemployment rate, inflation rate, exchange rate, real interest rate and lending rate are significant determinants of bank NPL in European countries. Bank capital to asset ratio, bank liquid reserve to bank asset ratio and inflation rate in Africa are significant determinants of bank NPL in African countries. The implication of the results is that the determinants of bank NPL in European countries are not necessarily the drivers of bank NPL in African countries.

Globalization has had a significant impact on the dynamics of the global economy, including the international business practices carried out by multinational companies. One of the consequences of this phenomenon is the increasing flow of transactions between related entities, known as Transfer Pricing. Transfer pricing refers to a company's policy in determining the transfer price of transactions involving goods, services, intangible assets, or financial transactions between affiliated companies. Multinational companies often utilize transfer pricing by manipulating transfer prices to reduce the amount of tax payable to the state in order to maximize profits. The purpose of this study is to examine the effect of Intangible Assets and Tunneling Incentive on the decision of companies to engage in Transfer Pricing. The observation period of this study covers the years 2020 to 2024. The research sample consists of consumer non-cyclical sector companies listed on the Indonesia Stock Exchange (IDX) during the 2020–2024 period. The sampling method employed was purposive sampling, resulting in a total of 115 observations. Data were processed using E-Views with panel data regression analysis under the Random Effect Model (REM). Transfer Pricing was measured using the ratio of related-party receivables to total receivables, Intangible Assets were measured using the ratio of intangible assets to total assets, and Tunneling Incentive was measured using the ratio of the largest shareholding to total outstanding shares. The results indicate that Intangible Assets have no significant effect on the decision to engage in Transfer Pricing, whereas Tunneling Incentive has a significant effect, suggesting that the proportion of share ownership influences a company's tendency to conduct Transfer Pricing. Furthermore, the study finds no simultaneous effect of Intangible Assets and Tunneling Incentive on the decision to engage in Transfer Pricing.

A sound banking environment immensely espouses a country’s economic growth. Measuring bank competence provides opportunities to ameliorate the financial system. The Indian government's 2019 mega-merger of public sector banks (PSBs) envisioned to increase their financial and operational performance. The moral hazard risk is more serious with PSBs due to crony capitalism and governance issues. The government augments recapitalization whenever necessary and henceforth alters banks' perspectives on apt risk management perspectives. Despite geopolitical conflicts, geoeconomic fragmentation, commodity price volatility, climate change, aging populations, and declining productivity, the global banking industry is resilient with robust capital buffers, increased profitability, and other indicators. The banking sector strife in several advanced countries in 2023 was put down by swift policy interferences that prevented a systemic predicament. Formidable macroeconomic groundworks in India have enriched the health and performance of the country's banking. In 2023-24, banks saw a sixth consecutive year of increased profitability and by 31st March 2024, their gross non-performing assets (GNPAs) ratio dropped to 2.7%, the lowest level in past 13 years. This article examines the financial performance of six PSBs (Bank of Baroda-BOB, Canara Bank-CB, Indian Bank-IB, Punjab National Bank-PNB, State Bank of India-SBI, and Union Bank of India-UBI) pre-and post-merger. Outcomes divulge that not significant variation between financial evaluation (return on assets: ROA; return on equity: ROE; earnings per share: EPS; and net interest margin: NIM) in the pre-and post-merger periods except there lies the significant difference in NIM of BOB and UBI. The study's conclusion will help various stakeholders including banks, customers, and regulators to develop effective measures that boost the financial performance of PSBs, shareholder earnings, and synergy implications of mergers and acquisitions (M&As).

Inflation accounting, which aims to eliminate the distortive effects of high inflation on financial statements, allows for a more realistic and comparable analysis of business performance. The study aims to analyze the impact of inflation accounting on business performance. In this context, the financial data of the enterprises operating in the Metal Goods, Machinery, Electrical Equipment and Transportation Vehicles Sub-Sector of Borsa Istanbul for the year 2022 were analyzed. The Enhanced Entropy method was used for objective-based weighting of financial ratios, and the Gray Relational Analysis (GRA) method was used for performance ranking. Based on inflation-adjusted and non-inflation-adjusted financial statements, the comparative performance rankings of the enterprises were calculated and the change in the rankings was revealed. Moreover, these performance rankings are compared with stock returns to assess the extent to which financial indicators are consistent with capital market reactions. The findings reveal that inflation adjustment leads to significant changes in firm performance rankings and that this change varies across firms, especially in financial ratios such as financial leverage, return on assets, inventory dependency and fixed asset ratios. A high level of correlation was found between performance rankings before and after inflation adjustment. However, no significant and consistent relationship was observed between performance rankings and stock returns.

This research was conducted to analyze the effect of business strategy on firm performance with earnings management as an intervening variable. Business strategy is a tool used by firms to achieve competitive advantage. This research refers to the concept of Miles and Snow to determine business strategies with the prospector and defender dimensions as different extreme strategies. In this research, business strategy is measured using three proxies: employee per sales ratio (EMPSAL), capital expenditure per total asset ratio (), and dividend payout ratio (DPR). Earnings management is measured by calculating discretionary accruals using the modified Jones model approach. Firm performance is measured using the profitability ratio with the net profit margin indicator. The population in this research consists of manufacturing companies listed on the Indonesia Stock Exchange from 2022 to 2024. The data used are secondary data by determining the research sample using the purposive sampling method and obtained as 16 firms from 171 firms with a total of 48 samples. Companies with prospector and defender strategies in this research are grouped using cluster analysis. The intervening variable in this research was tested using path analysis aided by SPSS 24. The results showed that (1) business strategy had no significant effect on earnings management, (2) earnings management had no significant effect on firm performance, and (3) business strategy had a significant effect on firm performance.

Abstract This paper generalizes the results in Lo (2002) by allowing the distributions of asset returns exhibiting fat tails with the tail index 0 < κ < 4. We show that the asymptotic behavior of the estimated Sharpe ratio is biased and converges at a slower rate as 2< κ < 4. Therefore, the risk-return payoff information provided by the estimated Sharpe ratio can be misleading. It is also shown that the asymptotic unbiasedness and normality of the estimated Sharpe ratio can only be established as asset returns have a finite (4 + ε ) th moment with ε ≥ 0, i.e., κ > 4. Of particular interest are the cases as the tail index 2 < κ < 4, where the ex ante Sharpe ratio is well defined, and an inference method is developed. Empirical examples are used to illustrate the derived results, and an empirical study on major ETFs and mutual funds suggests caution in using the Sharpe ratio for comparing investment performance.