This study examines the impact of bank soundness ratio (based on the Risk-Based Bank Rating (RBBR) method) on financial performance via the environmental, social, and governance (ESG) framework within Islamic governance and social responsibility in Indonesian Islamic commercial banks (Sharia banks). The sustainability of Islamic banks is attained not solely through corporate performance, dictated by financial ratios, but also by a focus on global concerns, specifically environmental, social, and banking governance (Adu et al., 2024; Pasko et al., 2022). This study utilizes data from all Islamic commercial banks in Indonesia, with a sample for 2018–2022, and employs moderating regression analysis. The findings indicate that liquidity risk, financing, operating, and capital adequacy (RBBR), Islamic social reporting (ISR), and Islamic corporate governance (ICG) significantly impact Sharia banking performance. ICG effectively moderates the relationship between RBBR and ISR on Sharia banking financial performance. ICG can enhance the health of Sharia banks and their awareness of social responsibility, thereby positively influencing their performance. The Financial Authority can use these insights to enhance risk management and ISR regulations in Sharia banks. This study reveals discrepancies with prior studies, where return on equity (ROE) yielded superior outcomes to return on assets (ROA). This study identifies ICG’s significance in enhancing RBBR and ISR impact on banking performance within Sharia banking, a topic unexplored by scholars.

Fund similarity is important for investors when constructing diversified portfolios. Because mutual funds do not always adhere closely to their stated investment policies, investors may unintentionally hold funds with overlapping exposures, leading to reduced diversification and instead causing “diworsification”, which is an investment term for when too much complexity leads to worse results. As a result, various quantitative methods have been proposed in the literature to investigate fund similarity, primarily using portfolio holdings. Recently, machine learning tools such as clustering and graph theory have been introduced to capture fund similarity. This paper builds on this literature by applying bipartite graphs and node2vec embeddings to a comprehensive dataset that covers 3874 funds over a nearly 6-year period. Our empirical evidence suggests that, bipartiteness is not preserved for non-index (active) funds. Furthermore, while graph embeddings yield higher similarity scores than holding-based measures, they do not necessarily outperform holding-based similarity in explaining returns. These findings suggest that graph-based embeddings capture structural relationships among funds that are distinct from direct portfolio overlap but are not sufficient substitutes when similarity is evaluated solely through returns. As a result, we recommend a more comprehensive similarity measure that includes important risk metrics such as volatility risk, liquidity risk, and systemic risk.

Islamic banks in Africa have operated for over two decades in an effort to expand and compete with conventional financial institutions, particularly in a region with a significant Muslim population. Despite this potential, the growth of Islamic banking across the continent has been shaped by unique structural and economic challenges that influence how these banks manage financial risks. Among these risks, liquidity risk remains a critical factor affecting the stability and profitability of Islamic banks. This study investigates the impact of liquidity risk on the financial performance of Islamic banks in Africa from 2014 to 2019. Using panel data from 34 Islamic banks across 11 African countries, the research applies a fixed effects regression model to analyze the relationship between liquidity risk and key financial performance indicators. The findings reveal that liquidity risk, measured through the liquidity ratio, has a significant positive relationship with the financial performance of Islamic banks. This suggests that maintaining adequate liquidity enhances banks’ profitability and resilience in the face of regional economic volatility. The study concludes that effective liquidity risk management is essential for improving the financial performance of Islamic banks in Africa. It recommends that policymakers and bank managers adopt stronger liquidity monitoring frameworks and regulatory support mechanisms to ensure sustainable growth and competitiveness within the African financial sector.

Purpose The study aims to review the impact of liquidity vulnerabilities, internal resilience, exposure to loan losses, and macroeconomic dynamics on performance uncertainty among US commercial banks. Design/methodology/approach The data for empirical inquiry were analyzed using the two-step system GMM panel estimation technique. Further empirical analyses were performed using the dynamic panel threshold model propounded by Seo et al. (2019) to verify the existence of non-linear threshold effects or otherwise for surmised study interactions. Findings The results suggest that worsening liquidity position and exposure to loan losses among US commercial banks contribute significantly to performance uncertainty. Internal resilience, however, is found to lessen or reduce performance uncertainty among commercial banks. The findings suggest that improved or strengthened internal banking dynamics, because of appreciable growth in deposits, asset growth, and healthy liquidity reserves, tend to minimize volatility associated with US commercial bank performance. Finally, this study found that although internal resilience is critical in dampening volatility associated with commercial bank performance, the conclusion is feasible for commercial banks characterized by yield on assets equal to or more than 4.5% and 3.6%, respectively, for the two volatility performance measures used in the study. Originality/value The contributions and significance of this study stem from the unique approach adopted. The approach adopted in this study differs significantly from related studies in terms of the methodology used, the nature of interactions examined, and the core variables featured in the examination of the prime objectives of the study.

The last passage time before ruin: Theory and applications in liquidation risk management

Despite of the theoretical and empirical information extant to the topic of funding structure of a firm, the existing literature focuses mostly on the funding structure itself as it does not affect overall firm performance and there is an inherent neglect of the sector-specific features on market-based measure of the valuation such as the Market value added. The difference in the operational and financial environment in different industries such as consumer goods, industrial goods, conglomerates, natural resources and healthcare industries, that is the need for tailored approach. The paper analysed the correlation between the funding structure and the market value of the quoted manufacturing firms between the years 2014 and 2023. Particularly, the research analysed the relationship between short-term debt, long-term debt, share capital and retained earnings and the market value added and Market value added. The positivist research paradigm has been presumed in the study and ex- post facto research design has been adopted. The information used in the research was the central bank of Nigeria statistical bulletin, the annual audited financial reports of the quoted companies used in the research. Descriptive statistics, Hausman test and random effect panel ordinary least square at 5% level of significance were used in the study. The Hausman test came up with correct outcome which justified the use the random effect panel regression method. The research established that short-term debt had a positive significant relationship with the Market value added, the long-term debt showed significant negative relationship with the Market value Added, the share capital had a positive significant relationship with the Market value Added, the retained earning had a positive but not significant relationship with the Market value Added. This research concluded that the percentage of funding play a very important role in decision making process of financial performance of quoted manufacturing companies in Nigeria. The study among others admonished manufacturing companies in Nigeria when listed publicly in the stock exchange, to have a strategic decision to increase their dependence on short-term debt in order to finance their operations. This will help in management of cash flow in an effective manner and timely repayments which are important in mitigating the liquidity risks. Another good contribution in the study is that it is focused on the manufacturing industry in Nigeria. This study is also different to the past studies in respect that the target industry was the manufacturing industry.

The object of the study is the processes of cash flow management in the banking system of Ukraine, which is characterized by high dynamism, increased risks caused by war and economic instability, as well as rapid adaptation to digital technologies and European standards. The article emphasizes the critical importance of effective cash flow management to maintain financial stability and ensure uninterrupted operations of the bank in the face of uncertainty. Problem statement. The main problem studied in the article is the lack of efficiency of traditional methods of forecasting and managing cash flows in modern realities. These methods are unable to adequately process large amounts of data, take into account complex nonlinear dependencies, and respond quickly to unpredictable changes caused by both war and digital transformation. This creates liquidity risks, leads to suboptimal use of capital, and reduces the overall resilience of the banking system. Unresolved aspects of the problem. Today, there are gaps in the integration of the latest intelligent systems directly into the bank's operational and strategic processes. There are still unanswered questions about how to turn highly accurate predictions obtained through machine learning into concrete, managerial decisions that will minimize risks. Purpose of the article. The aim of the article is to develop comprehensive recommendations for improving cash flow management in a bank using intelligent systems. For this purpose, a three-dimensional approach is used, which combines Big Data analysis, improving the accuracy of forecasts using machine learning, and their integration into a management decision support system. Presentation of the main material. The authors of the article use a three-dimensional coordinate system of “analysis-prediction-integration” to structure the research. Practical examples for forecasting liquidity, assessing borrowers' solvency, and the effectiveness of marketing campaigns are considered. The use of LSTM, SVM, Random Forest, and RNN models to improve forecasting accuracy is detailed. To integrate the forecasting results into the bank's risk management system, specific solutions are proposed, such as the use of automated dashboards, early warning systems, and dynamic scoring. Conclusions. The recommendations proposed in this article allow banks to move from reactive to proactive cash flow management. This helps to significantly reduce operational risks, optimize capital, increase profitability and strengthen competitive positions. The practical value of the study lies in the provision of specific tools and scenarios for the implementation of intelligent systems in daily operations, which is extremely important for ensuring the financial stability of the Ukrainian banking system in the face of uncertainty.

Abstract Extreme changes in the weather conditions can lead to operational inefficiencies and physical degradation of a firm’s assets. As a consequence, firms with a weak capacity to adapt to such changes can be confronted with financial difficulties. Using explainable artificial intelligence modelling (XAI), we examine the performance of variables assessing climate change risks and the magnitude of the climate change phenomenon in predicting corporate failure. The experimental findings, which are supported by real-world datasets from France, show that climate change risk (CCR) and climate change magnitude (CCM) variables combined with accounting ratios can better predict the firm’s outcome, which is either survival or liquidation. The findings based on the XGBoost model, which incorporates the CCR and CCM variables, demonstrate high predictive performance, with an area under the receiver operating characteristic curve (AUC) of 0.995. By relying upon such variables, the Extremely Randomized Trees (ERT) model also reveals a strong bankruptcy prediction capacity, achieving an AUC of 0.999 for the transport sector and 0.996 for the industrial sector. Additionally, the effects of CCM variables are heterogeneous across sectors. The liquidation risk of trade firms tends to be higher in French counties exposed to a low degree of humidity, higher temperatures, and strong winds. Transport and industrial firms benefit from a low likelihood of failure in counties with more precipitations that can prevent the risk of droughts or heat waves.

The theoretical link between bank credit expansion and financial vulnerability is explored in this paper. The study shows that during economic booms, banks often over-expand credit scale, which, while promoting economic growth in the short term, also accumulates systemic financial risks. The article first reviews key theoretical frameworks such as Minsky's financial instability hypothesis and the financial accelerator theory and analyzes the micro drivers and macro environmental factors of bank credit expansion, and then constructs theoretical models to reveal the intrinsic linkage mechanism between bank credit expansion and asset price fluctuations, rising leverage ratios, maturity mismatch, and liquidity risk. The study also shows that bank credit expansion alters the balance sheet structure of economic entities, increases the interconnectivity and complexity of the financial system, and thereby raises financial vulnerability. Finally, the article discusses counter-cyclical adjustment mechanisms within the macroprudential policy framework and the role of regulatory tools such as capital adequacy ratios and liquidity coverage ratios in curbing excessive credit expansion, and puts forward policy recommendations such as improving the financial regulatory system, optimizing the credit structure, and strengthening the risk early warning mechanism in the hope of finding a balance between promoting economic growth and maintaining financial stability.

This study aims to analyze the influence of credit risk, liquidity risk, and operational risk on banking financial performance, especially profitability as measured using Return on Assets (ROA). The research population includes all conventional banks listed on the Indonesia Stock Exchange during the 2022–2024 period. The sample was determined using a purposive sampling technique, yielding 43 banks that met the research criteria. Data were collected from the banks' annual reports and official financial statements, which contain information related to NPL (Non-Performing Loan), LDR (Loan to Deposit Ratio), and BOPO (Operating Costs to Operating Income). The analysis was carried out using panel data regression, classical assumption tests (normality and multicollinearity), and hypothesis tests such as t-tests and determination tests (Adjusted R²). The results showed that partially, credit risk (NPL) had no significant effect on profitability, suggesting that the credit quality of the sample banks was relatively maintained or that the NPL factor was not yet a major pressure on performance. On the other hand, liquidity risk (LDR) had a significant positive effect on ROA, indicating that optimal liquidity management supports increased profitability. Operational risk (BOPO) had a significant negative effect on ROA, confirming that operational efficiency is a key factor in maintaining the bank's financial performance. These findings provide practical implications for bank management in optimizing liquidity and reducing operational costs to increase profitability in a sustainable manner.

ABSTRAK Penelitian ini bertujuan untuk menganalisis pengaruh risiko kredit, risiko likuiditas, dan risiko operasional terhadap profitabilitas bank BUMN yang terdaftar di Bursa Efek Indonesia (BEI) periode tahun 2015–2024. Variabel independen risiko kredit (NPL), risiko likuiditas (LDR), dan risiko operasional (OER) dan variabel dependennya adalah Profitabilitas (ROA). Data bersumber dari data sekunder laporan keuangan perusahaan perbankan BUMN konvensional yang terdaftar di BEI tahun 2015-2024. Teknik pengambilan sampel menggunakan Purposive sampling, Analisis data yang digunakan regresi linear berganda dan alat uji menggunakan software SPSS versi 30. Hasil tersebut menunjukkan bahwa secara parsial Risiko Kredit (NPL) tidak berpengaruh signifikan terhadap Profitabilitas (ROA) , serta berkorelasi positif dengan nilai 0,750 > 0,05. Secara parsial Risiko Likuiditas juga tidak berpengaruh terhadap Profitabilitas (ROA) dan berkorelasi positif dengan nilai signifikansi 0,88 > 0,05. Risiko Operasional secara parsial berpengaruh negatif terhadap Profitabilitas (ROA), dengan nilai signifikansi 0,001< 0,05. Kata kunci : Profitabilitas, Risiko Kredit, Risiko Likuiditas, Risiko Operasional, Bank BUMN, Bursa Efek Indonesia ABSTRACT This study aims to analyze the effect of credit risk, liquidity risk, and operational risk on the profitability of state-owned banks (Bank BUMN) listed on the Indonesia Stock Exchange (IDX) during the period 2015–2024. The independent variables include credit risk (NPL), liquidity risk (LDR), and operational risk (OER), while the dependent variable is profitability (ROA). The data used in this study is secondary data sourced from the financial statements of conventional state-owned banks listed on the IDX for the years 2015–2024. The sampling technique used is purposive sampling. The data analysis method applied is multiple linear regression, and the testing tool used is SPSS software version 30. the findings show that, partially, credit risk (NPL) has no significant effect on profitability (ROA) and is positively correlated, with a significance value of 0.750 > 0.05. Similarly, liquidity risk (LDR) also does not have a significant effect on profitability (ROA) and shows a positive correlation, with a significance value of 0.880 > 0.05. Meanwhile, operational risk (OER) has a significant negative effect on profitability (ROA), with a significance value of 0.001 < 0.05. Keywords: Profitability, Credit Risk, Liquidity Risk, Operational Risk, State-Owned Banks, Indonesia Stock Exchange

ABSTRACT This paper examines the impact of value jumps on bond liquidity and credit spreads, an area of growing importance in structured risk. Despite the recognition of the predictive role of value jumps in asset pricing, their impact on the bond market, particularly on liquidity spreads, remains less explored. Using ChinaBond Valuation, an authoritative third‐party valuation data, this study overcomes limitations posed by sparse transaction data and biased ratings in the developing credit bond market, and reveals that value jumps increase both bond liquidity and credit risk spreads, with downward jumps exerting a stronger influence. We identify that, value jumps do not lead to significantly higher spreads for bonds with inherently high default risk but for those opaque and illiquid, implying that value jumps in the bond market transmit more of a sentimental liquidity risk than substantive default risk. Meanwhile, we confirm that value jumps incorporate both public and private information, and extreme heterogeneous risk mainly elevates bond credit spreads.

This paper empirically examines the effect of financial inclusion on the resilience to short-term liquidity stress of commercial Tunisian banks using data for the period 2010–2021. We utilize the two-step generalized method of moments (GMM) estimator. We mainly draw the inference that financial inclusion and bank profitability have a positive influence on bank liquidity risk resilience. We also give proof that the bank size has a negative impact. Nevertheless, bank capital and economic growth have no impact. Eventually, the findings support the view that bank resilience to short-term liquidity shocks can be enhanced by a stable macroeconomic environment. According to the author's current knowledge, no previous empirical research has been carried out in the banking sector in Tunisia in which the impact of financial inclusion on bank resilience to liquidity stress is examined

Financing risk is the most vital aspect in banking, so it must be managed so that it does not increase. The purpose of this study is to test the factors that determine financing risk. Financing risk is measured by non-performance financing (NPF), while the factors that are suspected of influencing financing risk consist of internal and external factors. Internal factors consist of profitability as measured by return on assets (ROA), capital as measured by capital adequacy ratio (CAR), liquidity risk as measured by financing to deposit ratio (FDR), and bank efficiency level as measured by operating expense to operating income ratio (EIR). While external factors consist of inflation rate, and Bank Indonesia interest rate. The population in this study were Islamic Rural Banks (IRBs) operating in Indonesia as many as 164 IRBs. Samples were taken as many as 75 IRBs with purposive sampling. The observation period was 4 years with quarterly data so that 1,200 observation data were collected. Hypothesis testing used panel data regression analysis by selecting three panel data regression models. After being tested with chow-test, Hausman-test, and Lagrange Multiplier-test, the common effect model was obtained as the best model. The results of the study with the common effect model are profitability (ROA), capital (CAR), efficiency level (EIR), and BI-rate have a positive and significant effect on financing risk. While liquidity risk (FDR) and inflation do not affect financing risk.

Liquidity risk, defined as the potential inability of an organization to meet its financial obligations in a timely manner without significant losses, becomes especially acute during periods of economic crisis. Geopolitical tensions and economic sanctions have significantly narrowed the range of available liquidity management tools, forcing Russian economic entities to seek new approaches to ensuring financial stability. The purpose of this study is to analyze a liquidity risk management strategy that can ensure the financial stability of an organization in an economic crisis. According to the study, companies that take into account the dual nature of liquidity, its behavioral factors, and adapt their strategies to all stages of an economic crisis can meet their financial obligations in a timely manner, minimizing negative consequences and strengthening their financial stability. The scientific novelty of the study is the integration of behavioral liquidity factors into the traditional risk management model and the development of differentiated strategic approaches for each stage of the economic crisis in the context of sanctions restrictions.

Liquidity risk, defined as the potential inability of an organization to meet its financial obligations in a timely manner without significant losses, becomes especially acute during periods of economic crisis. Geopolitical tensions and economic sanctions have significantly narrowed the range of available liquidity management tools, forcing Russian economic entities to seek new approaches to ensuring financial stability. The purpose of this study is to analyze a liquidity risk management strategy that can ensure the financial stability of an organization in an economic crisis. According to the study, companies that take into account the dual nature of liquidity, its behavioral factors, and adapt their strategies to all stages of an economic crisis can meet their financial obligations in a timely manner, minimizing negative consequences and strengthening their financial stability. The scientific novelty of the study is the integration of behavioral liquidity factors into the traditional risk management model and the development of differentiated strategic approaches for each stage of the economic crisis in the context of sanctions restrictions.

Investing involves allocating funds in the present with the expectation of obtaining future returns. One common type of financial investment is stock investment. Diversifying investments across various companies enables investors to reduce the risks associated with individual stock performance. One approach used in constructing a stock portfolio is the Liquidity-Adjusted Capital Asset Pricing Model (LCAPM), a modification of the traditional CAPM that incorporates liquidity risk. The Sharpe Index is employed to evaluate the performance of the constructed portfolio. This study utilizes closing price data, trading volume, and total transaction volume of stocks listed in the Infobank15 Index, along with the Composite Stock Price Index (JCI) and the Bank Indonesia Certificate interest rate, covering the period from January 2020 to December 2023. The results indicate that the optimal portfolio consists of three stocks, with allocation proportions of 12.31% for BRIS, 75.88% for BBCA, and 11.81% for BBNI.

Purpose This study aims to develop the traditional financial ratio-based earnings prediction model and the earnings management predictors-based prediction model to access each firm’s earnings status to minimize agency costs. Design/methodology/approach This study utilizes data from 322 companies from 2009 to 2022, which accounted for a total of 3,220 firms-year observations. The forty-five (45) traditional financial ratios and eight (8) earnings management predictors are chosen from previous literature. Stepwise logistic regression is used to develop the earnings prediction models, while the maximum likelihood ratio is used to determine the selection of the final model. The first step begins with the development of the traditional financial ratio-based earnings prediction model, followed by the earnings management predictors-based earnings prediction model and finally the earnings prediction model. Findings The findings of the last step identified the five discriminatory traditional financial ratios, where four (4) variables belonged to the statement of financial position and one (1) variable belonged to the statement of profit and loss, with a predictive accuracy of 80% of the overall model. Research limitations/implications The external validity of the developed earnings prediction model can be tested on large data sets of different financial markets. Practical implications The traditional financial ratio-based earnings prediction model developed will reduce agency conflict, which will benefit the firm. Using the predictive model, stakeholders can access a firm’s earnings status and management’s strategy for short-term and long-term firm decisions. Originality/value The developed earnings prediction model’s discriminatory variables are the proxies of working capital policy, working capital investment policy, leverage, short-term liquidity risk and firm size.

This study aims to compare financial risk management between conventional and Shariah banks in Indonesia. Financial risk is measured using three key indicators: Net Interest Margin (NIM) as a proxy for market risk, Current Ratio (CR) for liquidity risk, and Non-Performing Loan (NPL) for credit risk. The study employs the Mann–Whitney U Test to examine differences between the two types of banks using a sample of conventional and Shariah banks registered in Indonesia. The results indicate no significant differences between conventional and Shariah banks in terms of NIM and CR, although Shariah banks exhibit a higher yet more volatile NIM and a more stable CR. However, a significant difference is found in NPL, where Shariah banks display higher NPL ratios than conventional banks. These findings suggest that although both types of banks differ in their approaches to managing market and liquidity risks, Shariah banks are more vulnerable to credit risk, as reflected in their higher NPL levels. This study is expected to provide insights for banks and regulators to enhance risk management in both banking systems and serve as a foundation for developing more effective risk management policies in the conventional and Shariah banking sectors.

This paper examines the Financial Accounting Standards Board’s (FASB) recent changes to define crypto assets, focusing on why non-fungible tokens (NFTs), utility tokens and asset-backed tokens (ABTs) were not included. By examining the core features of these excluded assets, the research unpacks the reasoning behind their omission. The absence of these assets from the standard definition creates challenges for financial institutions. Without clear accounting guidance, companies face uncertainty in valuation, liquidity risk and difficulty meeting compliance requirements. Risk managers are left guessing how to assess and report these holdings. This has an impact on everything from disclosures to capital planning. The findings highlight a critical need for accounting standards that keep pace with the complexity and growth of digital assets. Institutions may misprice assets, misjudge exposure and fall short of regulatory expectations without up-to-date guidance. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.