Abstract This study assesses the influence of media sentiment on liquidity risk during financial crises within the context of the Russian financial market. Trade sanctions, especially the suspension of the SWIFT network, have profoundly impacted Russian banking sector liquidity. Given this vulnerable situation, the study utilizes media sentiment data to analyze banking sector performance via monthly data from January 2012 to December 2024. This study applies big data modeling techniques to construct positive, negative, and mixed media sentiment indices to evaluate their relationship with bank liquidity risk. The findings, which are based on GMM estimation, reveal that the media sentiment index is significantly and positively associated with bank liquidity risk. Moreover, negative and mixed media sentiment indices have a detrimental effect on bank performance, leading to an appreciation of liquidity risk. Additionally, the study finds that interbank networks play a vital role in mitigating liquidity risk. Overall, media sentiment significantly impacts banking performance. The findings provide practical implications for banking sector policymakers, financial market regulators, and investors in making informed decisions during both normal and crisis periods.

This article presents a model for pricing an exchange option considering stochastic volatility and liquidity risk. The impact of liquidity risk on an asset price is considered by utilizing a liquidity discount process that is influenced by both market and asset-specific liquidity. Girsanov’s theorem is applied to transform from the real-world probability measure to equivalent probability measures, such as the risk-neutral probability measure. The Feynman–Kac theorem is applied to transform the exchange option pricing formula into the vanilla option pricing formula. The analytical expression is derived through the characteristic function approach. The accuracy of the proposed formula is validated through comparisons with Monte Carlo simulation, where the relative error remains below 0.93% across different values of S(0) and τ. Furthermore, numerical experiments highlight that incorporating liquidity risk leads to higher option prices. As the maturity increases from 0.1 to 2.0, the percentage gap between the option prices increases from 1.65% to 20.2%. Finally, sensitivity analysis is conducted to examine the influence of various parameters and to demonstrate the impact of stochastic volatility and liquidity in exchange option valuation.

In the branch banking system, branches with surplus funds cannot be profitable without an internal fund transfer pricing (FTP) system. Therefore, a dynamic FTP model is critical to enhance a bank’s sustainability. This article has designed a market-oriented multiple FTP model by factoring in liquidity risk, market interest rate, credit demand, and portfolio diversification. The FTP rates are estimated using the 151 branch-level daily data of a bank in India. We reduced the endogeneity issue related to variable selection with the 2SLS system equation. The findings suggest that dual-functioning branches are profitable if they reduce their fund borrowings from the corporate head office (HO). Corporate treasury provides liquidity support to banks, but their profit gets impacted by the increase in the policy and HO-transfer rate. The study showed that corporate spread is significantly affected by increased liquidity cost and market borrowing rate. It is also empirically established that internal FTP pricing may hamper branches’ earnings when branch managers mobilize funds beyond the cost of lending as per FTP rates. The dynamic FTP model is linked to the yield curve, making it market oriented and enabling managers to price products effectively. JEL Codes: G21, D02

Liquidity Risk and Profitability of Listed Deposit Money Banks in Nigeria

ABSTRACT The study sought to examine the impact of artificial intelligence (AI) on reducing financial risks in commercial banks listed at Amman Stock Exchange. To attain this purpose, a descriptive methodology was used to examine the AI variables by focusing on machine learning, deep learning, expert systems, and financial risk (credit and liquidity risk)—to demonstrate how to reduce these risks in commercial banks. The study depended on primary data to collect the sources. The study population included all 12 commercial banks listed at Amman Stock Exchange, and the study sample included staff members from commercial banks listed at Amman Stock Exchange, with 323 computerized questionnaires sent, and it was all legitimate for analysis. by using SPSS to do a linear regression analysis to assess the research hypotheses. The findings showed that AI has a big and important impact on reducing financial risks in commercial banks. The study recommended that banks by taking advantage of their AI-assisted banking and financial operations to formulate adaptable strategies that help manage and reduce the financial risk of their assets and liabilities to diverse risks that are faced.

ABSTRACT The credit spread of green bonds—the “green premium”—fundamentally reflects the market’s monetary valuation of environmental benefits as a key financing instrument for green projects. This study constructs an environmental information disclosure (EID) quality index through content analysis and the entropy method to empirically examine EID’s impact on credit spreads in primary and secondary markets using data from China’s green bond market (2016–2023). The findings reveal that: (1) high-quality EID significantly reduces credit spreads; (2) environmental regulation can weaken the effect of EID in reducing credit spreads; (3) investor subscription willingness in the primary market and liquidity risk expectations in the secondary market mediate the relationship between EID and credit spreads; (4) heterogeneity analysis indicates that labeled green bonds, non-listed firms, and first-time issuers benefit more prominently from EID in lowering credit spreads; and (5) high-quality EID can also reduce the pricing discrepancy between the primary and secondary markets. The results have significant policy implications for improving the green bond market pricing efficiency and advancing sustainable finance.

Purpose: This study aims to analyze and compare the financial risks of Islamic Rural Banks (BPRS) and Conventional Rural Banks (BPR) in Indonesia, focusing on liquidity and credit risks. The objective is to provide a comprehensive understanding of risk management patterns and performance differences between these two types of banks. Methodology/approach: This study aims to analyze and compare the financial risks of Islamic Rural Banks (BPRS) and Conventional Rural Banks (BPR) in Indonesia, focusing on liquidity and credit risks. The data analysis used is time series analysis with the ARIMA method. Results/findings: The findings reveal that BPRS generally maintains higher liquidity ratios with lower liquidity risk compared to BPR, although both bank types exhibit credit risk above the recommended threshold. The forecasting results indicate that BPRS is likely to maintain its liquidity and reduce non-performing financing in the coming period, while BPR faces declining liquidity and persistent non-performing loan issues. Conclusion: Islamic and conventional rural banks show different financial risk profiles, with conventional banks facing higher liquidity and loan risks, while Islamic banks generally maintain stronger liquidity but remain exposed to financing risks. Limitations: This study is limited by its focus on quantitative indicators and historical data, without incorporating qualitative or macroeconomic factors. The use of ARIMA may not fully capture sudden structural changes or regulatory impacts. Contribution: The study offers insights for improving risk management and compares Islamic and conventional rural banks in a developing country.

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.

Risk management has become a critical function in modern banking due to increasing financial volatility, regulatory pressures, and globalization. This study examines the impact of risk management practices on bank performance by comparing banks operating in emerging and developed markets. Using secondary data from selected commercial banks across both market categories, the study analyzes how credit risk, liquidity risk, operational risk, and capital adequacy influence profitability and financial stability. Bank performance is measured using Return on Assets (ROA), Return on Equity (ROE), and Net Interest Margin (NIM). The findings reveal that effective risk management practices significantly enhance bank performance in both emerging and developed markets, although the magnitude and direction of impact vary across regions. The study provides empirical evidence supporting the importance of strong risk governance frameworks and offers policy implications for regulators and banking institutions.

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.

Digital power generalized exchange option pricing considering liquidity risk

In an evolving stock market landscape characterized by diversified participants, the personal financial behavior of corporate insiders has garnered much attention, especially concerning its impact on stock liquidity. A key financial behavior under scrutiny is share pledging, where corporate insiders use their shareholdings as collateral for personal loans. Using a sample of Chinese listed firms from 2007 to 2021, this study examines the relationship between insiders’ share pledging and the stock liquidity of listed firms, as well as possible underlying mechanisms. Our analysis reveals a positive association between share pledging and stock liquidity. The findings withstand a series of robustness tests. Further analysis shows that stock repurchases and increased shareholdings may be the underlying mechanisms driving the positive effect of share pledging on stock liquidity. Our results have significant implications for regulators managing liquidity risk and offer valuable insights for other stakeholders. As insiders’ personal financial behavior significantly impacts the sustainable quality of securities markets, investors, managers, and analysts can benefit from understanding the influence of share pledging on stock liquidity.

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.