Journal of Risk and Financial Management doi: 10.3390/jrfm19080627

Authors: Aleena Varekat Charly Tetiana Paientko

The Corporate Sustainability Reporting Directive (CSRD) extends mandatory, assured and standardised sustainability reporting to a European reporting population several times larger than that of its predecessor, on the premise that such disclosure is priced by capital markets. This paper examines whether equity prices responded to the five legislative and standard-setting milestones through which the mandate became public between April 2021 and July 2023. German DAX constituents falling within the scope of Article 19a are compared with matched S&P 500 firms, using an annual difference-in-differences design and a daily market model event study. The annual estimator yields a positive and significant coefficient of +0.204 that is robust to alternative specifications, standard error corrections and influence diagnostics. Four diagnostics nevertheless indicate that it does not identify a regulatory effect: the same design applied to year pairs containing no CSRD or ESRS event yields estimates of comparable magnitude and mixed sign; parallel pre-trends are rejected; the coefficient is concentrated among poorly matched firm pairs and falls to +0.046 once a caliper is imposed; and the design is underpowered for effects of the magnitude it reports. The event study, which measures each firm against its own home market and therefore does not rely on the cross-country comparison, detects no abnormal return at any milestone once multiple testing and cross-sectional dependence are taken into account: the cumulative 3-day reaction across all five events is +0.8 percentage points, with a 95% confidence interval of [−2.5, +4.0], which excludes a repricing of the magnitude the annual estimate implies. The paper contributes a set of design diagnostics that distinguish an identified estimate from one that is merely stable, and shows that the two-country annual comparisons common in this literature do not survive them.

Journal of Risk and Financial Management doi: 10.3390/jrfm19080626

Authors: Lena Bedawi Elfadli Elmonshid

This study examines the determinants of non-performing financing (NPF) using country-level Islamic banking system aggregates for the six Gulf Cooperation Council (GCC) countries, with particular attention to the role of global economic policy uncertainty and U.S. monetary policy uncertainty. Using a panel dataset covering the period 2014Q4–2024Q3, this study applies fixed-effects estimation and panel quantile regression to capture both average effects and distributional heterogeneity in financing risk. The findings reveal that the determinants of NPF vary significantly across the conditional NPF distribution. Profitability and capital adequacy are positively associated with NPF, whereas GDP is negatively associated with NPF. Liquidity has a negative and statistically significant association mainly in the middle and upper quantiles, indicating a stronger stabilizing role under elevated risk conditions. Global economic policy uncertainty is significant only in the upper quantiles and has a negative coefficient, while U.S. monetary policy uncertainty shows limited statistical relevance. These results indicate that mean-based models may conceal important differences across financing risk regimes. The findings are interpreted as statistical associations rather than causal effects, given the country-level aggregation, limited cross-sectional dimension, and potential measurement and model specification constraints. This study contributes distribution-sensitive evidence on GCC Islamic banking systems and offers cautious implications for risk monitoring, liquidity management, and macroprudential supervision.

Journal of Risk and Financial Management doi: 10.3390/jrfm19080625

Authors: Omar Shawkey Taha Mohamed Gaber Esmail Mohamed Ahmed Hassanein Yara Ibrahim

Forecasting corporate leverage adjustments remains challenging due to persistent financing behavior, firm heterogeneity, and changing macroeconomic conditions. This study investigates whether increasing model complexity improves the forecasting of corporate leverage adjustment by comparing dynamic econometric models, machine learning algorithms, and a neuro-symbolic artificial intelligence framework. The analysis is based on an unbalanced panel of 39,226 firm-year observations from 3001 publicly listed non-financial firms across 18 countries. The empirical analysis compares Fixed Effects and two-step Difference GMM estimators with regularized regression, gradient boosting, artificial neural networks, and a theory-guided neuro-symbolic framework that incorporates economically meaningful financial constraints through a resampling-based approximation of a differentiable rule-based penalty. Model performance is evaluated using out-of-sample forecasting accuracy measured by the Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and the coefficient of determination (R2). The results indicate that corporate leverage exhibits substantial persistence, with estimated adjustment speeds of approximately 29–36% annually. Machine learning algorithms do not improve forecasting accuracy relative to benchmark dynamic econometric models when evaluated out of sample, while incorporating symbolic financial constraints provides only limited additional predictive benefits. These findings suggest that leverage persistence dominates model complexity and that parsimonious dynamic econometric models remain highly effective for forecasting corporate leverage adjustment. The study contributes to the growing literature on explainable artificial intelligence in corporate finance by providing a comprehensive comparison of dynamic econometric, machine learning, and neuro-symbolic approaches within a unified forecasting framework for emerging economies in the MENA region.

Journal of Risk and Financial Management doi: 10.3390/jrfm19080624

Authors: Chayapat Phonlaboon Nuttavong Poonpool Salakjit Ninlaphay

Environmental, social, and governance (ESG) disclosure has received increasing attention in capital markets as investors place greater emphasis on sustainability information alongside financial information when evaluating firms. In this study, the authors examine the relationship between ESG disclosure and investor reactions among firms listed on the Stock Exchange of Thailand using Bloomberg ESG disclosure scores and an event study approach. The analysis is based on secondary data over the period of 2019–2022, employing a fixed-effects model on unbalanced panel data. The findings indicate that overall ESG disclosure is positively and statistically significantly associated with investor reactions. These results show that each ESG dimension is positively associated with investor reactions. While the environmental and social dimensions are significant at the 0.01% level, governance disclosure remains statistically significant at the 0.05 level. The empirical evidence suggests that ESG disclosure provides information that investors may consider when evaluating firms. Moreover, this study provides evidence that changes in the level of ESG disclosure are associated with changes in cumulative abnormal returns (CARs). This study contributes to the literature on ESG disclosure and corporate sustainability in emerging markets. Its results have practical implications for listed companies, investors, and regulators by highlighting the importance of ESG disclosure in corporate reporting and investment evaluation.

Journal of Risk and Financial Management doi: 10.3390/jrfm19080623

Authors: Hyunjung Choi

Ownership–control disparity, defined as the difference between controlling shareholders’ voting rights and cash-flow rights, is a distinctive feature of corporate governance in Korean business groups. Although prior studies have examined its association with firm value and credit ratings, relatively little evidence is available on whether ownership–control disparity is reflected in corporate bond pricing. This study examines the association between ownership–control disparity and corporate bond yield spreads using a sample of publicly listed Korean manufacturing firms from 2011 to 2022. Corporate bond yield spreads are used as a market-based measure of debt financing costs because they incorporate investors’ assessments of credit risk. The empirical results show that firms with greater ownership–control disparity exhibit significantly lower bond yield spreads. The findings suggest that bond investors may view greater ownership–control disparity as being associated with lower corporate credit risk despite potential agency concerns. Consistent results are also obtained when credit ratings are used as an alternative measure of debt financing costs. This study contributes to the literature on corporate governance and debt financing costs by providing market-based evidence on how ownership–control disparity is reflected in corporate bond pricing within the institutional setting of Korean business groups.

Journal of Risk and Financial Management doi: 10.3390/jrfm19080622

Authors: Vanya Georgieva Radosveta Krasteva-Hristova

Digitalisation is increasingly viewed as an instrument for narrowing the VAT gap in the European Union, yet the relative relevance of voluntary business digitalisation and mandatory administrative reporting remains unclear. This study distinguishes cloud accounting from mandatory transaction reporting and analyses an unbalanced EU-27 panel for 2013–2024, with estimations limited to 2013–2023. Sequential pooled OLS, two-way fixed-effects models and robustness checks are applied to European Commission, Eurostat and World Bank data. The initially negative association between cloud accounting and the VAT gap disappears after controlling for income and government effectiveness. Mandatory digital reporting is associated with a VAT gap of about 3–4 percentage points lower, although the small number of adopters and pre-adoption trends preclude causal claims. Theoretically, first, the findings distinguish firm-level digital capability from information directly accessible to tax administrations; second, they show that technology adoption and institutional capacity must be analysed separately. Practically, first, the results support interoperable systems providing timely, structured and verifiable transaction data; second, they indicate that cloud accounting should complement, rather than replace, mandatory reporting infrastructure.

Journal of Risk and Financial Management doi: 10.3390/jrfm19080621

Authors: Sauda Nerjaku Valentina Sinaj

In recent years, the banking system has been affected by several economic and financial shocks, increasing the importance of analyzing bank profitability and its determinants. This study examines bank profitability in selected Balkan countries over the period 2010–2024 by combining econometric panel data methods with machine learning techniques. Bank profitability is proxied by two commonly used indicators, ROA and ROE, while the explanatory variables include bank-specific factors such as efficiency, capital adequacy, non-performing loans, net interest margin, and the credit-to-deposit ratio, as well as macroeconomic variables such as GDP, inflation and unemployment. The econometric results indicate that efficiency and capital adequacy are key determinants of bank profitability, with efficiency negatively associated with ROA and ROE, while capital adequacy is positively associated with both indicators. The machine learning analysis, based on Random Forest and XGBoost, further evaluates the predictive role of the explanatory variables. Overall, the results show that bank-specific variables have a stronger influence on profitability than macroeconomic variables. In particular, feature importance highlights the relevance of the credit-to-deposit ratio, while SHAP values emphasize the contribution of NPLs. Overall, the findings suggest that bank profitability in selected Balkan countries is mainly driven by internal banking factors rather than macroeconomic conditions.

Journal of Risk and Financial Management doi: 10.3390/jrfm19080620

Authors: Kamran Quddus Sougata Banerjee

Given the rising global interest in Environmental, Social, and Governance (ESG), this paper investigates whether a company’s ESG news affects equity investors’ sentiment, addressing a gap in the relevant research. Interest in ESG investing has grown rapidly, yet existing research measures investor sentiment only indirectly—through market-wide proxies such as the CBOE Volatility Index, low-frequency investor surveys, or realized stock returns—measures that conflate sentiment with risk aversion and fundamentals and cannot isolate firm-specific reactions to ESG news. This study measures investor sentiment directly from investors’ own expressions: we pair firm-day ESG news-sentiment scores for all S&P 500 constituents with investor sentiment extracted from stock-related tweets using a finance-tuned RoBERTa model. Using Bayesian Ridge Regression (BRR), the study finds that ESG news significantly impacts equity investors’ sentiment. This study contributes to both academic and managerial practice by establishing the association and sensitivity of ESG news and investor sentiment in academic literature and proposing a framework for firms to practice effective sustainability management.

Journal of Risk and Financial Management doi: 10.3390/jrfm19080618

Authors: Hany Elzahar Jamal Ali Al-Khasawneh Ahmed Hassanein Hosam Abdelrasheed

This study investigates the impact of different cases of armed conflict on the US stock market. It examines whether the market reacts differently in cases of direct versus indirect US military involvement in the conflict and at the initiation or termination of the conflict. The analysis is based on nine armed conflicts that took place from 2003 to 2022. The study utilizes an event study methodology and focuses on three sectors in the US stock market: Defense and Aerospace, Oil and Gas, and Alternative Energy. The results indicate that direct military involvement is generally associated with less favorable market responses, particularly during conflict initiation stages and within energy-related sectors. In contrast, conflict termination events frequently generate more positive market reactions, reflecting lower geopolitical uncertainty and reduced military exposure. Indirect involvement events, especially those related to the Russia–Ukraine conflict and the Russian intervention in Syria, are associated with more favorable responses in several sectors, particularly Aerospace and Defense. The study contributes to the literature by adopting a multi-conflict framework, distinguishing between direct and indirect military involvement, comparing conflict initiation and termination phases, and providing sector-level evidence on the heterogeneous effects of armed conflicts on financial markets.

Journal of Risk and Financial Management doi: 10.3390/jrfm19080619

Authors: Omkar Hirlekar Ashutosh Kolte Rajesh Pahurkar

The global energy sector is undergoing rapid and, in many respects, irreversible transformation driven by the convergence of digital disruption, sustainability mandates, and shifting investor expectations. Technologies such as artificial intelligence (AI), blockchain, and digital twin systems are fundamentally reshaping energy operations and strategic decision-making, while ESG governance quality and renewable energy adoption have emerged as two of the most consequential determinants of corporate financial competitiveness and equity valuation. Despite growing practitioner and regulatory interest in these dynamics, limited empirical evidence exists on how ESG governance, renewable adoption, and digital disruption jointly influence financial performance and environmental outcomes across multiple sectors simultaneously. This study addresses that gap using panel data from 26 large-cap US-listed firms across five sectors over 2015–2022 (N = 208 firm-year observations for Revenue/Market Cap/ROA models; N = 91 for the CO2 model). A multi-method econometric framework is employed, comprising Fixed Effects and Random Effects panel regression with Hausman specification testing, Difference in Differences quasi-experimental analysis, and sequential OLS path analysis with HC3 robust standard errors. Three of four hypotheses are supported. ESG governance quality generates a significant market capitalisation premium of approximately 10–14% per unit Bloomberg ESG Score improvement, after controlling for firm size and R&D intensity; no significant revenue channel effect is found once firm size is properly accounted for. Renewable energy adoption shows a marginal association with market capitalisation at the 10% significance level (FE β = 0.019, p = 0.086; RE β = 0.016, p = 0.077), suggesting capital markets may price clean energy adoption as a forward-looking signal. ESG governance quality drives within-firm CO2 emission reduction substantially more powerfully than renewable energy quantity alone, with the Fixed Effects estimator identifying a governance-led eco-efficiency mechanism. Firm profitability functions as a cross-model financial capacity moderator, enabling simultaneous ESG investment and environmental improvement. The findings carry direct implications for corporate managers, institutional investors, and policymakers aligned with SDG 7, SDG 9, and SDG 13.

Journal of Risk and Financial Management doi: 10.3390/jrfm19080617

Authors: Ying Deng Thien Sang Lim

Digital technologies are increasingly adopted in corporate liquidity management, yet whether financial digitalization enables firms to achieve more effective working capital adjustment remains insufficiently understood. Financial shared services (FSS) may strengthen information integration, process standardization, and operational coordination, but existing research provides limited evidence on how external supply chain conditions shape the relationship between FSS and working capital adjustment effectiveness. Prior studies have focused primarily on adjustment speed rather than adjustment effectiveness, namely the extent to which firms maintain working capital close to target levels. Using panel data from Chinese A-share listed firms from 2014 to 2023, this study examines whether FSS is associated with the effect of working capital adjustment (DEV) and whether supply chain concentration moderates this relationship. Drawing on dynamic trade-off theory and information asymmetry theory, this study employs high-dimensional fixed-effects models to examine how internal information capabilities and external supply chain conditions jointly shape working capital adjustment. The findings show that firms adopting FSS tend to exhibit smaller deviations from target working capital levels, which is consistent with more effective adjustment. However, this association becomes weaker as supply chain concentration increases, suggesting that external dependence may constrain firms’ ability to translate enhanced internal information capabilities into improved working capital outcomes. Further analysis suggests that customer concentration plays a more prominent moderating role. This study extends the understanding of digital-enabled financial management beyond internal process improvement and identifies supply chain structure as an important boundary condition relevant to the value of FSS.

Journal of Risk and Financial Management doi: 10.3390/jrfm19080616

Authors: Salma Hamrouni Montassar Zayati Kamel Naoui

The present study investigates extreme co-movements and financial contagion across a broad set of global financial markets, including ten developed and emerging stock market indices, commodities (gold and oil), and cryptocurrencies (Bitcoin), over the period from January 2007 to May 2023. In the context of the increasing interconnectedness of global financial markets, it is imperative to comprehend the propagation of systemic shocks across asset classes for the purpose of effective risk management. In order to achieve this objective, a Copula-GARCH framework is employed, in which the Student’s t-copula is selected for its superior ability to capture nonlinear dependence and tail co-movements. The analysis compares dependence structures during the pre-crisis and the COVID-19 crisis periods. The present study diverges from the majority of previous research in its utilisation of a combined approach, integrating Copula-GARCH modelling with wavelet analysis. This novel method is employed to collectively examine tail dependence and multi-scale contagion dynamics, thereby facilitating a more comprehensive evaluation of financial interconnectedness during periods of market stress. The empirical evidence indicates significant and largely symmetric tail dependence across the majority of market pairs. This finding suggests the presence of stronger co-movements during periods of extreme market conditions, a phenomenon that was particularly evident throughout the course of the global pandemic. The robustness of these findings is further confirmed by wavelet analysis, which provides a multi-scale perspective on shock transmission across markets. The results demonstrate that financial contagion intensified during the pandemic, with important implications for international portfolio diversification and risk management. Furthermore, the role of gold as a potential safe-haven asset during periods of severe financial stress is highlighted, providing valuable insights for investors and policymakers.

Journal of Risk and Financial Management doi: 10.3390/jrfm19080615

Authors: Ifigeneia Persaki Fotios Siokis

This paper examines the relationship between revenue diversification, profitability, and risk in European banks, with particular emphasis on the structural break induced by the COVID-19 shock. Using quarterly supervisory data from the European Banking Authority (EBA) over the period 2016Q1–2024Q4, we distinguish between pre- and post-pandemic regimes and estimate dynamic fixed-effects models that account for unobserved heterogeneity and persistence in bank performance. The results reveal a pattern consistent with regime dependence. Descriptive (quintile-based) comparisons suggest that banks with greater reliance on non-interest income tended to report higher profitability prior to COVID-19, although data limitations prevent us from confirming this pattern in a full multivariate regression for the pre-COVID subsample. In the post-COVID period, once bank and time fixed effects, persistence, and balance-sheet characteristics are properly controlled for, revenue diversification does not exert a statistically significant effect on either profitability or earnings volatility; this result is robust across bank fixed effects only, two-way (bank and time) clustered, and one-way (bank) clustered specifications. We show that diversification is systematically associated with differences in bank size, capitalization, and lending intensity, indicating that income structure is closely linked to underlying business model characteristics. These findings suggest that the observed diversification–performance relationship largely reflects cross-sectional heterogeneity rather than a stable causal effect. Overall, the evidence indicates that revenue diversification does not provide a consistent improvement in risk-adjusted performance in European banking. Instead, performance and risk dynamics are primarily driven by balance-sheet composition and persistence. The results highlight the importance of accounting for structural heterogeneity and macroeconomic regimes when evaluating the role of non-interest income in bank performance.

Journal of Risk and Financial Management doi: 10.3390/jrfm19080614

Authors: Ali Matar

This mixed-methods study examines the associations among fintech advancement, green finance, and financial inclusion in Jordan, an emerging economy. It draws on a distinctive three-part dataset: survey data from 21 commercial banks (N = 21), a national household survey, and semi-structured interviews with stakeholders. The quantitative results indicate that the positive association between fintech adoption and the provision of green finance is statistically consistent with full mediation by banks’ absorptive capacity, particularly their digital maturity and data analytics capabilities. Proactive regulatory support significantly moderates this mediated relationship. Market demand, by contrast, has no statistically significant moderating effect. At the household level, the combined use of digital and green financial products is associated with higher formal account ownership and with the use of a greater number of financial products. The interviews support these results, pointing to institutional capacity and regulatory clarity as essential enabling factors. Given the cross-sectional bank-level data (N = 21) and the exploratory scope of the mediation analysis, causal interpretations should be avoided. Future longitudinal research is needed to examine temporal dynamics. Even so, these findings offer policymakers an initial empirical framework: channeling fintech toward sustainable development will likely require targeted interventions to build institutional digital capacity and establish clear regulatory frameworks, rather than depending solely on market forces.

Journal of Risk and Financial Management doi: 10.3390/jrfm19080613

Authors: Ana Lorena Jiménez-Preciado Francisco Venegas-Martínez Cesar Gurrola-Ríos Ricardo Jacob Mendoza-Rivera

How does the Federal Reserve sound when it speaks? We measure the delivery of the Chair across 84 FOMC press conferences from 2011 to 2026, using Google’s Gemini 2.5 Flash to annotate 11,156 sixty-second segments spanning the Bernanke, Yellen, and Powell eras. The model returns ten paralinguistic variables, among them emotion, firmness, speech rate, and hesitation counts. Rather than trust these labels, we test them. Checked against regex-based transcript counts of disfluencies and against Praat, hesitations and speech rate prove reliable (Pearson r up to 0.92); firmness and emotion do not, showing little acoustic grounding and shifting when the prompt changes, so they are better read as sentiment drawn from the words than from the voice. Using the measures that survive this test, we find that calm delivery dominates, as one would expect of a Fed Chair, yet the three Chairs differ in ways that hold up under a hierarchical model, with the sharpest gaps in the unscripted question-and-answer session. Measured disfluency rises around episodes such as the 2022 Ukraine war, but not across the pandemic as a whole. The wider lesson is that multimodal models must be validated feature by feature before they are trusted as instruments.

Journal of Risk and Financial Management doi: 10.3390/jrfm19080612

Authors: Lutz Plümer

Whereas existing approaches to financial regime detection calibrate thresholds to historical price-and-return statistics, we propose a framework built on immunisation: a particle filter whose observation model is calibrated to the distributional shape of market stress rather than specific historical episodes. The particle filter—with theoretical foundations in probabilistic robotics and autonomous driving, and adapted to financial markets—forms the inferential backbone. Its observation model stacks distributional shape descriptors—skewness, tail asymmetry, kurtosis, and the share of leading sector-eigenvalue energy in cross-asset return covariance—computed across a hierarchy of temporal windows and injected as structured distributional archetypes, replacing the random initialisation of Thrun and Burgard, and of Reisinger. Applied to S&P 500 across four distinct crises (Dotcom 2002, Lehman 2009, COVID-19 2020, and the 2022 inflation-driven bear market), the descriptors show pre-crisis discrimination, with effect sizes (Cohen’s d) of 2.9 or more for realised volatility, Bowley downside skewness, and tail-quantile features, and 1.3 for sector concentration (λ1 ratio). Out of sample (2015–2026), the pipeline confirms endogenous regime transitions with lead-time before the market trough. With the flexibility of particle filters, the richness of the observation model is the primary enabler of real-time regime detection. We frame the system as a distributional-shape monitor that detects regime transitions in real time, not a pre-peak forecaster: highly sensitive, it registers deformation as stress becomes measurable, with the attendant sensitivity–specificity trade-off. On the abrupt COVID-19 shock, it confirms the transition sixteen trading days before the trough, without claiming pre-peak detection; confirmation timing scales with each crisis’s own duration, from roughly two to three weeks for the fastest episodes to several months for the slowest.

Journal of Risk and Financial Management doi: 10.3390/jrfm19080611

Authors: W. Wardhiah M. Shabri Abd. Majid Said Musnadi A. Sakir

Background: Corporate energy transition can create efficiency, financing, and valuation benefits, but it also exposes firms to implementation, information, and transition risks. This study examines whether digital transformation helps firms convert energy-transition strategies into financial value. Unlike prior studies that mainly treated digitalization or sustainability as broad direct predictors, this study examines an implementation-based, multidimensional digital capability as a boundary condition across three distinct energy-transition strategies and both accounting- and market-based financial outcomes. Methods: Using an unbalanced panel of 30 firms associated with Indonesia’s LQ45 Low Carbon Leaders Index (120 firm years, 2020–2025), we construct a 30-item implementation-based Digital Transformation Index and estimate two-way fixed-effects models with firm-level wild-cluster-bootstrap inference, conditional marginal effects, false-discovery-rate adjustment, and prespecified robustness checks. Results: Clean energy use is positively associated with return on assets, return on equity, and Tobin’s Q. Low-carbon operational efficiency is most clearly associated with return on assets, whereas renewable energy use is primarily reflected in Tobin’s Q. Digital transformation is positively associated with all three outcomes and selectively strengthens the financial effects of the three transition strategies. Conclusions: Digital transformation is not a universal performance amplifier. It functions as a strategy- and outcome-specific value-conversion and risk-management capability that improves the monitoring, coordination, financing, verification, and communication of energy-transition investments.

Journal of Risk and Financial Management doi: 10.3390/jrfm19080610

Authors: Youngshin Kim Jing Han

This study investigates whether Chinese sectoral stock returns respond heterogeneously to international oil price movements and whether such responses vary across crisis regimes. Using daily data from January 2015 to June 2026, we analyze ten major sectoral indices of the Shanghai Stock Exchange, Dubai crude oil returns, oil price volatility, and the US dollar–Chinese yuan exchange rate. Dubai crude oil is used as the benchmark because it reflects Asia-oriented crude oil pricing and China’s imported energy cost conditions. The empirical analysis proceeds in several steps. First, baseline regressions are estimated to examine the average effect of oil returns on sectoral stock returns while controlling for domestic market-wide movements and exchange rate changes. Second, market-adjusted sectoral returns are used to isolate genuine sector-specific oil transmission from common market shocks. Third, GARCH(1,1)-based oil volatility and crisis-period interaction terms are introduced to identify the uncertainty effect of oil price movements during the COVID-19 pandemic, the post-pandemic period, and the US–Iran/Middle East geopolitical conflict period. Finally, DCC-GARCH dynamic conditional correlations are used as a robustness check. The results show that Chinese sectoral stock returns do not respond uniformly to oil price movements. The timing-adjusted results provide little evidence that lagged Dubai oil returns systematically predict next-day sectoral returns. Nevertheless, oil-price uncertainty exhibits selective and regime-dependent effects, particularly for Energy, Materials, Consumer Staples, Consumer Discretionary, Industrials, and Utilities.

Journal of Risk and Financial Management doi: 10.3390/jrfm19080609

Authors: Akshat Gupta Jianguo Liu

This paper develops a six-component diagnostic protocol for evaluating confidence-based selective prediction on autocorrelated financial labels, demonstrating it on VIX regime classification across 5000 trading days (July 2006–May 2026). The protocol combines coverage-matched baseline comparison, joint-coverage decomposition, regime-transition auditing, risk–coverage analysis, feature attribution, and abstention confound testing. Applied to Random Forest, Histogram Gradient Boosting, and XGBoost classifiers with 36 features, the protocol indicates that headline selective accuracy of 90–94% largely reflects label persistence: on jointly covered days, the Random Forest and a persistence rule calibrated on training data make identical predictions at three of five horizons (McNemar b=c=0) and disagree on at most 3 of 708 days elsewhere. All three classifiers and a forecast-then-threshold HAR model achieve 0% covered accuracy on calm-to-high transitions—across 16 distinct transition episodes at the shortest horizon—and an LSTM performs below chance on the same days. The pattern is consistent with an informational constraint of the daily feature set rather than a model deficiency, and it replicates on S&P 500 trend regimes. The protocol is modular and directly applicable to other selective prediction systems on serially correlated outcomes.

Journal of Risk and Financial Management doi: 10.3390/jrfm19080608

Authors: Kiril Luchkov Aleksey Potebnya

This study analyzes the relationship between digital disclosure of information on Environmental, Social and Governance (ESG) factors, publicly verifiable environmental evidence, corporate responsibility, trust, and greenwashing risk in building construction in Bulgaria. The article applies a combined research design that integrates the Environmental Benchmarking Index for Building Construction Companies (EBI-C41), calculated at the company level for 12 construction companies, with a survey of 297 informed respondents. The results show that EBI-C41 has consistent positive associations with transparency, corporate responsibility, and trust, as well as a negative association with greenwashing risk. On this basis, the findings suggest that, in building construction, verifiable sustainability evidence is important for trust-related stakeholder evaluations and provides information beyond ESG communication visibility alone. Diagnostic indices are also proposed to assess the gap between communication, evidence, and trust. The results should be interpreted as exploratory associations within a purposive and non-representative sample, rather than as evidence of causal relationships or as an assessment of the full internal environmental performance of companies in the sector.

Journal of Risk and Financial Management doi: 10.3390/jrfm19080607

Authors: Bolun Zhang Jun Luo Ruobing Wu Jie Wei Zuzhuang Luo Hongbo Shen

Credit risk assessment forms a cornerstone of banking risk management and the stability of the wider financial system. Over the past decade, the rapid development of machine learning (ML) techniques has substantially enhanced traditional credit risk assessment methodologies. ML has now emerged as a core technological pillar for the banking sector, strengthening risk identification capabilities, optimising credit decision-making, and advancing financial inclusion. Conventional credit scoring models, dominated by logistic regression (LR) and scorecard approaches, offer inherent strengths in interpretability and regulatory compliance. However, constrained by their linear assumptions, these methods struggle to capture complex non-linear relationships within credit data and deliver insufficient predictive accuracy for the “credit-invisible” population lacking formal credit histories. This paper presents a systematic literature review (SLR) of ML applications in credit risk assessment (CRA), covering publications from January 2016 to May 2026. A total of 894 papers were retrieved from five digital libraries, and following a rigorous multi-stage screening process, 129 studies were selected for final inclusion. Our analysis reveals that tree-based ensemble models and deep learning (DL) architectures predominate in contemporary research in this field. Meanwhile, post hoc explanation methods and machine learning operations (MLOps) are gaining significant traction as solutions to address fairness, transparency, and system maintenance challenges in real-world production environments. We synthesise prevailing methodologies into a unified end-to-end credit risk modelling framework spanning data preprocessing, feature engineering, model training, evaluation, and operational deployment. Through a critical assessment of the advantages, limitations, and inherent trade-offs of existing approaches, this SLR not only identifies current research gaps and future directions for the academic community, but also provides practical guidance for the banking sector to build compliant, fair, and efficient intelligent risk assessment systems.

Journal of Risk and Financial Management doi: 10.3390/jrfm19080606

Authors: Nasareldeen Hamed Ahmed Alnor

Artificial Intelligence (AI) is now part of accounting systems, changing the way that accounting data is handled and the quality of the financial reporting. The impact of the qualifications of accountants and the adoption of AI has been studied separately in previous studies and the mediating variable of AI between the two variables, accounting education and information quality, has not been studied yet. This research aims to explore the relationship between the academic qualifications of accountants, AI in accounting systems, and accounting information quality between the two major variables explored in this study. The study design adopted for this study is quantitative research which involved 353 respondents in Saudi Arabia. The proposed measurement and structure models were tested using a software program known as SmartPLS which is partial least squares structural equation modeling (PLS-SEM), including mediation analysis. The results indicate that the academic qualification of an accountant does not directly significantly affect the quality of accounting information but it has an indirect significant effect via AI. Moreover, AI has strong positive effects on the quality of accounting information and acts as an important mediator between the academic qualification of the accountants and quality of accounting information. This research’s results have shown that the use of the AI capabilities in an accountant’s education and professional training is important in improving the quality of financial reporting. It also adds to the socio-technical systems literature by highlighting AI’s potential to link human capital and accounting information quality in the evolving digital accounting environment.

Journal of Risk and Financial Management doi: 10.3390/jrfm19080605

Authors: Varun Kesavan Aruna Polisetty Rajkumar Subbaiyan

Decentralised finance (DeFi) is a relatively new trend in finance that uses blockchain, smart contracts, and distributed ledger technology to offer financial services in a decentralised manner. Although scholars have made many theoretical advances in decentralised finance in recent years, knowledge of its theoretical structure and future research areas remains limited. This is why this study provides a bibliometric analysis of 1002 articles on DeFi published in Scopus between 2012 and 2026. The analysis uses performance analysis and a science mapping approach based on citation analysis, co-authorship, bibliographic coupling and keyword co-occurrence analysis. The results reveal a remarkably high annual growth rate of 39.34% and DeFi’s dynamism and interdisciplinary nature. The three main countries involved in DeFi research are the USA, China, and the UK. Management Science, Energy Economics and Technological Forecasting and Social Change became the main scientific journals for disseminating knowledge about DeFi. Analysis of thematic changes showed a transition of scientific interests from blockchain and cryptocurrencies to new topics, like artificial intelligence, sustainability, governance, and financial inclusion. Overall, the current study provides a better understanding of the intellectual, conceptual, and social basis of DeFi and highlights possible research areas in the use of artificial intelligence in DeFi, decentralised governance, and sustainable digital financial system development.

Journal of Risk and Financial Management doi: 10.3390/jrfm19080604

Authors: Abolfazl Soleimani Davood Askarany

This study investigates whether pandemic-induced fear undermines auditors’ professional judgment directly or through a more debilitating psychological mechanism—coronaphobia. We surveyed 146 audit partners and senior managers in Iran during the peak of the COVID-19 pandemic, in a “meta-crisis” context characterised by international sanctions and strained healthcare. Using PLS-SEM, we traced the pathway from pandemic anxiety to perceived audit quality, testing coronaphobia as a mediating variable. Results reveal that coronaphobia mediated approximately 65% of the total adverse effect on audit quality. While general anxiety exerted a direct negative influence, its transformation into pathological fear consumed cognitive resources essential for professional scepticism and sound judgment. We advance a psychological mediation model of audit quality vulnerability, demonstrating how external shocks degrade professional judgment by targeting its human foundations. For practice, this reframes auditors’ psychological well-being as a core control objective; for research, it argues that accounting systems are only as reliable as the minds that operate them—especially during crises requiring rapid adaptation.

Journal of Risk and Financial Management doi: 10.3390/jrfm19080603

Authors: Christopher Jungesblut

The decision-oriented conception of enterprise risk management (ERM), in which risks are quantified, aggregated, and weighed against return when business decisions are prepared, is increasingly regarded as the core of value-based management. In Germany, this conception acquired a legal foundation in 2021: Section 1 of the Stabilization and Restructuring Framework for Enterprises Act (StaRUG), the Financial Market Integrity Strengthening Act (FISG), and the amended Section 91(3) of the German Stock Corporation Act (AktG) require continuous monitoring of developments that may jeopardize the company’s continued existence, the initiation of “appropriate countermeasures” once a critical threshold is exceeded, and direct communication of the risk situation to the supervisory board. This paper argues that these obligations are difficult to satisfy without risk aggregation by Monte Carlo simulation and a quantitative risk-bearing-capacity concept, the same apparatus that underpins simulation-based valuation. The study asks whether listed firms report using it. The 2021 annual reports of 83 DAX- and MDAX-listed companies (excluding banks, exchanges, and insurers) are scored against eleven criteria capturing disclosed risk management practice. Because the instrument reads public reporting rather than internal process, the scores are interpreted throughout as a lower bound on practice. The average score is 0.73 of a possible 2.0 (about 37%). StaRUG is named by no company; FISG is by roughly 31%; and only a minority disclose adequate risk aggregation or a risk-bearing-capacity concept with a defined threshold. The pattern, near-universal assertion of readiness to act, combined with near-absence of the quantitative apparatus that would make such action triggerable, is consistent with ceremonial conformity decoupled from substantive practice. The value-relevant core of risk management thus remains largely unreported. Because the same apparatus generates the cost of capital and the decision value used in simulation-based valuation, the scores also function as a diagnostic of valuation capability: a firm that cannot aggregate its risks must import a discount rate rather than derive one. This has implications for valuation, governance, supervisory oard liability, and audit.

Journal of Risk and Financial Management doi: 10.3390/jrfm19080602

Authors: Nada Jabbour Al Maalouf Layal Sfeir Anthony Nehme Jeanne Laure Mawad

Prolonged financial crises force households to adopt strategies intended to preserve wealth and support financial resilience. Among these strategies, physical gold has become an increasingly attractive safe-haven asset, yet limited evidence exists on the behavioral factors associated with consumers’ intentions to purchase gold in economies experiencing prolonged financial crises. Addressing this gap, this study investigates the cognitive, behavioral, social, and economic predictors of gold purchase intention in Lebanon, one of the countries most severely affected by financial collapse, currency depreciation, and institutional distrust. Drawing on the Theory of Planned Behavior (TPB) and Consumer Behavior Theory, the study develops an extended conceptual framework by incorporating financial literacy as an additional antecedent and perceived affordability as both a direct predictor and a moderating variable. Data were collected through a cross-sectional survey of 268 Lebanese adults and analyzed using partial least squares structural equation modeling (PLS-SEM) in SmartPLS 4. The findings reveal that financial literacy, attitude toward gold, subjective norms, perceived behavioral control, and perceived affordability are all significant positive predictors of purchase intention. Furthermore, the positive associations of attitude and subjective norms with purchase intention are stronger at higher levels of perceived affordability, highlighting its dual role as both an economic determinant and a potential contextual boundary condition. The study extends the TPB by distinguishing between psychosocial predictors of gold purchase intention and the financial capability and resource constraints represented by financial literacy and perceived affordability. By examining perceived affordability as a potential boundary condition for the associations of attitudes and subjective norms with purchase intention, the study clarifies when favorable evaluations and social influences are more strongly associated with intentions to purchase gold as a resilience-oriented financial asset.

Journal of Risk and Financial Management doi: 10.3390/jrfm19080601

Authors: Abootaleb Shirvani Mahshid Fahandezhsadi Svetlozar T. Rachev Thisari K. Mahanama Frank J. Fabozzi

The increasing importance of climate change, natural resource constraints, and sustainability-related risks has created a growing need for quantitative measures that connect environmental performance with economic and financial decision-making. Existing environmental and ESG indicators provide valuable assessments of sustainability conditions, but they are generally designed as ranking or reporting measures rather than dynamic financial indices suitable for risk modeling, portfolio analysis, and long-term economic evaluation. This paper proposes a sovereign-level environmental wealth framework that translates environmental performance into financially interpretable time-series indices. Using fourteen World Development Indicators (WDIs) covering environmental conditions and environmentally relevant economic characteristics for ten major economies, we construct Dollar Environmental Financial Indices (DEFIs). The proposed framework combines standardized environmental information with economic capacity, represented by GDP per capita, to measure the economic value associated with national environmental performance. The resulting indices are not intended to represent directly traded financial securities, but rather synthetic environmental wealth benchmarks that allow sustainability-related risks to be analyzed using established tools from financial economics. We further construct a Global Dollar Environmental Financial Index (GDEFI), which represents the common global component of environmental performance and serves as a benchmark for evaluating systematic environmental exposure across countries. Using robust regression, dynamic econometric models, and volatility analysis, we estimate environmental beta coefficients and examine how country-level environmental conditions respond to global environmental movements. Estimated environmental betas range from −0.708 (Australia) to 1.617 (China), while the explanatory power of the global benchmark reaches an adjusted R2 of 0.886 for the United Kingdom, demonstrating substantial cross-country heterogeneity in environmental exposure, risk dynamics, and resilience. To demonstrate the usefulness of the proposed framework, we apply risk-adjusted performance measures, including Jensen’s alpha, Sharpe ratio, Sortino ratio, and the Rachev ratio, together with mean–variance and CVaR-based portfolio optimization methods. These analyses show that environmental exposures exhibit distinct risk–return and tail-risk characteristics, highlighting potential diversification benefits in sustainability-oriented decision frameworks. Maximum-likelihood factor analysis further indicates that a three-factor structure provides an adequate representation of the common variation in environmental performance across countries. Finally, we provide a conceptual illustration of how environmental index-based derivatives could support future sustainability risk management. While DEFIs are not currently tradable assets, the proposed framework establishes a bridge between environmental measurement and financial modeling by allowing environmental risk to be evaluated through the analytical tools traditionally applied to financial markets.

Journal of Risk and Financial Management doi: 10.3390/jrfm19080600

Authors: Faiza Abdalla Sheikh Batoun Ayesha Mohamed Ali Abdulla Batoun

This study investigates the relationship between financial leverage and firm performance and examines the moderating role of macroeconomic conditions among GCC-listed firms. While prior studies have primarily focused on the direct effect of leverage on firm performance, comparatively limited attention has been devoted to understanding how macroeconomic conditions shape the relationship between financial leverage and firm performance in emerging markets. Drawing on Trade-off Theory, Agency Theory, and Contingency Theory, this study argues that the effectiveness of financial leverage depends on prevailing macroeconomic conditions. Using a panel of GCC-listed firms over the period 2016–2023, the analysis employs a dynamic two-step System Generalized Method of Moments (System GMM) estimator to address endogeneity, unobserved firm heterogeneity, and the persistence of firm performance. Firm performance is measured using return on assets (ROA), return on equity (ROE), and Tobin’s Q. The baseline results reveal that financial leverage has a significant negative effect on both accounting-based and market-based measures of firm performance, suggesting that the costs associated with excessive debt outweigh its financing benefits. The moderation analysis further reveals that macroeconomic conditions exert heterogeneous effects on this relationship. Specifically, GDP growth mitigates the adverse effect of financial leverage on ROA, whereas inflation mitigates the adverse effect on ROE but reinforces the adverse effect on Tobin’s Q. These findings demonstrate that the impact of financial leverage on firm performance is contingent upon prevailing macroeconomic conditions and varies across accounting-based and market-based performance measures. The study contributes to the capital structure literature by integrating firm-level financing decisions with macroeconomic conditions and extending the explanatory power of Trade-off Theory, Agency Theory, and Contingency Theory by demonstrating how macroeconomic conditions shape the relationship between financial leverage and firm performance in the GCC context. The findings also have important implications for corporate managers, investors, and policymakers by emphasizing the need to incorporate macroeconomic conditions into capital structure decisions and adopt adaptive financing strategies to promote sustainable firm performance under changing macroeconomic conditions.

Journal of Risk and Financial Management doi: 10.3390/jrfm19080599

Authors: Huda Aldhahi Abdulrahman Alsamaani

Can the distress of a cryptocurrency be predicted from its market behavior, and is that predictability reliable when it matters most? Using daily data for 609 USD-quoted coins traded on Kraken between 2013 and 2025, we built a survivorship-inclusive coin-quarter panel and model the onset of severe, sustained price distress—a deep, non-recovering drawdown relative to a trailing peak. A panel logit confirmed that realized volatility, illiquidity, weak momentum, and asset youth predict distress, with a coin-stratified cross-validated out-of-sample AUC of about 0.68. Our central contribution was to show that this predictability is regime-dependent. Interactions between coin-level signals and contemporaneous market-wide volatility are jointly significant (likelihood-ratio p < 0.001), and a rolling-origin evaluation reveals prospective accuracy swinging from no better than chance (AUC 0.43) to strong (0.79) across years. This regime-dependence is robust across alternative distress thresholds, regime proxies, data frequencies, cluster-bootstrap inference, and replication on a second exchange (Binance), though the individual signal channels are not. Testing the most natural mechanism—rising cross-asset co-movement in turbulent markets—we find no support. Microstructure-based early-warning signals for crypto distress are thus conditionally reliable: informative in calm markets but unreliable in the turbulent conditions where warning is most valuable.

Journal of Risk and Financial Management doi: 10.3390/jrfm19080598

Authors: Mishelle Doorasamy Oladapo Fapetu Pelumi Abdulmalik Adewumi

The study examined the relationship among climate finance (CF), Environmental Risk Accounting (ERA), and firm value for publicly listed non-financial firms in Nigeria and South Africa between 2010 and 2022. Using a carefully balanced panel sample consisting of 520 observations, we construct our independent variables as follows: Climate Finance (CF); Climate Financial Exposure (CFEI), using an AI-powered textual analysis approach; and Greenwashing Gap (GWG). Through fixed-effects panel regression, our results indicate that while climate finance does not directly influence firm value, CF and the quality of ERA practices interact positively, showing that CF only creates value conditional on high-quality ERA. Greenwashing risk is negatively associated with firm value, while environmental-risk-accounting quality is separately associated with higher firm value. Institutional differences across countries have consequences for the role of ERA. These results are examined using a double-theoretic approach that integrates institutional theory to justify how the regulation pressure leads to differences in accounting disclosures in different countries, and the resource-based theory, to justify how these differences influence firm value. The application of difference-in-difference analysis through the adoption of the King IV code by South African firms provides evidence consistent with an appreciable valuation premium by firms in South Africa after the intervention. The findings are broadly consistent across methods such as IV-2SLS, System GMM, Propensity Score Matching, and the Heckman Selection Model. There are important ramifications of the findings for accounting practice and environmental policy within sub-Saharan Africa.

Journal of Risk and Financial Management doi: 10.3390/jrfm19080597

Authors: Vanya Georgieva Radosveta Krasteva-Hristova

The sharp rise in nominal interest rates after 2022 constitutes a substantial test for European non-financial corporations after a prolonged period of exceptionally cheap debt. This paper examines how the cost of debt financing—proxied by the lagged, ex post real long-term sovereign yield, interpreted throughout as an indicator of economy-wide financing conditions rather than a direct corporate borrowing rate—is associated with the gross investment rate of non-financial corporations in the EU-27 over 2000–2025, using harmonised annual sector accounts and two-way fixed-effects panel models, interaction designs and local projections. Three findings emerge. First, the conditional association is stronger for the real than for the nominal cost of debt: a one percentage point increase in the lagged real yield is associated with a decline of roughly 0.3–0.4 percentage points in the investment rate, and a formal test does not reject treating the nominal yield and inflation as components of the real rate. Second, this association is not stable over time: it weakens markedly after 2020, and the weakening is robust to an alternative 2022 breakpoint and to wild cluster bootstrap inference. Third, direct tests with predetermined leverage and profit shares do not account for this weakening, so stronger corporate balance sheets—including the pronounced deleveraging from around 477% to around 226% of income—remain only one candidate explanation among several. The profit-share interaction is positive, but the evidence of attenuation is weak and specification-dependent: it is not statistically significant with the one-year-lagged measure and reaches only marginal significance under two alternative measures.

Journal of Risk and Financial Management doi: 10.3390/jrfm19080596

Authors: Enggar Diah Puspa Arum Helpan Alfaridzi Rico Wijaya Wiralestari Aulia Beatrice Brilliant Ilham Wahyudi

Financial statement fraud (FSF) remains a persistent concern in emerging markets, where institutional weaknesses and ineffective monitoring increase the risk of financial misreporting. Although prior studies have largely relied on the Fraud Triangle and its extensions, empirical evidence on the applicability of the GONE Theory remains limited, particularly in emerging economies. This study investigates the effects of greed (proxied by managerial ownership), opportunity (proxied by board characteristics), need (proxied by financial target and remuneration), and exposure (proxied by audit characteristics) on FSF (proxied by the likelihood of earnings manipulation measured by the Beneish M-Score). A total of 260 firm-year observations from F&B companies listed on the Indonesia Stock Exchange during the 2019–2023 period were analyzed using the PLS-SEM. The results show that greed, opportunity, and need increase the likelihood of FSF, while exposure has no effect. These findings provide empirical support for the GONE Theory and expand the literature on FSF by highlighting the dominance of internal motivations and organizational conditions, suggesting that managerial incentives, board characteristics, and financial targets are the primary drivers of FSF, as opposed to external preventive mechanisms. This study offers insights for strengthening governance and internal control systems to mitigate fraud risk.

Journal of Risk and Financial Management doi: 10.3390/jrfm19080595

Authors: Gergana Mihaylova-Borisova

This article investigates the factors that determine the dynamics of mortgage lending in Bulgaria and the Euro area by using ordinary least squares (OLS) regression models based on stationary time series over the period 2010–2025. The results show that in Bulgaria, the dynamics of mortgage lending are determined primarily by wage growth, inflation, and the high liquidity of the banking system, which increases banks’ capacity to extend new loans. In contrast, in the Euro area, the main factor driving mortgage lending trends is interest rates on mortgage loans, with the development of the real estate market, as measured by house price index, also exerting a significant influence. The findings further indicate that, despite the high degree of economic integration between Bulgaria and the European Union, the factors determining mortgage lending differ, which justifies the need for separate modeling of mortgage loans in the two economies. Moreover, mortgage lending transmission mechanisms differ substantially across the two economies despite their close monetary integration, highlighting the importance of country-specific institutional characteristics. The faster growth of mortgage lending by Bulgarian banks compared to those in the Euro area does not yet pose risks to the stability of Bulgaria’s banking system.

Journal of Risk and Financial Management doi: 10.3390/jrfm19080594

Authors: Marco Antonio Ledesma Munive Alejandro Anibal Aguirre-Rojas Graciela Soledad Verastegui Velasquez William Huanca Pilar Zevallos Nivaneth Valencia

This study examines whether the accumulated stock of private credit provides early-warning information for subsequent deterioration in banking-sector asset quality. It combines annual Passport banking indicators with World Development Indicators for 58 countries over 2010–2024; the preferred sample contains 746 country–year observations. A second-order dynamic fixed-effects model links log(1 + NPL), where NPL denotes the non-performing loan ratio, to lagged private credit to gross domestic product (GDP), real credit growth, lending rates, bank capital, GDP growth, inflation, and unemployment. Its preferred credit-depth coefficient is 0.00377, implying that a 10-percentage-point increase is associated with approximately 0.15 percentage points more NPLs one year later at the sample median. To operationalize early-warning calibration without claiming a universal cutoff, the paper reports the sample credit-depth quartiles and estimates a country fixed-effects linear probability model using the European Banking Authority’s 5% gross-NPL supervisory trigger. In that alternative outcome, a 10-percentage-point increase in credit depth is associated with a 2.78-percentage-point higher conditional probability of NPLs reaching 5% or more (p = 0.002). On a strictly common 609-observation sample, the credit-depth coefficients at one-, two-, and three-year horizons are 0.00501, 0.00960, and 0.01266. Lending rates and unemployment are positive, whereas annual credit growth and capital ratios are not robust predictors. Pooled interactions do not reject equal slopes across broad country partitions. System generalized method of moments (GMM) passes conventional tests but violates a persistence-bound credibility check. The evidence supports an early-warning interpretation, not a causal claim.

Journal of Risk and Financial Management doi: 10.3390/jrfm19080593

Authors: Yoly Tatiana Polania Cerinza Osval Armando Ibáñez-Díaz Hugo Fernando Guerrero-Sierra Jaime Edison Rojas-Mora

This paper analyses the short-run dynamic relationship between monetary policy and banking market structure in Colombia during a period of post-pandemic inflation and aggressive policy tightening. Using monthly credit portfolio data for 2017–2024, we compute several concentration indicators (the Herfindahl–Hirschman Index (HHI), CRk ratios, and a dominance index) and employ three complementary identification strategies to evaluate the causal effect of monetary policy innovations on banking concentration. First, a structural VAR model identified through sign restrictions finds that contractionary shocks are associated with a short-run increase in banking concentration (median peak response: +0.60 HHI points at h = 3; 90% credible set: [+0.12, +1.16]), contrasting with the negative short-run response obtained under recursive reduced-form identification. Second, an extended VAR including credit portfolio growth as a mechanism variable confirms that contractionary shocks compress aggregate lending but do not generate robust, persistent changes in concentration. Third, local projections with regime-interaction terms formally test the nonlinear mechanisms discussed in the literature and find evidence of state-dependent transmission: the concentration response is larger in the low-inflation regime and attenuates during high-inflation episodes. All estimated effects are transitory and horizon-sensitive, reinforcing a cautious interpretation. The paper contributes new evidence from an emerging economy on the structural consequences of monetary policy and highlights the importance of identification assumptions in determining the direction of this effect.

Journal of Risk and Financial Management doi: 10.3390/jrfm19080592

Authors: Odeta Pileckaitė Rasa Subačienė

This study investigates the misalignment between ESG environmental (E) scores and actual sustainable investment activities, addressing a critical gap in the literature regarding the reliability of ESG metrics. While prior research has highlighted concerns about greenwashing, few studies have systematically linked ESG ratings to EU Taxonomy-based capital expenditure (CapEx) indicators. This study aims to bridge this gap by developing a novel firm-level typology that captures discrepancies between reported environmental performance and real investment commitments. The empirical analysis is based on Bloomberg data for European firms over the 2022–2024 period and employs cluster analysis alongside non-parametric statistical testing (Kruskal–Wallis) to assess intergroup differences. The findings reveal substantial heterogeneity across firms, including cases where high ESG ‘E’ scores are not supported by aligned sustainable investments and instances of under-recognised investment activity. These inconsistencies suggest potential distortions in ESG signalling and indicate limitations in current rating methodologies. The study contributes to the literature by integrating ESG evaluation with EU Taxonomy metrics and proposing a refined analytical framework for detecting greenwashing risks. From a practical perspective, the results provide valuable insights for investors, regulators and policymakers seeking to enhance the credibility, comparability and transparency of sustainability disclosures.

Journal of Risk and Financial Management doi: 10.3390/jrfm19080590

Authors: Chaiyathad Phutthadet Ausawatap Akartwipart

Real Estate Investment Trusts (REITs), as highly leveraged, income-generating vehicles, are among the asset classes most structurally exposed to interest rate risk. This study examines the extent of environmental, social, and governance (ESG) disclosure—rather than underlying ESG performance—as a signal associated with the risk-adjusted performance of REITs listed in Thailand and Singapore and whether interest rate conditions moderate this relationship. Using a new 15-indicator ESG Disclosure Index (ESGDI) applied to a panel of 43 REITs comprising 215 REIT-year disclosure observations (2021–2025), panel regressions selected via Hausman and Breusch–Pagan Lagrange Multiplier tests show that ESG disclosure is significantly associated with lower REIT risk in the baseline specification using cluster-robust standard errors (β = −0.033, p = 0.035) but has no significant association with returns. This baseline association is not robust: it becomes insignificant under a one-year lagged specification (β = −0.020, p = 0.378) and indistinguishable from zero once year fixed effects are added (β = 0.001, p = 0.975). The ESGDI × interest-rate interaction is insignificant in the baseline model, with only a marginal effect under year fixed effects. No statistically significant cross-country heterogeneity is detected, though statistical power is limited. Any risk-reducing signal from ESG disclosure in this setting appears fragile and specification-sensitive rather than a reliable risk driver.

Journal of Risk and Financial Management doi: 10.3390/jrfm19080591

Authors: Shu Inoue

This study examines whether female governance representation and the presence of a female signing audit partner are associated with key audit matter (KAM) reporting in Japan. The descriptive sample comprises 9808 firm-year observations for Japanese listed companies from 2021 to 2023; primary multivariate analyses use 9794 complete cases. KAM headings were manually collected and classified as account- or entity-level matters. Poisson and zero-truncated Poisson models with firm-clustered standard errors are the primary KAM-count specifications; Tobit is retained only as a supplementary check. The interaction between female audit-partner involvement (FEAUD) and female governance representation (FEBOARD) is negative in both count models, but evidence is limited and model-dependent; the corresponding p-values are 0.067 in the Poisson model and 0.293 in the zero-truncated Poisson model. Supplementary estimates are also negative, although statistical significance varies across specifications. Conditional KAM-count differences are small and emerge mainly at higher levels of female governance representation. Evidence for total KAM-section length is weaker, and length checks indicate that any negative total-length association reflects fewer KAMs rather than shorter descriptions per KAM. Type-specific analyses provide only weak descriptive evidence. The findings are associational and do not directly measure communication, coordination, or disclosure quality.

Journal of Risk and Financial Management doi: 10.3390/jrfm19080589

Authors: Mohammad Al-Dwiry Weaam Amira

This study examines the relationship between environmental, social, and governance (ESG) performance and bank credit risk among publicly listed U.S. banks over the period 2016–2025. It distinguishes between forward-looking and realized credit risk by using the loan loss provision ratio (LLPR) as the primary measure of expected credit risk and the non-performing loan ratio (NPLR) as a robustness measure. Using fixed-effects and dynamic System Generalized Method of Moments (System GMM) estimations, the results show that stronger ESG performance is associated with lower forward-looking expected credit risk. The ESG pillar analysis indicates that the social dimension exerts the strongest risk-reducing effect, followed by governance and environmental performance. In addition, economic policy uncertainty weakens the beneficial effect of ESG on bank credit risk. By contrast, ESG performance is not significantly associated with realized credit deterioration measured using NPLR, suggesting that ESG primarily influences banks’ expectations of future credit losses rather than realized loan performance. Overall, the findings demonstrate that the impact of ESG on bank credit risk depends on both the measurement of credit risk and the surrounding macroeconomic environment.

Journal of Risk and Financial Management doi: 10.3390/jrfm19080588

Authors: Randheer Ramsoomer Hermann Azemtsa Donfack Adri Drotskie

Physical cash remains a critical component of payment systems worldwide due to its accessibility, liquidity, anonymity, and role in promoting financial inclusion and resilience during systemic shocks. Despite rapid digitalisation, cash retains its relevance in economies such as South Africa, where it supports both formal and informal market activity. Understanding the determinants of cash demand is therefore essential for managing operational and policy risks faced by central banks. This study examines the factors influencing cash demand in South Africa and their implications for the South African Reserve Bank’s (SARB) currency management and risk mitigation strategies. Using a Vector Error Correction Model (VECM), Impulse Response Functions (IRFs), and advanced forecasting techniques, the analysis integrates key macroeconomic and technological variables, including GDP, interest rates, mobile penetration, ATMs, EFTs, and tax ratios. The study also benchmarks its results against international empirical evidence to contextualise South Africa’s evolving cash dynamics. The results highlight the significant impact of payment technology, especially mobile banking, on reducing cash usage. While ATMs and bank branches still support cash demand to some extent, the growing preference for digital transactions, notably through EFTs and mobile platforms, is reshaping financial behaviour. Macroeconomic variables like GDP and interest rates continue to influence demand, but their role is increasingly mediated by digital adoption. The forecasting analysis reveals that neural network models, particularly NNETAR, outperform traditional linear models (like VECM and Exponential Smoothing), especially over longer horizons. These models better capture non-linearities and evolve structural dynamics in cash usage. These insights hold material implications for SARB’s operational and financial risk frameworks. As cash demand becomes more unpredictable and technology-driven, adaptive forecasting and policy strategies are required to ensure efficient currency management and financial system stability.

Journal of Risk and Financial Management doi: 10.3390/jrfm19080587

Authors: Olena Lytvyn Oleksii Chugaiev Nataliia Reznikova Andrii Onyshchenko Oleksandr Ostapenko Oleksandr Pravdyvets

This study examines the impact of the Russian–Ukrainian war on global financial stability, focusing on currency crises, exchange-rate dynamics, and economic vulnerability during 2003–2024 with an outlook for subsequent years. The objective is to assess how geopolitical shocks, combined with global monetary tightening, influenced the frequency and intensity of currency crises across developed and emerging economies. The study applies a quantitative comparative methodology based on a modified Exchange Market Pressure Index (EMPI) using monthly IMF data on exchange rates, reserves, interest rates, and depreciation dynamics. Currency crises are identified through threshold-based criteria, enabling cross-country and temporal comparison. A conceptual framework explains how geopolitical risk affects currency markets, financial stability, and macroeconomic performance. The findings show that crisis episodes were more frequently concentrated around the Great Recession, the COVID-19 pandemic, and the Russian–Ukrainian war. Emerging economies were more vulnerable, experiencing stronger capital outflows, sharper currency depreciation, and more frequent crises, while developed economies were affected mainly through inflation and energy price shocks. The war intensified financial fragmentation, increased safe-haven flows toward the US dollar, gold, and Swiss franc, and raised systemic risks in debt, banking, and corporate sectors. The study concludes that differentiated macroeconomic strategies, stronger external buffers, and enhanced international financial coordination are necessary to reduce risks and preserve currency stability.

Journal of Risk and Financial Management doi: 10.3390/jrfm19080586

Authors: Stephanus Paulus Lumintang Noermijati Noermijati Fatchur Rohman Sri Palupi Prabandari

Interorganizational trust constitutes a foundational pillar of relational governance that facilitates coordination and enhances organizational performance. However, recent scholarly debate suggests that an overreliance on trusted interorganizational relationships may engender unintended counterproductive consequences. Integrating Social Exchange Theory (SET) and the Knowledge-Based View (KBV) through a capability-based perspective, this study examines a capability-based mechanism through which interorganizational trust influences firm performance via the mediating role of problem-solving capability (PSC). Based on a census dataset of all 48 licensed futures brokerage firms operating under the Jakarta Futures Exchange (JFX), primary survey data gathered from Chief Executive Officers were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The empirical evidence indicates that interorganizational trust is negatively associated with both problem-solving capability and firm performance. In contrast, problem-solving capability is positively related to firm performance and likely to significantly mediate the relationship between interorganizational trust and firm performance. These findings suggest that the organizational implications of relational trust are better understood by examining the internal adaptive capabilities that transform relational resources into firm performance. From this capability-based standpoint, problem-solving capability serves as a critical internal mechanism linking external relational conditions to organizational performance. Meanwhile, the concept of capability erosion offers a plausible theoretical explanation for how excessive reliance on relational support may reduce organizations’ incentives to sustain internal problem-solving capability. Overall, this paper contributes to the relational governance literature by providing a capability-based perspective on the dark side of interorganizational trust, offering practical insights for industry executives and market regulators on balancing relational governance with internal capability development.

Journal of Risk and Financial Management doi: 10.3390/jrfm19080585

Authors: Ibrahim Bakirtas Muhammed Rasid Bakir Gokay Canberk Bulus

This study re-assesses the root causes of inflation through an innovative hybrid analytical framework integrating deep neural networks, random forest algorithms, and causal inference (DoWhy) within the Quantity-Theoretical Inflation Theory, using monthly data from Turkey for the period 2003M05–2025M01. The findings reveal that inflation expectations and exchange rate fluctuations are the most influential drivers of inflation, overshadowing other variables such as money supply, real interest rates, and global oil prices. Although the exact ordering of predictors is model dependent, and the random forest assigns the leading role to the real interest rate, the signal shared across all three methodological pathways rests on expectations and the exchange rate. While money supply growth aligns with monetarist theory and remains a consistent source of upward price pressure, its impact is often mediated through expectations and currency depreciation. Causal estimates confirm that Turkey’s persistent inflation is fueled not just by macroeconomic imbalances but by the erosion of policy credibility and weak anchoring of expectations, particularly after 2017. The study contributes methodologically by introducing AI-powered modeling to inflation analysis in emerging markets, and empirically by validating the central role of expectations and exchange rate pass-through in a structurally fragile, import-dependent economy. The results suggest that sustainable price stability in Turkey hinges on reestablishing central bank independence, adopting orthodox inflation targeting, and regaining public trust through consistent and transparent communication.

Journal of Risk and Financial Management doi: 10.3390/jrfm19080584

Authors: Wissam El Khoury Sebouh Aintablian

This study examines the relatively underexplored phenomenon of hostile takeovers within the insurance industry, a sector that played a significant role in the events surrounding the 2008 global financial crisis. Using a sample of 754 insurance merger and acquisition (M&A) transactions completed between 2008 and 2021, we investigate the determinants of hostile takeover activity. The dataset comprises acquiring and target firms from 46 countries and special administrative regions (SARs), providing a broad international perspective on post-crisis insurance-sector M&A dynamics. The findings reveal that several transaction- and firm-specific factors significantly affect the likelihood of hostile takeovers. In particular, a target firm’s prior M&A experience, increases in target book value, higher bidder research and development expenditures, bid revisions, and acquisition premia are positively associated with takeover hostility. While descriptive analyses document notable variation across jurisdictions, the primary empirical evidence is derived from pooled regression models incorporating country fixed effects. The results contribute to the literature on insurance-sector consolidation by identifying industry-specific factors associated with hostile acquisition activity and enhancing understanding of how information asymmetries, strategic considerations, and governance mechanisms shape takeover outcomes. These findings offer valuable implications for corporate managers, investors, and policymakers operating within a globally interconnected and highly regulated insurance industry.

Journal of Risk and Financial Management doi: 10.3390/jrfm19080583

Authors: Sara Almarri Hamza El Kaddouri

Background: The Chief Financial Officer (CFO) has become a central strategic actor in capital-intensive firms; however, little evidence links CFO risk-taking behaviour to firm performance outside developed markets. This study examines how CFO risk-taking affects corporate financial performance in the Industrial, Energy and Petrochemical sectors of the Gulf Cooperation Council (GCC) countries. Methods: Using 260 firm-year observations (2015–2024) from 26 listed firms, this study measures CFO risk-taking through financial leverage, capital expenditure intensity, earnings volatility, and cash flow volatility, and firm performance through Return on Assets (ROA), Return on Equity (ROE), and Earnings Per Share (EPS). Panel Fixed-Effects regression and a Vector Autoregression (VAR) model are used to estimate contemporaneous and dynamic relationships, guided by Agency Theory, Upper Echelons Theory and Prospect Theory. Results: CFO risk-taking proxies are significantly associated with ROA: leverage and cash flow volatility reduce ROA, while earnings volatility and capital expenditure raise it. The ROE model is a robust null finding, and EPS evidence is limited to earnings volatility. The VAR results indicate time-varying, exploratory, and dynamic relationships between risk-taking and performance. Conclusions: This study contributes to the literature in three ways: it shifts the analytical focus from the widely studied CEO to the increasingly influential CFO; it provides the first large-scale empirical evidence on CFO risk-taking for the under-researched GCC region; and it operationalises CFO risk-taking through a finer set of proxies than prior work. The findings imply that GCC boards and investors should treat financial leverage and cash flow stability as behavioural risk indicators and that regulators may benefit from encouraging more granular CFO-level risk disclosure.

Journal of Risk and Financial Management doi: 10.3390/jrfm19080582

Authors: Gharmili Meryem Boudri Imane Alj Abdelkamel

Quantitative portfolio optimization has accelerated sharply since 2018, with deep learning and reinforcement learning agents now competing with the mean–variance framework that defined six decades of research. Existing narrative reviews struggle to track this expansion. We screen 832 documents from Scopus and Web of Science under PRISMA 2020 and retain 589 unique articles spanning 2003–2025. Applying BERTopic with SPECTER scientific embeddings, UMAP and HDBSCAN, we identify five coherent topics with a mean coherence of 0.864: classical mean–variance (T0; n = 270), deep reinforcement learning (T1; n = 116), machine learning return forecasting (T2; n = 87), covariance estimation and robust optimization (T3; n = 52)—and metaheuristics (T4; n = 56). A rank-weighted similarity analysis, designed to neutralise the c-TF-IDF collinearity artefact, shows that deep reinforcement learning is the most isolated paradigm. The two methodological families bifurcate over time: AI/deep learning approaches grow from 3.6% of annual output before 2018 to 40.2% afterwards, while classical methods retain volume but lose share. We synthesise the empirical practices of each family along five dimensions critical to applied finance and identify three under-explored integration frontiers.

Journal of Risk and Financial Management doi: 10.3390/jrfm19080581

Authors: Hajar Bouladasse Said El Ganich Taoufiq Yahyaoui

Purpose: This study provides a comprehensive bibliometric analysis of AI integration in banking, mapping research trends, intellectual structures, and performance-related themes. While AI’s growing importance in financial services has attracted scholarly attention, bibliometric studies specifically focusing on the AI-banking-performance triad remain limited. This study addresses this gap by systematically mapping the research dynamics, intellectual structure, and thematic evolution of this domain. Materials and Methods: Publications were retrieved from the Scopus database using predefined search criteria, resulting in 891 articles published between 2014 and 2024. Bibliometric indicators were employed to examine publication trends, authorship patterns, and institutional contributions. VOSviewer (version 1.6.20) and the R-based Bibliometrix (version 4.3.3) package were used to construct co-authorship networks, keyword co-occurrence maps, co-citation structures, and thematic maps. Results: Findings reveal exponential growth, particularly after 2018, with a peak of 277 articles in 2024. IEEE Access and Expert Systems with Applications are the leading sources, while Baesens B. emerges as a highly influential author. China and India dominate output, though European countries achieve higher per-article impact. Highly cited works focus on credit scoring, fraud detection, fintech, and financial inclusion. Conceptual mapping identifies five thematic clusters, with “AI in banking” as a motor theme, NLP as a niche, and credit detection as emerging. Conclusions: AI research in banking is rapidly expanding, interdisciplinary, and globally distributed. Theoretically, the findings are framed by TAM, TPB, DOI, and Dynamic Capabilities Theory, revealing that AI adoption represents a multi-level phenomenon spanning individual acceptance, institutional diffusion, and strategic reconfiguration. These theoretical lenses explain why fraud detection and credit scoring dominate as early adoptions while NLP and governance remain underdeveloped. The study highlights key contributors, emerging themes, and future research directions. However, findings are constrained by reliance on a single database (Scopus), exclusion of non-English and non-peer-reviewed sources, and inherent limitations of bibliometric methods.

Journal of Risk and Financial Management doi: 10.3390/jrfm19080580

Authors: Qian Fang Nuttawut Rojniruttikul

This study examines how non-interest income diversification affects bank performance and risk in selected emerging Asian economies. Drawing on panel data from 44 banks across China (36) and Thailand (8) over 2022–2025, the analysis employs fixed-effects regressions, mediation analysis, and subsample testing to unpack the performance implications of revenue diversification. The non-interest income ratio (NII) serves as the proxy for income diversification, capturing the strategic shift away from traditional net-interest margins toward fee-based and digitally facilitated activities in markets where mobile payment ecosystems and virtual banking frameworks have reshaped competitive dynamics. Results indicate that NII exerts a statistically significant positive effect on bank profitability (ROA and ROE), with no corresponding increase in risk exposure as measured by Z-score. The relationship is markedly stronger among large banks, consistent with scale advantages in technology infrastructure, network effects, and regulatory compliance cost amortization. Cost efficiency does not mediate the NII-performance nexus, suggesting that revenue-side mechanisms dominate in this context. Cross-country exploratory patterns reveal stable but modest effects in China’s mature diversification ecosystem against larger but statistically imprecise coefficients in Thailand’s early-stage transition. These findings offer a qualified complement to the Western-centric complexity-risk narrative and highlight institutional boundary conditions governing bank diversification outcomes in emerging markets.

Journal of Risk and Financial Management doi: 10.3390/jrfm19080579

Authors: Ayrin Sultana A. H. M. Ziaul Haq Md. Nur Alam Siddik Sajal Kabiraj

Financial innovation is recognized as a major catalyst of long-term economic progress. The main aim of this study is to analyze the nexus between financial innovation and economic growth in Bangladesh. While financial innovation plays a crucial role, empirical studies examining its impacts on the economy, specifically in the context of Bangladesh, are scarce. This study is designed to address this existing gap. Based on time-series data covering the period from 2004 to 2023, this research employed the Autoregressive Distributed Lag (ARDL) bounds testing procedure to examine long-run cointegration among the variables. Robust findings indicate significant long-run effects of financial innovation, measured by the number of automated teller machines, on economic growth. The short-run analysis reveals temporary adjustment effects following financial innovation, while the error-correction mechanism confirms convergence toward the long-run equilibrium after short-run shocks. Granger causality analysis showed a strong unidirectional causality from ATM development to GDP growth. The empirical findings of this study will be of greater importance to developing nations like Bangladesh because they will encourage bank management and policymakers to pursue policies that promote financial innovations. This study enriches the empirical literature by confirming or disproving the findings of previous studies.

Journal of Risk and Financial Management doi: 10.3390/jrfm19080578

Authors: Kiril Luchkov Mihail Chipriyanov Galina Chipriyanova Marin Marinov

This study develops a scenario-based analytical framework for assessing smart microgrids in industrial zones as infrastructure investments and for analyzing multi-actor risk allocation and governance. No empirically validated return is reported for a specific Bulgarian industrial zone. Public institutional, market, financial, technology and environmental sources are instead used to benchmark the scenario assumptions. Three scenarios are evaluated for a reference zone with annual consumption of 12,000 MWh. The model incorporates photovoltaic (PV) degradation, battery round-trip efficiency of 85–90%, annual usable-capacity degradation, one modeled battery replacement within a 12–15-year service interval, component-based capital and operating expenditures, and an author-defined semi-quantitative likelihood–impact risk matrix. Net present value (NPV) is EUR −1,114,726 in the conservative scenario, EUR 697,836 in the baseline scenario and EUR 3,424,835 in the favorable scenario. Discounted payback is not achieved within 20 years in the conservative scenario and is approximately 14.1 and 7.0 years in the baseline and favorable scenarios, respectively. Deterministic one-at-a-time sensitivity analysis identifies electricity price, capital expenditures, and the direct PV self-consumption ratio as the dominant financial drivers.The indicative reduction in location-based emissions associated with grid electricity purchases is 802.9–1385.5 tonnes of carbon dioxide equivalent (tCO2e) per year under the selected grid-average electricity emission factor. The contribution lies in integrating public-data availability assessment, external parameter benchmarking, battery service life, degradation and replacement economics, investment appraisal, threshold analysis, semi-quantitative risk prioritization and contractual risk allocation within one reproducible framework. The outputs remain illustrative and require project-level validation using measured hourly loads, binding prices, financing terms and enforceable contracts.

Journal of Risk and Financial Management doi: 10.3390/jrfm19080577

Authors: María Andrea Arias-Serna Luis Fernando Móntes-Gómez María Alejandra Lasso-López Jhon Quiza-Montealegre

Prudential regulation traditionally evaluates credit, market, and liquidity risks through separate indicators, providing a fragmented assessment of institutions’ financial soundness. This study proposes the Risk Capacity Index (ICR) as an integrated measure of the structural risk-bearing capacity of organizations in the Colombian solidarity sector. Rather than measuring individual risks in isolation, the proposed framework evaluates the capacity of available equity to absorb aggregate financial exposure by integrating Expected Loss, Value at Risk, and the Liquidity Gap within a single prudential metric. The conceptual design of the ICR is grounded in the notion that equity constitutes the institution’s ultimate loss-absorbing constraint, while its operational specification is developed using supervisory risk measures applicable to cooperative financial institutions. The methodology combines analytical sensitivity analysis with a forward-looking stress-testing framework based on the Prudential Regulation Authority approach. Results demonstrate that the ICR exhibits nonlinear deterioration as aggregate risk exposure increases, with liquidity risk emerging as the principal determinant of financial fragility and the viability threshold. The theoretical contribution of the ICR lies not in replacing existing prudential ratios, but in providing an integrated institution-level measure that jointly relates available loss-absorbing capital to simultaneous exposures across multiple financial risks within a common analytical framework. The proposed index therefore complements established measures of capital adequacy, liquidity resilience, and financial soundness by offering a consolidated perspective on institutional risk-bearing capacity.

Journal of Risk and Financial Management doi: 10.3390/jrfm19080576

Authors: Vajinder Kaur Eugene Pinsky

This study examines behavior among mutual funds across five sectors, including energy, utilities, real estate, technology, and healthcare, over a 27-year period (1999–2025). For each year, using daily NAV returns, each fund is regressed against the S&P 500 to separate fund-specific performance from broader market movements. All the resulting residual vectors for each year and fund are clustered together. These annual cluster assignments are then linked across time to construct each fund’s trajectory, showing how its relative performance position shifts from year to year. These trajectories capture long-term behavioral divergence and provide a simple, intuitive visualization. To identify and characterize fund trajectory and distinctiveness, we apply methods based on residual magnitudes, quantile migration patterns, and Hamming distance measures of (cluster, time) fund trajectories. These trajectories reveal how certain funds consistently diverge from market behavior, thereby contributing to portfolio diversity in terms of trajectory separation. We introduce a portfolio Hamming diversification index that measures separation between trajectories. Using trajectories and Hamming distances, we examine how fund behavior changes during major market disruptions, including the dot-com crash (2001), the financial crisis (2009), and the COVID-19 pandemic (2020–2021), and identify sector-specific differences in how fund trajectories respond to these events. The proposed methodology is intended as an exploratory descriptive methodology for studying long-term behavioral trajectories rather than as a replacement for traditional asset-pricing or performance-evaluation models.

Journal of Risk and Financial Management doi: 10.3390/jrfm19080575

Authors: Foued Badr Gabsi Sirine Sahnoun

The environmental implications of globalization and financial inclusion have become a major concern for both policymakers and researchers. This study examines the heterogeneous relationships between Global Value Chain (GVC) participation, financial inclusion, and CO2 emissions in a panel of 41 developed and emerging economies over the period 2004–2022. To capture differences across emission levels, the analysis employs the Method of Moments Quantile Regression (MMQR), complemented by Fixed Effects (FE), System GMM, and Common Correlated Effects Mean Group (CCEMG) estimators for robustness. The findings reveal substantial heterogeneity across the conditional distribution of CO2 emissions. Forward and backward GVC participation exhibit distinct environmental associations across emission levels, while financial inclusion plays a differentiated moderating role in these relationships. The conditional marginal effects further show that the environmental implications of GVC participation depend on the level of financial inclusion. The robustness analysis confirms the consistency of these findings across alternative estimators. Overall, the results suggest that the environmental consequences of globalization depend on both countries’ emission levels and the role of financial inclusion in shaping the effects of GVC participation. From a policy perspective, the findings highlight the need to align financial development with environmental objectives. Strengthening green finance frameworks and environmental regulations can help ensure that deeper integration into global production networks supports sustainable development rather than increasing environmental degradation.

Journal of Risk and Financial Management doi: 10.3390/jrfm19080574

Authors: Ivana Miklošević Andreja Todorović Andrija Popović

This paper examines how economic downturns and currency movements affect the quality of bank loans in Central, Eastern, and Southeastern Europe, using annual data for 14 national banking systems over 2008–2023. We estimate a bias-corrected dynamic fixed-effects model, verify inference with Driscoll–Kraay, cluster-robust, and wild cluster bootstrap procedures, run formal threshold tests, and conduct scenario simulations. Credit risk is highly persistent. The bias-corrected autoregressive coefficient of 0.944 implies a half-life of 12.0 years, although the bootstrap confidence interval of 0.601 to 1.048 does not rule out near-unit-root behavior. Exchange-rate depreciation predicts higher non-performing loan (NPL) ratios and survives both the strictest few-cluster test (p = 0.028) and a correction for euro-adoption breaks, while lower real GDP per capita growth is marginal under the same test (p = 0.060). Threshold tests that re-estimate the threshold in every bootstrap replication do not reject linearity in any of 14 configurations (minimum p-value of 0.071). Institutional quality does not measurably moderate the exchange-rate channel. A severe combined adverse scenario raises the projected NPL ratio from 6.54 to 12.32 percent over five years (90 percent interval: 8.2 to 24.6 percent). Together, the surviving channels and the disciplined null results delimit nonlinear transmission in emerging Europe.

Journal of Risk and Financial Management doi: 10.3390/jrfm19080573

Authors: Arusaya Thamaree Simon Zaby

The study investigated the relationship between female board representation and corporate social responsibility (CSR) in Thai firms in the period between 2015 and 2021. We examined whether a higher proportion of female directors would enhance CSR and sustainable corporate development. The findings provide no empirical evidence of a relationship, suggesting that female board representation may not influence CSR in Thailand. The role and impact of female directors appear to be indirect and complex, making it difficult to assess and quantify their contribution to CSR. We also examined whether female chief executive officers (CEOs) and/or chief financial officers (CFOs) moderate the relationship between female board representation and improved CSR. The findings do not support this hypothesis, suggesting that female CEOs and/or CFOs do not moderate the relationship between the presence of female directors and CSR. The presence of a female CEO or a member of a minority group in a firm may not be enough to influence the company’s charitable giving decisions. This study contributes to the corporate governance and CSR literature by examining how female board representation and female executive leadership jointly shape ESG performance and disclosure in an emerging market context, and by distinguishing between performance-based and disclosure-based ESG outcomes using two major global databases.

Journal of Risk and Financial Management doi: 10.3390/jrfm19080572

Authors: Kosla Hin Bunthe Hor Siphat Lim

This research adds to the existing literature by examining the impact of IFRS 9 in a context where accounting reforms, prudential regulation and credit growth are strongly intertwined. Studies examining the impact of IFRS 9 implementation on profitability for banks provide inconclusive evidence. We find that our results are both robust to static panel estimators and strengthened by dynamic specifications. The transition from an incurred loss to an expected credit loss (ECL) model thus entails short-term profitability costs by bringing forward credit impairment recognition, increasing provisioning and lowering reported earnings. Results show that NPLs and leverage have a persistent negative impact on ROA, while a larger bank size and cash contribute positively to profitability. Liquidity is negatively related but statistically insignificant, indicating that its role is primarily a prudential rather than an earnings component. Given that, in dynamic models, higher GDP and inflation lead to lower profitability at the macroeconomic level, and expansionary conditions do not improve bank performance even if expected loss recognition increases or operating expense falls. IFRS 9 is hence a possible source of major institutional reform with real influence on financial outcomes, risk appetites and the strength of Cambodia’s banking sector in general.

Journal of Risk and Financial Management doi: 10.3390/jrfm19080569

Authors: Sparsha Mandreker Guntur Anjana Raju

This study investigates the oversubscription in public issues of Small and Medium Enterprises in India, across different quantiles. This study further explores the non-linearity in factors through machine learning. Utilising data from 1014 IPOs during the period 2012 to 2024 listed on SME exchanges of NSE and BSE, this study applies OLS regression, quantile regression and a Random Forest model. SHapley Additive exPlanations values are computed to validate and extend econometric findings. Underpricing, listing delay and hot markets are the prominent factors affecting oversubscription. The Random Forest model outperforms OLS, revealing non-linear effects of predictors. This study offers crucial insights enabling policymakers and regulators to refine disclosure regulations and strengthening investor protection measures, while firms can efficiently structure their offerings and attract significant investor interest.

Journal of Risk and Financial Management doi: 10.3390/jrfm19080571

Authors: Salma El Faroui Mimoun Benali

Climate-related risks are now perceived as financially material, affecting banks, investors, firms, regulators, and central banks. This article provides a systematic literature review of the relationship between climate-related risks and financial decision-making. The review, which is based on Scopus and Web of Science and follows a PRISMA-based selection process, includes a final sample of 80 studies retrieved using database publication-year filters for 2015–2025, including three online-first records subsequently assigned to 2026 issues. A Quality Appraisal Matrix, descriptive analysis, keyword co-occurrence mapping with VOSviewer version 1.6.20, and cluster-based thematic synthesis are used. The results reveal four key streams in the literature: climate risk, Environmental, Social, and Governance (ESG), and financial modeling; climate change, sustainable finance, and systemic stability; transition risk, investment, and risk assessment; and banks, performance, and financial impact. The review underscores the role of climate risks in asset pricing, portfolio allocation, lending, credit-risk assessment, disclosure, stress testing, and financial stability supervision, and identifies key gaps with respect to emerging economies, data quality, and the practical incorporation of climate risk considerations into financial decisions.

Journal of Risk and Financial Management doi: 10.3390/jrfm19080570

Authors: Erekle Pirveli Iza Gigauri Claudia Covucci

Drawing on stakeholder theory, agency theory, and critical mass theory, this study investigates the relation between board diversity and firm profitability channeled through sustainability disclosure. Employing structural equation modeling, this study analyzes data from annual reports of all publicly listed Georgian entities from 2018 to 2023, covering 246 firm-year observations. The research findings reveal that board diversity (including gender representation, nationality, and number of members) significantly enhances sustainability disclosure, but does not have a measurable impact on firm profitability. Moreover, the results indicate a nonsignificant effect of sustainability disclosure on firm profitability. This study exhibits that control variables such as ownership concentration and CEO duality negatively impact sustainability scores weakening the relationship between ESG disclosure and firm profitability. Report language negatively affects sustainability score and company size plays a favourable role in ESG performance. The results are explained by the country context. This research contributes to the academic discourse on sustainability disclosure, board diversity, and firm profitability and provides new insight into board diversity’s role in fostering a sustainable corporate environment from an emerging market’s perspective.

Journal of Risk and Financial Management doi: 10.3390/jrfm19080568

Authors: Mariem Turki Imed Chkir Kamel Naoui

This paper examines the threshold impact of media-based attention to transition and physical climate risks on banks’ credit risk among the 230 largest US commercial banks from 2011 to 2022. Using a dynamic panel threshold model, our analysis reveals a non-linear relationship between media-based climate risk and banks’ credit risk. The empirical results indicate the existence of a significant threshold dividing the data into lower and upper regimes for both climate transition risks and physical climate risks. More specifically, the estimated threshold levels are 0.500 for the transition risk index and 0.571 for the physical climate risk index. Below these critical thresholds, banks appear resilient to increased media attention to climate risks; however, once these thresholds are exceeded, growing concern about climate risks significantly increases banks’ vulnerability to credit risk. These findings highlight the critical implications of physical and transition risks for financial stability. Our results are robust to a range of alternative measures and model specifications, providing valuable insights for bank managers, regulators, and policymakers, while emphasizing the need to integrate media-based climate risk considerations into credit risk assessments and policy frameworks to strengthen the banking sector’s resilience.

Journal of Risk and Financial Management doi: 10.3390/jrfm19080567

Authors: Dan Wang Anis Suriati Binti Ahmad Nur Amirah Binti Borhan

This study examines whether environmental, social, and governance (ESG) performance enhances firm value in China’s A-share market, how this relationship operates, and under what conditions it becomes stronger. Drawing on stakeholder theory, the natural resource-based view, and the dynamic capabilities perspective, this study develops a moderated mediation framework in which green innovation mediates the ESG–firm value relationship and digital transformation strengthen the ESG–green innovation link. Using panel data for 4423 Chinese A-share listed firms comprising 27,254 firm-year observations from 2009 to 2023, the hypotheses are tested using two-way fixed-effects models, mediation and moderated mediation analyses, robustness tests, and instrumental-variable estimation. The results show that overall ESG performance is positively associated with firm value, although its dimensions exhibit heterogeneous effects: environmental performance is negatively associated with firm value, whereas social and governance performance show positive associations. Green innovation partially mediates the ESG–firm value relationship, indicating that ESG creates greater economic value when sustainability commitments are translated into substantive green innovation. Digital transformation further strengthens the indirect effect of ESG performance on firm value through green innovation. Heterogeneity analyses reveal that the value relevance of ESG varies across firm size, ownership type, and industry pollution intensity. The findings suggest that ESG does not create firm value automatically; rather, its economic value depends on firms’ ability to transform sustainability commitments into innovation, with digital transformation enhancing this process. By identifying both the mechanism and the boundary condition of ESG value creation, this study provides new evidence on how and under what conditions ESG contributes to firm value in an emerging market.

Journal of Risk and Financial Management doi: 10.3390/jrfm19080566

Authors: Ibraheem Alaskar Ibrahim Khatatbeh Reyadh Faras Ahmad Bash

The determinants of bank credit quality have been studied extensively, yet much of the existing evidence rests on a single regression specification, so a variable’s apparent significance may be conditioned on which controls a researcher chooses to include. We confront this problem directly for the Gulf Cooperation Council (GCC) countries, providing a robustness analysis of non-performing loans (NPL) determinants for the region’s banks. We employ a balanced panel of 45 listed commercial banks drawn from all six GCC countries over the period 2010 to 2024. We examine fifteen bank-specific and four macroeconomic potential determinants of NPLs, utilizing two variants of extreme bounds analysis (EBA), namely, the strict criterion of Leamer and the more lenient criterion of Sala-i-Martin, estimated within a panel fixed-effects framework. The findings show that of the nineteen determinants routinely cited in the literature, seventeen prove fragile once their coefficients are tested across the full range of possible model specifications. None survives Leamer’s strict criterion, whereas Sala-i-Martin’s less restricted test suggests that only two variables are robust, namely, asset quality (loan intensity), which enters positively, and capital adequacy, which enters negatively, while all four macroeconomic variables are fragile on both tests. For regulators, bank-level balance sheet indicators, especially loan intensity and capital adequacy, offer a more robust starting point for NPL early-warning and stress-testing frameworks and complement macroeconomic forecasts.

Journal of Risk and Financial Management doi: 10.3390/jrfm19080565

Authors: Fahad Alrobai

Purpose: This research aims to examine the impact of accounting conservatism on investment efficiency and the cost of capital within the Saudi Arabian corporate context following the implementation of Saudi Vision 2030. Methodology: This study analyzes panel data from 105 non-financial listed firms on the Saudi Stock Exchange (Tadawul) from 2016 to 2024. To fulfill the structural requirements for measuring investment efficiency, the sample is restricted to sectors containing a minimum of 10 firms. The empirical framework relies on four robust Ordinary Least Squares (OLS) econometric models to evaluate the hypothesized relationships. Findings: The empirical findings indicate two primary results. First, accounting conservatism exerts a significant positive impact on investment efficiency. Second, statistical tests reveal that accounting conservatism has a nuanced, asymmetric, and non-linear impact on the components of the cost of capital—specifically, the weighted average cost of capital (WACC), cost of equity (COE), and cost of debt (COD)—when conditioned across three distinct regimes: the full sample, underinvesting firms, and overinvesting firms. These results challenge traditional linear assumptions, indicating that a state-contingent framework better explains market reactions to financial reporting strategies. Implications and Recommendations: The findings suggest that decision makers should abandon the assumption that maximizing accounting conservatism is a universally risk-averse or beneficial strategy. Instead, corporate managers should treat accounting conservatism as a strategic instrument governed by definite thresholds, as its impact on financing costs is deeply tied to a firm’s structural investment realities. Regulatory bodies and standard setters in the Saudi market are encouraged to integrate these non-linear insights when evaluating the capital market effects of financial transparency reforms.

Journal of Risk and Financial Management doi: 10.3390/jrfm19080564

Authors: Rania Al-Nsour Antonio Menor-Campos

This study investigates the impact of tax avoidance on dividend policy in listed non-financial firms in Portugal, Italy, Greece, and Spain from 2018 to 2024. Using Refinitiv Eikon panel data for 368 firms—yielding approximately 2200 firm-year observations before lagging—and firm fixed-effects models, the study examines whether tax avoidance increases dividend payouts and whether board independence, financial constraints, and economic policy uncertainty condition this relationship. Tax avoidance is measured using reverse-coded effective tax rate proxies and book–tax differences, while dividend policy is proxied by the dividend payout ratio. The results reveal a positive association between tax avoidance and dividend payout, suggesting that tax planning can operate as a channel for generating additional distributable liquidity in classical double-tax systems. However, this pass-through is not mechanical. The effect of tax avoidance on dividends is weaker in firms with more independent boards and stronger among financially constrained firms. It is also attenuated under heightened policy uncertainty. These findings support a three-dimensional conditionality model in which tax avoidance creates the capacity for higher dividends, but governance quality, financing frictions, and macro-level risk jointly determine whether tax-generated liquidity is paid out, retained, or used for dividend smoothing.

Journal of Risk and Financial Management doi: 10.3390/jrfm19080563

Authors: Mohammed Nawlo Fadi Alkaraan Hasan Radwan Katalo

Digital transformation is reshaping industries, business models, and investment opportunities, creating new challenges for international portfolio management. The European communication services sector has become a strategic component of the digital economy, driven by advances in artificial intelligence (AI), digital platforms, 5G infrastructure, cloud computing, cybersecurity, and data-driven business models. Despite its importance, limited evidence exists regarding the effectiveness of portfolio optimisation strategies within digitally transforming sectors. This study investigates international portfolio optimisation using constituent firms of the MSCI Europe Communication Services 35/20 Capped Index. Drawing upon Modern Portfolio Theory and the Treynor–Black framework, an actively managed portfolio is constructed and evaluated against the SPDR® MSCI Europe Communication Services UCITS ETF and an equal-weight portfolio. Using daily market data, the analysis estimates asset returns, alpha and beta coefficients, portfolio weights, and risk-adjusted performance measures, including the Sharpe and Treynor ratios. Paired-samples t-tests are employed to assess the statistical significance of performance differences among investment strategies. The findings show that the Treynor–Black portfolio generated the highest annual return (27.32%), outperforming both the benchmark and equal-weight portfolios, and the highest percentage of Sharpe ratios (1.2159), suggesting that diversification benefits outweighed the advantages of active security selection. Hypothesis testing indicates no statistically significant difference between the Treynor–Black and equal-weight portfolios, and no statistically significant difference exists between the proposed and benchmark portfolios. The study extends the international portfolio management literature by applying the Treynor–Black model to a digitally transforming sector. The findings suggest that portfolio performance is influenced not only by firm-level financial characteristics but also by broader digital and institutional environments. Firms operating within digitally advanced and well-governed economies appear better positioned to exploit technological innovation and generate sustainable long-term value. Overall, the results demonstrate that successful international portfolio optimization requires balancing active security selection with diversification while recognizing the role of digital transformation, governance quality, and innovation ecosystems in shaping investment performance within the digital economy.

Journal of Risk and Financial Management doi: 10.3390/jrfm19080562

Authors: Bouthaina Ben Othman Rihab Bedoui Ben Salem Heni Boubaker

Carbon price instability within the EU Emissions Trading System (EU ETS) is associated with financial stress that propagates across green finance markets, yet the system-level dynamics linking carbon allowance instruments, clean energy equities, green bonds, and oil volatility remain insufficiently characterized over the turbulent 2021–2026 period. This paper applies the DCC-GARCH R2 decomposed connectedness framework to five exchange-traded funds and one volatility index spanning the principal channels through which EU ETS regulatory shocks propagate to financial markets, and derives a novel Connectedness-Based Hierarchy Index (CBHI) that translates the transmitter–receiver hierarchy into a time-varying portfolio desirability index. Five key findings emerge. First, the carbon allowance futures ETF (KRBN) and the Paris-aligned equity ETF (CARB) form a near-closed systemic bloc within this asset universe: bilateral connectedness reaches 0.867, with to and from directional connectedness values both approaching 80%. Second, this co-transmitter structure is highly contingent on the joint inclusion of both instruments; excluding CARB raises KRBN’s CBHI from 0.251 to 22.204, reclassifying it as a structural diversifier and reducing the mean Total Connectedness Index (TCI) from 52.18% to 30.81%. Third, system-wide connectedness averages 52.18% but surges to nearly 73% during EU ETS regulatory crises. Fourth, connectedness-aware portfolios outperform the minimum-variance benchmark across all risk-adjusted metrics, yielding an annualized Sharpe ratio of 0.148 vs. −1.066 (T=1035 observations, daily rebalancing, zero transaction costs); this improvement is driven by reallocation toward crude oil volatility (OVX), the sole non-ETF and most peripheral instrument (to = 5.55%, CBHI=14.310), which the CBHI identifies as the system’s dominant structural diversifier and whose weight rises from 0.4% in the benchmark to 31.6%. Fifth, the CBHI uncovers a structural tension in Paris-aligned mandates: CARB records the second-lowest CBHI (0.254), indicating that climate alignment and systemic risk minimization are partially conflicting objectives.

Journal of Risk and Financial Management doi: 10.3390/jrfm19080561

Authors: Lumengo Bonga-Bonga Bereket Abayneh Ataro

Against the backdrop of rapid technological innovation and the growing use of alternative investment instruments, this study examines the dynamic connectedness among decentralized finance assets, AI-based stocks, Islamic stocks and commodities. Covering the period from December 2019 to June 2022, we use the time-varying parameter vector autoregression (TVP-VAR) model to measure the magnitude, direction and evolution of return spillovers across Chainlink, Maker, Basic Attention Token, NVIDIA, Amazon, Google, Microsoft, DJIM World, DJIM EM, gold, crude oil and Global X Lithium and Battery Tech. The connectedness literature has examined spillovers across different asset classes during crisis periods. However, much of this literature focuses mainly on pairwise relationships among traditional asset classes, with limited attention to how emerging, alternative and technology-driven assets interact within a single network. We further assess the role of investor sentiment and network topology in identifying systemic transmitters and receivers. The results show strong interconnectedness, with an average total connectedness index (TCI) of 68.81%. Notably, AI-based stocks, especially Microsoft and NVIDIA, consistently emerge as net transmitters of return shocks, while commodities like gold and crude oil serve as absorbers of shocks. The portfolio results show that network centrality improves risk-adjusted performance by reducing volatility and downside risk. These insights have practical implications for policymakers and market participants, offering guidance for developing effective regulatory frameworks, investment strategies and risk management approaches in an increasingly interconnected financial landscape.

Journal of Risk and Financial Management doi: 10.3390/jrfm19080560

Authors: Thirawat Chantuk Pornthip Jatupornmongkolchai Wiwatwong Bunnun

Most public investment feasibility studies rely on the linear assumption model (LAM), which assumes constant growth in revenues and costs and may overlook the underlying production structure. This study develops a Production Function-Based Financial Feasibility Model (PFFM) by integrating a Cobb–Douglas production function into cash flow analysis. It also applies the model to a 15-year electric bus project operated by the Khon Kaen Provincial Administrative Organization, covering three routes and a total investment of THB 285.05 million. The analysis uses deterministic projections based on expert-validated growth assumptions. Ordinary least squares estimation yields an energy elasticity of 0.990; however, because energy consumption is derived from a fixed rate per kilometre, this coefficient primarily reflects the internal consistency of the projections rather than an independently estimated behavioural relationship. Returns to scale are statistically indistinguishable from unity (RTS = 1.006; p = 0.95). Based solely on farebox and advertising revenues, both the LAM and PFFM indicate that the project is financially infeasible, with base-case NPVs of −THB 271.74 million and −THB 271.61 million, respectively, B/C ratios of approximately 0.54, and unattainable IRRs. This conclusion remains unchanged under a 30% increase in electricity prices. Although the two models produce similar results because RTS is close to unity, the PFFM provides a more transparent decomposition of fixed and marginal costs and clearer diagnostic insights into production technology and cost risk. In addition, an SROI analysis covering environmental, health, safety, and time-saving benefits, together with net-impact adjustments, yields a ratio of 3.45, indicating that the project generates substantial social value despite its financial infeasibility. The proposed framework therefore provides public agencies with a more comprehensive basis for investment decisions, subsidy design, and public service obligation policies.

Journal of Risk and Financial Management doi: 10.3390/jrfm19080559

Authors: Saša Obradović Nemanja Lojanica Sergej Gričar Štefan Bojnec

This study investigates the relationships among military expenditure, economic growth, energy consumption, and carbon dioxide (CO2) emissions using panel data for eight Central and Eastern European countries over the period 2000–2023. The analysis employs the Pooled Mean Group Autoregressive Distributed Lag (PMG-ARDL) estimator to examine both the short-run and long-run dynamics. To ensure the robustness of the findings, the CS-ARDL, Common Correlated Effects Mean Group (CCEMG), and panel FMOLS estimators are additionally applied. The results of the Pedroni, Kao, and Johansen–Fisher panel cointegration tests provide robust evidence of cointegration, confirming the existence of a stable long-run equilibrium relationship among the variables. The long-run estimates indicate that military expenditure and economic growth contribute to lower CO2 emissions, whereas higher energy consumption increases environmental degradation. In the short run, military expenditure has no statistically significant effect on CO2 emissions, suggesting that its environmental implications emerge only over a longer time horizon. These findings imply that strategically directed investments in the defense sector, particularly those promoting environmentally friendly technologies, energy efficiency, and technological innovation, can support the alignment of national security objectives with environmental sustainability. The results provide important policy implications for Central and Eastern European countries, highlighting that coordinated policies promoting cleaner energy use, sustainable economic growth, and environmentally conscious defense modernisation can contribute to long-term environmental and economic sustainability.

Journal of Risk and Financial Management doi: 10.3390/jrfm19080558

Authors: Domenico Vicinanza

During financial crises, markets do not only fall or become more volatile. They may also become more dynamically coupled, with the behaviour of one market becoming more recoverable from another. The purpose of this study is to examine whether crisis periods strengthen nonlinear coupling between major US and European equity indices, using Convergent Cross-Mapping as a state-space reconstruction method. Daily log returns for the Dow Jones Industrial Average, S&P 500, FTSE 100 and DAX are analysed across pre-crisis, crisis and post-crisis windows for the COVID-19 market shock and the Global Financial Crisis. Pairwise bidirectional Convergent Cross-Mapping is used to estimate cross-map skill, convergence and directional asymmetry, with a focused lagged analysis of key transatlantic pairs during COVID-19. Cross-map skill is interpreted as the strength of the recoverable dynamical footprint between markets. The results show that nonlinear coupling, measured as recoverable dynamical information between reconstructed market states, increases during crisis phases. During COVID-19, mean late-library cross-map skill rises from the pre-crisis to the crisis period, and all tested directional relationships satisfy the convergence criterion. The Global Financial Crisis also shows increased crisis-period coupling, with stronger persistence into the post-crisis phase. Lagged COVID-19 results indicate that the strongest recoverability occurs over a short contemporaneous-to-three-trading-day alignment window. The findings position Convergent Cross-Mapping as a complementary mathematical modelling framework for identifying recoverable dynamical information between markets during financial stress.

Journal of Risk and Financial Management doi: 10.3390/jrfm19080557

Authors: Salah Kayed Abdulhadi H. Ramadan Ruaa BinSaddig Bahaa Subhi Awwad Raneem Fawarseh

Based on agency theory, this study examines the association between the optimistic tone of earnings conference calls and the cost of equity capital using an unbalanced panel of 342 non-financial FTSE All-Share companies (987 firm-year observations) over the period 2010–2024. Earnings conference call tone is measured using the financial sentiment dictionary and analysed using NVivo 14 software. The cost of equity capital is estimated using an implied cost of equity model. Panel specification is determined using appropriate panel-data diagnostic tests, while robustness is assessed through lagged-tone regressions, an alternative cost of equity measure, and two-stage least-squares (2SLS) estimation to address potential endogeneity. The results show a significant negative association between optimistic earnings conference call tone and the cost of equity capital (β = −7.787, p < 0.01). A statistically significant reverse association is also documented: A statistically significant reverse association is also documented: a lower cost of equity is associated with a more optimistic tone in subsequent conference calls (β = −0.001, p < 0.01). This result is interpreted as evidence of an association rather than a causal effect. Both results remain robust across alternative model specifications, lagged-tone analyses, alternative cost of equity measures, and endogeneity controls. The findings indicate that positive and transparent voluntary communication, particularly through earnings conference calls, is associated with lower information asymmetry and a lower cost of equity capital. Firms that have not yet adopted this communication channel may consider incorporating earnings conference calls into their investor-relations strategies to enhance voluntary communication with investors. This study contributes to the disclosure literature by documenting statistically significant associations between earnings conference call tone and the cost of equity capital under two model specifications in the UK market and by providing comprehensive robustness evidence supporting the stability of the reported associations.

Journal of Risk and Financial Management doi: 10.3390/jrfm19080556

Authors: Vita Jagrič Polona Tominc Maja Rožman

Digital payment systems have become increasingly important in contemporary financial environments; however, understanding the factors that drive their adoption remains a significant research challenge. This study examines the determinants of behavioral intention and actual use of digital payments by applying an extended Technology Acceptance Model (TAM) that incorporates perceived risk and trust alongside the traditional TAM constructs. Data were collected through an online survey of 154 Slovenian and international students enrolled in finance-related programs at the University of Maribor, Slovenia, and analyzed using structural equation modeling (SEM) using WarpPLS (version: 8.0). The results indicate that perceived ease of use positively affects perceived usefulness and behavioral intention, while perceived usefulness significantly increases behavioral intention. Perceived risk negatively influences trust, whereas trust positively affects behavioral intention. Furthermore, behavioral intention is the strongest predictor of actual use. All hypothesized relationships were statistically significant. The findings confirm the suitability of the extended TAM for explaining digital payment adoption in the studied group of young people and highlight the importance of usability, perceived benefits, trust, and risk perceptions in shaping digital payment behavior.

Journal of Risk and Financial Management doi: 10.3390/jrfm19080555

Authors: Sugeng Suroso Sri Wulandari Chajar Matari Fath Mala

Geopolitical uncertainty represents a growing source of systemic risk that reshapes international markets, disrupts cross-border operations, and challenges firms’ ability to sustain financial performance. This research examines the mechanisms through which geopolitical instability relates to firm financial outcomes in Southeast Asian economies by assessing the mediating roles of supply chain resilience, currency volatility, and foreign investment confidence. Based on a quantitative cross-sectional design, data were collected from 308 firms across Southeast Asian Economies and analyzed using partial least squares structural equation modeling (PLS-SEM). The findings indicate that geopolitical risks significantly influence financial performance, with the strongest effects transmitted through financial channels. Currency volatility and foreign investment confidence emerge as critical mediators, demonstrating that exchange rate instability and investor risk perceptions substantially shape firm performance under geopolitical pressure. While supply chain resilience enhances firms’ capacity to adapt to external disruptions, its direct contribution to financial performance remains insignificant. The model explains 66.7% of the variance in financial performance, reflecting strong explanatory capability. These findings extend existing knowledge by integrating financial, operational, and institutional mechanisms to clarify how geopolitical disruptions propagate into firm-level outcomes. The results underscore the importance of financial preparedness, institutional effectiveness, governance quality, and adaptive capabilities in managing geopolitical uncertainty.

Journal of Risk and Financial Management doi: 10.3390/jrfm19080554

Authors: Byung-Kook Kang

This study examines Average True Range (ATR)-based take-profit (TP) and stop-loss (SL) rules as components of adaptive risk-management design within robust algorithmic trading systems in the USD/JPY foreign exchange market. Rather than simply evaluating whether volatility-adaptive exit rules improve trading performance, the study investigates the conditions under which they contribute to trading outcomes. To this end, the study adopts a systematic framework combining a broad Moving Average Convergence Divergence (MACD) parameter space with ATR-based TP/SL multiplier settings and distinguishes heterogeneous outcome patterns before and after ATR implementation. The results show that the effectiveness of ATR-based exit rules is conditional. Performance improvements occur only for specific combinations of model structures, exit-rule specifications, and market conditions. A notable finding is that optimizing trading-model parameters plays a primary role in determining the effectiveness of exit rules, while ATR-based exit rules function as complementary components that reinforce well-specified trading models. Moreover, under mildly mean-reverting market conditions, profit-enhancement cases expand across a broader range of optimized MACD parameter configurations, suggesting a previously underexplored interaction between market dynamics, model structure, and volatility-adaptive exit-rule design. Overall, the findings provide new evidence that the effectiveness of adaptive risk-management mechanisms depends on the interaction between model structure, exit-rule design, and market conditions, thereby offering broader insights into adaptive trading-system design under changing market conditions. For practitioners, the results suggest that adaptive ATR-based exit rules are most effective when combined with appropriately optimized trading models.

Journal of Risk and Financial Management doi: 10.3390/jrfm19080553

Authors: Gökhan Özkul Özen Akçakanat Ozan Özdemir

This study comprehensively examines the banking, macroeconomic, and institutional quality dynamics determining the return on equity (ROE) of the banking sector in 27 European Union countries over the 2014–2024 period. Adopting a purely explanatory framework rather than a predictive exercise, the primary aim is to identify and rank the factors driving cross-country profitability variability. The traditional multiple linear regression (MLR) method and three machine learning models (CatBoost, Extra Trees, Gradient Boosting) are comparatively analyzed, with model transparency ensured via the Shapley Additive Explanations (SHAP) algorithm. Empirical findings provide evidence consistent with strong, non-linear interactions among profitability dynamics that traditional econometric models tend to overlook. Comparative analyses indicate that the best-performing CatBoost algorithm possesses notably higher explanatory power compared to the MLR model, an advantage that persists when the linear benchmark is augmented with country fixed effects. According to SHAP results, the non-performing loan (NPL) ratio is the most dominant factor eroding profitability. Conversely, inflation is associated with a positive impact on ROE through the repricing channel up to a certain threshold, after which its marginal contribution flattens, exhibiting a concave structure. These thresholds should be read as model-implied patterns within the present sample rather than as general economic constants. The direct explanatory power of the institutional quality indicators employed here—and of a principal-component composite of the broader governance set—remains relatively limited, suggesting an indirect role operating through macroeconomic channels. These findings, supported by leave-one-country-out (LOCO), fixed-effects, and lagged-regressor robustness checks, suggest that explainable machine learning offers a valuable analytical infrastructure for characterizing the asymmetric effects of macro-financial shocks on bank performance.

Journal of Risk and Financial Management doi: 10.3390/jrfm19080552

Authors: Ramya Haravu Paramesh Hemalatha Krishnamoorthy Gunasekaran Deepak Raghava Naik

Although employed women represent one of the fastest-growing segments of the investor population in emerging economies, their investment decision-making is still largely modelled through fragmented, single-determinant frameworks that treat women as a homogeneous group. This study develops and tests an integrated structural model of financial-goal-directed investment orientation among employed women, drawing together Behavioural Finance Theory, the Theory of Planned Behaviour, and the Life-Cycle Hypothesis. Primary data were collected through a structured questionnaire from 951 employed women across the four administrative zones of Bengaluru, India, using stratified random sampling. The measurement model was validated through exploratory and confirmatory factor analysis, and four competing structural specifications were estimated by maximum likelihood; the best-fitting model was selected on the basis of the corrected Akaike Information Criterion and approximate fit indices. The results indicate that risk tolerance is the strongest direct correlate of financial-goal-directed investment orientation, that confidence and self-efficacy operates as the pivotal psychological mediator linking macroeconomic perception to risk-taking, and that market sentiments are the strongest external correlate of investor confidence. Peer influence shows a theoretically important dual association, positively related to risk tolerance while negatively related to confidence. A serial mediation pathway running from market sentiments through confidence and risk tolerance to financial goals is supported. Because the design is cross-sectional, the associations are interpreted as structural relationships consistent with the proposed theoretical framework rather than as established causal effects. This study is exploratory and hypothesis-generating in character. The findings reframe financial-inclusion interventions for employed women around confidence-building rather than information provision, with implications for product design, advisory practice, and policy.

Journal of Risk and Financial Management doi: 10.3390/jrfm19080551

Authors: Ziad Albaraki Abdelhakim Abdelhadi Talal Al-Sulaiman

As Saudi Arabia accelerates its Vision 2030 economic diversification, the domestic banking sector serves as the critical engine for capital deployment. However, evaluating these institutions is complicated by conflicting performance indicators, where high profitability is often offset by elevated market valuation multiples. This study applies an objective, established multi-criteria decision-making (MCDM) framework—combining Shannon’s Entropy for objective weighting with TOPSIS for ranking—to evaluate ten major banks listed on the Saudi Stock Exchange (Tadawul), tracked by the Tadawul All Share Index (TASI), over the 2021–2025 period. The contribution is contextual and empirical rather than methodological: the systematic application of established objective MCDM methods to the Saudi banking sector during the pivotal Vision 2030 window, with an investor-oriented criterion set. Comparative validation was executed using the CRITIC weighting algorithm and the VIKOR ranking method, complemented by a four-dimensional sensitivity analysis (Weight Perturbation, Leave-One-Criterion-Out, Alternative Normalization, and Equal-Weight scenarios). Spearman correlation coefficients (>0.86) confirm that the framework produces empirically stable rankings resistant to methodological perturbation, providing policymakers and investors with a data-driven decision-support tool for the Saudi banking sector under Vision 2030.

Journal of Risk and Financial Management doi: 10.3390/jrfm19080550

Authors: Abdulrahman Alsamaani Huda Aldhahi

Does the pricing of uncertainty in cryptocurrency markets depend on where in the return distribution one looks, and does investor attention carry information beyond uncertainty itself? We address these questions for ten cryptocurrencies spanning dominant and less-dominant coins over September 2018 to December 2021, using quantile regression, joint (multivariate) quantile regression, and Granger causality tests, together with a COVID-19 sub-sample. Grounding the analysis in the uncertainty- and attention-based asset-pricing literature, we test three hypotheses: that uncertainty is priced in the tails of the return distribution rather than at its centre; that cryptocurrency-specific uncertainty matters more than broad macroeconomic uncertainty; and that uncertainty and attention are complementary, so that pairing an uncertainty index with an attention index explains tail returns better than either index alone. The evidence supports all three. Single indices are largely irrelevant at the median but become influential in bear-market tails and over longer horizons; the cryptocurrency-specific UCRY indices dominate the broader macro proxies; and price-or-policy-plus-attention pairs show stronger and broader tail effects than either index alone, though part of this reflects the additional regressor in paired specifications. Causality runs mainly from indices to returns at longer horizons. The study shows that uncertainty is a tail phenomenon in cryptocurrency markets and that attention operates as a distinct, complementary channel.

Journal of Risk and Financial Management doi: 10.3390/jrfm19080549

Authors: Sami Ullah Resham Iftikhar Muhammad Mohiuddin Ijaz Hussain Ishfaq Ahmad

This article investigates how mediating variables such as financial literacy and social capital can be used in the relationship between financial inclusion and sustainable development in the Pakistani educational sector. A quantitative survey design was used to gather data on educators, students, and stakeholders, in order to quantify financial inclusion, financial literacy, social capital, and sustainable development. Partial least squares structural equation modeling (PLS-SEM) was employed to analyze the proposed relationships with SmartPLS. The results show that there are positive and significant correlations between financial inclusion and financial literacy, along with social capital and sustainable development. The findings also indicate that the connection between financial inclusion and sustainable development is associated with financial literacy and social capital. The present study can be useful because it describes the connection between financial access and sustainable results—based on financial knowledge, trust, cooperation, and networks—and provides implications for policymakers, educators, and financial institutions in practice.

Journal of Risk and Financial Management doi: 10.3390/jrfm19080548

Authors: Nada Jabbour Al Maalouf Layal Sfeir

In an increasingly complex financial landscape, individual financial behavior is shaped by a range of cognitive, technological, and psychological factors. Existing research on financial behavior often examines financial literacy, FinTech adoption, and financial attitude separately, with limited attention to their combined effects or to whether these relationships remain consistent across contrasting economic environments. To address this gap, this study examines the associations of financial literacy and FinTech adoption with financial behavior, both directly and indirectly through the mediating role of financial attitude. Grounded in the Theory of Planned Behavior and the Technology Acceptance Model, the study proposes an integrated behavioral model using primary data from two contrasting contexts: Lebanon, a financially constrained and unstable environment, and the United Arab Emirates (UAE), a stable, high-income country with advanced FinTech infrastructure. Data were collected through a survey of 400 respondents and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings indicate that financial literacy and FinTech adoption are positively associated with financial behavior in both countries. Moreover, financial attitude significantly mediates both relationships. Measurement invariance was established prior to cross-country comparisons, and the multi-group analysis indicated that the structural relationships were generally comparable across Lebanon and the UAE despite descriptive differences in several path coefficients. The study contributes to the behavioral finance and sustainable finance literature by integrating cognitive, technological, and psychological predictors within a unified framework, validating the mediating role of financial attitude, and providing cross-national evidence from two contrasting economic contexts. The findings suggest that strengthening financial literacy alongside responsible FinTech adoption may support more sustainable and inclusive financial behaviors, particularly in environments characterized by economic instability and unequal access to financial services. Practical and policy implications are offered for educators, FinTech providers, financial institutions, and policymakers, emphasizing the importance of context-sensitive initiatives that promote financial resilience, financial inclusion, and the development of sustainable financial systems.

Journal of Risk and Financial Management doi: 10.3390/jrfm19070547

Authors: Alessio Faccia

Machine-readable financial reporting changes how corporate disclosures become visible, verified, and answerable. Inline eXtensible Business Reporting Language (Inline XBRL) combines a human-readable report with embedded structured data, while artificial intelligence expands automated extraction and screening. The study develops Machine-Readable Accountability as a bounded socio-technical construct organised around dynamic visibility, distributed judgement, and responsibility displacement. A qualitative documentary analysis examines four primary archives: regulatory rules, official filing-evidence records, verification materials, and algorithmic-governance documents. Peer-reviewed studies serve as contextual framing and external corroboration. They do not form primary documentary observations. The corpus covers the mature United States Securities and Exchange Commission regime and the European Single Electronic Format from 2020 to July 2026, with earlier sources retained for historical grounding. A seven-family codebook guides analysis of classification, visibility, validation, judgement, audit, artificial intelligence, and accountability. Results arise from coded rules, official filing observations, assurance requirements, and cross-archive role mapping. Structured reporting reduces extraction costs, supports comparison, and permits automated quality checks. It also places institutional weight on taxonomy fit, extension design, validation logic, and software-mediated review. Documentary evidence supports the relocation of judgement across preparers, taxonomy designers, software vendors, auditors, and regulators. Evidence for deliberate narrative optimisation aimed at artificial intelligence remains indirect, so algorithmic answerability remains a bounded theoretical proposition. The framework links market-efficiency research with studies of quantification, professional judgement, and digital governance, and specifies controls for data lineage, extension approval, model documentation, human review, and responsibility assignment.

Journal of Risk and Financial Management doi: 10.3390/jrfm19070546

Authors: Wei Gao Quan Fang Ting Sun

In the context of advocating the high-quality development of companies, this study explores the mechanism underlying the relationship between ESG performance and profit quality (PQ) and the moderating effect of media reputation. Given the wide variety and large number of indicators of corporate profitability, establishing evaluation methods for assessing PQ presents a critical challenge. We adopted the game theory combination weight method to construct a multi-dimensional PQ evaluation system. Using a sample of Chinese A-share listed companies from 2011 to 2024, the study applies two-way fixed effects estimation and instrumental variable analysis to test study hypotheses. The research findings indicate that (1) ESG performance and its sub-dimensions positively influence PQ and that (2) media reputation positively moderates the relationship between ESG performance and PQ. We further discovered that different ESG dimensions have distinct effects on various dimensions of PQ. Therefore, this study contributes to the literature on ESG and PQ while providing practical guidance for companies pursuing high-quality development.

Journal of Risk and Financial Management doi: 10.3390/jrfm19070545

Authors: Radosveta Krasteva-Hristova Vanya Georgieva

Green bond markets have expanded rapidly across the European Union, but development remains uneven across Member States. Using a balanced EU-27 panel for 2021–2025, this study distinguishes sovereign market entry from the depth of the overall green debt market. Pooled Probit and Tobit estimates show that larger economies are substantially more likely to issue sovereign green bonds, whereas government debt and the budget balance are not significantly associated with entry. Market depth is positively associated with economic scale and a stronger budget balance, although the latter relationship partly overlaps with institutional quality. Environmental taxation is not significantly associated with market depth, providing no evidence of substitution for green debt financing. The findings support measures to lower entry costs and strengthen institutional capacity in smaller Member States.

Journal of Risk and Financial Management doi: 10.3390/jrfm19070544

Authors: Godfred Afrifa Ahmad Alshehabi Mariam Alsabah

Our study investigated the interaction of credit from suppliers (trade payables) and credit given to customers (trade receivables) in order to better understand how the reliance on credit from suppliers and credit given to customers interact with each other to affect firms’ performance. Using a sample of 26,731 firm-year observations from 28 European countries, we found new empirical evidence that both trade payables and trade receivables have a more positive effect on firm performance than would be the case if their individual effects were considered in isolation; thus, a complementarity may exist between the credit from suppliers and credit given to customers, affecting firms’ performance. Interestingly, our results showed greater sensitivity to certain firm-specific characteristics. In particular, the interaction effect of trade payables and trade receivables was stronger for young firms, firms with growth potential, and financially constrained firms. Further analysis also revealed that the interaction effect of trade payables and trade receivables was stronger for small- and medium-sized enterprises (SMEs), and firms in countries with French/German legal origins, or countries with more debt-reliant bank-based economies.

Journal of Risk and Financial Management doi: 10.3390/jrfm19070543

Authors: Uk Jo Jae Goo Kim

In South Korea, apartments dominate the residential housing market, accounting for 67.5% of total housing transactions in the fourth quarter of 2018. With this figure continuing to rise, apartments are the most significant asset for many families. Consequently, precise and timely valuations are crucial for stakeholders, including homeowners, buyers, and mortgage lenders. Traditionally, these stakeholders have relied on the qualitative judgments of certified real estate agents. Because of market opacity and low liquidity, agents often use a comparative approach, referencing the most recent transaction prices of nearby comparable apartments. However, this method is subjective, potentially biased, time-consuming, and costly. Our study seeks to offer a more objective and quantitative method for determining fair apartment prices in Korea, helping market participants make informed decisions. The prediction target is the monthly representative price of an apartment complex (the within-complex average of transaction prices), from which a complex-level price index is subsequently constructed; we distinguish this target from individual transaction prices throughout. By employing clustering methods to identify similar apartments and imputation techniques for missing values, our model demonstrates promising results, with a mean absolute percentage error as low as 5.38% in the worst-case (consecutive-mask) setting and 4.76% in the typical (random-mask) setting. Because the training (2006–2015) and test (2016–2022) periods are temporally disjoint, these figures reflect out-of-sample performance rather than in-sample fit. We further validate the resulting series against external references: it attains a 5.03% MAPE against actual transactions nationwide—outperforming the appraiser-based Kookmin Bank index (7.29%)—and, once aggregated, closely tracks the official KREB transaction-based index while becoming available earlier; complex-level Granger tests confirm that our series temporally leads the appraiser-based series about 1.5 times as often as the reverse. We also outline the missing-data assumptions under which the approach is valid.

Journal of Risk and Financial Management doi: 10.3390/jrfm19070542

Authors: Phoompat Dangwung Jay Junghun Lee Junwoo Kim

This study examines the associations among product market competition, commodity hedging, and income smoothing in the metals industry. Using a text-based measure of competition intensity, we find that firms facing stronger competitive pressures are more likely to hedge commodity price risk. We also find a positive association between commodity hedging and income smoothing through discretionary accruals, suggesting a complementary relationship in reducing performance volatility. Moreover, this positive association weakens as product market competition intensifies. Collectively, these results contribute to our understanding of the associations between product market competition, firms’ risk management, and financial reporting behavior.

Journal of Risk and Financial Management doi: 10.3390/jrfm19070541

Authors: Ngoc Toan Pham Hieu Le Tran Trung

Whether environmental, social, and governance (ESG) disclosure stabilizes share prices or merely masks bad news, it remains unsettled, and the evidence is conspicuously weak whenever the relationship is assumed to be linear. This study revisits the question by allowing the effect of ESG disclosure on future stock price crash risk to be nonlinear and by breaking down disclosure into its environmental, social, and governance components. Using an unbalanced panel of non-financial firms listed on the Ho Chi Minh Stock Exchange over 2018–2024, we estimate firm and year fixed effects models with firm-clustered standard errors, measuring one-year-ahead crash risk by negative conditional skewness (NCSKEW) and down-to-up volatility (DUVOL). Consistent with prior work, the linear association between overall ESG disclosure and crash risk is statistically insignificant. Once a quadratic term is introduced, however, a U-shaped relationship emerges, and dimension-level tests show that this curvature is driven almost entirely by social disclosure: the linear term is negative and the squared term positive and significant for both crash risk proxies, with turning points of 0.3316 (NCSKEW) and 0.2918 (DUVOL). The U shape is confirmed by the formal test of Lind and Mehlum for both proxies, is robust to additional profitability and valuation controls and, most strongly for NCSKEW, to panel-corrected and feasible-GLS estimators. Low variance inflation factors confirm that multicollinearity does not affect the estimates. The findings support a “too-much-of-a-good-thing” interpretation: social disclosure improves transparency and reduces crash risk up to a moderate threshold, beyond which incremental, hard-to-verify narrative disclosure becomes consistent with impression management and heightens crash risk. Because the turning point lies below the first quartile of social disclosure, most sample firms already operate where additional disclosure raises crash risk. This study reframes the ESG crash risk debate around the level and dimension of disclosure rather than its mere quantity.

Journal of Risk and Financial Management doi: 10.3390/jrfm19070540

Authors: Sasawalai Tonsakunthaweeteam Siwarit Pongsakornrungsilp Pimlapas Pongsakornrungsilp Rachawit Photiyarach Salucknai Outtanasith Vikas Kumar

This study investigates the impact of environmental factors, financial conditions, and trade-related policy on Thailand’s textile and clothing exports under the ASEAN–China Free Trade Agreement (ACFTA) and the WTO’s Agreement on Textiles and Clothing (ATC), focusing on export performance and trade creation between 1990 and 2024, using strong panel data across 31 countries from 11 ASEAN–China member countries and 20 non-member countries. This study has been guided by Porter’s competitive advantage theory and the gravity trade framework. The analysis was conducted using STATA 18 to analyze the fixed-effects regression alongside a robust Poisson Pseudo-Maximum Likelihood (PPML) estimation. The results indicate that (1) improvements in environmental, trade, and financial conditions are associated with higher export performance, (2) the results do not support trade creation under ACFTA, with the estimated ACFTA coefficient being negatively associated with export volume, and (3) the post-ATC effect is not consistently supported across model specifications and is not statistically significant in the combined specification. The findings suggest that export performance is jointly influenced by these dimensions rather than by trade liberalization and the post-ATC agreement alone. We recommend that policymakers support firms in managing the transition costs associated with green production, strengthen international trade cooperation, and maintain macroeconomic stability to enhance the long-term export performance of Thailand’s textile and clothing industry. These findings provide evidence that environmental sustainability and macro-financial conditions remain important determinants of export performance under changing global trade conditions. Future research may incorporate additional variables, broader datasets, and alternative econometric approaches to further validate the robustness of the findings.

Journal of Risk and Financial Management doi: 10.3390/jrfm19070539

Authors: Nguyen Thi Hong Duyen Le Quoc Diem Nguyen Thao Hoa

How the maturity structure of corporate debt shapes firms’ capacity to withstand financial pressure remains understudied, particularly in bank-dependent emerging markets. This study examines whether greater reliance on short-term debt weakens firms’ ability to absorb financial shocks. Using quarterly panel data for non-financial listed firms on the Vietnamese stock market from 2015 to 2025, we construct an accounting-based measure of financial resilience (FR), defined as the ratio of earnings before interest, taxes, depreciation and amortization (EBITDA) to the sum of short-term debt and interest expense, and measure debt maturity structure (DMS) as the proportion of short-term debt in total interest-bearing debt. Firm fixed-effects models with quarterly time fixed effects and firm-clustered standard errors are used to estimate the relationship. The results consistently show that firms with a higher proportion of short-term interest-bearing debt exhibit significantly lower financial resilience across all model specifications. This negative relationship remains robust after controlling for alternative measures of financial leverage and using a logarithmic transformation of the dependent variable. The findings highlight the importance of debt maturity management as a key component of corporate financing strategy for firms and policymakers seeking to enhance financial resilience.

Journal of Risk and Financial Management doi: 10.3390/jrfm19070538

Authors: Nguyen Thi Mai Anh

This study investigates the determinants of the intention to adopt Environmental Management Accounting (EMA) in Vietnam’s steel industry by integrating the Technology–Organization–Environment (TOE) framework with Rational Choice Theory (RCT). While prior research often assumes that external pressure is directly associated with EMA adoption, this study argues that such effects are contingent upon managerial evaluation of perceived net benefits (PNB). Using survey data from 420 respondents and applying Partial Least Squares Structural Equation Modeling (PLS-SEM), the empirical results show that top management support has the strongest associations with adoption intentions and PNB is the strongest predictor of adoption intention and serves as the theorized mediating variable. This suggests that EMA adoption is more associated with PNB, as firms are more likely to adopt EMA when its expected benefits are perceived to outweigh implementation costs, organizational risks, resource commitments, and operating burdens. Given the limitations of cross-sectional data, these findings represent theoretically grounded associations rather than conclusive causal inferences. This study refines TOE-based explanation by incorporating a rational-actor perspective, while providing practical guidance for steel-firm managers to integrate EMA into existing operational routines.

Journal of Risk and Financial Management doi: 10.3390/jrfm19070537

Authors: Triana Arias Abelaira María Jesús Guillén Palomino Lázaro Rodríguez Ariza Carlos Díaz Caro

This study analyses the evolution of the financial literature on climate risk, examining the integration of artificial intelligence techniques into its measurement and management. To this end, a bibliometric approach is employed based on 221 articles indexed in the Web of Science Core Collection, using the Bibliometrix package. Moving beyond existing descriptive bibliometric reviews on ESG and green finance, the novelty of this paper lies in its analytical focus on how financial science operationalises quantitative AI mechanisms to price and integrate climate transition risk into asset and portfolio valuation. The structural analysis reveals that natural language processing (NLP) and digital transformation acting as driving motor themes, suggesting that the reviewed literature associates AI innovation policies with the mitigation of corporate greenwashing and enhance information transparency. Furthermore, while machine learning algorithms establish the cross-cutting predictive foundation for risk assessment, empirical evidence unveils a critical academic shift of traditional ‘financial performance’ towards a declining quadrant, indicating that empirical studies frequently find that that multi-phase investments in risk technologies do not yield immediate financial returns. Finally, the study maps a persistent geographical gap where emerging markets lack the data infrastructure of advanced economies, alongside isolated high-dimensional causal econometric niches like double machine learning. This analytical mapping provides key implications for global risk management and future quantitative research avenues.

Journal of Risk and Financial Management doi: 10.3390/jrfm19070536

Authors: Yan Noviar Nasution Donny Maha Putra

This study examines whether recognized intangible assets carry information about bank performance in an emerging market, and whether their information value is conditioned by the maturity of macro digital infrastructure. Using a balanced panel of 28 Indonesian commercial banks over 2015–2024 (280 firm-year observations), we estimate two-way fixed-effects models with macro digital infrastructure, an economy-wide principal component index of internet penetration, mobile and broadband subscriptions, and electronic payment volume as a cross-layer moderator. Intangible investment intensity, proxied by the ratio of reported intangible assets to total assets, shows weak direct associations with performance; only the operating efficiency ratio displays a marginally significant short-run cost, consistent with transition-cost dynamics. The central result is conditional: the interaction between intangible intensity and macro digital maturity is strongly significant for operating efficiency (β = −2.587, p = 0.005), with the implied efficiency cost contracting by a model-implied 88 percent across the observed range of digital maturity (an estimate computed from the estimated coefficients over the observed sample variation, not a structural causal magnitude). Heterogeneity is pronounced across regulator-defined bank tiers (KBMI): the four largest banks realize positive profitability effects, whereas mid-tier banks bear transition costs. Results are robust to Driscoll–Kraay standard errors, system GMM, sub-sample splits, and outlier exclusion. The findings show that the information value of recognized intangibles in banking is state-contingent, extending the intangible-asset and digitalization literature to emerging-market banking.

Journal of Risk and Financial Management doi: 10.3390/jrfm19070535

Authors: Fatma Zehri Raghad Alsudays Laila Aladwey

A This study examines the interplay between environmental, social, and governance (ESG) practices, artificial intelligence (AI) adoption, and financial performance within Saudi Arabia’s financial sector. It investigates whether AI adoption moderates the ESG–performance relationship, reflecting the sector’s ongoing digital transformation under Vision 2030. Drawing on 224 firm-year observations across banks, diversified financials, real estate investment trusts (REITs), and insurance companies, the study employs content analysis of annual reports to identify AI implementation. Panel regression models are used to test the effects of ESG practices on both accounting-based (ROE) and market-based (Tobin’s Q) performance measures, while examining AI’s moderating role. The results reveal that ESG practices significantly enhance accounting-based performance, particularly return on equity, while board size exerts a positive and board independence a negative influence. However, ESG does not significantly affect market-based valuation (Tobin’s Q). Notably, AI adoption negatively moderates the ESG–financial performance link, suggesting short-term challenges in integrating digital transformation with sustainability strategies. This study contributes to literature in three key ways. First, it provides new evidence from financial institutions in a developing economy—Saudi Arabia—where ESG and AI integration remains underexplored. Second, unlike previous research that proxies AI adoption through R&D expenditure, this study captures actual deployment of AI tools in operational activities. Third, it extends the ESG–performance debate by introducing AI adoption as a novel moderating factor. The findings offer actionable insights for managers and policymakers in emerging markets, underscoring the importance of developing organizational capabilities that harmonize AI-driven innovation with ESG principles to foster sustainable long-term value creation.

Journal of Risk and Financial Management doi: 10.3390/jrfm19070534

Authors: Luis Varona Castillo Jorge R. Gonzales Castillo

This study investigates the macroeconomic and microeconomic factors influencing the valuation of Bitcoin (BTC) from January 2011 to December 2025 utilizing an Autoregressive Distributed Lag (ARDL) model. The empirical results provide robust evidence supporting a long-term equilibrium relationship (cointegration) among the variables. Furthermore, the findings reveal a procyclical dynamic aligned with the US Federal Reserve’s monetary policy, alongside significant positive influences from the network’s active address count and computing power hash rate. Conversely, global market volatility exerts a statistically significant negative impact on Bitcoin’s price trajectories.

Journal of Risk and Financial Management doi: 10.3390/jrfm19070532

Authors: Ruben Gevorgyan Alisa Tanyan

Investors’ behavior in the auctions for government bonds is closely associated with the processing and valuing of information. This study explores investors’ behavior in relation to information in the context of the sovereign debt market in Armenia in August 2017 to December 2025. Armenia has a small financial market, which is relatively deep and involves only a small number of investors. This particular situation allows for testing the applicability of the rational inattention theory. While information in a small open economy can be abundant, it does not follow that information is valued in the same way. Investors in a small open economy focus their attention on monitoring some salient policy variables, including the central bank policy interest rate and headline inflation but ignore some more specific signals such as demand dynamics. We suggest that the spread between the cut-off yield and the weighted average yield in the auction can be used as a measure of information inattention. According to the rational inattention theory, investors allocate their attention strategically and focus on those signals that can be obtained easily and publicly. Therefore, our hypothesis is that the auction spread is consistent with partial information processing, whereby demand signals are underweighted relative to the policy rate. Indeed, the analysis suggests that the cut-off yield remains correlated with the policy rate, whereas the spread does not increase. This is consistent with the hypothesis that yield spreads reflect bounded rationality in attention allocation. During the periods of increased need for government borrowing, auctions become the key sources of signaling and thus need to be studied.

Journal of Risk and Financial Management doi: 10.3390/jrfm19070533

Authors: Alexander Vladimir Velez Flores Arturo Rafael Chayña Rodriguez Wildor Jazmany Jara Vilca Carlos Paul Hancco Ramos Esteban Marín Paucara Lucio Quea-Gutierrez Juan Carlos Chayña-Contreras Julian Apaza-Chino Mario Serafín Cuentas Alvarado Yesenia Fátima Llanque Añacata Anibal Sucari León

Gold’s price reflects currency, opportunity-cost, and safe-haven channels whose strength shifts across regimes, motivating an empirical, data-driven forecasting approach. This study develops a monthly gold price forecasting system for ASM sales-timing decisions in Peru (January 2020–June 2026) using macro-financial predictors including a geopolitical risk index and three U.S. monetary indicators, none of which were Granger-causal and were therefore excluded from the production set. After confirming non-stationarity and Johansen cointegration (four vectors), thirty-two model-feature-set combinations, including Elastic Net, Bayesian Ridge, and a PCA factor, were compared under strict temporal validation with bounded hyperparameter search. The selected model, Ridge regression on the CONTROL feature set, achieved a cross-validation MAPE of 2.29% and test MAPE of 3.62% (official)/3.15% (extended sensitivity window). It was benchmarked against random walk, historical mean, and exponential smoothing and evaluated via the Diebold–Mariano, Clark–West, encompassing, and Model Confidence Set tests (low-power caveats given the small sample). A dual-horizon Monte Carlo simulation, robust to heavy-tailed shocks, projected USD 4482/oz (December 2026) and USD 5106/oz (December 2027). A sales-timing backtest showed a statistically significant result (−0.67%) versus a passive strategy, indicating calibrated price information alone does not yet yield a reliable trading edge, supporting the model’s role as decision support rather than an autonomous trading signal.

Journal of Risk and Financial Management doi: 10.3390/jrfm19070531

Authors: Serkan Cantürk

Cities in the Majority World face a widening climate investment gap that is often attributed to the absence of suitable financing instruments. Green bonds promise to mobilise private capital for low-carbon urban infrastructure, yet they have diffused unevenly, leaving the economies with the greatest needs at the market’s margins. This study asks whether macroeconomic constraints—the cost of finance, monetary instability, and public indebtedness—systematically shape green bond issuance across emerging and developing economies. We assemble an original panel of 24 such economies over 2015–2024 (240 country-year observations) and estimate pooled ordinary least squares (OLS), random-effects, two-way fixed-effects, Tobit, and probit models with robust standard errors. The public debt-to-GDP ratio is positively associated with issuance in most specifications, though the strength of this relationship varies across estimators and it is not statistically significant in the preferred two-way fixed-effects model; the renewable energy share is consistently positive, while consumer price inflation shows no significant suppressive effect. A probit model of the extensive margin shows that public debt, the renewable energy share, and income per capita raise the probability of issuing among the economies for which the data permit estimation. The four lower-income Sub-Saharan economies in the sample fall outside this estimation owing to missing data, yet record no issuance whatsoever over the decade—a descriptive pattern consistent with the structural barriers the model identifies. The findings challenge the assumption that monetary stabilisation is a precondition for climate finance, pointing instead to capital-market depth and subnational fiscal capacity as the more binding constraints.

Journal of Risk and Financial Management doi: 10.3390/jrfm19070530

Authors: Natcha Saramas Supasuta Tuncharo Aroonrak Tunpanit

This study examines the short-run effect of carbon taxation on the growth rate of GDP per capita, the annual first difference in log GDP per capita, using a panel of 16 carbon-pricing economies spanning Europe, the Americas, Asia and Africa over 2020–2024. Country fixed-effects estimation with country-clustered robust standard errors follows formal model selection (F-test, Hausman test), checked for cross-sectional dependence. Three baseline specifications are estimated, Model 3 excluding the COVID-19 dummy as a robustness check; a fourth adds carbon tax interaction terms with inflation, investment, energy intensity and political stability to test whether these factors condition the relationship. A higher carbon tax rate has a small but statistically significant negative effect on growth across all three baseline models (a USD 10 increase implies roughly a 1.2-percentage-point reduction in annual growth, preferred specification); none of the interaction terms is significant, indicating no detectable conditioning effect. Investment shows a robust positive association with growth; inflation, a robust negative one. Energy intensity and the COVID-19 dummy enter with signs contrary to expectations once year fixed effects are excluded, and the carbon tax coefficient loses significance under a lagged specification, cautioning against a strictly causal reading. Findings support pairing carbon tax design with investment and price-stability policies.

Journal of Risk and Financial Management doi: 10.3390/jrfm19070529

Authors: Ioannis Kalialakis Antonios Kostas Vasileios Zoumpoulidis Christos Grose Dimitrios N. Koufopoulos Michail Fygkioris

Environmental, Social, and Governance (ESG) criteria are increasingly central to corporate transparency, risk oversight, and sustainable finance, yet evidence on the governance pillar in Greece remains limited. This study examines a pooled cohort of 68 firms appearing in the Athens Stock Exchange ESG Index during 2021–2023, with annual analytical samples of 66, 68, and 65 firms. Using annual reports, corporate governance statements, and sustainability disclosures, it evaluates board size, board independence, gender diversity, CEO–Chair structure, committee architecture, internal audit disclosure visibility, and external audit concentration. Board size remained stable. Proportional board independence peaked in 2022 and remained slightly above its 2021 level in 2023. The aggregate female board seat share increased, although the 2023 rise partly reflected a smaller denominator. CEO–Chair duality was persistent but non-monotonic, internal audit disclosure visibility changed only modestly, and top-two audit provider concentration increased in 2023. The findings are interpreted as selective governance institutionalization: visible, threshold-based arrangements adjust more readily than capability-intensive mechanisms involving authority, internal controls, specialist oversight, and assurance capacity. Greek, EU, and OECD benchmarks indicate partial regulatory readiness. The study provides a longitudinal governance baseline but does not estimate causal performance effects or certify firm-level legal compliance.

Journal of Risk and Financial Management doi: 10.3390/jrfm19070528

Authors: Jampal Dolma Annop Thananchana Tirapot Chandarasupsang

Regulatory permissiveness is widely prescribed as the primary institutional lever for digital asset adoption. This study challenges that prescription. Analyzing NFT and DeFi adoption across 105 countries using Principal Component Analysis (PCA)-constructed composite indices and multivariate Ordinary Least Squares (OLS) regression, we find that the Frontier Technology Readiness Index (FTRI) is the dominant structural correlate across all specifications, consistently outperforming competing explanatory variables. Regulatory environments neither independently explain adoption nor are associated with it linearly: both permissive and restrictive environments outperform mostly prohibited jurisdictions, suggesting that regulatory clarity rather than permissiveness is the operative institutional dimension. NFT and DeFi markets follow empirically distinct pathways: NFT adoption shows stronger associations with digital marketplace maturity while DeFi is more closely associated with technological infrastructure, suggesting that treating Web3 as a homogeneous policy category is unwarranted. National income conditions how effectively technological readiness is associated with adoption gains, with structural determinants exhibiting considerably reduced explanatory power in lower-middle-income economies. For policymakers, these findings reframe the debate: the primary structural correlate of digital asset adoption is technological capacity, not regulatory stance, and below a development threshold, neither intervention is reliably associated with adoption gains.