Journal of Risk and Financial Management doi: 10.3390/jrfm19060449

Authors: Muhammad Enamul Haque Mahmood Osman Imam

The study investigates the state-dependent dynamics of overconfidence in the Bangladesh equity market by exploring the relationship between market returns and trading volume within a nonlinear information-theoretic framework. Building up on the traditional return–volume literature, the study differentiates between total market returns and unexpected returns, with the latter representing unexpected information shocks obtained using the Market Index Model. Transfer Entropy with bootstrap inference estimates the directional and asymmetric information flows across five different market states, namely: bullish, bearish, crisis, extended crisis, and COVID-19. The evidence suggests that the overconfidence biases in aggregate market returns are small and intermittent and are reflected in poor and unstable information flow between market returns and trading volume. In comparison, unexpected market returns have a directionally significant impact on trading behavior, which supports the behavior of state-dependent overconfidence. The findings also reveal that overconfidence is higher in normal and bullish market situations but drops significantly in crisis-based situations. The asymmetric analysis indicates increased trading responses to negative returns shocks, as it is more evident that investors are more sensitive to losses and recovery expectations. The research adds to behavioral finance literature on frontier markets through an unexpected return decomposition with nonlinear causality model. The results have serious implications on market surveillance, assessment of investor behavior and design of regulatory policies.

Journal of Risk and Financial Management doi: 10.3390/jrfm19060448

Authors: Ioana Andrioaia Veronica Grosu Svetlana Mihaila Alina Butnaru Ciobotar

Corporate sustainability and the reliability of ESG reporting have gained relevance in the evaluation of listed companies, particularly in emerging capital markets, where reporting practices are still in their early stages of development. The purpose of this study is to analyze the relationship between the quality of ESG reporting, the risk of greenwashing estimated using a proxy derived from reported information, and the market value of companies listed on the Bucharest Stock Exchange. The research employs a mixed-methods design, involving content analysis of annual reports, sustainability reports, and sustainability statements for 25 companies over the 2020–2024 period. The scores corresponding to the Environmental, Social, and Governance dimensions, as well as the proxy for greenwashing risk, were developed using an ordinal scoring grid, which was validated through inter-rater assessment. During the course of the study, the empirical relationships were tested using pooled OLS specifications on short panel data, incorporating the natural logarithm of market capitalization, financial controls, year effects, and sector dummy variables. The results highlight the presence of an association between the quality of ESG reporting and market value, particularly for environmental and social dimensions, while the greenwashing risk proxy exhibits a limited statistical influence. The study contributes to the literature on ESG reporting in emerging markets and highlights the need for a cautious interpretation of indicators constructed based on corporate disclosures.

Journal of Risk and Financial Management doi: 10.3390/jrfm19060447

Authors: Rola Shawat Abanoub Wassef Yara Ibrahim Ahmed Hassanein Hosam Moubarak Hebatallah Badawy

This study examines how assurance level and assurer type jointly influence non-professional investors’ reactions to sustainability disclosures in an emerging market context. It employs a controlled 2 × 2 mixed-design experiment that manipulates assurance level (limited vs. reasonable) and assurer type (audit firm vs. non-audit firm). Data were collected from MBA and DBA students in Egypt as proxies for non-professional investors. Investor reaction is captured through multiple measures, including perceived sustainability performance, reliance on sustainability information, investment intention, stock valuation, and decision confidence. Non-parametric statistical techniques are used to test hypotheses, complemented by exploratory machine learning using SHAP values. The results provide strong and consistent evidence that the assurance level is the dominant factor shaping investor reactions. Reasonable assurance significantly enhances investor judgments across all key measures, whereas the type of assurer does not have a statistically significant independent effect. Additional analyses reveal that reasonable assurance from a non-audit firm elicits more favorable reactions than limited assurance from an audit firm, underscoring the primacy of assurance strength over provider identity. Exploratory findings further indicate that assurance influences investment decisions primarily through perceived sustainability performance and reliance on information. This study contributes to the literature by clarifying the relative roles of assurance level and assurer type and providing novel evidence from an emerging market setting (i.e., Egypt). The findings offer important implications for firms, assurance providers, and regulators seeking to enhance the credibility and decision usefulness of sustainability reporting.

Journal of Risk and Financial Management doi: 10.3390/jrfm19060446

Authors: Wa’el Al-Karaki Aldi Ardilo Ahmed Eltweri Yuan Zhai Gbemisola Ogbolu

This study examines the relationship between emotional intelligence and enterprise risk management maturity among risk leaders in Indonesia’s financial services sector, adopting a workplace accountability perspective to explain how leadership behavioural competencies support effective risk ownership, risk communication, and accountable risk decision-making. Drawing on survey data from 280 board-level executives holding the Qualified Risk Governance Professional credential, the study measures emotional intelligence using the Bar-On EQ-i and enterprise risk management maturity using the RIMS Risk Maturity Model. The findings reveal a strong and positive association between emotional intelligence and enterprise risk management maturity, with interpersonal competence and adaptability exhibiting the strongest associations with ERM maturity, while no significant differences are observed across job roles or organisational size. By empirically examining the association between leadership emotional capabilities and the institutionalisation of risk governance, the study contributes to global management and the literature on risk by extending enterprise risk management research beyond technical frameworks and compliance models, particularly within emerging market contexts. The results suggest that emotional intelligence may represent a transferable governance capability that is relevant to organisations operating in complex, uncertain, and globally interconnected environments. Practically, the study suggests that emotional intelligence development may represent a useful complement to leadership and risk capability programmes aimed at supporting risk culture, cross-functional engagement, and accountability.

Journal of Risk and Financial Management doi: 10.3390/jrfm19060445

Authors: Bochra Issa Sana Ben Abdallah Foued Badr Gabsi

This study investigates how eco-efficiency and socio-efficiency influence firms’ default risk across the European financial, industrial, and consumer service sectors from 2010 to 2024. This study aims to determine whether integrating environmental and social performance into corporate strategies mitigates financial distress over time. The Pooled Mean Group ARDL estimator was employed to capture the short- and long-term dynamics. The results indicate that higher eco- and socio-efficiency significantly reduce long-term default risk, particularly in the financial and industrial sectors. Short-term effects were found to be insignificant, suggesting that sustainability benefits gradually emerged. This study offers novel sector-specific evidence linking sustainability efficiency to default risk in European firms and provides insights into how environmental and social efficiencies enhance corporate resilience and financial stability.

Journal of Risk and Financial Management doi: 10.3390/jrfm19060444

Authors: Yara Ibrahim Khaled Hussainey Taghred Mokhtar Sayed Moawad

This study investigates the determinants and predictability of stock return volatility by integrating firm-specific financial characteristics with advanced econometric and volatility modeling techniques. Using an unbalanced panel dataset comprising 1596 firms and 19,752 firm-year observations from MENA stock markets over the period 2010–2024, the analysis employs fixed-effects panel regression models, conditional volatility models, and machine learning-based forecasting approaches. Following extensive diagnostic testing, including tests for heteroskedasticity, serial correlation, cross-sectional dependence, and model specification, a two-way fixed-effects model with Driscoll–Kraay standard errors is adopted as the preferred estimation framework. The results indicate that liquidity ratio, cash ratio, sales growth, firm age, lagged volatility, and lagged returns are significant determinants of stock return volatility, whereas leverage, tangibility, board independence, firm size, Tobin’s Q, and profitability do not exhibit statistically significant effects after controlling for firm-specific and time-specific heterogeneity. The volatility analysis reveals substantial persistence in stock return volatility, with the EGARCH-t specification providing the best fit among the competing GARCH-family models according to the Akaike Information Criterion. The estimated asymmetry parameters indicate that volatility responds differently to positive and negative shocks, supporting the presence of asymmetric volatility dynamics and the suitability of asymmetric volatility models. The forecasting analysis shows that advanced machine learning and deep learning models achieve competitive predictive performance; however, differences in predictive accuracy across models are generally modest.

Journal of Risk and Financial Management doi: 10.3390/jrfm19060443

Authors: Mohammed Alhashim

The study investigates the relationship between political signal quality, uncertainty measures, and sovereign CDS connectedness among major oil-exporting countries using a time-varying parameter vector autoregressive (TVP-VAR) approach along with regression and quantile-based techniques. The findings indicate a moderate degree of sovereign connectedness, suggesting the presence of cross-country spillover effects in sovereign risk markets. The results further show that Qindex is negatively associated with sovereign connectedness both in the case of normal market conditions and mild stress levels. In contrast, conventional uncertainty indicators appear to exert relatively weaker effects across model specifications. Overall, the findings suggest that the informational quality of political communication may play a role in shaping sovereign spillover dynamics alongside broader macroeconomic and financial conditions.

Journal of Risk and Financial Management doi: 10.3390/jrfm19060442

Authors: Rajib Chakraborty Lan Sun Urmee Ghose Ayub Islam

This present study aims to investigate the influence of board characteristics on the level of climate change disclosures and the extent to which the implementation of the corporate governance code (CGC) moderates these factors. The ordinary least squares statistical method is used to analyze the panel data. In addition, the Tobit regression model is also estimated to check the robustness of the study findings. This study suggests that larger board sizes, more independent directors, and board meeting frequency are positively associated with higher levels of climate change disclosure. However, the study does not find any association between CEO duality, foreign ownership, and climate change disclosure. In addition, it is also observed that CGC can enhance the influence of board characteristics on the likelihood of disclosing climate information. The study offers necessary directions for regulatory authorities, business firms, and practitioners to be more transparent in disclosing climate information and extends guidelines to tackle climate change disclosure issues.

Journal of Risk and Financial Management doi: 10.3390/jrfm19060441

Authors: Batuhan Karabiber Tayfun Tuncay Tosun

This study empirically aims to analyze the impact of primary monetary policy stance and transmission mechanisms of the European Central Bank (ECB)—such as the total assets of the ECB, long-term interest rate based on the government bond yields, and the EURUSD exchange rate—on major volatile cryptocurrencies like Bitcoin and Ethereum, as well as the leading stablecoin Tether. To this end, the study employs the linear Autoregressive Distributed Lag (ARDL) and the Bootstrap ARDL (BA-ARDL) procedures, robust approaches with limited data in time series analysis. The dataset consists of monthly data over the period from January 2019 to December 2025. We summarize the novel and robust primary empirical results of our study as follows: First, (i) it is revealed that the ECB’s balance sheet expansion has encouraged Bitcoin and Ethereum, yet has also, to a limited extent, suppressed Tether. Secondly, (ii) while the ECB’s long-term interest rate negatively impacts the prices of Bitcoin, Ethereum, and Tether, the negative impact on Tether is relatively weaker. Finally, (iii) the EURUSD exchange rate positively affects Ethereum, while its effect on Bitcoin is not statistically significant. On the other hand, at a 10% significance level, EURUSD has a weak negative effect on Tether. In conclusion, the empirical evidence demonstrates that the primary monetary policy stance and transmission mechanisms of the ECB influence the leading digital assets in distinct ways. Taking our findings into account is crucial for designing the digital euro in terms of financial stability and regulatory framework. Finally, we offer sound policy implications for the ECB based on empirical findings.

Journal of Risk and Financial Management doi: 10.3390/jrfm19060440

Authors: Wiwik Utami Erna Setiany Rieke Pernamasari Anwar Allah Pitchay

This study examines how innovation and green management influence business sustainability and firm value in Indonesian healthcare companies. Innovation is measured using Value-Added Intellectual Capital (VAIC) efficiency, green management through Environmental, Social, and Governance (ESG) scores, business sustainability as carbon emission disclosure (CEDI), and firm value as Market Value Added (MVA). The sample consists of 123 firm-year observations from healthcare firms listed on the Indonesia Stock Exchange (2019–2023). Based on the capital-based theory of sustainability and stakeholder theory, hypotheses are tested using fixed-effect panel regression, Baron and Kenny mediation analysis, and Structural Equation Modelling (SEM). The results show that VAIC is the only significant predictor of MVA, with a consistent positive effect across all model specifications. Neither ESG Score nor CEDI shows a significant effect on market value, indicating that sustainability disclosure has not yet translated into measurable financial returns in this context. Within the structural model, ESG governance is the strongest predictor of carbon disclosure, while firms with higher VAIC tend to prioritise value creation over environmental reporting. All mediation hypotheses are rejected. These findings suggest that intellectual capital and sustainability practices currently function as separate strategic priorities in Indonesian healthcare. Intellectual capital produces tangible market value in the short term, while the financial benefits of sustainability disclosure are likely to emerge only as Indonesia’s ESG reporting standards and investor awareness continue to develop.

Journal of Risk and Financial Management doi: 10.3390/jrfm19060439

Authors: Sunnatov Yusuf Usmonovich

Corporate balance sheets report aggregate equity and liability totals but conceal the internal allocation of financing sources across asset categories—an identification problem that conventional econometric methods cannot resolve without additional parametric assumptions. This paper develops a maximum entropy (ME) panel estimator to recover two latent scalar parameters: x ∈ (0,1), the share of equity capital directed toward long-term asset financing, and y ∈ (0,1), the corresponding debt allocation share. Grounded in maximum entropy principle, the estimator selects the unique parameter vector that satisfies the mean-level balance-sheet constraint while maximising joint Shannon entropy—the least-biassed solution consistent with observable data. The closed-form logistic representation yields a scalar Lagrange multiplier λ*, interpreted as a financing pressure index, recoverable via bisection in at most 21 iterations at tolerance ε = 10−5. Building on the ME estimates, we introduce a continuous matching alignment index M* = x* − y* that measures the degree of compliance with the financial matching principle along a continuous spectrum rather than as a binary categorisation. Applied to a ten-firm, cross-sectoral panel spanning Technology, Finance, Energy, and Automotive sectors over an observation window spanning 2001 to 2025 (with firm-specific subperiods reflecting differences in IPO dates and data availability), the framework reveals substantial heterogeneity in latent financing flows: equity allocation shares range from 30.1% (NVIDIA) to 75.1% (ExxonMobil), while debt allocation shares span 37.1% to 77.5%. Across the panel, only Meta exhibits substantial positive matching alignment, while Microsoft, ExxonMobil, Apple, and Tesla show only very slight differences that fall within the neutral band, and the remaining firms show varying degrees of structural departure from the matching benchmark; the thresholds used to summarise these descriptive labels are interpretive aids rather than re-imposed binary criteria, and the substantive ranking of firms along M* does not depend on the specific threshold values adopted. The ME solution’s entropy H(x*, y*) and the normalised diversification index D(x*, y*) describe allocation balance under the estimator’s information–theoretic criterion rather than independently observed firm complexity; in the present sample, the cross-firm ordering of these values is not recovered by firm size, leverage, or sector classification alone. These findings, based on a ten-firm case-study panel with time-invariant allocation parameters, should be interpreted as descriptive patterns of the present sample rather than statistically validated regularities. They provide a theoretically rigorous and computationally tractable identification of unobservable corporate financing flows, with potential implications for capital structure theory, financial risk assessment, and balance sheet analysis that would benefit from validation on larger and more representative samples in future work.

Journal of Risk and Financial Management doi: 10.3390/jrfm19060438

Authors: Mohamed Haffar Shatha Mustafa Hussain Amer Alaya Serap Emik Mohammad Jammal

This study examines how the relationship between perceived financial accounting disclosures (FAD) and investor reactions to negative financial news (IRNFN) is conditioned by two individual-level moderators among 310 retail investors holding shares in project-based organisations (PBOs) listed on the Dubai Financial Market and Abu Dhabi Securities Exchange. The two moderators are framing bias susceptibility, a cognitive predisposition to be influenced by presentational form, and AI channel reliance (AICR), the extent to which investors rely on AI-mediated information channels—including algorithmic news aggregators, robo-advisory tools, AI-curated social media feeds, and automated sentiment-scored financial alerts—for receiving and interpreting corporate disclosures. Drawing on Behavioural Finance Theory and the Theory of Planned Behaviour, the study investigates whether the strength of the FAD–IRNFN association depends on these cognitive and informational processing conditions. The measurement model was estimated using confirmatory factor analysis in AMOS 25, and the moderation hypotheses were tested through path analysis with mean-centred composite scores and bias-corrected bootstrap inference, with a latent interaction robustness check reported in parallel. AI channel reliance emerged as a substantial moderator of the FAD–IRNFN relationship, while framing bias provided a smaller, marginally significant moderating effect. The findings are consistent with the theoretical expectation that, in AI-mediated information environments, the perceived quality and presentation of complex disclosures are associated with stronger, rather than weaker, investor reactions to negative news. Because the design is cross-sectional and based on self-reported data, the results are interpreted as associations rather than causal effects, with implications for disclosure regulation, corporate communication, and AI platform design in the UAE and comparable emerging markets.

Journal of Risk and Financial Management doi: 10.3390/jrfm19060437

Authors: Önder Dorak Duygu Şengül Çelikay

This study aims to examine whether and how environmental, social, and governance (ESG) performance is related to corporate tax avoidance in a non-linear and threshold-dependent manner using explainable machine learning. Based on 6461 firm-year observations of publicly listed European firms over the 2018–2023 period, this study employs a multi-algorithmic machine-learning classification framework. Model interpretability is achieved through SHAP, which identifies feature importance, marginal effects, interaction patterns, and ESG-related threshold dynamics. The results demonstrate that the ESG–tax relationship is highly non-linear. While the Country and Industry factors establish baseline tax risks, ESG sub-dimensions act as critical firm-level determinants. Specifically, high Corporate Social Responsibility (CSR) and Human Rights scores effectively constrain tax avoidance. In contrast, exceptionally high Management scores correlate with increased tax-avoidance risk. These findings support the legitimacy buffer argument and show that strong governance may also reflect managerial sophistication and capacity for less visible tax planning. The study contributes by revealing non-linear ESG threshold effects and by demonstrating how XAI/SHAP can distinguish between symbolic and substantive sustainability practices in corporate tax behavior.

Journal of Risk and Financial Management doi: 10.3390/jrfm19060436

Authors: Souvik Banerjee Amarnath Mitra Shalini Aggarwal

The journal retracts the article titled, “Impact of Environmental, Social, and Governance Parameters on Financial Performance of Firms: A Cross-Country Analysis” (Banerjee et al [...]

Journal of Risk and Financial Management doi: 10.3390/jrfm19060435

Authors: Luyanda Majenge Simiso Msomi

This study investigates whether government spending stimulates economic growth by applying the Keynesian theoretical framework across varying economic conditions. The analysis uses annual data from 1980 to 2024 to explore how fiscal dynamics change over time and across regimes. It employs the NARDL model to evaluate asymmetric effects, the STAR model to capture regime dependence, and threshold Granger causality tests to assess causal relationships across spending regimes. These approaches enable a detailed examination of asymmetry, structural breaks, and nonlinear adjustment in the spending–growth relationship. The results show that Keynesian effects remain present across economic regimes but operate only in the short run without generating sustained long-term output gains. The absence of long-run cointegration is consistent with the presence of short-run dynamic multipliers, because these multipliers reflect temporary adjustments rather than permanent effects. The findings indicate that increases and decreases in government spending have proportionate effects on output, confirming a symmetrical Keynesian response. Government debt demonstrates a consistently negative and statistically robust influence on short-run growth. Corruption, measured using an index capturing governance quality, heightens policy ineffectiveness during periods of high public expenditure. Threshold causality tests reveal that government spending Granger causes economic growth in both low and high spending regimes, confirming the short-run stimulative potential of fiscal policy. Consequently, the study supports countercyclical fiscal interventions while emphasising the importance of prudent debt management and governance reforms to reduce fiscal risks.

Journal of Risk and Financial Management doi: 10.3390/jrfm19060434

Authors: Ni Wayan Rustiarini Ni Putu Shinta Dewi Ni Made Sunarsih Sharifah Norzehan Syed Yusuf

This study investigates whether female representation on corporate boards and carbon emissions disclosure (CED) are interrelated in an emerging market. Using critical mass theory (CMT), which posits that female directors can surely impact the decisions of boards once they reach critical mass, we examine whether the presence of three women on the board or approximately 30% board membership is necessary in Indonesia. This context is important since (i) boards are still a long way from representing the demographics of Indonesians due to low female representation on boards; (ii) in many cases board sizes are too small for meaningful communication between two directors; and (iii) regulations surrounding environmental disclosure barely exist relative to more developed markets. Based on panel data from Indonesian manufacturing firms, the study demonstrates that the effect of board gender diversity on CED is nonlinear and contextually dependent. The results demonstrate that the core idea of CMT is not fully supported in this setting. The presence of even a single female director is linked to higher levels of carbon emissions disclosure, signaling that female directors likely play a substantive role and serve more than just symbolic purposes. That said, improvements associated with having women on the board do not increase progressively with more females taking a seat around the table. However, the positive effect is diminished and becomes statistically insignificant at higher levels of female representation. The results also imply that firms whose board of directors contain moderate levels of gender diversity (with 20–40% women on the board) engage in Type I CED to the highest extent. However, boards nearing a gender balance do not seem to garner any further benefits from disclosure.

Journal of Risk and Financial Management doi: 10.3390/jrfm19060433

Authors: Faycal Chiad

As countries accelerate their transition toward low-carbon economies, understanding the drivers of green finance is essential for shaping effective sustainability policies. This study investigates how FinTech development, digitalization, financial access, and structural factors influence public renewable energy investment—a measurable dimension of green finance—across 29 European countries over 2000–2022, using the Method of Moments Quantile Regression (MMQR). Results reveal strong distributional heterogeneity: FinTech consistently promotes green investment across all quantiles, digital infrastructure amplifies this effect in advanced regimes, and financial access is most binding at lower quantiles. Natural resource dependence exerts a persistent resource curse constraint that intensifies at higher quantiles. Three robustness strategies—2SLS-IV and quantile fixed effects QFE confirm a causal positive FinTech effect. Quantile-specific policy implications are derived: early-stage green investors should prioritize financial access and digital infrastructure, while advanced economies should deepen FinTech adoption and address resource-dependence constraints.

Journal of Risk and Financial Management doi: 10.3390/jrfm19060432

Authors: N’Adoi Aboagye Saralees Nadarajah

Exchange-rate forecasting requires modelling non-stationary dynamics, heavy-tailed shocks, and complex temporal dependencies. However, forecasting performance in emerging-market currencies is fundamentally constrained by intrinsic dynamical instability, while most existing approaches are evaluated primarily through predictive accuracy rather than the predictability limits of the underlying system. This paper develops a predictability-aware framework that combines nonlinear dynamical diagnostics with a Lévy-driven neural stochastic differential equation model. Drift and diffusion are parameterized by neural networks and driven by α-stable Lévy motion, enabling the representation of non-Gaussian fluctuations, abrupt shocks, and regime changes. To learn under discontinuous dynamics, we introduce a structurally constrained training objective based on a strong-form discretization of the underlying SDE. To characterise intrinsic predictability, we employ phase-space reconstruction and maximal Lyapunov exponent estimation. These diagnostics are interpreted as finite-sample measures of trajectory divergence and effective instability in a stochastic system, rather than evidence of low-dimensional deterministic chaos—a distinction motivated by well-documented limitations of chaos testing in financial data. Experiments on multiple West African currency pairs demonstrate competitive short-horizon forecasting performance relative to econometric and neural baselines while providing a principled framework for analysing predictability degradation under heavy-tailed stochastic dynamics. Across currencies and model classes, forecasting accuracy deteriorates beyond horizons comparable to the estimated Lyapunov time, suggesting that forecast degradation reflects intrinsic dynamical instability rather than model-specific limitations. The results support the view that reliable exchange-rate prediction is fundamentally a short-horizon problem and illustrate how stochastic dynamical modelling and predictability diagnostics can be combined to characterise forecasting limits in heavy-tailed financial systems.

Journal of Risk and Financial Management doi: 10.3390/jrfm19060431

Authors: Mattia Iotti Giovanni Ferri Alberto Calugi

Under Article 2135 of the Italian Civil Code, agricultural biogas firms represent a strategic expansion of traditional farming boundaries. By driving corporate diversification, environmental sustainability, and circular economy objectives, these firms are attracting substantial investment within the European Union and particularly in Italy. However, the bioenergy sector is structurally characterized by high capital intensity and low asset turnover efficiency, necessitating extensive external financing. Despite these unique dynamics, empirical evidence regarding their capital structure remains scarce. To address this literature gap, this study analyzes a 10-year balanced panel dataset comprising 350 firm-year observations, representing the most extensive research conducted to date on specialized Italian agricultural biogas firms. To answer the research questions (RQs), financial ratios (FRs) were calculated from financial statement (FINSTAT) data by applying the DuPont decomposition framework. The main findings are that (1) firms exhibit high profitability, but with some cases of loss and equity erosion; (2) firms exhibit low capital turnover and some cases of short-term financial unsustainability; (3) capital structure is often characterized by excessive debt. Our findings reveal a capital-intensive sector that, while profitable, remains vulnerable to financial instability. We provide actionable insights for practitioners and policymakers to foster a culture of financial sustainability. Our findings help mitigate information asymmetries, fostering more transparent market operations and ensuring that public subsidies are channeled into resilient capital structures.

Journal of Risk and Financial Management doi: 10.3390/jrfm19060429

Authors: Iulian Dascalu Bogdan-Ștefan Ionescu Veronica Grosu Alina Butnaru Ciobotar

This exploratory study investigates the application of the EU Taxonomy within the Romanian capital market, focusing on companies included in the BET Index and analyzing discrepancies between declarative sustainability commitments and actual technical compliance. Employing documentary content analysis and descriptive statistical design, the research introduces a convergence matrix that compares declarative intensity with taxonomic potential, mainly reflected through eligible turnover and complemented by CapEx-related indicators. The findings suggest a systemic execution gap. Despite significant eligibility in certain cases, technical alignment tends to remain very limited, mainly due to the bureaucratic and practical constraints involved in demonstrating compliance with the Do No Significant Harm criteria. Consequently, the analysis identifies a possible cross-sectional decoupling pattern: entities with low eligibility tend to compensate through extensive narrative disclosures, whereas those with higher eligibility maintain more pragmatic and technical communication. Furthermore, Capital Expenditure appears to be used as a forward-looking mechanism to project future transition efforts. The study concludes that, in emerging markets, the EU Taxonomy currently operates as a regulatory-technical proxy, emphasizing the need to distinguish substantive compliance from formal, narrative-driven disclosure.

Journal of Risk and Financial Management doi: 10.3390/jrfm19060430

Authors: Domenico Vicinanza

Financial crises are usually identified through drawdowns, volatility, and changes in returns, but these indicators do not directly describe whether the recurrence structure of market behaviour changes during a shock. This study tests Laminarity, a Recurrence Quantification Analysis measure derived from vertical structures in recurrence plots, as a nonlinear diagnostic of persistence and market-regime structure during the COVID-19 market shock. Daily data for the Dow Jones Industrial Average, S&P 500, and NASDAQ Composite from 2018 to 2022 are analysed using adjusted prices and log returns. Rolling-window Recurrence Quantification Analysis is applied across alternative window lengths and recurrence thresholds, testing crisis-responsive and longer robustness windows, as well as sparse, intermediate, and denser recurrence definitions. Drawdown and rolling volatility are used as descriptive benchmarks for cumulative loss and fluctuation intensity over the same stress episode. The results show that conventional indicators identify the COVID-19 shock clearly. Price-based Laminarity generally increases during the stress period, consistent with a more persistent crisis trajectory in price levels. Return-based Laminarity is more heterogeneous, with some specifications showing Laminarity loss and others increases. The findings do not support Laminarity as a universal crisis-warning signal, but as a parameter-sensitive diagnostic of recurrence structure, especially when interpreted alongside related RQA metrics.

Journal of Risk and Financial Management doi: 10.3390/jrfm19060428

Authors: Dinko Primorac Ivona Huđek Kanižaj Ana Mulović Trgovac Željka Marčinko Trkulja

Efficient allocation of capital toward environmental, social, and governance (ESG) objectives has become a critical challenge for emerging economies pursuing sustainable development and financial resilience. While prior research has primarily focused on ESG investment volumes, considerably less attention has been devoted to the efficiency with which financial and institutional systems transform capital into measurable sustainability outcomes. This study introduces the concept of ESG-Oriented Capital Allocation Efficiency (ECAE) and develops a hybrid multicriteria decision-making (MCDM) framework to evaluate its performance across 24 emerging market economies during the period 2021–2025. The proposed framework integrates DEMATEL, ANP, entropy weighting, TOPSIS, and VIKOR methods to capture causal relationships, interdependencies, weighting structures, and comparative efficiency rankings. The results identify governance effectiveness, ESG policy stability, and regulatory quality as the most influential drivers of ECAE, while higher ESG investment volumes alone do not necessarily generate superior sustainability outcomes. Sensitivity analysis confirms the robustness of the ranking results across alternative weighting scenarios. The findings suggest that strengthening institutional quality, policy coherence, and governance effectiveness is essential for improving sustainable finance outcomes. The study contributes to the sustainable finance literature by providing a policy-oriented framework for evaluating how effectively emerging market economies translate ESG-oriented capital into tangible sustainability performance.

Journal of Risk and Financial Management doi: 10.3390/jrfm19060427

Authors: Mostafa E. Shahen Mahmoud Otaify Hanan Amin Mohamed Ahmed Rady

The future of Earth depends on the investment choices of companies and individuals. Over the past decade, investment decisions of individuals have been intensely studied by researchers in developed countries. Yet, very few studies focused on these investment decisions in developing countries using an experimental approach. This study adopts an experimental approach to examine the impact of ESG awareness on the sustainable investment decisions of undergraduate students in Egypt. In the experiment, subjects were asked to watch a video on investment basics (control) and investment and ESG basics (treatment). After that, the subjects were asked to choose between two choices, one sustainable (ESG choice) and one unsustainable with and without a return difference. After controlling for demographic characteristics, personal traits, financial knowledge, and risk tolerance, the results indicate that ESG awareness increases the probability of making sustainable investment decisions. Moreover, the findings indicate that sustainable investing among individuals is conditional on higher or equal returns than conventional investing. The study provides practical insights for professionals and policy recommendations.

Journal of Risk and Financial Management doi: 10.3390/jrfm19060426

Authors: Sana’ Atari Ruaa BinSaddig Reem Khamis Bahaa Subhi Awwad

This study investigates the key determinants of bank stability and profitability in commercial and Islamic banks listed on the Amman Stock Exchange (ASE) in Jordan, with a focus on credit risk and capital adequacy during the period 2018–2024. Using panel data from 15 banks, the study applies fixed effects regression models with clustered standard errors. Liquidity is proxied by the loan-to-deposit ratio (LDR), credit risk by the loans loss provisions-to-total loans ratio, and capital strength by the equity-to-assets ratio, alongside a COVID-19 dummy and an interaction term between liquidity and credit risk. Financial performance and stability are measured using return on assets (ROA), return on equity (ROE), and the logarithmic Z-score. The findings indicate that credit risk has a significant negative effect on both bank performance and financial stability, whereas capital adequacy exerts a positive and significant effect. The COVID-19 pandemic negatively affected financial performance and stability, while liquidity (LDR) shows no significant direct effect. The interaction between liquidity and credit risk was statistically insignificant across all estimated models, suggesting that credit risk remains the dominant determinant regardless of liquidity conditions. The study highlights the importance of effective credit risk management and strong capital buffers in enhancing bank resilience. It contributes to the literature by providing recent evidence from the Jordanian banking sector and by incorporating multiple performance measures, a pandemic shock variable, and risk interaction effects to better understand bank stability within a unified empirical framework for an emerging banking market.

Journal of Risk and Financial Management doi: 10.3390/jrfm19060425

Authors: Salvador Cumaio Zélia Serrasqueiro Mara Madaleno

This paper aims to analyse the contributions of studies that link financial literacy (FL) and behavioural finance (BF) in relation to saving and debt behaviours, considering both global and developing economy perspectives. This study employs a semi-systematic literature review (S-SLR) to examine 109 articles sourced from Scopus and Web of Science, published between 2011 and 2024. The evidence shows mixed results regarding the influence of FL and behavioural factors on saving and debt behaviours, with saving receiving greater attention. Most research is quantitative and concentrated in developed economies, although some developing Asian economies are also represented. The in-depth analysis of developing economies indicates that, while FL training and intervention-based approaches are relatively well established, studies integrating FL and BF remain scarce, limiting a comprehensive understanding of financing decisions. Future research should therefore prioritise the developing contexts, adopt more diverse methodologies, and incorporate psychological variables. This S-SLR offers an integrated perspective on FL and BF in relation to saving and debt behaviours as components of financing decisions, contrasting with existing literature reviews, which typically treat these fields separately, focus on investment decisions, and provide limited in-depth analysis of developing economy contexts, while also generating insights to support future research on this interconnection.

Journal of Risk and Financial Management doi: 10.3390/jrfm19060424

Authors: Mesbah Fathy Sharaf Abdelhalem Mahmoud Shahen

Digital financial services have expanded rapidly across emerging economies and are often presented as tools for advancing women’s economic inclusion. However, the extent to which digital finance is associated with lower gender inequality depends on the broader structural conditions in which women live and work. This study examines the relationship between digital financial participation, labor market integration, and gender inequality in Brazil using nationally representative microdata from the 2025 Global Findex survey. Three outcomes are examined: digital account ownership, use of any digital payment, and engagement in merchant digital payments. Multivariate logit models show moderate gender gaps at early stages of digital financial participation. However, these gaps are not uniform across the population. The interaction results show that gender differences are concentrated mainly among individuals outside employment and among those without internet access. Among employed and digitally connected individuals, the gender gap becomes small and statistically insignificant across the three outcomes. A nonlinear decomposition shows that observable socioeconomic characteristics explain only a small share of the aggregate gender gap, especially for account ownership and any digital payment use. Additional robustness checks using probit and complementary log-log models support the main pattern of results. This suggests that the gender gap cannot be explained only by differences in education, income, employment, or internet access, and may also reflect unobserved household, institutional, or social constraints. The findings suggest that digital finance alone does not equalize participation. Rather, women’s digital financial participation is closely associated with their position in the labor market and their access to digital infrastructure. Because the analysis is based on cross-sectional data, the results should be interpreted as conditional associations rather than causal effects. Digital financial expansion is therefore more likely to support gender inclusion when it is linked to broader policies that strengthen women’s labor force attachment, digital connectivity, and economic autonomy.

Journal of Risk and Financial Management doi: 10.3390/jrfm19060423

Authors: Ranganatham Gangineni Komal Singh Satyanarayana Parayitam Panduranga Venkataramulu Suneetha Baddela Venkataramanaiah Malepati

The Behavioral Finance (BF) has undergone significant developments due to the transformative influence of Environmental, Social and Governance (ESG) practices. BF and Sustainable Investment (SI) are closely intertwined domains, both of which bring into line with the broader framework of ESG. Integrating BF into the field of SI expands the understanding of how psychological biases, emotional factors, and cognitive constraints influence investors decisions connected to sustainability focused assets. Despite their growing relevance, the existing literature lacks a comprehensive review that provides holistic reviewing of research integrating into these areas. To address this gap, we provide an overview of BF and SI research in Socially Responsible Investments (SRI). Using both co-citation and bibliometric-coupling analysis, we infer the thematic structure of key words of BF and SI for a period of 20 years starting from 2004 to September 2025. Additionally using performance analysis and co-occurrence analysis, we highlighted trends and research directions regarding BF and SI. Further, seven thematic clusters and coupling networks were also identified which are offering to the researchers a structured foundation to explore emerging trends and consolidate knowledge within the BF and SI field. This Bibliometric study aids in recognizing the emerging topics for research in the domain of BF and SI.

Journal of Risk and Financial Management doi: 10.3390/jrfm19060422

Authors: Jorjin Godoy

This study examines the interconnectedness of the Philippine banking system across three contagion channels: interbank loans, interbank deposits, and payment systems. Using network data from 481 banks supervised by the Bangko Sentral ng Pilipinas (BSP), the study applies topology-based measures to assess the structure and strength of interbank linkages. It introduces two metrics: the Overall Interconnectedness Index (OII), which measures the level of connectedness of the network, and the Core Connectivity Index (CCI), which identifies robustly linked banks within the system. The results show that payments are more interconnected than loans and deposits, but the overall interconnectedness remains very low across all channels. For the full banking system, OII values range from 0.06 to 0.65%, indicating a sparse network structure. In the core network of universal and commercial banks, loans and deposits show modestly higher interconnectedness, while payments display a much stronger core–periphery pattern. The CCI results are consistent with these findings, confirming weak connectedness in the loans and deposits networks and relatively stronger connectedness in the payments network. These findings suggest that the Philippine interbank network has limited potential for contagion through small shocks, but its sparse structure may also reduce risk-sharing capacity and weaken the system’s ability to absorb larger shocks. The proposed measures offer a useful framework for monitoring systemic risk and identifying banks that contribute most to interconnectedness. They also provide policy implications for financial regulators, like BSP, in strengthening financial stability through improved market access, payment system participation, and macroprudential surveillance.

Journal of Risk and Financial Management doi: 10.3390/jrfm19060421

Authors: Wahyu Meiranto Tengku Ahmad Sandi Abbad Adi Firman Ramadhan Marsono Marsono

This research investigates the factors influencing the behavioral intention and actual use of e-wallets among Generation Z by extending the UTAUT3 model to include personal innovativeness and perceived security. The study employs a quantitative approach using Partial Least Squares Structural Equation Modeling (PLS-SEM). Data were collected from 535 Generation Z e-wallet users between 15 January and 28 February 2026. The results reveal that traditional determinants such as performance expectancy, effort expectancy, facilitating conditions, and hedonic motivation do not significantly influence behavioral intention in a mature digital environment. In contrast, social influence, price value, habit, personal innovativeness, and perceived security significantly shape users’ intentions. Furthermore, the findings indicate that behavioral intention fully mediates the relationship between personal innovativeness and perceived security with actual usage behavior. This suggests that although users may possess innovative tendencies and perceive strong security, these factors influence usage only through the formation of intention. The study also shows that Generation Z demonstrates a strong ability to manage financial activities independently within digital platforms, reflecting high levels of digital and financial literacy. At the same time, users remain highly aware of potential risks, particularly regarding data privacy and transaction security, which significantly affect their intention to adopt e-wallet services. Additionally, actual usage behavior is primarily driven by habit and behavioral intention, indicating routinized usage patterns. Overall, this study highlights the critical roles of trust, social influence, and behavioral reinforcement in explaining technology adoption among Generation Z.

Journal of Risk and Financial Management doi: 10.3390/jrfm19060420

Authors: Kosla Hin Bunthe Hor Siphat Lim

This study explores the effect that the adoption of International Financial Reporting Standards (IFRS) 9 has on credit risk in commercial banks in Cambodia, focused primarily on non-performing loans (NPLs) as a significant indicator. In the static and dynamic panel estimations, the analysis shows that the NPL behavior is best characterized using a dynamic specification, which passes relevant diagnostic tests and leads to evidence of persistence and endogeneity, which has not been conducted in Cambodia yet. The study covered the period from 2013 to 2024. During this period, 26 commercial banks had complete datasets. Combining time-series and cross-sectional data, the total sample size was 312 observations. The results show substantial path dependence in NPLs, suggesting credit deterioration is persistent and that early measures are needed. We find evidence that the adoption of IFRS 9 is positively and significantly associated with increased measures of NPLs, though we interpret this as consistent with improved transparency and forward-looking recognition of expected credit losses—and not indicative of deterioration in underlying asset quality. Bank-specific determinants such as profitability, size, leverage, and liquidity emerge as key predictors of credit risk; banks with stronger financial fundamentals experience improved asset quality. Macroeconomic factors like economic growth are key to decreasing NPLs in the dynamic framework as well. The results highlight the need for forward-looking accounting standards, prudent bank-level practices, and macroeconomic stability. Policy issues include increased supervisory vigilance, legal conservatism when assessing IFRS 9-related indicators, a revision of the capital and liquidity regulatory framework in relation to counterparties operating with them, as well as coordinated macroeconomic policies aiming at boosting the financial system—economy arterial connection.

Journal of Risk and Financial Management doi: 10.3390/jrfm19060419

Authors: Berislav Andrlić Marko Šostar Verica Budimir

This study examines how investment priorities for sustainable rural development are shaped when financial, environmental, social, and institutional criteria are evaluated simultaneously. Using the Analytic Hierarchy Process (AHP), the study assesses six investment alternatives: eco-tourism, agro-tourism, renewable energy, digital tourism, sustainable agriculture, and cultural tourism. The results reveal the dominance of financial performance and risk considerations, which together account for more than two-thirds of total decision weight. Renewable energy emerges as the highest-ranked investment alternative, whereas agro-tourism and sustainable agriculture remain under-prioritized despite their environmental and social benefits. A comparative scenario analysis demonstrates that policy-oriented weighting structures substantially alter investment rankings, increasing the attractiveness of locally embedded and sustainability-oriented activities. The findings suggest a structural divergence between market-driven capital allocation and broader rural development objectives. By integrating sustainable finance and rural development within a multi-criteria decision-making framework, the study provides practical insights for investors and policymakers seeking to align investment decisions with long-term sustainability goals.

Journal of Risk and Financial Management doi: 10.3390/jrfm19060418

Authors: El Mehdi Douhabi Zineb Drissi

Behavioral finance challenges classical rational-investor models by demonstrating that psychological biases shape financial decisions. Evidence indicates that overconfidence, herding, loss aversion, and the disposition effect are not uniformly distributed but are shaped by gender, age, financial literacy, income, and investment experience. However, the literature remains fragmented across contexts and geographies. This PRISMA 2020 systematic review synthesizes 57 empirical studies (2010–2025) screened from 172 Scopus records and appraised against eight quality criteria. Findings confirm overconfidence (31 studies) and herding (26) as the most prevalent biases, concentrated among younger, male, and less experienced investors, whereas loss and risk aversion are more common among female, older, and financially insecure investors. Financial literacy emerges as the strongest moderator, reducing most biases while paradoxically amplifying overconfidence at moderate levels, consistent with the Dunning–Kruger effect. Formal moderation analyses (14 studies) support literacy as a significant boundary condition, and investment experience exhibits a non-linear pattern favoring moderate levels. This review contributes a structured, quality-appraised synthesis and a research agenda addressing intersectionality, longitudinal designs, and geographic diversity.

Journal of Risk and Financial Management doi: 10.3390/jrfm19060417

Authors: Max Wong Patrick Ge

Climate risk is increasingly recognized as an important factor in financial modelling, with applications including stress testing where climate risk factors are used to influence market risk and credit risk. However, this “transmission channel” modelling faces several challenges, particularly in terms of data availability and the mismatch between the time horizons of climate risks and financial risks. Recent research, especially from central banks and regulatory bodies, is beginning to address these challenges. The International Swaps and Derivatives Association (ISDA) has developed methodologies to compute very short-term scenarios. In this paper, we illustrate how outputs from ISDA and other sources can be integrated for climate stress testing of key products listed on the Singapore Exchange (SGX). The main contribution of this study is the development of a structured “last-mile modelling” framework that combines country-level climate sensitivity scaling, stressed correlation inference modelling, and direct carbon cost transmission mechanisms to bridge macro-level climate scenarios with product-level financial risk. It provides a practical and extensible approach for climate stress testing across both listed and over the counter (OTC) markets.

Journal of Risk and Financial Management doi: 10.3390/jrfm19060416

Authors: Muhammad Ziyad Hari Sukarno Sumani Hadi Paramu

Islamic banking is increasingly expected to align Sharia-based intermediation with sustainability objectives, yet empirical evidence remains limited on how sustainability disclosure links financing structures with bank performance. This study examines whether Islamic Sustainable Banking (ISB) functions as a mediating mechanism between profit-sharing financing, debt-based financing, and financial performance in Islamic banks in Indonesia and Malaysia. ISB is measured using an Islamic Sustainable Banking Disclosure Index that integrates Maqasid al-Shariah principles with SDG-oriented disclosure indicators. Using panel data from 23 Islamic banks over 2018–2023 and applying partial least squares structural equation modeling, mediation analysis, PLS-MGA, and permutation tests, the study finds that both profit-sharing and debt-based financing are negatively associated with ISB disclosure, while ISB is positively associated with net profit margin but not return on assets. The mediation results indicate statistically significant negative indirect associations through ISB, suggesting that sustainability disclosure operates as a conditional transmission mechanism rather than an automatic performance driver within the specified PLS-SEM model. Cross-country tests reveal significant differences between Indonesia and Malaysia, particularly in the associations between financing structures and profitability. The study contributes to Islamic sustainable finance by clarifying how Maqasid-oriented disclosure connects financing composition, governance capacity, and profitability, while offering practical implications for bank managers, regulators, and policymakers seeking to integrate sustainability into Islamic banking governance and financing decisions.

Journal of Risk and Financial Management doi: 10.3390/jrfm19060415

Authors: Pedro Eche-Querevalú Amador Grover Mejía-Osorio Emilio Javier Rojas-Villanueva Fiorella Helka Vega-Lazo Jorge Miguel Chávez-Díaz

Financial literacy among university students is increasingly important in contexts marked by digital payments, accessible credit and growing financial-product complexity. This study analyzes the explanatory relationships between technical-financial knowledge (TFK), perception/attitude toward financial education (PS), practical application of financial knowledge (PAK), and financial self-management (PFS) among Peruvian university students. A total of 422 surveys were collected, and the final PLS-SEM analysis was conducted with 358 complete cases. The model was estimated in ADANCO using consistent PLS for reflective constructs and Mode B for PFS as a formative construct, with 5000 bootstrap replicates. The results show that TFK positively predicts PS (β = 0.711; p < 0.001) and PAK (β = 0.709; p < 0.001). PFS is explained by both PS (β = 0.282; p < 0.001) and, more strongly, PAK (β = 0.558; p < 0.001), with moderate-to-high explanatory power (R2 = 0.568). The total indirect effect of TFK on PFS was significant (β = 0.596; p < 0.001), and the TFK → PAK → PFS pathway was the dominant mechanism. These findings suggest that university financial education should move beyond conceptual content and prioritize practice-oriented learning strategies, including budgeting, savings planning, product comparison and digitally mediated decision-making.

Journal of Risk and Financial Management doi: 10.3390/jrfm19060414

Authors: Luís Pacheco António Carvalho

Capital structure theory remains a central concern within corporate finance, despite more than six decades of sustained scholarly inquiry. The seminal contributions of Modigliani and Miller established the analytical foundations from which subsequent frameworks emerged, notably the static trade-off theory and its later evolution into dynamic adjustment models. Although competing theoretical perspectives have advanced the debate, their respective limitations have increasingly encouraged a more integrative understanding of firms’ financing behaviour. This study critically examines the limitations of the dynamic trade-off model in explaining the financing decisions of Portuguese small and medium-sized enterprises (SMEs) during the period 2015–2024. The article contributes to the literature by proposing an original comparative methodological framework and introducing an empirical indicator designed to assess the divergence between the model’s theoretical assumptions and observed financing practices. Using dynamic panel estimations based on the Generalized Method of Moments (GMM), the findings reveal that, although SMEs exhibit partial adjustment behaviour towards target leverage rations, several core determinants predicted by the dynamic trade-off framework lose explanatory power when confronted with observed data. In particular, profitability displays patterns more consistent with pecking order behaviour, while variables traditionally associated with debt optimization and collateral effects become statistically weak or inconsistent. These results suggest that the financing behaviour of Portuguese SMEs cannot be fully explained by a single theoretical framework and is strongly shaped by institutional constraints, internal financing preferences, and contextual factors. The study therefore highlights both the continuing relevance and the empirical limitations of the dynamic trade-off model, while reinforcing the need for more pluralistic approaches to capital structure analysis. From a practical perspective, the findings indicate that SME financing decisions should not be interpreted solely through leverage optimization logic, carrying implications for managers, financial institutions, and policymakers involved in SME financing and fiscal policy design.

Journal of Risk and Financial Management doi: 10.3390/jrfm19060413

Authors: Arie Pratama Nanny Dewi Tanzil Poppy Sofia Koeswayo Kamaruzzaman Muhammad Lokita Rizky Megawati

Amid growing global attention to corporate sustainability and responsible investment, the disclosure of Sustainable Development Goals (SDGs) has emerged as an important component of non-financial reporting. However, the extent to which SDG disclosure contributes to firm value remains underexplored, particularly in emerging markets. This study examines the association between SDG disclosure in corporate reports and firm value among 660 publicly listed companies across four Southeast Asian countries: Indonesia, Malaysia, Thailand, and Singapore. SDG disclosure is measured using 17 SDG indicators derived from the Refinitiv database and should be interpreted as a measure of disclosure breadth rather than disclosure quality or depth. The analysis begins with descriptive statistics to illustrate the distribution of key variables, followed by ANOVA to assess differences in SDG disclosure across countries and industries. Hypothesis testing is then conducted using multiple regression analysis with robust standard errors, with firm value proxied by price-to-book value (PBV). Several robustness checks are performed, including winsorised regression, year-by-year regressions, and regression models incorporating country and industry dummy variables. The results indicate that SDG disclosure is positively associated with firm value, although the relationship is interpreted as correlational rather than causal because of the short observation period and potential endogeneity. The findings also show that SDG disclosure is unevenly distributed across goals and countries, with SDG 8 and SDG 13 receiving the highest attention, while SDG 2 and SDG 14 remain among the least disclosed. These results highlight the importance of sustainability transparency in shaping market valuation and underscore the need for more balanced, comparable, and quality-oriented sustainability reporting frameworks across the region.

Journal of Risk and Financial Management doi: 10.3390/jrfm19060412

Authors: Bapon Shm Fakhruddin Shaily Gandhi

Climate-related disasters are escalating in frequency and severity, yet global adaptation finance remains critically insufficiently structured to respond after disasters occur rather than before. This study empirically examines disaster loss data, climate finance flows, and financial instrument evidence to test two hypotheses: whether climate finance is disaster-reactive, and whether policy uncertainty constrains it. We integrate data from the Emergency Events Database (EM-DAT), covering seven climate-induced hazard types (droughts, extreme temperatures, floods, glacial lake outburst floods, wet mass movements, storms, and wildfires), in addition to the OECD Creditor Reporting System (CRS), the World Uncertainty Index (WUI), the ND-GAIN vulnerability index, and the World Governance Indicators, the Green Climate Fund Open Data Library, and the Artemis Deal Directory across 131 countries (2011–2024) for Hypothesis 1 and 100 countries (2012–2024) for Hypothesis 2. Fixed-effects panel regressions with Driscoll–Kraay standard errors confirm that prior-year disaster losses significantly predict subsequent climate finance flows (β = 0.040, p = 0.009; N = 1769 country-year observations), establishing a reactive financing pattern. Policy uncertainty interacting with high vulnerability is found to suppress adaptation finance flows (β = −2.587, p = 0.080, N = 878 country-year observations), with the effect concentrated among the most climate-exposed economies. We propose a risk-layered climate finance architecture aligning instruments with distinct hazard tiers across the Global South. Credible policy signals, strategic public investment, and systematic integration of insurance mechanisms are essential preconditions for unlocking scalable, forward-looking resilience finance.

Journal of Risk and Financial Management doi: 10.3390/jrfm19060411

Authors: Alina Ciobotar Butnaru Veronica Grosu Ioana Andrioaia

This paper analyzes the measurement of intellectual capital in the context of the increasing importance of ESG factors and current economic changes, highlighting the role of intangible assets in supporting company performance. The existing literature emphasizes the limitations of traditional models, such as the Value-Added Intellectual Coefficient (VAIC), which do not adequately capture the contribution of the modern dimensions of intellectual capital. The study is based on a quantitative approach and uses a sample of 75 companies listed on the Bucharest Stock Exchange. The data were analyzed using IBM SPSS Statistics, Version 26.0 through the application of principal component analysis (PCA), linear regression, and ANOVA tests. The results show that the traditional model does not significantly explain market performance measured by Tobin’s Q, while the modern model, based on human, structural, relational capital, and ESG factors, provides a more comprehensive conceptual perspective, although its statistical explanatory power remains limited. The paper contributes to the existing literature by proposing an extended approach to intellectual capital evaluation, adapted to the current context, and offers useful implications for investors, managers, and other users of financial information.

Journal of Risk and Financial Management doi: 10.3390/jrfm19060410

Authors: Wan Wei Susan Pozo Shen Chen

This paper examines the impacts of monetary policy announcements on exchange rate volatility of the Mexican peso, a currency that is representative of emerging market currencies, against the US dollar. Narrow windows around policy announcements and high-frequency second-by-second intraday data are used in the analysis. To examine the impact of announcements on exchange rate volatility, we divide the announcement period into a pre-announcement period (five minutes before the announcement), a contemporaneous period (five minutes after the announcement), and a post-announcement period (fifteen minutes after the “contemporaneous period”). While incorporating monetary policy announcements from both the US and Mexico, we find that US monetary policy announcements have greater impacts on the volatility relative to Mexican monetary policy announcements, although both of them lead to significant increases in the volatility around announcements. Furthermore, the increase in volatility resulting from the US announcements lasts for all of the sub-periods, while the Mexican announcements cause an increase in volatility only over the first two periods. In other words, the impact of US monetary policy tends to be more persistent than Mexican monetary policy with respect to peso/dollar volatility.

Journal of Risk and Financial Management doi: 10.3390/jrfm19060409

Authors: Vijay Kiran Battula

The rapid advancement of payment technologies and potential disintermediation pressure make it important to monitor how actively commercial banks participate in payment and settlement systems. This study conceptualizes bank participation as a multidimensional construct and develops a Bank Payment Participation Index (BPPI or ANR BPPI) using publicly available Reserve Bank of India data for 2011–2012 to 2022–2023. BPPI integrates Financial Capacity (FC), Technological Readiness (TR), Payment Performance (PI), and a PPI-based Technological Advancement/Disintermediation proxy (TAD). TAD, measured as the share of PPI transactions in total payment volumes, enters the index as (1−TAD) because rising non-bank payment penetration reduces banks’ intermediation share; a higher TAD represents a structural drag on bank payment participation, and (1−TAD) converts this drag into a participation-compatible scale. The index applies min–max normalisation, equal-weighted sub-index aggregation, and geometric mean composition with lagged input dimensions. Computations show that the four-component BPPI rises from 230.794 in 2013–2014 to 797.453 in 2022–2023, indicating a strong long-run increase in banking-system participation. The BPPI remains strongly associated with GDP over the 2013–2014 to 2022–2023 sample, with R Square = 0.906 and adjusted R Square = 0.894. Diagnostic tests indicate that the validation is best interpreted as association-based evidence rather than causal proof. The BPPI is proposed as a decomposable monitoring and diagnostic framework that equips regulators and banks to track participation trends and detect structural vulnerabilities over time, subject to future refinement using fixed policy goalposts, bank-level data, and CBDC-specific transaction data. In its present form, the BPPI constitutes a model-stage prototype framework subject to future operationalisation with fixed expert-determined benchmarks and bank-level disaggregated data.

Journal of Risk and Financial Management doi: 10.3390/jrfm19060408

Authors: Shanhong Wu Jing Jiang

We reexamine the predictive roles of DuPont decomposition components—asset turnover (ATO) and profit margin (PM)—in forecasting future operating profitability. We demonstrate that ΔPM is conditionally informative, rather than weak per se, by integrating mean reversion and competitiveness and connecting findings to earnings persistence and industry structure. The prior literature generally finds that changes in ATO dominate changes in PM toward predicting future return on net operating assets (ΔRNOA), despite theoretical arguments suggesting that both components should contain predictive information. Using a sample of 99,938 U.S. firm-year observations from 1981 to 2020, we reconcile these findings by incorporating heterogeneous mean reversion and firm competitiveness into the DuPont framework. We first document that PM exhibits substantially faster mean reversion than ATO, which weakens the unconditional predictive power of ΔPM. We then introduce industry-relative measures of ATO and PM to capture firms’ competitive positioning within industries. The results show that both ΔATO and ΔPM become significantly more informative for firms with stronger relative competitive advantages. In addition, firms with higher relative ATO and PM exhibit higher earnings and revenue persistence and are more likely to sustain consecutive earnings increases. We further find that competitiveness effects are stronger in diversified industries, while mean reversion is slower in concentrated industries. Overall, our findings provide a unified framework linking DuPont analysis, competitiveness, and profitability persistence, with important implications for forecasting, valuation, and financial statement analysis.

Journal of Risk and Financial Management doi: 10.3390/jrfm19060407

Authors: Edman Padilla Flores

Following the global financial crisis, the transition to IFRS 9’s forward-looking Expected Credit Loss (ECL) model has introduced significant implementation complexity, particularly in emerging markets facing data limitations. This study investigates the heterogeneous ECL compliance strategies adopted within the Cambodian banking sector during a period of heightened credit stress, marked by a system-wide non-performing loan ratio of 8.6%. Utilizing a multiple-case study design and replication logic, a qualitative content analysis was conducted on the 2024 audited financial statements of 13 representative institutions, ranging from market leaders to international subsidiaries. The findings reveal a pronounced technical divide: market leaders utilize advanced internal statistical methods, such as cohort analysis, whereas international subsidiaries rely on top-down parent-group proxy models to bridge local data gaps. A “macro-correlation paradox” was identified, where certain institutions prioritize faithful representation by excluding macroeconomic variables when statistical links to historical defaults remain weak. Furthermore, a significant transparency gap exists, where granular disclosures are consistent with a signaling interpretation regarding institutional safety. These results suggest that ECL compliance in data-limited environments may be interpreted as a strategic management choice rather than a standardized technical exercise, highlighting the need for regulatory standardization of modeling assumptions to improve inter-bank comparability.

Journal of Risk and Financial Management doi: 10.3390/jrfm19060406

Authors: Eda Oruç Erdoğan Murat Erdoğan Durmuş Acar İlker Kıymetli Şen

Increasing attention has been directed toward the consistency between sustainability disclosures and financial statements, giving rise to the concept of financial connectivity. A prevailing assumption in this debate is that climate-related risks and opportunities will directly translate into quantifiable impacts on financial statement amounts under International Financial Reporting Standards (IFRS). This study challenges that assumption by arguing that connectivity does not necessarily materialise through immediate recognition outcomes in financial statements. To address this gap, the paper develops a three-stage, judgement-based connectivity framework that links climate-related disclosures under IFRS S1 and IFRS S2 to recognition, measurement, and disclosure decisions under IFRS Accounting Standards. Rather than treating sustainability disclosures as direct valuation inputs, the framework evaluates each disclosed risk or opportunity through structured accounting judgements. The framework is illustrated using the 2024 climate-related disclosures of a listed manufacturing entity (Company A). The illustrative application suggest that significant climate exposures do not automatically result in recognised provisions under IAS 37. Instead, connectivity primarily operates through assumption-setting mechanisms embedded in existing measurement models, including impairment testing (IAS 36), asset life assessments (IAS 16), and deferred tax evaluations (IAS 12). The study makes three interrelated contributions: it reconceptualises financial connectivity as a structured judgement process rather than a numerical reconciliation exercise; it operationalises this reconceptualisation through a replicable step-by-step mapping framework that links IFRS S2 disclosures to specific IFRS recognition, measurement, and disclosure requirements without expanding existing accounting rules; and it clarifies that disciplined non-recognition may represent adherence to accounting integrity rather than a reporting deficiency. These contributions distinguish the framework from existing professional guidance by making the underlying judgement logic explicit and replicable within the scope of IFRS-based financial reporting.

Journal of Risk and Financial Management doi: 10.3390/jrfm19060405

Authors: Nouran Nabil Abdelsalam Mahmoud Ellelly Saleh Aly Saleh Aly Sherif El-Halaby Abdelmoneim Bahyeldin Mohamed Metwally

This study aims to explore the impact of artificial intelligence adoption in accounting systems (AIAS) on organizational performance (OP). Further, the study explores the mediating role of financial decision-making quality (FDMQ) on the AIAS-OP relationship. The sample comprises 583 accountants, finance managers, CFOs, and auditors in all firms listed on the Egyptian Stock Exchange (EGX), covering banking, IT, manufacturing, and service sectors. Data were analyzed using Smart-PLS 4 software. The results revealed a positive and significant impact of AIAS on both FDMQ and OP. Further, the results revealed a positive and significant impact of FDMQ on OP. Finally, FDMQ showed a significant mediating role between AIAS and OP. These results have significant implications for policymakers, investors, regulators, and corporate executives, emphasizing the crucial role played by AIAS and FDMQ in shaping OP, particularly within emerging markets such as Egypt. This study provides a valuable contribution to the accounting literature by highlighting the impactful consequences of AIAS and FDMQ on OP in a unique and unexplored context. Furthermore, this research underscores the vital role that FDMQ assumes in mediating the relationship between AIAS and OP, contrasting with earlier studies in the literature which primarily examined the direct impact of AIAS or FDMQ on OP.

Journal of Risk and Financial Management doi: 10.3390/jrfm19060404

Authors: Johannes Eybers Gary van Vuuren

This paper examines the effectiveness of deposit insurance in reducing bank run risk using an agent-based model with heterogeneous depositor behavior, including random withdrawals, risk-based responses, and peer-driven contagion. The results reveal a nonlinear stability pattern with a narrow transition region separating solvency from collapse. Within this region, deposit insurance mainly improves stability by shifting the critical threshold and extending time-to-failure. Across all scenarios, behavioral and structural factors, including wealth inequality, risk aversion, depositor awareness, and contagion, systematically affect the location and sharpness of this transition without removing it. Fragility rises sharply beyond moderate inequality (Gini ≈ 0.5), while depositor awareness and peer effects act as coordination mechanisms that accelerate collapse. Overall, deposit insurance is a powerful but limited stabilization tool: it strengthens resilience but does not alter the underlying dynamics of systemic risk. These findings suggest that effective policy must also address the behavioral and informational drivers of bank runs.

Journal of Risk and Financial Management doi: 10.3390/jrfm19060403

Authors: Khwazi Magubane

This study evaluates the relative effectiveness and feasibility of synchronized and country-specific macroprudential policies in advanced systemic economies (ASEs) and systemic middle-income countries (SMICs). The analysis is motivated by the growing policy tension between the potential global financial stability gains from macroprudential coordination and the loss of domestic policy autonomy that such coordination may impose. To address this issue, the study develops a common macroprudential policy index (CMPI), which captures the shared component of macroprudential actions across countries and serves as a proxy for cross-country macroprudential policy coordination. In doing so, the study provides an empirical framework for assessing whether synchronized macroprudential policies generate more effective outcomes than country-specific interventions, thereby offering insights into the practical feasibility of international macroprudential coordination. Using a Dynamic Common Correlated Effects (DCCE) model and a Panel Structural VAR (PSVAR), the study examines the effects of domestic and coordinated macroprudential policies on capital flows, credit growth, and house prices. The findings reveal important differences across short-run and long-run horizons. In the long run, both domestic macroprudential policy (MPI) and coordinated macroprudential policy (CMPI) exert contractionary effects on capital flows, consistent with tighter credit conditions and higher lending costs. However, PSVAR results show that synchronized macroprudential shocks can temporarily increase capital flows, credit, and house prices through volatility reduction, portfolio reallocation, and cross-border spillover channels. These effects are transitory, indicating that coordinated policies primarily shape short-run financial adjustment dynamics rather than permanently increasing global liquidity. Overall, the results suggest that macroprudential coordination between ASEs and SMICs is feasible, particularly in areas related to systemic risk containment, volatility management, and the mitigation of destabilizing cross-border spillovers. However, the heterogeneous responses across countries also indicate that effective coordination requires a flexible framework in which broad common principles are coordinated internationally while domestic authorities retain discretion to calibrate instruments according to local financial conditions and vulnerabilities.

Journal of Risk and Financial Management doi: 10.3390/jrfm19060402

Authors: Michail Dadopoulos Stratos Moschidis

Accurate product-to-catalog invoice matching is a foundational internal control for financial oversight and audit quality, yet it is bottlenecked by inconsistent vendor descriptions and the resulting ‘long tail’ of supplier heterogeneity, driving costly manual reconciliation in Enterprise Resource Planning (ERP) environments. This study pursues three objectives: (i) to design a Retrieval-Augmented Generation (RAG) architecture that matches invoice line items to a product catalog under conditions of optical character recognition noise, vendor-specific abbreviations, and multilingual heterogeneity; (ii) to evaluate this architecture on three public entity resolution benchmarks against established lexical and Dense retrieval baselines; and (iii) to assess its viability as a decision support system in a real accounts payable workflow with audit-trail requirements. To address (i), we introduce a novel ‘augment-both-sides’ strategy: large language models (LLMs) proactively enrich each catalog Stock Keeping Unit (SKU) with synonyms and alternative descriptions before vectorization, while invoice lines undergo runtime query expansion, and an LLM-based reranker produces the final Top-3 candidates. For (ii), evaluation on the Abt-Buy, Amazon-Google, and Walmart-Amazon datasets yields Top-3 Recall of 91.60% to 97.96%, matching or exceeding the strongest non-LLM baseline on every benchmark. For (iii), a production deployment on approximately 200 manually verified Greek invoice lines (proprietary dataset, anecdotal observation) yields a Top-3 hit rate of approximately 97%, consistent with the public-benchmark results. The architecture functions as a reliable intelligent decision aid, narrowing the search space from thousands of SKUs to a precise candidate set for structured human verification.

Journal of Risk and Financial Management doi: 10.3390/jrfm19060401

Authors: Rim El Houcine

This study investigates the impact of board gender diversity and the presence of a critical mass of female directors on corporate carbon emissions. Grounded in agency, legitimacy, and critical mass theories, it explores how the gender composition of corporate boards shapes firms’ environmental governance. Using panel data from 37 non-financial CAC 40 firms between 2020 and 2023, the analysis employs Fixed Effect regression models with robustness checks. The results reveal a non-linear relationship between gender diversity and emissions: a higher proportion of female directors reduces emissions only when the board reaches a critical mass, supporting the idea that women’s influence becomes significant beyond token representation. CEO duality negatively affects environmental outcomes, while firm size and profitability are positively associated with emission performance. The study contributes to corporate governance research by showing that meaningful female representation enhances environmental accountability, highlighting the need for policies promoting gender balance and sustainability-oriented board practices.

Journal of Risk and Financial Management doi: 10.3390/jrfm19060400

Authors: Imad Jabbouri Omar Farooq Ahmed Ankit Maryem Naili

This paper examines the relationship between social capital and trade credit among private firms headquartered in 111 developing economies. The paper shows that firms headquartered in countries with higher levels of social capital receive more trade credit from their suppliers and extend more trade credit to their customers than firms headquartered in countries with lower social capital. The findings remain robust after controlling for a wide range of firm-level and country-level characteristics. Additional analyses show that the relationship between social capital and trade credit is more pronounced in countries with strong institutional environments. The results further indicate that specific dimensions of social capital, particularly interpersonal trust and social tolerance, are positively associated with both supplier-provided and firm-provided trade credit. Overall, the findings highlight the importance of informal institutional environments in facilitating relational financing among private firms operating in developing economies.

Journal of Risk and Financial Management doi: 10.3390/jrfm19060399

Authors: Xinyu Wang

Topic modeling is one of the most widely used Natural Language Processing models in business fields. In this survey, by collecting and reviewing 140 topic modeling-related articles published in 40 finance and related business journals, I document the trend of topic modeling across journals and time, review the main algorithms used in the literature, and organize the evidence by research areas, research methodologies, and data sources. The survey shows that Latent Dirichlet Allocation is the dominant approach especially in early studies, but newer variants, such as supervised LDA, correlated topic modeling, sentence-level models, and structural topic models, are being adopted when researchers need better model performances under specific cases. Recent work increasingly uses topic-based methods to summarize documents, construct new measures, classify disclosures, and compare text information from firms, market participants, and policymakers. Though topic modeling algorithms are powerful, challenges such as noisy documents, topic labeling, and Blackbox issues still exist. Overall, topic modeling has moved from a supplementary textual analysis tool to a main research tool in finance research, and topic modeling will accelerate the development of finance research in the near future.

Journal of Risk and Financial Management doi: 10.3390/jrfm19060398

Authors: Alina Ciobotar Butnaru Anastasia Mihaila Geanina Măciucă Iulian Dascălu

Companies are increasingly valued according to sustainability criteria, so governance policies represent a credible source of information on the entity’s ability to create value for employees and the community. Intellectual capital becomes a valuable source of innovation, using non-financial factors as essential tools in sustainability reporting. The accounting professional is an important balancing point, supporting the processing and validation of non-financial information in digital reporting contexts. Numerous studies address these concepts separately without highlighting causal links between non-financial factors, professional accountants and sustainability reporting. This paper explores intellectual capital valorization through integrative perspectives in the context of sustainable performance, based on documentary synthesis and content analysis of non-financial information from 30 Romanian companies listed on the Bucharest Stock Exchange. The paper clarifies the contribution of extra-financial factors in measuring intellectual capital and the role of professional accountants in developing valid and compliant reports through intelligent information systems. Results indicate that non-financial indicators play an integrative role in developing global intellectual capital, while human expert reasoning maintains its primary role in interpreting and validating information. The proposed conceptual model highlights links between the main concepts, serving as a starting point for future quantitative studies.

Journal of Risk and Financial Management doi: 10.3390/jrfm19060397

Authors: Cecilia Hermansson Kent Eriksson Carin Segerlind

This study examines the nonlinear relationship between customer satisfaction (CS) and both the levels and growth of customer revenue (CR) at the individual level in the banking sector. Utilizing a unique data on 19,054 Swedish bank customers (2013–2017), the analysis combines subjective satisfaction measures with objective financial and demographic register data. Regression models test for diminishing returns at high satisfaction levels while assessing the persistence of these effects over a four-year period. The findings indicate that while CS is positively associated with both revenue level and revenue growth, the relationship with revenue level is nonlinear. Specifically, customers scoring 80–89 generate higher revenues than those scoring 90–100, providing weak evidence of a ceiling effect (at the 10% significance level) that is notably absent for revenue growth. Furthermore, CS explains less than 1% of revenue variation, highlighting the inherent limits of satisfaction-based revenue models. These ceiling effects are more pronounced among older, lower-income women without debt, whereas wealth has no observable impact. Finally, the nonlinear effects fade after one year, though gender remains a consistent moderator. These tentative findings suggest limited financial returns from maximizing satisfaction, thereby supporting the implementation of more differentiated customer segmentation strategies.

Journal of Risk and Financial Management doi: 10.3390/jrfm19060396

Authors: Bruce Rishel Melissa Rishel

The most widely used bankruptcy predictor, Altman’s Z-Score, assigns a positive coefficient to asset turnover; faster firms are rated safer. Under crisis conditions, that assumption reverses. We introduce the Solvency Margin (SM), a diagnostic calculable from standard financial statements that measures, in dollars, how far an organization is from the threshold where operations become impossible. Unlike static liquidity ratios, the SM yields a concrete speed limit: the maximum operating velocity at which an organization can survive a defined shock. We validated the SM against pre-crisis financial data across three crises in two domains. Regarding the automotive sector, SM computed from FY2019 filings showed directional predictive power among ten major automakers in both the 2021 semiconductor shortage (ρ = 0.50, p = 0.14) and the 2020 COVID-19 pandemic (ρ = 0.53, p = 0.12; ρ = 0.70, p = 0.036 excluding one governance-driven outlier). With reference to the 2023 U.S. banking crisis, SM augmented with a Deposit Stability Factor predicted crisis outcomes among eighteen regional banks (Spearman ρ = 0.62, p = 0.006), correctly ranking three of four failed institutions in the bottom three positions. Monte Carlo simulation (450,000+ runs) confirmed threshold behavior. We present a five-step calculation method and a three-lever decision framework for practitioners.

Journal of Risk and Financial Management doi: 10.3390/jrfm19060395

Authors: Vladimir Ristanović Dinko Primorac Nataša Stevandić

This paper investigates the relationship between green vocational education and training (VET), structural economic features, and green employment in Central and Eastern European (CEE) economies. For the purpose of the research, an initial database covering the post-2010 period was assembled from Eurostat and related statistical sources. Due to data availability and cross-country comparability constraints, the final empirical analysis employs a balanced panel of six EU Member States covering the period 2018–2022. The empirical analysis employs pooled OLS and fixed-effects estimators over the period 2018–2022, following a stepwise modeling strategy to assess baseline relationships and robustness. The results show that VET enrollment alone is not a reliable predictor of green employment growth, while VET graduation rates exhibit a more consistent—yet not robust—association once country-specific heterogeneity is controlled for. By contrast, structural reliance on industrial sectors is consistently linked to lower green employment shares, while environmental tax revenues demonstrate modest positive effects. Overall, the findings suggest that green employment dynamics are driven primarily by structural and macroeconomic conditions rather than by skill formation alone. The study contributes to the literature on the green transition by providing an integrated perspective on the interaction between skills, structural transformation, and policy incentives in shaping sustainable labor market outcomes.

Journal of Risk and Financial Management doi: 10.3390/jrfm19060394

Authors: Ahmad Salim Moh’d Abderrahman Naser Makarem

Missing Acknowledgments [...]

Journal of Risk and Financial Management doi: 10.3390/jrfm19060393

Authors: Samuel Kwadjo Akukumah Sam Kris Hilton

This study investigates the interplay of internal audit system, cultural complexity and corruption control in Ghana’s state-owned enterprises (SOEs), examining how these factors influence anti-corruption efforts. Employing a quantitative and cross-sectional survey design, we gather data from 1150 internal auditors and use EFA, descriptive statistics and macro-process modeling for analysis. The results show that internal audit effectiveness, quality, independence, and resources are all positively related to corruption control (prevention, detection and response), with internal audit independence having the greatest effect on corruption control. Power distance culture (PDC) moderates these relationships, but the direction and significance of the moderation vary across the different aspects of corruption control. This study highlights the importance of strengthening internal audit system and addressing cultural barriers to enhance corruption control in SOEs, informing governance strategies in emerging economies. It has demonstrated that PDC plays a complex role in shaping the effectiveness of internal audit system in controlling corruption. Thus, this research contributes to the limited literature on the intersection of internal audit, PDC and corruption control in a developing country context, offering insights for policymakers and practitioners.

Journal of Risk and Financial Management doi: 10.3390/jrfm19060392

Authors: Karama Saadaoui Rym Belgaroui Salah Ben Hamad Houda Hadj Kacem

This study examines how credit risk affects bank valuation and whether mergers and acquisitions (M&A) moderate this effect, using a panel of 102 listed Eurozone and UK banks from 2004 to 2024. Applying MM-quantile regression, we find that credit losses reduce valuation across all quantiles, especially for low-valued banks. M&A activity boosts valuation mainly for high-valued banks, while for weaker banks, acquisitions exacerbate the negative impact of credit deterioration. Robustness checks confirm these results. Our findings highlight that credit risk and consolidation have heterogeneous effects, suggesting that supervisory policies should consider banks’ market positions and distributional risk dynamics.

Journal of Risk and Financial Management doi: 10.3390/jrfm19060391

Authors: Yiyang Wu

This study explores the informativeness of forward-looking disclosures in managers’ speeches in U.S. quarterly earnings conference calls, focusing on time-frame specificity—whether statements provide precise temporal horizons. Using a keyword search, forward-looking statements (FLSs) in managers’ speeches in U.S. quarterly earnings conference calls are classified into those with and without specific time frames, and tests of their determinants, market responses, and implications for firms’ future performance are conducted. First, uncertainty is positively associated only with FLSs without time frames, likely because managers find it more difficult to specify time frames under uncertainty or are less willing to be held accountable. Second, investors respond more quickly to FLSs with time frames and more slowly to those without, while analysts use both types to improve forecasts; however, FLSs without time frames increase forecast dispersion, whereas those with time frames reduce it, suggesting greater information processing difficulty. Third, larger changes in future earnings and discretionary accruals are associated with more FLSs without time frames, while capital investment increases only with more FLSs with time frames. Collectively, these findings indicate that time-frame specificity conveys differential informational value.

Journal of Risk and Financial Management doi: 10.3390/jrfm19060390

Authors: Ardit Gjeçi Andromahi Kufo Rovena Vangjel Troplini Athina Tori Denis Hoxha

This study examines the ability of asymmetric GARCH-family models, specifically EGARCH and GJR-GARCH, to capture and forecast the volatility of major decentralized cryptocurrencies. We analyzed the returns of seven leading assets (BTC, ETH, ADA, XRP, LTC, XLM, DASH). We used the Crypto Fear & Greed Index (CFGI) as a dummy variable, covering a period when all cryptocurrencies were active simultaneously. Notably, the Student-t distribution provided the best in-sample results with the lowest AIC and BIC for both models. When comparing the models directly, EGARCH consistently outperforms GJR-GARCH across in-sample metrics. The use of the CFGI dummy variable marginally improves in-sample results for only three of the seven cryptocurrencies, suggesting it may be adding noise to the models for some coins. Additionally, there is no clear rule of asymmetry across all cryptocurrencies, suggesting a fundamental structural difference from the traditional stock market. Out-of-sample metrics and performance vary more than in-sample metrics, with normal and GJR-GARCH models yielding better performance and lower QLIKE values for specific cryptocurrencies. This study contributes to the growing literature on volatility modeling and forecasting in cryptocurrencies, highlighting the importance of asset-specific valuation in the cryptocurrency market. It also provides a framework for integrating specific market indicators into the modeling framework.

Journal of Risk and Financial Management doi: 10.3390/jrfm19060389

Authors: Rashmi Malhotra Davinder Malhotra Robert Nydick Nathan Coates

This research explores whether boosting model complexity enhances the forecasting of corporate financial outlook in a multiclass credit outlook setup. Instead of viewing distress as simply a yes-or-no result, companies are divided into negative, neutral, and positive outlook categories to better reflect shifting credit conditions. The study evaluates a parametric baseline against several nonlinear classifiers—including ensemble, kernel-based, and similarity-driven approaches—while applying a consistent validation process and statistical testing. On average, nonlinear models outperform the linear specification in terms of out-of-sample accuracy and provide more homogeneous classification across the three outlook categories. Importantly, they substantially improve the identification of firms with financial vulnerabilities. Among nonlinear models, average performance differences are economically small and statistically insignificant. These findings suggest that there are diminishing returns to additional complexity once nonlinear structure is allowed for in the models. SHAP-based interpretability provides exploratory evidence that model decisions are economically intuitive and broadly consistent with nonlinear, state-dependent credit risk dynamics. Negative financial surprises tend to be penalized more heavily than positive ones are appreciated, demonstrating the convex nature of the underlying risk dynamics.

Journal of Risk and Financial Management doi: 10.3390/jrfm19060388

Authors: Simon Osiregbemhe Ilogho Heinz Eckart Klingelhöfer

As mobility and transport activities declined during the COVID-19 lockdowns, transactions and operations became increasingly dependent on digitalisation. This shift reduced the need for carbon-emissions-intensive fossil-fuel-based transportation. Using a panel of thirteen (13) Southern African Development Community (SADC) countries over the period 2002–2021, the analysis captures financial inclusion through indicators of ATM density and commercial bank accessibility, while digital inclusion is measured using mobile phone subscriptions and internet penetration. On this basis, it investigates the effects of (a) financial and (b) digital inclusion, and (c) the moderation of financial and digital inclusion on transport-related carbon emissions. Employing the Panel Two-Stage Estimated Generalised Least Square (EGLS) analysis on data obtained from the World Bank database and Our World in Data, the findings reveal statistically significant outcomes. Increasing ATM accessibility, commercial bank branch accessibility and mobile phone subscription rates are associated with reduced transport-related emissions. In contrast, enhanced internet access does not contribute to transport-related carbon emissions. Moderation analyses further indicate that the interaction of the accessibility of ATMs or commercial bank branches with internet access do not lead to a further reduction in carbon emissions than the individual ones but might have a slightly opposing direction (that still do not annihilate the individual effects). Findings show that only the moderation of ATM accessibility and mobile subscriptions reduce transport-related carbon emissions further than the individual effects. Taking the economic development of most SADC countries in the last 20 years into account, the study recommends strategic investment in advanced digital innovations, particularly linked with mobile devices, to strengthen digital banking efficiency and improve customer service while supporting emission-reducing pathways.

Journal of Risk and Financial Management doi: 10.3390/jrfm19060387

Authors: Silvia-Andreea Peliu Georgiana Danilov Nicoleta Tiloiu Ștefan Cristian Gherghina

This research responds to recent calls to explore the independence conditions under which boards’ leadership becomes economically meaningful for performance and risk in continental European governance systems. Using an unbalanced panel dataset of 223 non-financial publicly listed companies from Western Europe over 10 years between 2015 and 2024, this research examines how boards and their committee independence influence return on assets and return on equity, return volatility, indebtedness and liquidity volatility. Econometric methods include OLS regressions, industry fixed effects, linear and nonlinear models, including alternative specifications. The results highlight a U-shaped relationship between board and audit committee independence and operational efficiency, consistent with the critical mass interpretation. Board, audit and nomination committee independence reduce return volatility, reflected in a linear relationship. Audit committee independence is likely to reduce indebtedness beyond a balanced level, while the relationship of nomination committee independence with debt level is linear and negative across specifications. All governance mechanisms related to independence exhibit nonlinear relationships with liquidity volatility, with an immediate negative effect, while excessive independence oversight reduces their marginal effect. The findings suggest the existence of an optimal level of board and committee independence that is economically meaningful, providing practical guidance for shaping board and committee composition, to enhance performance and control risk.

Journal of Risk and Financial Management doi: 10.3390/jrfm19060386

Authors: Ebere Ume Kalu Innocent Odekina Idachaba Eleje Emmanuel Ben Etim Udoh Zeeshan Syed

This paper is an investigation into the degree to which regulatory quality and economic policy uncertainty influence loan performance in 15 Sub-Saharan African countries. The data for the study were drawn from the International Monetary Fund (IMF), World Bank and Federal Reserve Bank of St. Louis, covering the period 2008Q1–2024Q4. Using quarterly panel data, we employ a Panel autoregressive distributed lag (PARDL) with the addition of a Quantile ARDL (QARDL) approach to account for non-homogeneous effects of different levels of non-performing loans. Empirical feedback reveals that sound and effective regulatory quality substantially reduces non-performing loans, most especially in fragile financial regimes. Also, it was established that monetary and fiscal and economic policy uncertainty always enhances non-performing loans, especially during stress conditions, and this is an indication of the asymmetric state-dependent nature of the policy risk in the weak banking systems. The study concludes that increasing the quality of the regulatory system should be a key objective of financial sector reforms in Sub Saharan Africa (SSA). In addition, there is a need for regional coordination, such as regulatory harmonization and policy signalling, between countries in SSA, to reduce cross-border spillover effects and increase financial stability in a more interdependent financial system.

Journal of Risk and Financial Management doi: 10.3390/jrfm19060385

Authors: Dilmi C. W. Hettiachchi-Halpe-Kankanamalage Abootaleb Shirvani Nicholas Appiah Svetlozar T. Rachev W. Brent Lindquist Frank J. Fabozzi

We employ an empirical framework for real estate securities that incorporates portfolio optimization, return distribution tail diagnostics, risk metrics, modeling of long-range dependence in return volatility, regression against benchmark indices, and option pricing, treating these as necessary layers of a risk-management structure that concentrates on downside risk. Optimization compared mean–variance against downside-sensitive conditional value at risk. Tail behavior was assessed via skewness, kurtosis, and extreme value theory; volatility persistence was examined using ARMA–FIGARCH models. Benchmark dependence was examined via the capital asset pricing model (CAPM), employing endogenous and exogenous market proxies. Insurance instruments via European options were priced using a doubly subordinated normal inverse Gaussian pricing model capable of modeling skewed, heavy-tailed return distributions. Significant findings for the optimized portfolios include return distributions with losses that are heavier-tailed than gains; a transition in time from moderate-to-high long-range dependence in conditional volatility; smaller values of CAPM “alpha” and “beta” for minimum-risk portfolios compared to tangent portfolios; and significant implied volatility values.

Journal of Risk and Financial Management doi: 10.3390/jrfm19060384

Authors: Shefali Saini Mduduzi Biyase Gurpreet Kaur

This study empirically investigates the impact of exchange rate volatility on foreign direct investment inflows to India from 1990 to 2023, addressing a crucial dimension of macroeconomic stability in emerging economies. Recognizing that currency fluctuations significantly influence multinational corporations’ investment decisions, understanding this impact is vital for effective economic policy. Utilizing annual time series data from the Reserve Bank of India and the World Bank, the study employs the Quantile Autoregressive Distributed Lag (QARDL) modeling framework to capture both short-run and long-run dynamics. Unlike conventional mean-based estimators, the QARDL framework captures heterogeneous effects across different points of the FDI distribution, allowing for a more comprehensive understanding of how macroeconomic factors influence investment under varying economic conditions. The empirical results reveal significant asymmetries in the relationship between exchange rate fluctuations and FDI inflows. In the long run, exchange rate depreciation positively influences FDI inflows, particularly at the median and upper quantiles of the FDI distribution, suggesting that currency competitiveness becomes more important when investment inflows are already moderate or strong. In contrast, the exchange rate effect is statistically insignificant at lower quantiles, indicating that currency movements alone are insufficient to attract foreign investment when inflows are weak. These results offer valuable empirical insights for policymakers seeking to enhance macroeconomic resilience and promote long-term capital inflows in developing countries.

Journal of Risk and Financial Management doi: 10.3390/jrfm19060383

Authors: Kwoloong T. Liaw

University endowments provide long-term support for academic activities. Universities rely on the investment returns of endowments to continuously fund these activities. To pursue better investment performance, university endowments of all sizes have adopted the endowment model, which reduces holdings of public securities and increases allocation to alternative assets such as hedge funds, private equity, commodities, and real estate. This study documents the trend toward increasing allocation to alternative assets and evaluates the investment performance. Large university endowment funds have allocated a higher portion to alternatives and have higher rates of returns. Conversely, smaller university endowments have increased a lower percentage to alternatives and their performance trails that of larger peers, supporting prior studies showing that smaller endowments would achieve better performance by adopting the conventional 60/40 allocation in equity and fixed income strategy. We perform a regression analysis to examine the link between asset allocation and investment performance. The empirical results show that the impacts of equities and alternatives on performance are positive and significant. Furthermore, a comparative analysis indicates that investment returns exhibit high year-to-year volatility while the spending rates are stable and that the average rate of return is higher than the average spending rate.

Journal of Risk and Financial Management doi: 10.3390/jrfm19060382

Authors: Christopher Regan Ying Xie

Deploying large language models (LLMs) for domain-specific analysis raises a critical validation challenge: distinguishing genuine structural reasoning from training data memorization. We address this through temporal obfuscation testing, which strips calendar dates, ticker symbols, and contextual markers from input sequences, forcing models to reason from numerical structure alone. Applying this framework to options dealer gamma exposure (GEX) patterns across two temporal scales, we validate detection using 2221 evaluations (1412 real windows plus 809 synthetic controls) spanning 2020–2025. At the single-day scale, obfuscation testing achieves 71.5% detection of dealer hedging patterns with 91.2% predictive accuracy; raw strike-level data outperforms pre-calculated GEX metrics by 30.8 percentage points (92.3% vs. 61.5%), establishing that parametric aggregation represents lossy compression of structural signal. At the multi-day scale, 30-day regime detection achieves 81.2% detection in 2024 (95% CI [75.8, 86.1]%) versus 12.1% in 2020 (95% CI [8.1, 16.6]%)—a 69.1 percentage point separation (φ = 0.69, Fisher’s exact p = 1.8 × 10−52)—with 0% false positives on synthetic controls. Multi-year analysis reveals regime evolution tracking zero-days-to-expiration (0DTE) adoption—detection rising from 3.7% (2021) to 100% (2024)—with GEX magnitude growing from $3.0B to $20.3B. Stable detection despite collapsing profitability (Sharpe 1.8 → 0.1) confirms structural market mechanics rather than exploitable inefficiencies, establishing temporal obfuscation as a generalizable methodology for validating LLM reasoning in quantitative domains.

Journal of Risk and Financial Management doi: 10.3390/jrfm19060381

Authors: Babatunde Lawrence Sune Ferreira-Schenk Adefemi A. Obalade

Using the recently created contemporaneous and lagged R2-decomposed connectedness paradigm, this study examines the dynamics of return transmission between large South African banks and two top international banks, J.P. Morgan and BNP Paribas. The analysis makes a distinction between delayed (liquidity-driven) propagation mechanisms and instantaneous (information-driven) spillovers, using daily stock returns from 2015 to 2024. With a Total Connectedness Index of 44.14%, which is driven mostly by contemporaneous transmission, the results demonstrate a high degree of systemic interdependence and rapid assimilation of global information across banking stocks. We find smaller lagged spillovers which become much more intense during stressful events like COVID-19 in 2020, the conflict between Russia and Ukraine in 2022, and the banking instability involving the United States and Switzerland in 2023. These findings are conditioned by funding pressures, liquidity limits, and slow portfolio rebalancing. In the South African financial system, Standard Bank and Nedbank consistently act as net transmitters of shocks, whereas J.P. Morgan and BNP Paribas primarily act as net receivers, indicating asymmetric cross-border contagion pathways. However, their spillover transmission roles switch during crises. Overall, the results offer fresh empirical insight on how global shocks are absorbed and retransmitted by emerging-market banking systems, providing policy-relevant information for cross-border supervisory coordination, macroprudential design, and systemic risk monitoring.

Journal of Risk and Financial Management doi: 10.3390/jrfm19060380

Authors: Alexandro Damar Tirta Rizkyanzah Chusnul Maulidina Hidayat Prasetyo Hartanto

This study investigates whether stock market volatility of the Indonesian Composite Index (IHSG) during presidential elections is predominantly driven by global or domestic factors. Using an event study framework covering four election cycles (2009, 2014, 2019, and 2024), we examine three transmission channels, macroeconomic conditions (interest rate, inflation, exchange rate), trading activity (volume, frequency, traded value), and global risk sentiment (VIX), across a five-month pre- and post-election window, with the election month excluded to avoid short-term shock distortion. Channel variables are constructed using Principal Component Analysis (PCA) and tested through multiple linear regression with dummy interaction terms. To address autocorrelation in the initial OLS model (Durbin–Watson = 1.189), the Cochrane–Orcutt procedure is applied, which resolves the issue (Durbin–Watson = 1.990) and yields the primary results. The corrected model explains 55.8% of variation in IHSG volatility (Adjusted R2 = 0.558, F = 7.862, p < 0.001). Results consistently show that global risk sentiment (VIX) is the only significant positive driver of volatility (β = 0.003, p < 0.001), while macroeconomic and trading activity channels show no robust significance. No significant interaction effects are found, indicating that the influence of these channels does not differ statistically between pre- and post-election periods. We conclude that stock market volatility around Indonesian presidential elections is more strongly associated with global risk sentiment than with domestic macroeconomic or trading activity factors. The VIX consistently emerges as the dominant explanatory variable in the estimated model.

Journal of Risk and Financial Management doi: 10.3390/jrfm19060379

Authors: Jorge Marino-Romero Ángel-Sabino Sanguino Eva Crespo-Cebada Carlos Díaz-Caro

Artificial intelligence (AI) is reshaping tourism businesses by improving decision making, service personalization, operational efficiency, and data-driven management. Beyond these organizational benefits, AI may also strengthen firms’ capacity to cope with market volatility, demand shocks, cost pressures, and other sources of financial fragility. This study provides a systematic literature review and bibliometric analysis of 146 Web of Science articles on AI in tourism published between 2019 and 2023. Following a structured screening process, it identifies the intellectual structure, thematic evolution, and main performance-related drivers associated with AI adoption. The findings show a rapidly expanding field centered on business performance, information technology, big data, robotics, and AI-enabled service innovation. The literature suggests that AI contributes to resilience by enhancing forecasting, resource allocation, customer management, and organizational adaptability under uncertainty. However, explicitly financial perspectives—such as financial vulnerability, resilience, liquidity, solvency, and risk management—remain underdeveloped. This study contributes by reframing AI in tourism as a potential resilience-building capability rather than only a tool for service innovation. Its main limitations are the reliance on Web of Science and a fixed 2019–2023 bibliometric corpus, which future research should extend.

Journal of Risk and Financial Management doi: 10.3390/jrfm19060378

Authors: Shuangzhe Liu Ross Maller Svetlozar T. Rachev

This Perspective is dedicated to the memory of Professor Joseph Mark (Joe) Gani (1924–2016) and Professor Christopher Charles (Chris) Heyde (1939–2008), two scholars whose intellectual leadership profoundly shaped applied probability, mathematical statistics, and their interface with finance, insurance, and risk management. Their contributions extend beyond specific technical results to the development of research cultures grounded in probabilistic rigor, empirical relevance, and methodological transparency. We emphasize three enduring themes central to modern quantitative risk analysis. First, the systematic incorporation of heavy-tailed and non-Gaussian features in stochastic modeling, reflecting persistent empirical deviations from classical Gaussian assumptions in financial data. Second, the development of stochastic and time-series methodologies capable of handling dependence structures, including conditional heteroskedasticity and long-range dependence. Third, the principled integration of probabilistic modeling with data-driven and machine learning approaches, ensuring predictive performance is accompanied by interpretability and robustness. We situate these contributions within contemporary challenges in financial risk management, including systemic risk, environmental, social and governance (ESG) considerations, and climate finance. In particular, climate-related financial risks arise from both physical impacts (such as extreme weather events and long-term environmental change) and transition dynamics associated with the shift toward a low-carbon economy (including policy, technological, and market adjustments). These sources of risk introduce additional forms of dependence, nonlinearity, and model uncertainty, particularly in high-dimensional, data-rich settings. This Perspective highlights a forward-looking research agenda that preserves the foundational principles of applied probability while adapting them to modern financial systems characterized by real-time information flows and evolving risk structures. This legacy continues to shape how financial risk is modeled, measured, and understood in increasingly complex and interconnected environments.

Journal of Risk and Financial Management doi: 10.3390/jrfm19060377

Authors: Amina Ladhari Heni Boubaker

This study investigates the predictive performance of decomposition-based deep learning models through a focused case study on Ethereum price forecasting. Using hourly Ethereum price data from 5 September 2020 to 13 July 2025, we develop hybrid forecasting frameworks that integrate three signal decomposition techniques—Wavelet Decomposition (WD), Variational Mode Decomposition (VMD), and Empirical Mode Decomposition (EMD)—with a Long Short-Term Memory network enhanced by an attention mechanism (LSTM–Attention). The decomposition methods are first applied to extract multiple frequency components from the original time series, allowing the forecasting model to capture both short-term fluctuations and long-term dynamics inherent in this specific digital asset. Each decomposed component is then modeled using the LSTM–Attention architecture, and the forecasts are aggregated to produce the final prediction. The predictive performance of the proposed models is evaluated using MAE, MSE, RMSE, and MAPE, and the results are compared with benchmark models including ARIMA-GARCH and standard LSTM–Attention. Forecast accuracy is assessed through out-of-sample one-step-ahead predictions, and robustness is ensured by averaging results across 10 independent runs. The empirical results demonstrate that incorporating decomposition techniques substantially improves forecasting accuracy. Among the tested models, the EMD–LSTM–Attention framework achieves the best performance, producing the lowest forecasting errors. While focused on the Ethereum market, these findings highlight the effectiveness of combining signal decomposition and attention-based deep learning architectures to enhance predictive performance in high-volatility cryptocurrency environments.

Journal of Risk and Financial Management doi: 10.3390/jrfm19060376

Authors: Penprapak Manapreechadeelert Kanokwan Meesook Somnuk Aujirapongpan Jorge Miguel Chávez-Díaz

The study investigates how minority shareholding affects dividend payout policy in Thai listed companies, with firm growth and debt burden positioned as joint financial moderators. Using a moderated moderation design through Hayes’ PROCESS Macro Model 3, the analysis draws on 2430 firm-year observations from 532 companies listed on the Stock Exchange of Thailand during 2019–2023. Grounded in Agency Theory, Signaling Theory, and Pecking Order Theory, the results reveal a positive association between minority shareholding and dividend payouts, lending empirical support to the agency-based view of dividends as a governance mechanism. Nevertheless, corporate debt plays a negative moderating role, consistent with Pecking Order Theory’s prediction that leverage constraints reduce payout flexibility. The three-way interaction analysis further demonstrates that minority shareholder influence is contingent upon the firm’s financial context, particularly the joint configuration of growth prospects and leverage. In firms with simultaneously high growth and high debt, the governance role of minority shareholders is substantially diminished. On the other hand, in firms with high growth and low leverage, minority shareholders exert a stronger influence, consistent with Signaling Theory. The results reveal minority shareholder governance as financially contingent and highlight the need to assess financial constraints alongside ownership-based governance mechanisms in emerging capital markets.

Journal of Risk and Financial Management doi: 10.3390/jrfm19050375

Authors: Nour M. Mazen Lababidi Hasan Radwan Katalo Yahya Kamakhli

Research Purpose: This study investigates the role of digital investor behavior, measured through Google Trends, alongside technical indicators such as RSI and Bollinger Bands, in forecasting volatility in the Dubai Financial Market. The aim is to develop a hybrid analytical framework that integrates behavioral and technical dimensions to enhance predictive accuracy in emerging markets. Study Methodology: Daily data from 2020 to 2025 were collected, covering both crisis and post-crisis periods. Digital attention was quantified using Google Trends search indices, while technical indicators included RSI and Bollinger Bands calculated over a 7-day horizon. Volatility was modeled using ARCH, GARCH, and EGARCH frameworks, with Max Drawdown employed as a complementary risk metric to capture extreme market movements. Findings: Digital investor attention shows a predictive association with volatility, particularly when combined with technical indicators. Models incorporating both behavioral and technical variables demonstrated superior predictive performance. The EGARCH model successfully captured the asymmetric impact of negative shocks (γ < 0, p < 0.05), while Max Drawdown provided additional insights into risk exposure during periods of heightened market stress, achieving an R2 of 95.36%. Scientific value: This study positions digital attention as a complementary variable that improves forecasting, moving beyond conventional price-based models in volatility modeling; by integrating Google Trends with technical analysis, the research introduces a hybrid forecasting framework that can be adapted to other emerging markets. Practical Implications: The findings offer practical value for policymakers and investors. Regulators can use digital attention measures as early warning signals to anticipate volatility, while investors can integrate behavioral and technical indicators to improve risk management and trading strategies. From a foresight perspective, the study contributes to building more resilient financial systems by embedding behavioral data into predictive tools.

Journal of Risk and Financial Management doi: 10.3390/jrfm19050374

Authors: Melvien Deisie Christin Welang Juli Hendri Sung Suk Kim

This study examines the relationship between managerial overconfidence and environmental, social, and governance (ESG) performance through firm-level financial policy channels in an emerging-market context. Using panel data from non-financial firms listed on the Indonesia Stock Exchange during 2015–2024, this study adopts a multidimensional channel-based perspective in which managerial overconfidence is indirectly reflected through financing, liquidity, and investment decisions. Fixed-effects estimation with Driscoll–Kraay standard errors is employed as the baseline approach and complemented by lagged specifications, system GMM estimation, alternative measurements, and quantile regressions to assess robustness. The findings suggest that managerial overconfidence does not exert a direct and uniform influence on ESG performance but operates indirectly through heterogeneous financial policy behavior. The financing channel provides weak and unstable evidence, whereas the liquidity channel shows a relatively stronger positive association with ESG performance. The investment channel appears most sensitive to measurement and model specification, indicating that different operationalizations may capture distinct dimensions of managerial overconfidence. This study contributes to the behavioral corporate finance and ESG literature by showing that managerial overconfidence influences sustainability outcomes indirectly through heterogeneous financial policy mechanisms in an emerging market setting while highlighting the importance of temporal dynamics, endogeneity, and measurement sensitivity.

Journal of Risk and Financial Management doi: 10.3390/jrfm19050372

Authors: Samir Varma

This paper develops a queueing-organized framework for within-venue monitoring of BTC/USDT liquidity, signed-flow pressure, and resiliency on Binance. The model treats latent buy and sell pressure as occupancy processes and organizes three empirical diagnostics: the variance-per-BTC liquidity measure Rr, the mean-reversion rate θeff, and the companion signed-flow proxy βeffproxy. Using Binance trade data from 2020–2025, the empirical results show a pooled first-order variance–volume regularity away from the highest-volume tail, material rolling variation in liquidity and resiliency, and stronger next-day risk sorting from rolling Rr than from Amihud illiquidity or a Kyle-style minute-impact benchmark. In overlapping 30-day windows, θeff is positive by point estimate in roughly two-thirds of windows but clearly positive in only about two-fifths under a simple uncertainty buffer, implying that local recovery is often fragile or ambiguous. The one-hour symmetric benchmark is useful as a coarse center benchmark, but the heaviest tails require a Student-t VaR overlay. The framework is intended for risk managers, execution desks, market makers, and exchange surveillance teams that need indicators of thinning liquidity, weakening recovery, and one-sided signed flow. Queueing is useful because it turns those signals into one monitoring dashboard for market quality and short-horizon risk.

Journal of Risk and Financial Management doi: 10.3390/jrfm19050373

Authors: Donia Khalfallah Oumaima Haj Ammar Hana Bejaoui Abderahman Rejeb Sándor Remsei

Climate-related financial risks have become a central concern for financial institutions and regulators, particularly within the European financial system. This paper examines how climate-related risks are integrated into governance, risk assessment, and regulatory practices in European financial institutions. Using a structured narrative literature review of academic and institutional sources published between 2015 and 2026, the study synthesizes evidence on physical, transition, and liability risks, as well as the frameworks and tools used to assess them, including climate stress testing, scenario analysis, and climate value-at-risk models. The findings indicate that climate considerations are increasingly embedded within governance structures and supervisory frameworks; however, implementation remains fragmented due to inconsistent data, methodological limitations, and institutional barriers. The review further highlights that existing risk models often struggle to capture the long-term and non-linear nature of climate-related uncertainty. This paper contributes to the literature by linking financial stability theory and institutional theory to explain the persistent gap between regulatory ambition and institutional practice within the European context. The study concludes by discussing implications for supervisory policy, disclosure standardization, and climate-risk integration in financial decision-making.

Journal of Risk and Financial Management doi: 10.3390/jrfm19050371

Authors: Fan Zhang Haobing Wang

This study investigates whether CNN-based front-end feature extraction improves the predictive performance of deep learning models applied to 1 min intraday CSI 300 index data. Three baseline sequence models, LSTM, GRU, and TCN, are compared against their CNN hybrid and dual-branch fusion variants across five input window sizes, with all comparisons using identical back-end configurations. A total of 45 model configurations are trained and evaluated across 20 independent runs, with performance assessed on four metrics (MAE, RMSE, Directional Accuracy, and Information Coefficient) and statistical significance evaluated by paired t-tests. After standardisation, adding a CNN front-end does not consistently improve performance over the raw baseline and reduces IC for LSTM- and GRU-based models in many cases (e.g., IC of 0.0187 vs. 0.1031 for CNN-LSTM vs. LSTM at W=1), suggesting that standardised recurrent models can extract useful patterns directly from the raw sequence without CNN preprocessing. The dual-branch fusion architecture, which retains both the raw and CNN-compressed sequence branches, consistently outperforms the pure CNN hybrid on MAE, RMSE, and IC for LSTM- and GRU-based models (e.g., LSTMDualBranchFusion achieves statistically significant MAE reductions over CNN-LSTM at W=1, W=2, W=4, and W=5), indicating that the raw sequence carries complementary predictive information that the CNN front-end discards. TCN-based models produce near-zero or negative IC values regardless of architecture variant, suggesting a possible limitation of dilated convolutional architectures for return rank-ordering on this dataset and sample period. These findings are consistent across all five window sizes examined.

Journal of Risk and Financial Management doi: 10.3390/jrfm19050370

Authors: James C. N. Mbugua Ibrahim Tirimba Ondabu Fred Ochogo Sporta

The objective of this paper was to explore how financial development affects the relationship between sustainability practices and sustainable development in Sub-Saharan Africa, where poor institutional quality and shallow financial markets may prevent sustainability gains from translating into measurable improvements in human development, poverty reduction, and environmental outcomes. Both descriptive and explanatory components were included in the study, which employed a longitudinal panel design. Using a positivist, longitudinal panel design, this study analyzes data from 49 Sub-Saharan African countries (2000–2023) sourced from the World Bank, United Nations Development Programme, and Sustainable Development Reports. Data analysis was done using regression models and descriptive analysis. The findings show that financial development does not serve as an effective transmission channel through which sustainability practices impact the achievement of sustainable development. The research concluded that policy interventions should include developing sustainable banking regulations, creating green finance incentives, establishing sustainability-linked lending criteria, and strengthening financial inclusion policies that target sustainable development sectors.

Journal of Risk and Financial Management doi: 10.3390/jrfm19050369

Authors: Elisabet Ruiz-Dotras Josep Llados-Masllorens

Several studies show that financial vulnerability is not determined solely by low levels of wealth, but also by behavioural and social factors that shape financial behaviour. From this perspective, the social environment and financial knowledge can influence how investors evaluate their investment experiences. However, most of the literature has focused on how these aspects affect participation in financial markets, rather than on how they shape perceptions of the investment experience itself. This study explores how interactions with one’s social environment and both objective and subjective levels of financial knowledge contribute to how people evaluate the outcomes of their investments. To do so, we analyse a sample of undergraduate students using multivariate regression and Oaxaca–Blinder decompositions across three social environments—family, workplace, and banking advisors—and three types of financial assets: stocks, investment funds, and pension funds. The results show that perceptions of investment experience are shaped not only by individual factors but also by financial knowledge and the social environment—and these effects differ between men and women. There are also differences across types of financial assets, suggesting varying levels of vulnerability. These findings highlight the importance of personal characteristics, financial knowledge, and social context in explaining investment perceptions and differences in financial vulnerability.

Journal of Risk and Financial Management doi: 10.3390/jrfm19050368

Authors: Janah Nada El Ganich Said Yahyaoui Taoufiq Kouchrad Ikhlass

In this research, the primary aim is to conduct a systematic review of the thematic evolution of green finance, which remains fragmented and unevenly represented in global academic debates. The objective of this analysis is to scientifically map out the scholarly output on green finance from 2015 to 2026, detailing its intellectual structure, trends, thematic clusters, and emerging lacunae in the field. Primary data extraction from Web of Science was employed to construct the bibliometric database, whereas the identification, screening, and selection of the final dataset were conducted in accordance with the PRISMA guidelines to ensure the study’s transparency and reliability. The main findings highlighted an increasing scholarly interest in the field’s publications from 2019 onward. Key occurrences and citation maps, using RStudio (version 4.1) and Biblioshiny (version 4.5.2), indicate dispersed clusters comprising sustainability transitions, digital finance, bibliometric methods, and a weak link to governance and behavioral perspectives. The co-authorship and country analyses confirm a pronounced geographic imbalance of green finance-related research in academia, with an overrepresentation in the Global North and an underrepresentation in Africa, Latin America, and the MENA region. The analysis further emphasizes the growing role of institutional and ESG regulatory frameworks in shaping research trajectories, while also identifying a limited integration of emerging technological dimensions such as digital finance and artificial intelligence. Thus, the study’s contribution to the literature relies on its critical understanding and structuring of the field’s evolution. The implications include synthesizing research gaps and the need for outcome-oriented impact assessments and mechanism-based models of green finance to ensure significant inclusivity and resilience in the subject’s future agenda.

Journal of Risk and Financial Management doi: 10.3390/jrfm19050367

Authors: Shuangzhe Liu Svetlozar T. Rachev

This featured volume is dedicated to the memory of Professor Joe Gani (1924–2016) and Professor Chris Heyde (1939–2008), two outstanding scholars whose research, intellectual leadership, and mentorship had a lasting influence on applied probability, mathematical statistics, stochastic processes, actuarial science, and financial risk analysis [...]

Journal of Risk and Financial Management doi: 10.3390/jrfm19050366

Authors: Adile Aktar Ömer Tekşen

Financial reporting standards aim to increase transparency; however, the expansion in disclosure volume may also create an information overload paradox for investors, an issue that remains underexplored in the context of institutional investors. Excess information beyond mandatory requirements may complicate decision environments and create cognitive burden. When information exceeds cognitive processing capacities, attention may become fragmented, making it more difficult to distinguish signal from noise and potentially leading to analysis paralysis and changes in risk perception. Drawing on bounded rationality and cognitive load theory, this study conceptualizes information overload as a behavioral constraint associated with perceived limitations in decision quality and speed and, accordingly, examines its influence on institutional investors’ decision processes through a phenomenological approach. The study employs thematic analysis based on in-depth interviews with 19 professionals in institutional investment organizations in Türkiye. The findings suggest that information overload is experienced as cognitive strain that may prolong decision processes, may be associated with analysis paralysis and perceived changes in decision quality, and may be associated with increased uncertainty and potential challenges in interpreting risk. These findings provide exploratory insight into how information density may influence risk interpretation and portfolio assessment, and how institutional investors perceive decision-making efficiency.

Journal of Risk and Financial Management doi: 10.3390/jrfm19050365

Authors: Olajumoke Oyewo Omobolanle Korede Oluwalana Kolawole Alo Gbenga Ekundayo

This study examines the association between foreign exchange (FX) governance and financial stability by analysing empirical evidence from multinational entities. We analyse a 16-year panel (2009–2024) comprising 6613 firm-year observations using OLS regression with industry and year fixed effects. Firm-level data on financial sustainability, FX governance, board attributes, and controls are drawn from the London Stock Exchange Group (formerly Refinitiv), while country-level institutional and economic indicators are obtained from the World Bank. The result suggests that FX governance is negatively associated with earnings volatility, implying that FX governance enhances the financial stability of organisations. The baseline result is robustness to endogeneity and selection bias. However, our subsample analysis reveals that the impact of FX governance on financial stability varies based on institutional quality and industry. Whereas FX governance is negatively associated with earnings volatility thus enhancing financial stability in high-institutional-quality settings, the impact is not significant in low-institutional-quality environments. This study contributes to knowledge by empirically validating the relevance of FX governance to financial stability. Our study also contributes to the limited studies on the role of FX governance in diminishing earnings volatility, thus exposing FX management as a strategy for achieving financial sustainability. The international sample analysed in the study contributes to the generalisability of results.

Journal of Risk and Financial Management doi: 10.3390/jrfm19050364

Authors: Siseko Mtunzi Merana Hilary Tinotenda Muguto Lorraine Muguto Paul-Francois Muzindutsi

This study examines whether investor sentiment drives volatility spillovers between socially responsible and traditional mutual funds. The rapid growth of responsible investing in emerging markets raises questions about whether higher costs deliver improved risk or diversification benefits, particularly in volatile, behaviourally driven settings. Using a sentiment-augmented Diebold–Yilmaz connectedness framework, a composite sentiment index is constructed from global and local indicators. The results show that spillovers are time-varying and regime-dependent. During periods of stress and pessimism, responsible funds act as net transmitters of volatility, while traditional funds absorb shocks. In bullish conditions, volatility transmission weakens. Overall, connectedness shifts across market states, and socially responsible funds do not consistently provide stabilising or diversification benefits, as these depend on prevailing sentiment and risk conditions. This study provides new evidence on how sentiment-driven volatility spillovers are transmitted between socially responsible and traditional funds in South Africa, with implications for systemic risk and ESG investment costs.

Journal of Risk and Financial Management doi: 10.3390/jrfm19050363

Authors: Hung-Hsi Huang Ya-Ting Li Ching-Ping Wang

This study examines the dynamic relationship between exchange rates and stock returns in Taiwan, focusing on asymmetry and time-varying dependence. Using monthly and daily data from 1994 to 2024, we employ ARDL, NARDL, and error correction models (ECM), together with a time-varying copula framework. We contribute to the literature in three ways. First, we provide a unified framework that jointly captures long-run equilibrium, short-run dynamics, and nonlinear dependence. Second, we document robust asymmetric effects, showing that currency depreciation stimulates stock returns, whereas appreciation exerts adverse effects, reflecting Taiwan’s export-oriented economic structure. Third, we show that the dependence between exchange rates and stock returns is time-varying and highly persistent. Overall, the findings highlight the importance of nonlinear and time-varying approaches in understanding exchange rate–stock market interactions and offer important implications for investors and policymakers.

Journal of Risk and Financial Management doi: 10.3390/jrfm19050362

Authors: Nurcan Kilinc-Ata Alia Mubarak Al-Fori

The intersection of financial innovation and environmental sustainability offers important opportunities for low- and middle-income (LMI) countries. This study examines the association between FinTech investment, geopolitical-economic uncertainty, urbanization, economic development, and carbon dioxide (CO2) emissions in LMI countries. CO2 emissions per capita are used as an environmental outcome indicator rather than as a direct measure of green finance. Using a panel dataset covering 2010–2021, the study applies fixed-effects panel regressions as the main empirical approach and reports one-step difference the Generalized Method of Moments (GMM) estimates as exploratory dynamic evidence. The fixed-effects results indicate that GDP per capita is positively and significantly associated with CO2 emissions, while FinTech investment and urbanization do not show consistent significant associations. Geopolitical risk is positively associated with CO2 emissions in some static specifications, but this association becomes insignificant once gross domestic product (GDP) per capita is included. The exploratory GMM results, estimated with collapsed instruments and restricted lag depth, do not provide statistically significant evidence that FinTech investment is associated with lower CO2 emissions. Overall, the findings suggest that FinTech investment may be relevant for environmental outcomes in LMI countries, but its role is neither automatic nor uniform and remains sensitive to model specification. Policy implications emphasize the need to strengthen digital financial infrastructure, regulatory transparency, institutional stability, urban planning, and climate-oriented investment channels to support FinTech-driven environmental performance.

Journal of Risk and Financial Management doi: 10.3390/jrfm19050361

Authors: Raja Kamal Ch K. Meenadevi Deepak Kumar D Rakesh Nagaraj

Credit risk evaluation is a key factor in financial intermediation, regulatory capital provision, and risk management in the portfolio. In this study, we compare the deep learning performance for probability-of-default (PD) estimation with a structured financial econometric model using loan-level data of an Indian non-banking financial agency between May and August 2025. Using the interpretation of PD as a conditional expectation, which is in line with reduced-form default-intensity models, we compare deep learning, logistic regression, and gradient boosting using a pure time-based out-of-sample design. Model assessment focuses on discrimination and calibration, where the area under the precision–recall curve (AUC-PR), Brier score, log-loss, and Hosmer–Lemeshow goodness-of-fit tests are utilized. The findings show that deep learning achieves higher accuracy in terms of calibration but a lower Brier score by about 18; this gap could be reduced by comparing logistic regression with statistically significant improvements in formal tests that compare forecasts. In portfolio back-testing, better probability scaling is translated into an actual loss reduction of about 12–13% for the August 2025 cohort. Although the improvements compared with the advanced ensemble techniques are moderate, the results indicate that deep learning improves the estimation of conditional default probabilities because of the better nonlinear modeling and upper-tail risk perception. This study contributes to the literature via its incorporation of machine learning and credit risk assessment into a formalized risk management and econometric assessment system.

Journal of Risk and Financial Management doi: 10.3390/jrfm19050360

Authors: Patrizia Gazzola Stefano Amelio Vincenza Vota

The integration of environmental, social and governance (ESG) criteria into corporate and financial decision-making has become one of the most significant transformations in today’s financial markets. Growing regulatory pressure, stakeholder expectations and increased awareness of sustainability challenges have led companies and investors to incorporate ESG considerations into strategic and investment decisions. Despite the rapid spread of ESG practices, the academic literature presents conflicting and sometimes contradictory evidence regarding their economic implications and practical effectiveness. This article provides a review of the literature on the main academic contributions to ESG integration, focusing on three key dimensions: the economic benefits associated with ESG practices, the methodological and credibility challenges relating to ESG measurement, and the organisational and technological factors that enable effective ESG implementation. The findings indicate that ESG integration is generally associated with positive organisational outcomes, including improved financial performance, lower cost of capital, greater stakeholder trust and a reduction in firm-specific risk. However, the realisation of these benefits is not automatic and depends to a large extent on the credibility of ESG practices and information. Rather than endorsing the widely held view that ESG criteria are inherently capable of creating value, the analysis shows that the value-creating effect of ESG criteria depends crucially on the credibility of ESG practices and the quality of their implementation. The literature highlights significant methodological challenges, including rating divergence, the lack of standardised metrics, methodological opacity and the growing risk of greenwashing, which can undermine the reliability of ESG information. This paper proposes an deductive conceptual framework in which ESG effectiveness emerges from the interaction between value creation mechanisms, credibility constraints, and enabling organisational and technological factors.

Journal of Risk and Financial Management doi: 10.3390/jrfm19050359

Authors: Aina Otarbayeva Akimzhan Arupov Madina Abaidullayeva Azizam Arupova Valeriy Abramov

This study assesses export concentration risk in four Central Asian economies (Kazakhstan, Kyrgyzstan, Tajikistan, and Uzbekistan) by examining trade specialization patterns in 31 mineral, chemical, textile, and metallurgical product groups over 2017–2024. Using a multi-index framework based on Revealed Symmetric Comparative Advantage (RSCA), Relative Trade Advantage (RTA), and the Lafay Index (LI), the paper distinguishes structurally embedded competitive advantages from export signals that are weak, import-dependent, or potentially transient. The revised analysis adds explicit data consistency checks, a clarified classification rule, and robustness tests based on sign concordance, majority-index rules, and RSCA-only thresholds. The results show that Central Asia’s risk profile is highly persistent but heterogeneous: Tajikistan is exposed to extreme single-commodity risk in aluminium and cotton-related segments; Kazakhstan remains vulnerable to mineral-fuel concentration and energy-price volatility; Uzbekistan has broader but still labour-intensive textile specialization; and Kyrgyzstan shows ambiguous competitiveness that may partly reflect re-export and transit-related trade. Fully competitive product groups are confined mainly to resource- and labour-intensive activities, while chemicals and technologically complex manufacturing remain non-competitive across the region. The findings support risk-differentiated policy responses, including commodity-price hedging, counter-cyclical stabilization tools, downstream processing, textile upgrading, and regional value-chain development.

Journal of Risk and Financial Management doi: 10.3390/jrfm19050358

Authors: John Lehlaka Masekoameng Sizwe Vincent Mbona Anisha Ananth Retius Chifurira

Credit risk management is critical in developing economies where high default rates threaten financial stability. This study compares traditional logistic regression (TLR) and Bayesian logistic regression (BLR) for predicting loan default using anonymized National Credit Regulator (NCR) data from 5000 South African loan borrowers (2018–2022). The NCR data included both bank and non-bank lenders. The findings indicate that the BLR model outperformed TLR, achieving an average precision of 0.94. Loan terms, inflation rates, and income bands of R5000–R10,000 and R20,000–R50,000 were associated with higher default risk, whereas higher credit scores and personal loan products were associated with lower default risk. Model performance improved when focusing on these predictors rather than all variables. Using a 0.5 probability threshold, BLR classified 94.5% of borrowers as high risk. Findings highlight the practical value of BLR for identifying key predictors and improving borrower risk classification. These insights can inform targeted strategies such as enhanced screening for long-term loans, monitoring during inflationary periods, and tailored repayment plans for vulnerable income groups, supporting responsible lending and portfolio stability.

Journal of Risk and Financial Management doi: 10.3390/jrfm19050357

Authors: Riza Andrian Ibrahim Heru Santoso Sukono

Empirical evidence shows that the El Niño-Southern Oscillation (ENSO) influences the frequency–damage relationship for floods. However, ENSO is generally not incorporated into indemnity-trigger modeling of Flood Catastrophe Bonds (FCBs), resulting in an incomplete representation of claim events. Therefore, this study aims to develop an FCB pricing model that incorporates ENSO as an external systematic risk factor affecting the indemnity trigger. The trigger is formulated as a doubly stochastic compound Poisson process, with its intensity modeled as an autoregressive integrated moving-average with exogenous variables. Bond prices are then derived by integrating the trigger process with the Cox-Ingersoll-Ross model under an arbitrage-free risk-neutral framework. To obtain stable numerical solutions, a Monte Carlo-based algorithm is also developed. Numerical simulations using data from Bandung Regency, Indonesia, show stable estimates under the relative Monte Carlo standard error measure. Then, incorporating ENSO empirically improves flood-intensity forecasting accuracy, as indicated by lower MAPE, MAE, RMSE, and Theil’s U. It also produces statistically significant price differences across all common maturities. This study advances the theoretical and practical pricing of FCBs by directly linking climate-driven flood intensity to indemnity triggers, equipping practitioners to quantify risk better and to set sustainable disaster risk financing, particularly in ENSO-affected regions.

Journal of Risk and Financial Management doi: 10.3390/jrfm19050356

Authors: Gihan M. Ali Mohammad Zaid Alaskar

Financial reporting integrity (FRI) plays a critical role in capital market efficiency, yet its determinants remain difficult to model due to nonlinear relationships, heterogeneous firm characteristics, and institutional differences across emerging markets. Prior research largely relies on linear econometric approaches, which may overlook threshold effects and complex governance dynamics. This study develops an explainable Optuna-optimized Extremely randomized trees (ET) ensemble framework to examine the association between controlling shareholders and FRI in Egypt and Saudi Arabia. Using a panel dataset of 1746 firm-year observations over the period 2014–2022, the model incorporates advanced preprocessing and mutual information-based feature selection to enhance predictive accuracy and robustness. The proposed model significantly outperforms regularized linear models, standalone machine learning models, and alternative ensemble techniques, achieving R2 values of 0.7935 in Egypt and 0.9231 in Saudi Arabia, alongside substantial reductions in RMSE and MAE. Diebold–Mariano tests confirm that these performance gains are statistically significant (p < 0.01). Explainability analysis using SHAP reveals that firm size and market share are the dominant drivers of FRI, while blockholder ownership exhibits a nonlinear and context-dependent association. Partial dependence results show a complex, non-monotonic relationship in Egypt—consistent with a monitoring–entrenchment trade-off—contrasted with a predominantly positive and monotonic association in Saudi Arabia. Importantly, these nonlinear patterns are not detected in conventional panel fixed effects models, highlighting the limitations of standard econometric specifications in capturing complex ownership dynamics. The findings highlight the importance of institutional context in shaping governance outcomes and demonstrate how explainable ensemble learning can uncover hidden nonlinearities in financial reporting behavior. This study contributes by identifying nonlinear thresholds and cross-country variation in ownership effects while integrating predictive performance with interpretability, offering a robust framework for analyzing corporate governance mechanisms in emerging markets and supporting more informed decision-making by investors, regulators, and policymakers.

Journal of Risk and Financial Management doi: 10.3390/jrfm19050355

Authors: Ashis SenGupta Barry C. Arnold Moumita Roy

In the spirit of the solution of for modeling price changes in high-volatility markets for univariate commodities, here we generalize an approach to the case of modeling price changes jointly for two related commodities. Often, conditional distributions are more easily understood in financial markets, where the fluctuations in one commodity can shed significant light on the behavior of a related commodity. With this observation, we enhance and characterize the entire family of bivariate joint densities for which both the conditional distributions are specified to be of the Laplace form. Such bivariate distributions will be referred to as bivariate Laplace conditional (BLC) distributions. We study the marginals of the BLC distributions and establish that they are not only sub-Gaussian but also super-Laplacian and, hence, super-Cauchy, i.e., they have heavier tails than Gaussian distributions but lighter tails than the usual Laplace and Cauchy distributions. Distance correlation is suggested as a measure of the association between the two marginal variables, as their product moment correlation is zero but they may be non-linearly dependent. A real-life data set is analyzed to illustrate the use of BCL distributions in practice. We believe that this is the first work using conditional specifications in bivariate financial data analysis.

Journal of Risk and Financial Management doi: 10.3390/jrfm19050354

Authors: Anas Azenzoul Nacer Mahouat Sophia Vandapuye Sara Nait Slimane Mourad Jbene Khalil Mokhlis

This study examines the transformative impact of artificial intelligence (AI) on tax auditing through a PRISMA-compliant systematic literature review and textometric analysis. By analyzing literature published between 2015 and 2025 using IRAMUTEQ, we uncover a nuanced perspective on AI’s evolving role. The results reveal a scholarly discourse highlighting significant advances in tax fraud prediction and financial risk assessment via deep learning and neural networks. This technological shift extends beyond operational efficiency to broader macroeconomic governance, simultaneously raising challenges regarding taxpayer equity and trust. Our findings underscore a transition in academic focus from purely technical applications to the ethical and psychological dimensions of AI. Finally, we propose the AI-Driven Tax Audit Model (ATAM), a framework designed to guide tax authorities in integrating these technologies by balancing algorithmic efficiency and financial risk mitigation with vertical equity and explainability.

Journal of Risk and Financial Management doi: 10.3390/jrfm19050353

Authors: Michael Masunda Haresh Barot Jayendrasinh Jadav

Forensic accounting faces increasing complexity as reactive approaches fail to address escalating risks. This study pioneers a Proactive Risk Intelligence Framework (PRIF) for Chartered Accountant (CA) firms, targeting gaps in risk anticipation, stakeholder communication, and compliance. Employing mixed-method-design interviews with 30 risk advisors, case studies, and analysis of 30 forensic reports, the PRIF was developed and validated using thematic coding, risk metrics, and Delphi panel refinement. Integration of AI and blockchain reduced the risk detection time from 47 days post-event to 9–22 days pre-event, with accuracy increasing from 62% to 89–94%. The Stakeholder Communication Index (SCI) revealed a strong correlation (r = 0.83) between report quality and client retention (91% for high SCI vs. 54% for low SCI). PRIF adoption reduced compliance resolution time by 58% and financial misstatements by 47%, yielding an average ROI of 83%. This integrated framework combines real-time monitoring, stakeholder-centric reporting, and dynamic compliance for CA firms. While the findings are based on India-focused samples, practical benefits include scalable toolkits for firms and policy guidance for regulators with a broader impact on financial governance. PRIF shifts forensic accounting from reactive detection to proactive prevention, advancing stakeholder trust and industry standards.

Journal of Risk and Financial Management doi: 10.3390/jrfm19050352

Authors: Yetunde Bernice Oyewole Grace Oluyemisi Akinola Odunayo M. Olarewaju Mustapha Bojuwon Victoria Temitope Ikulagba

The objective of this study was to explore the impact of exchange rate volatility on corporate financial stability in European corporations, with particular emphasis on the Eurozone and non-Eurozone. The data set of this study consisted of 80 publicly listed non-financial corporations in eight European countries over the period of 2010–2024. The model was able to capture the impact of various macroeconomic changes that affected European corporations in the past few years. The macroeconomic changes that were captured in this study were the European sovereign debt crisis, the COVID-19 pandemic in the world, and the conflict in Ukraine. The financial stability was measured by the Altman Z-score, the leverage ratio, and the current ratio. In this study, the financial impact of the exchange rate was measured by the rolling standard deviations and the conditional volatility with the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models. The fixed effects model estimation with the System Generalized Method of Moments (GMM) was used in this study. The results of this study showed that the exchange rate volatility was negatively correlated with financial stability in terms of the leverage ratio. However, the Eurozone provides protection against the financial impact of the exchange rate volatility in terms of the leverage ratio. The diagnostic tests in this study were carried out with the Hansen Test and the Arellano-Bond Test. The diagnostic tests confirmed that the results were valid. The significance of this study was that it provided longitudinal data on the impact of the exchange rate on the financial stability of European corporations with particular emphasis on the Eurozone and non-Eurozone. The study also provided new insights on the exchange rate in corporate finance. The Eurozone provides protection against the financial impact of the exchange rate.

Journal of Risk and Financial Management doi: 10.3390/jrfm19050351

Authors: Jihwan Choi

This study examines the association between account-level inherent risk and auditor–client disagreement. To measure disagreement directly, we introduce a novel proxy: the absolute magnitude of the gap between pre-audit and post-audit net income, constructed from unique disclosure data available through the Korean KIND system. We specifically focus on accounts characterized by elevated inherent risk, including accounts receivable, inventory, investments in subsidiaries, defined benefit obligations, and derivatives. Our empirical results reveal heterogeneous associations that reflect competing theoretical tensions. The relative magnitudes of traditional operational accounts–specifically accounts receivable and inventory–as well as defined benefit obligations are significantly and negatively associated with auditor–client disagreement. In contrast, the magnitude of complex valuation accounts, particularly investments in subsidiaries, is positively associated with disagreement. We interpret these divergent findings as follows. The negative associations likely reflect the constraining effect of modern audit technologies on traditional high-risk accounts, which standardize audit procedures and thereby limit managerial discretion. The positive association, conversely, underscores the inherently subjective nature of complex fair-value estimates, which remain susceptible to auditor–client friction. Taken together, this study shifts the analytical focus from firm-level determinants to account-level risk, demonstrating that the underlying economic nature of an account systematically shapes the extent of audit negotiations.

Journal of Risk and Financial Management doi: 10.3390/jrfm19050350

Authors: Suneel Maheshwari Deepak Raghava Naik Rajendar Garg

This study examines the risk–return performance of Nifty100 ESG and the Nifty Enhanced ESG equity indices listed on India’s National Stock Exchange (NSE) relative to the conventional Nifty 100 benchmark from April 2011 to June 2023. Rather than asserting formal “abnormal returns” in the asset-pricing sense as per CAPM or multi-factor alpha estimation, this study documents systematic return outperformance and time-varying volatility dynamics using unit root tests, month-of-the-year dummy regressions with ARIMA error correction, and GARCH-family conditional volatility models. The two ESG indices delivered cumulative returns much higher than the broader Nifty 100 index. Conditional volatility peaked for ESG indices when compared to Nifty 100 during the April 2020 COVID-19 shock, indicating marginally greater ESG resilience. A distinct March effect, which is analogous to the January effect in developed markets, is observed to be significant for the ESG indices. Our findings underscore the growing importance of responsible investing and time-varying risk premia in the Indian equity market.