Journal Description
Journal of Risk and Financial Management
Journal of Risk and Financial Management
is an international, peer-reviewed, open access journal on risk and financial management, published monthly online by MDPI (since Volume 6, Issue 1 - 2013).
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, EconBiz, EconLit, RePEc, and other databases.
- Journal Rank: CiteScore - Q1 (Business, Management and Accounting (miscellaneous))
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 18.8 days after submission; acceptance to publication is undertaken in 5.5 days (median values for papers published in this journal in the second half of 2025).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
Latest Articles
State-Dependent Dynamics of Overconfidence in Frontier Equity Markets: A Transfer Entropy Approach from Bangladesh
J. Risk Financial Manag. 2026, 19(6), 449; https://doi.org/10.3390/jrfm19060449 (registering DOI) - 21 Jun 2026
Abstract
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
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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.
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(This article belongs to the Section Financial Markets)
Open AccessArticle
The Impact of ESG Compliance and Greenwashing Risk on the Value of Companies Listed on the Bucharest Stock Exchange
by
Ioana Andrioaia, Veronica Grosu, Svetlana Mihaila and Alina Butnaru Ciobotar
J. Risk Financial Manag. 2026, 19(6), 448; https://doi.org/10.3390/jrfm19060448 (registering DOI) - 20 Jun 2026
Abstract
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
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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.
Full article
(This article belongs to the Section Sustainability and Finance)
Open AccessArticle
Investors’ Reaction to Sustainability Disclosures Under Varying Assurance Levels and Assurer Types: An Experimental Approach
by
Rola Shawat, Abanoub Wassef, Yara Ibrahim, Ahmed Hassanein, Hosam Moubarak and Hebatallah Badawy
J. Risk Financial Manag. 2026, 19(6), 447; https://doi.org/10.3390/jrfm19060447 (registering DOI) - 19 Jun 2026
Abstract
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
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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.
Full article
(This article belongs to the Special Issue Emerging Trends and Innovations in Corporate Finance and Governance)
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Open AccessArticle
Enhancing Enterprise Risk Management Through Emotional Intelligence: A Study of Risk Leadership in Indonesia
by
Wa’el Al-Karaki, Aldi Ardilo, Ahmed Eltweri, Yuan Zhai and Gbemisola Ogbolu
J. Risk Financial Manag. 2026, 19(6), 446; https://doi.org/10.3390/jrfm19060446 (registering DOI) - 19 Jun 2026
Abstract
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
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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.
Full article
(This article belongs to the Section Business and Entrepreneurship)
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Open AccessArticle
Eco- and Socio-Efficiency as Determinants of Default Risk: Evidence from European Firms
by
Bochra Issa, Sana Ben Abdallah and Foued Badr Gabsi
J. Risk Financial Manag. 2026, 19(6), 445; https://doi.org/10.3390/jrfm19060445 (registering DOI) - 19 Jun 2026
Abstract
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.
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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.
Full article
(This article belongs to the Section Sustainability and Finance)
Open AccessArticle
Predicting Stock Volatility Using Multidimensional Financial Risk: Evidence from Machine Learning and Hybrid GARCH–Deep Learning Models
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Yara Ibrahim, Khaled Hussainey and Taghred Mokhtar Sayed Moawad
J. Risk Financial Manag. 2026, 19(6), 444; https://doi.org/10.3390/jrfm19060444 (registering DOI) - 19 Jun 2026
Abstract
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,
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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.
Full article
(This article belongs to the Special Issue Editorial Board Members’ Collection Series: Journal of Risk and Financial Management, 2nd Edition)
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Open AccessArticle
Spillover Among Sovereign Credit Risk and the Role of Political Risk: Evidence from Oil-Exporting Economies
by
Mohammed Alhashim
J. Risk Financial Manag. 2026, 19(6), 443; https://doi.org/10.3390/jrfm19060443 (registering DOI) - 18 Jun 2026
Abstract
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
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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.
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(This article belongs to the Section Energy and Environment: Economics, Finance and Policy)
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Open AccessArticle
Board Characteristics, Climate Change Disclosures and the Moderating Role of Corporate Governance Code: Evidence from a Developing Economy
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Rajib Chakraborty, Lan Sun, Urmee Ghose and Ayub Islam
J. Risk Financial Manag. 2026, 19(6), 442; https://doi.org/10.3390/jrfm19060442 - 18 Jun 2026
Abstract
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
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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.
Full article
(This article belongs to the Special Issue Corporate Governance, Sustainability and Finance)
Open AccessArticle
The Impact of the ECB Policy Stance on Cryptocurrencies: Evidence and Policy Relevance
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Batuhan Karabiber and Tayfun Tuncay Tosun
J. Risk Financial Manag. 2026, 19(6), 441; https://doi.org/10.3390/jrfm19060441 - 18 Jun 2026
Abstract
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
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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.
Full article
(This article belongs to the Special Issue The Future of Money: Central Bank Digital Currencies, Cryptocurrencies and Stablecoins, 2nd Edition)
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Open AccessArticle
Innovation, Green Management, and Value Creation in Indonesian Healthcare: The Mediating Role of Business Sustainability
by
Wiwik Utami, Erna Setiany, Rieke Pernamasari and Anwar Allah Pitchay
J. Risk Financial Manag. 2026, 19(6), 440; https://doi.org/10.3390/jrfm19060440 - 17 Jun 2026
Abstract
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),
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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.
Full article
(This article belongs to the Special Issue Corporate Governance, Sustainability and Finance)
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Open AccessArticle
Maximum Entropy Identification of Latent Financing Flows in Corporate Balance Sheets: Cross-Sectoral Panel Evidence
by
Sunnatov Yusuf Usmonovich
J. Risk Financial Manag. 2026, 19(6), 439; https://doi.org/10.3390/jrfm19060439 - 17 Jun 2026
Abstract
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
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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.
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(This article belongs to the Special Issue Mathematical Modelling in Economics and Finance)
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Open AccessArticle
Financial Accounting Disclosures (FAD) in the UAE: Investor Reactions to Negative Financial News, Framing Bias and AI Channel Reliance
by
Mohamed Haffar, Shatha Mustafa Hussain, Amer Alaya, Serap Emik and Mohammad Jammal
J. Risk Financial Manag. 2026, 19(6), 438; https://doi.org/10.3390/jrfm19060438 - 17 Jun 2026
Abstract
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
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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.
Full article
(This article belongs to the Special Issue AI and Automation in Finance: Risk, Regulation, and Strategic Applications)
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Open AccessArticle
Non-Linear Effects of ESG Performance on Corporate Tax Avoidance: A Multi-Algorithmic Analysis via Explainable Artificial Intelligence
by
Önder Dorak and Duygu Şengül Çelikay
J. Risk Financial Manag. 2026, 19(6), 437; https://doi.org/10.3390/jrfm19060437 - 16 Jun 2026
Abstract
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
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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.
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(This article belongs to the Section Financial Technology and Innovation)
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Open AccessRetraction
RETRACTED: Banerjee et al. (2025). Impact of Environmental, Social, and Governance Parameters on Financial Performance of Firms: A Cross-Country Analysis. Journal of Risk and Financial Management, 18(12), 666
by
Souvik Banerjee, Amarnath Mitra and Shalini Aggarwal
J. Risk Financial Manag. 2026, 19(6), 436; https://doi.org/10.3390/jrfm19060436 - 16 Jun 2026
Abstract
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 [...]
Full article
(This article belongs to the Special Issue Corporate Sustainability and Firm Performance: Models, Practices and Policy Perspective)
Open AccessArticle
Fiscal Policy and Economic Growth in South Africa: Nonlinear Evidence for Transitory Keynesian Effects and Fiscal Risk
by
Luyanda Majenge and Simiso Msomi
J. Risk Financial Manag. 2026, 19(6), 435; https://doi.org/10.3390/jrfm19060435 - 16 Jun 2026
Abstract
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
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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.
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(This article belongs to the Section Economics and Finance)
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Open AccessArticle
Beyond Critical Mass: Nonlinear Effects of Female Directors on Carbon Emissions Disclosure in Emerging Markets
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Ni Wayan Rustiarini, Ni Putu Shinta Dewi, Ni Made Sunarsih and Sharifah Norzehan Syed Yusuf
J. Risk Financial Manag. 2026, 19(6), 434; https://doi.org/10.3390/jrfm19060434 - 16 Jun 2026
Abstract
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
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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.
Full article
(This article belongs to the Section Energy and Environment: Economics, Finance and Policy)
Open AccessArticle
Beyond Averages: FinTech, Digitalization, and the Heterogeneous Drivers of Green Finance in Europe
by
Faycal Chiad
J. Risk Financial Manag. 2026, 19(6), 433; https://doi.org/10.3390/jrfm19060433 - 16 Jun 2026
Abstract
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
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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.
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(This article belongs to the Section Financial Technology and Innovation)
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Open AccessArticle
SDE-Constrained Lévy-Driven Neural SDEs for Predictability-Aware Exchange Rate Forecasting
by
N’Adoi Aboagye and Saralees Nadarajah
J. Risk Financial Manag. 2026, 19(6), 432; https://doi.org/10.3390/jrfm19060432 - 16 Jun 2026
Abstract
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
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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 -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.
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(This article belongs to the Section Mathematics and Finance)
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Open AccessArticle
Economic and Financial Sustainability in the Biogas Sector: An Application to a Sample of Italian Agricultural Firms
by
Mattia Iotti, Giovanni Ferri and Alberto Calugi
J. Risk Financial Manag. 2026, 19(6), 431; https://doi.org/10.3390/jrfm19060431 - 15 Jun 2026
Abstract
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.
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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.
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(This article belongs to the Section Energy and Environment: Economics, Finance and Policy)
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Open AccessArticle
Laminarity and Market Stress: Testing an RQA-Based Diagnostic During the COVID-19 Shock
by
Domenico Vicinanza
J. Risk Financial Manag. 2026, 19(6), 430; https://doi.org/10.3390/jrfm19060430 - 15 Jun 2026
Abstract
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
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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.
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(This article belongs to the Special Issue Innovative Approaches to Financial Modeling and Decision-Making)
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