Journal of Risk and Financial Management doi: 10.3390/jrfm19080601

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

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

Journal of Risk and Financial Management doi: 10.3390/jrfm19080600

Authors: Faiza Abdalla Sheikh Batoun Ayesha Mohamed Ali Abdulla Batoun

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

Journal of Risk and Financial Management doi: 10.3390/jrfm19080599

Authors: Huda Aldhahi Abdulrahman Alsamaani

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

Journal of Risk and Financial Management doi: 10.3390/jrfm19080598

Authors: Mishelle Doorasamy Oladapo Fapetu Pelumi Abdulmalik Adewumi

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

Journal of Risk and Financial Management doi: 10.3390/jrfm19080597

Authors: Vanya Georgieva Radosveta Krasteva-Hristova

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

Journal of Risk and Financial Management doi: 10.3390/jrfm19080596

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

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

Journal of Risk and Financial Management doi: 10.3390/jrfm19080595

Authors: Gergana Mihaylova-Borisova

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

Journal of Risk and Financial Management doi: 10.3390/jrfm19080594

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

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

Journal of Risk and Financial Management doi: 10.3390/jrfm19080593

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

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

Journal of Risk and Financial Management doi: 10.3390/jrfm19080592

Authors: Odeta Pileckaitė Rasa Subačienė

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

Journal of Risk and Financial Management doi: 10.3390/jrfm19080590

Authors: Chaiyathad Phutthadet Ausawatap Akartwipart

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

Journal of Risk and Financial Management doi: 10.3390/jrfm19080591

Authors: Shu Inoue

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

Journal of Risk and Financial Management doi: 10.3390/jrfm19080589

Authors: Mohammad Al-Dwiry Weaam Amira

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

Journal of Risk and Financial Management doi: 10.3390/jrfm19080588

Authors: Randheer Ramsoomer Hermann Azemtsa Donfack Adri Drotskie

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

Journal of Risk and Financial Management doi: 10.3390/jrfm19080587

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

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

Journal of Risk and Financial Management doi: 10.3390/jrfm19080586

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

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

Journal of Risk and Financial Management doi: 10.3390/jrfm19080585

Authors: Ibrahim Bakirtas Muhammed Rasid Bakir Gokay Canberk Bulus

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

Journal of Risk and Financial Management doi: 10.3390/jrfm19080584

Authors: Wissam El Khoury Sebouh Aintablian

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

Journal of Risk and Financial Management doi: 10.3390/jrfm19080583

Authors: Sara Almarri Hamza El Kaddouri

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

Journal of Risk and Financial Management doi: 10.3390/jrfm19080582

Authors: Gharmili Meryem Boudri Imane Alj Abdelkamel

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

Journal of Risk and Financial Management doi: 10.3390/jrfm19080581

Authors: Hajar Bouladasse Said El Ganich Taoufiq Yahyaoui

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

Journal of Risk and Financial Management doi: 10.3390/jrfm19080580

Authors: Qian Fang Nuttawut Rojniruttikul

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

Journal of Risk and Financial Management doi: 10.3390/jrfm19080579

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

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

Journal of Risk and Financial Management doi: 10.3390/jrfm19080578

Authors: Kiril Luchkov Mihail Chipriyanov Galina Chipriyanova Marin Marinov

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

Journal of Risk and Financial Management doi: 10.3390/jrfm19080577

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

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

Journal of Risk and Financial Management doi: 10.3390/jrfm19080576

Authors: Vajinder Kaur Eugene Pinsky

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

Journal of Risk and Financial Management doi: 10.3390/jrfm19080575

Authors: Foued Badr Gabsi Sirine Sahnoun

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

Journal of Risk and Financial Management doi: 10.3390/jrfm19080574

Authors: Ivana Miklošević Andreja Todorović Andrija Popović

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

Journal of Risk and Financial Management doi: 10.3390/jrfm19080573

Authors: Arusaya Thamaree Simon Zaby

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

Journal of Risk and Financial Management doi: 10.3390/jrfm19080572

Authors: Kosla Hin Bunthe Hor Siphat Lim

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

Journal of Risk and Financial Management doi: 10.3390/jrfm19080569

Authors: Sparsha Mandreker Guntur Anjana Raju

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

Journal of Risk and Financial Management doi: 10.3390/jrfm19080571

Authors: Salma El Faroui Mimoun Benali

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

Journal of Risk and Financial Management doi: 10.3390/jrfm19080570

Authors: Erekle Pirveli Iza Gigauri Claudia Covucci

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

Journal of Risk and Financial Management doi: 10.3390/jrfm19080568

Authors: Mariem Turki Imed Chkir Kamel Naoui

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

Journal of Risk and Financial Management doi: 10.3390/jrfm19080567

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

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

Journal of Risk and Financial Management doi: 10.3390/jrfm19080566

Authors: Ibraheem Alaskar Ibrahim Khatatbeh Reyadh Faras Ahmad Bash

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

Journal of Risk and Financial Management doi: 10.3390/jrfm19080565

Authors: Fahad Alrobai

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

Journal of Risk and Financial Management doi: 10.3390/jrfm19080564

Authors: Rania Al-Nsour Antonio Menor-Campos

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

Journal of Risk and Financial Management doi: 10.3390/jrfm19080563

Authors: Mohammed Nawlo Fadi Alkaraan Hasan Radwan Katalo

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

Journal of Risk and Financial Management doi: 10.3390/jrfm19080562

Authors: Bouthaina Ben Othman Rihab Bedoui Ben Salem Heni Boubaker

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

Journal of Risk and Financial Management doi: 10.3390/jrfm19080561

Authors: Lumengo Bonga-Bonga Bereket Abayneh Ataro

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

Journal of Risk and Financial Management doi: 10.3390/jrfm19080560

Authors: Thirawat Chantuk Pornthip Jatupornmongkolchai Wiwatwong Bunnun

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

Journal of Risk and Financial Management doi: 10.3390/jrfm19080559

Authors: Saša Obradović Nemanja Lojanica Sergej Gričar Štefan Bojnec

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

Journal of Risk and Financial Management doi: 10.3390/jrfm19080558

Authors: Domenico Vicinanza

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

Journal of Risk and Financial Management doi: 10.3390/jrfm19080557

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

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

Journal of Risk and Financial Management doi: 10.3390/jrfm19080556

Authors: Vita Jagrič Polona Tominc Maja Rožman

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

Journal of Risk and Financial Management doi: 10.3390/jrfm19080555

Authors: Sugeng Suroso Sri Wulandari Chajar Matari Fath Mala

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

Journal of Risk and Financial Management doi: 10.3390/jrfm19080554

Authors: Byung-Kook Kang

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

Journal of Risk and Financial Management doi: 10.3390/jrfm19080553

Authors: Gökhan Özkul Özen Akçakanat Ozan Özdemir

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

Journal of Risk and Financial Management doi: 10.3390/jrfm19080552

Authors: Ramya Haravu Paramesh Hemalatha Krishnamoorthy Gunasekaran Deepak Raghava Naik

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

Journal of Risk and Financial Management doi: 10.3390/jrfm19080551

Authors: Ziad Albaraki Abdelhakim Abdelhadi Talal Al-Sulaiman

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

Journal of Risk and Financial Management doi: 10.3390/jrfm19080550

Authors: Abdulrahman Alsamaani Huda Aldhahi

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

Journal of Risk and Financial Management doi: 10.3390/jrfm19080549

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

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

Journal of Risk and Financial Management doi: 10.3390/jrfm19080548

Authors: Nada Jabbour Al Maalouf Layal Sfeir

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

Journal of Risk and Financial Management doi: 10.3390/jrfm19070547

Authors: Alessio Faccia

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

Journal of Risk and Financial Management doi: 10.3390/jrfm19070546

Authors: Wei Gao Quan Fang Ting Sun

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

Journal of Risk and Financial Management doi: 10.3390/jrfm19070545

Authors: Radosveta Krasteva-Hristova Vanya Georgieva

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

Journal of Risk and Financial Management doi: 10.3390/jrfm19070544

Authors: Godfred Afrifa Ahmad Alshehabi Mariam Alsabah

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

Journal of Risk and Financial Management doi: 10.3390/jrfm19070543

Authors: Uk Jo Jae Goo Kim

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

Journal of Risk and Financial Management doi: 10.3390/jrfm19070542

Authors: Phoompat Dangwung Jay Junghun Lee Junwoo Kim

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

Journal of Risk and Financial Management doi: 10.3390/jrfm19070541

Authors: Ngoc Toan Pham Hieu Le Tran Trung

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

Journal of Risk and Financial Management doi: 10.3390/jrfm19070540

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

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

Journal of Risk and Financial Management doi: 10.3390/jrfm19070539

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

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

Journal of Risk and Financial Management doi: 10.3390/jrfm19070538

Authors: Nguyen Thi Mai Anh

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

Journal of Risk and Financial Management doi: 10.3390/jrfm19070537

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

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

Journal of Risk and Financial Management doi: 10.3390/jrfm19070536

Authors: Yan Noviar Nasution Donny Maha Putra

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

Journal of Risk and Financial Management doi: 10.3390/jrfm19070535

Authors: Fatma Zehri Raghad Alsudays Laila Aladwey

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

Journal of Risk and Financial Management doi: 10.3390/jrfm19070534

Authors: Luis Varona Castillo Jorge R. Gonzales Castillo

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

Journal of Risk and Financial Management doi: 10.3390/jrfm19070532

Authors: Ruben Gevorgyan Alisa Tanyan

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

Journal of Risk and Financial Management doi: 10.3390/jrfm19070533

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

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

Journal of Risk and Financial Management doi: 10.3390/jrfm19070531

Authors: Serkan Cantürk

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

Journal of Risk and Financial Management doi: 10.3390/jrfm19070530

Authors: Natcha Saramas Supasuta Tuncharo Aroonrak Tunpanit

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

Journal of Risk and Financial Management doi: 10.3390/jrfm19070529

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

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

Journal of Risk and Financial Management doi: 10.3390/jrfm19070528

Authors: Jampal Dolma Annop Thananchana Tirapot Chandarasupsang

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

Journal of Risk and Financial Management doi: 10.3390/jrfm19070527

Authors: Mohammad Ismail Alawamreh Ahmed Razman Abdul Latiff Yusniyati Yusri Ibrahim Saleh Al-Radaideh Abutaber Thaer Mahmoud Abdelrehim Mohammad Mosleh Almousa

This paper examines the moderating effect of corporate digital transformation in the relationship between audit quality and aggressive tax avoidance in the sample of industrial companies listed on the ASE and operating during the 2020–2025 period. They were based on data of a balanced panel of 30 industrial companies listed on the ASE 180 observations. The primary estimator used was the feasible generalized least squares (FGLS) method that was employed after it was established that first-order autocorrelation, groupwise heteroskedasticity, and partial cross-sectional dependence existed. System-GMM estimator was used to confirm the robustness of the results, and to deal with the endogeneity that may arise due to reverse causality between auditor selection and result. There are three key findings of the study. First, there is a strong and consistent negative relationship between affiliation with one of the Big Four audit firms and aggressive tax avoidance in all the models studied, confirming that reputation-based audit quality is an effective institutional deterrent a finding of particular importance given that 73.3% of the Jordanian industrial firms in the sample rely on local auditors and therefore lack similar governance controls. Second, aggressive tax avoidance is positively related to higher audit fees, which are indicative of a more complex client base and an economic dependence on clients by the auditor, rather than a signal of greater monitoring rigour and, therefore, as a challenge to the fee-as-quality assumption common to the developed-market framework. Third, although digital transformation demonstrates a direct positive correlation with aggressive tax avoidance—indicating that firms can use digital capabilities to enhance tax planning and not compliance in the pre-JoFotara regulatory environment—its moderating effect on Big Four affiliation is not statistically significant. It is important to note that the relationship between the intensity of audit fees and digital transformation is positively significant, which is in line with the economic dependence argument. The implications of the findings are important to the Jordan Securities Commission, the tax authorities as well as regulatory bodies who are looking to enhance corporate tax compliance in a dynamic digital regulatory environment, and raise important questions of the portability of audit quality assumptions across institutional settings.

Journal of Risk and Financial Management doi: 10.3390/jrfm19070526

Authors: Radouane Aboulmaouda Khadija Slimani Nour El Houda Chaoui

With the broad application of machine learning (ML) and deep learning (DL) models in financial markets, it has become increasingly important to evaluate their performance compared with traditional methods, especially for credit risk scoring in financial services. This mechanism determines the probability of default (PD) for borrowers, in other words, how likely a client is to fail to repay their debt. With the rapid development and growing availability of ML/DL technologies, it is essential for banks, financial institutions, auditors and regulatory bodies to assess whether these approaches truly outperform traditional models in terms of accuracy, stability and interpretability or whether their complexity comes at a cost. A systematic review following PRISMA examined credit risk scoring models. From 520 initial articles, 117 were analyzed to compare ML/DL approaches with traditional methods. This review evaluates accuracy, stability and interpretability, offering guidance for model selection in real-world credit scoring. Logistic regression remains essential in regulated contexts requiring transparency, supporting informed decisions on balancing performance and explainability.

Journal of Risk and Financial Management doi: 10.3390/jrfm19070525

Authors: Ali Ibrahim Abueid Varadaraj Aravamudhan Mohammad Jamal Bataineh Tariq Talafha Mohanasundaram Karunanidhi Ananth Sengodan

Many investors have relied on Bitcoin as a hedge against inflation. However, differences in inflation measurement and monetary policies across countries make it difficult to determine whether Bitcoin effectively serves as an inflation hedge. This study examined Bitcoin’s effectiveness as an inflation hedge in the United States and India using monthly data on Bitcoin returns and Consumer Price Index (CPI) changes from January 2015 to December 2024 (N = 118, after first-differencing and lag alignment). The study employed Ordinary Least Squares (OLS) models, bivariate Vector Autoregression (VAR) Granger causality tests, Bai–Perron Structural Break Analysis, Impulse Response Functions (IRFs), and Quantile Regression analyses. The findings revealed no significant relationship between CPI and Bitcoin returns in either the United States or India, providing no empirical support for the Fisher Hypothesis. However, Granger causality results showed that lagged Bitcoin returns significantly predicted future U.S. CPI values, while no such relationship was observed for India. The predictive power of the model for the U.S. was lost after October 2022 due to the crypto winter phenomenon. This indicates that Bitcoin is not used as a hedge against inflation but is rather considered a financially driven information asset, which is subject to market influences.

Journal of Risk and Financial Management doi: 10.3390/jrfm19070524

Authors: Auliyah Rizky Suhasmoro Tanti Novianti Noer Azam Achsani Trias Andati

This study investigates how disaggregated environmental, social, and governance (ESG) indicators are associated with firm value, stock returns, and the cost of capital, emphasizing the moderating role of ESG controversies. Using panel data of publicly listed firms in the Asia-Pacific region from Refinitiv and applying firm and year fixed effects with forward-looking specifications (t to t + 3), the results show that the ESG–performance association is neither uniform across indicators nor stable over time: several environmental indicators lose statistical relevance beyond the short horizon, while selected social and governance indicators remain associated with outcomes only where materiality is high. The central finding is that ESG controversies do not merely add explanatory power but systematically reshape these relationships—attenuating or reversing the association between ESG and firm value or returns, while strengthening the association with the cost of capital. This pattern indicates that ESG is priced by the market only when it is perceived as credible, underscoring reputational risk—rather than ESG performance itself—as the dominant mechanism linking sustainability information to firm outcomes.

Journal of Risk and Financial Management doi: 10.3390/jrfm19070523

Authors: Bishala Maerdan Dinara Rakhmatullayeva Tatyana Kudasheva David Celetti

As ESG considerations increasingly influence access to finance, participation in global supply chains, and long-term competitiveness, understanding which factors experts perceive as most likely to influence future ESG adoption among small and medium-sized enterprises (SMEs) has become an important issue in sustainable finance and corporate management research. While existing studies largely focus on ESG performance, disclosure, and implementation outcomes, limited attention has been paid to the relative importance of competing ESG-related adoption factors in SMEs in Kazakhstan, an emerging-market context, particularly in contexts where ESG adoption remains in its early stages. This study investigates expert perceptions of the factors most likely to shape ESG implementation decisions among SMEs in Kazakhstan. Drawing on Institutional Theory, the Resource-Based View (RBV), and the concept of double materiality, the study applies the Analytic Hierarchy Process (AHP) to evaluate six ESG-related criteria: coercive institutional pressure, normative pressure, organizational capabilities, financial materiality, impact materiality, and internal governance and processes. Expert judgments were collected through pairwise comparisons and aggregated using the geometric mean within a group AHP framework. The aggregated matrix demonstrated acceptable group-level consistency (CR = 0.0193), providing a basis for exploratory interpretation of the aggregated priority structure, while not validating the consistency of all individual judgments. The findings indicate a structured priority pattern among the expert-perceived ESG-related decision factors. The financial dimension received the highest priority weight, followed by coercive and normative pressure. Internal governance occupied an intermediate position, whereas impact materiality and organizational capabilities received nearly identical lower weights. These results suggest that experts expect future ESG adoption among Kazakhstani SMEs to be influenced primarily by financial relevance and regulatory compliance rather than by impact-oriented sustainability objectives or internally developed sustainability capabilities. This study contributes to the literature in three ways. First, it advances understanding of ESG adoption readiness and prioritization mechanisms among SMEs in Kazakhstan, as an emerging-market context, by integrating institutional, resource-based, and materiality-oriented perspectives. Second, it extends the debate on double materiality by suggesting that financially relevant ESG considerations may play a particularly important role in ESG-related decision-making under conditions of resource scarcity and institutional uncertainty. Third, it provides evidence relevant to ESG regulation, sustainable finance, and SME support policies in developing economies. More broadly, the findings suggest that within the Kazakhstani SME context, ESG is expected to become financially relevant before it becomes fully internalized as a strategic sustainability practice.

Journal of Risk and Financial Management doi: 10.3390/jrfm19070522

Authors: Reem Abdalla Hassan Al Aaraj Yassir Alam

Background: This study examines how four canonical behavioral biases (overconfidence, herding, anchoring, and loss aversion) influence investor decision-making in the Saudi stock market (Tadawul), and whether overconfidence and loss aversion operate as moderating forces on herding and anchoring, respectively. Methods: Employing a quantitative, cross-sectional design with a stratified sample of 384 retail investors, the study applies Partial Least Squares Structural Equation Modelling (PLS-SEM) to test six hypotheses derived from Modern Portfolio Theory and Behavioral Finance frameworks. The measurement model satisfies established thresholds for reliability, convergent validity, and discriminant validity. Results: Results confirm that loss aversion is the dominant predictor of behaviorally influenced decision-making (β = 0.402, p < 0.001, f2 = 0.188), followed by herding (β = 0.234, p < 0.001) and overconfidence (β = 0.164, p = 0.001), while anchoring does not exert a statistically significant independent effect (β = 0.102, p = 0.084 one-tailed, p = 0.168 two-tailed). Neither the overconfidence × herding (β = 0.005, p = 0.920, two-tailed) nor the loss aversion × anchoring (β = −0.039, p = 0.330, two-tailed) interaction terms reach significance, indicating that these bias pairs operate as independent additive forces rather than compounding systems. The model explains 55.7% of the variance in investor decision-making (R2 = 0.557). Conclusion: The findings advance behavioral finance theory in GCC and Islamic equity markets by (1) demonstrating non-equivalence of anchoring effects relative to Western-market benchmarks, (2) resolving competing theoretical predictions about bias interaction effects, and (3) providing context-specific evidence that loss aversion subsumes anchoring cognition in the Saudi market. Practical implications for the Capital Market Authority, financial educators, and individual investors are discussed and contextualized within the Saudi market setting.

Journal of Risk and Financial Management doi: 10.3390/jrfm19070521

Authors: Radosveta Krasteva-Hristova Biser Krastev

This study addresses two distinct questions: (1) to what extent do ESRS-oriented corporate sustainability disclosures operationalize a life cycle perspective; and (2) how can Life Cycle Sustainability Assessment (LCSA) inform a conceptual model for improving the risk relevance of such disclosures? A theory-driven qualitative comparative content analysis is applied to the 2024 sustainability disclosures of Enel, Unilever, and Siemens AG across six analytical dimensions and four integration categories. Across the 18 company–dimension assessments, none met the criteria for systemically integrated disclosure; all were classified as partially integrated. The three cases show broad but uneven value-chain coverage: upstream and environmental information is more developed than downstream, end-of-life, social, and economic integration. Life cycle methods are used selectively, while cross-dimensional links and cradle-to-grave boundary transparency remain limited. Because the evidence concerns three purposively selected cases, the findings are analytical rather than statistically or sectorally generalizable. The study therefore proposes, rather than validates, an LCSA–ESRS Operationalization Model based on boundary reconfiguration, stage-based indicator mapping, dimensional harmonization, and an accounting translation layer. The model indicates how life cycle evidence could be connected to double materiality, systemic sustainability risks, and decision-useful disclosure. The study contributes by distinguishing life cycle-related disclosure from full LCSA integration and by positioning sustainability reporting as a boundary-alignment problem.

Journal of Risk and Financial Management doi: 10.3390/jrfm19070520

Authors: Lien Quynh Le

As sustainability reporting increasingly supports financial innovation by facilitating ESG-oriented investment, sustainable finance, and more informed capital allocation, understanding the governance factors associated with corporate social responsibility (CSR) disclosure has become increasingly important. This study examines the association between board of directors’ foreign experience and CSR disclosure among non-financial listed firms in Vietnam. Drawing on Upper Echelons Theory and Legitimacy Theory, the study argues that directors with international education and/or professional work experience possess broader governance perspectives, greater familiarity with global sustainability practices, and stronger awareness of stakeholder expectations, which may be associated with more transparent CSR disclosure. The study is based on a sample of 499 non-financial firms listed on the Ho Chi Minh Stock Exchange (HOSE) and the Hanoi Stock Exchange (HNX) during the 2015–2019 period, comprising 1185 firm-year observations. Information on board of directors’ foreign experience was manually collected from annual reports, corporate governance reports, and company websites. The findings indicate that board of directors’ foreign experience is positively associated with both the extent and quality of CSR disclosure. The study contributes to the corporate governance, sustainability, and financial innovation literature by providing one of the first empirical examinations of the relationship between board of directors’ foreign experience and CSR disclosure in Vietnam. The findings also offer practical implications for firms, investors, and policymakers seeking to strengthen sustainability reporting, improve transparency, facilitate access to sustainable finance, and promote innovative governance practices in emerging and transition economies.

Journal of Risk and Financial Management doi: 10.3390/jrfm19070519

Authors: Luis Ángel Meneses Cerón Eliana Fernández Rengifo Victoria Eugenia Pino Terán María Alejandra Albán Lara Rosa Cristina Vega Sánchez Jhon Jairo Fuentes Sánchez

This research analyzed the integration of artificial intelligence (AI) and machine learning in forensic accounting as tools for the detection of accounting fraud. Through a systematic review of the literature in Scopus under the PRISMA protocol, 76 documents published between 2019 and 2026 were selected and analyzed. The results show that supervised machine learning techniques, assembly methods, deep learning and natural language processing have demonstrated superior capacity to rule-based approaches to detect fraudulent patterns, when quality data and professionals capable of interpreting their results with forensic criteria are available. A recurring finding throughout the corpus is that machine learning models tend to perform better than rule-based approaches in contexts where sufficient quality data is available. Although this advantage is not universal, it depends on the type of fraud analyzed, the design of the model, the evaluation metrics used, and the institutional context of implementation. AI expands the practitioner’s ability to identify anomalous patterns in volumes of information that manual analysis cannot cover. However, significant barriers persist related to data quality, model interpretability, professional resistance to technological updating, and the absence of regulatory frameworks that grant evidentiary validity to algorithmic results. Latin America and Africa show a low investigative production in this field, despite concentrating high levels of vulnerability to financial fraud. The study concludes that the transformation of forensic accounting using AI is a social, institutional and educational process, not exclusively technical, which requires interdisciplinary training, data governance and specific ethical frameworks for the forensic field.

Journal of Risk and Financial Management doi: 10.3390/jrfm19070517

Authors: Bisma Dewabrata Nuttanan Wichitaksorn Yoichi Otsubo

This study applies intermarket analysis to forecast the New Zealand NZX 50 stock returns using a hybrid framework that combines econometric models with machine learning (ML) algorithms. Daily return data from 3 January 2001 to 31 December 2024 are employed to examine the interconnectedness between the New Zealand equity market and major global financial markets. This study follows a three-stage methodology: ML-based feature selection using LASSO, ridge, and elastic net regressions; econometric validation through a Global Vector Autoregressive (GVAR) model; and forecasting implementation using Support Vector Regression (SVR), Random Forest (RF), Long Short-Term Memory, and Artificial Neural Networks. Feature selection consistently identifies the Australian ASX 200, Japanese Nikkei 225, U.S. S&P 500, and U.S. 10-year Treasury yield as the most influential predictors, with Australia exerting the strongest impact. GVAR results reveal significant short-term spillover effects, but no long-term co-integrating relationships, indicating independent market trends. The U.S. market emerges as the dominant transmitter of shocks, while New Zealand acts as a net receiver. Forecasting results show RF and SVR outperform alternative models, with optimal performance achieved using a fifth-lag structure. The findings support short-term, spillover-based investment strategies and contribute to a transparent, replicable framework for forecasting equity markets in small open economies.

Journal of Risk and Financial Management doi: 10.3390/jrfm19070518

Authors: Pongsutti Phuensane Nantaphong Boonpong Arthit Apichottanakul

Purpose: This study investigates how financial and entrepreneurial capability configurations differentiate internationalized from domestically oriented Thai SMEs. By adopting a decision-science perspective, the study identifies the capability patterns that shape firms’ strategic international orientation. Design/methodology/approach: This research, which utilizes survey and financial data from 179 Thai SMEs (2021–2023), employs an inductive machine learning approach based on Extreme Gradient Boosting (XGBoost). The analytical framework integrates profitability, liquidity, leverage, operational efficiency, international experience, team readiness, market knowledge, and institutional connectivity. Feature importance scores and confirmatory statistical tests are used to validate differentiating capability structures. Findings: Internationalized SMEs are characterized by stronger financial agility, higher profitability, disciplined leverage, and more efficient resource utilization, together with entrepreneurial preparedness reflected in international experience, risk tolerance, team coordination, and institutional embeddedness. Localized SMEs, by contrast, display liquidity-heavy but lower-dynamism profiles, weaker network engagement, and more limited organizational readiness. Practical implications: The findings suggest that SME support should move beyond finance-only assistance and address capability alignment. Programs that strengthen financial agility, team readiness, international market knowledge, digital channels, and institutional networks may improve SME readiness for international engagement. Originality/value: The study contributes to international business theory by reframing SME internationalization as a selective and configurational process rather than a linear or binary outcome. It extends the resource-based view by emphasizing capability bundles rather than isolated resources and contributes to the dynamic capabilities perspective by showing how financial, entrepreneurial, and institutional capabilities are associated with SMEs’ differentiated participation in international markets.

Journal of Risk and Financial Management doi: 10.3390/jrfm19070516

Authors: Batuhan Karabiber

This study examines the effects of economic policy uncertainty (EPU) on three dimensions of financial market dynamics—stock market returns, stock market volatility, and exchange rate volatility—across 15 G20 economies over the period 2006 Q1–2024 Q4. Employing a rigorous second-generation panel econometric framework that accounts for cross-sectional dependence and slope heterogeneity, we apply the Common Correlated Effects Mean Group (CCEMG) and Augmented Mean Group (AMG) estimators as primary estimators, complemented by cross-sectionally augmented ARDL (CS-ARDL) for short- and long-run dynamics and the Method of Moments Quantile Regression (MMQR) for distributional analysis. EPU is significantly associated with lower stock market returns, particularly in advanced economies and at lower quantiles of the return distribution. The impact of EPU on stock market volatility is not strong for mean-based estimators even when global uncertainty (VIX) is controlled for, and global fear dominates domestic policy uncertainty as a driver of volatility. Exchange rate volatility is positively and significantly associated with EPU, particularly in higher-volatility regimes. Subgroup analysis shows that EPU–return effects are stronger in advanced economies, but EPU–exchange rate volatility responses are stronger in emerging markets. These results have important implications for portfolio allocation, hedging strategies and macroprudential policy decisions in G20 economies.

Journal of Risk and Financial Management doi: 10.3390/jrfm19070515

Authors: Sonia Abdennadher Eman Abukhousa Hajer Zarrouk

This study examines the determinants of investment decision quality among individual investors in the United Arab Emirates (UAE) financial market. As digital investment platforms continue to expand access to financial information and investment opportunities, understanding the drivers of effective investment decisions has become increasingly important for investors. Integrating insights from financial literacy, behavioral finance, trust and disclosure, and technology acceptance perspectives, the study develops a unified framework to examine how financial literacy and awareness, trust, transparency and disclosure, as well as digital platform support jointly shape the quality of investment decisions. Drawing on survey data from 208 individual investors with varying levels of investment experience, the study employs correlation and multiple regression analyses to examine the proposed relationships. The results reveal positive bivariate associations between investment decision quality and all three explanatory factors. In the multivariate model, however, only financial literacy and awareness and digital platform support remain statistically significant, with digital platform support emerging as exerting the largest statistically significant effect on decision quality. The independent effect of trust, transparency, and disclosure attenuates to non-significance, suggesting that its effect largely overlaps with financial literacy and digital platform support rather than representing a distinct independent contribution. The findings indicate that investor outcomes depend not only on access to information but also on investors’ ability to understand, interpret, and apply that information through effective digital support mechanisms. The study contributes to the investment decision-making literature by providing an integrated empirical framework and new evidence from the UAE as an underexplored market context. The findings provide indicative implications for investor education initiatives, financial institutions, and digital platform providers within the sampled context of an increasingly digital financial environment.

Journal of Risk and Financial Management doi: 10.3390/jrfm19070514

Authors: Fakhrurrazi Amir Mukhlis Yunus Teuku Roli Ilhamsyah Putra Sorayanti Utami

This study examines continuance intention toward Islamic mobile banking with AI-related or AI-supported features in Aceh, Indonesia, a regulated Sharia-based financial ecosystem. It develops and tests a model linking perceived AI usefulness, perceived AI security, trust in AI-based financial services, AI user acceptance, Islamic financial literacy, and continuance intention. Survey data from 250 Islamic mobile banking users were analyzed using PLS-SEM. The findings show that perceived AI usefulness, perceived AI security, and trust are positively associated with AI user acceptance and continuance intention. AI user acceptance is the strongest direct predictor of continuance intention and acts as a complementary partial mediator in the relationships between usefulness, security, trust, and continuance intention. Islamic financial literacy is also positively associated with continuance intention and strengthens the relationship between AI user acceptance and continuance intention, confirming its quasi-moderating role. These findings suggest that continuance intention toward Islamic mobile banking with AI-related or AI-supported features depends not only on technological value, security, and trust, but also on users’ ability to evaluate digital financial services through Islamic financial principles. The study contributes to financial technology, risk management, and Islamic fintech literature by offering a context-sensitive explanation of AI-related continuance behavior in a Sharia-based financial ecosystem.

Journal of Risk and Financial Management doi: 10.3390/jrfm19070513

Authors: Houda Zaim Siham Sahbani

This study presents a systematic literature review of 68 peer-reviewed articles (2015–2025) on artificial intelligence in tax compliance and evasion mitigation. Using the PRISMA 2020 protocol and textometric analysis via IRAMUTEQ software, we map publication trends, geographic distribution, and three research paradigms: machine learning and predictive modeling; artificial intelligence, technology and tax compliance; and government, financial development and revenue administration. The CIMO (Context–Intervention–Mechanism–Outcome) framework structures our synthesis of how institutional conditions shape intervention design and why identical technologies produce divergent outcomes across settings. While existing reviews have focused primarily on detection metrics without theorizing institutional boundary conditions, behavioral dynamics without addressing governance capacity, or ethical deficits without a theoretical framework, this study constructs the Adaptive AI Tax Compliance Framework (AAITCF), a context-sensitive implementation roadmap differentiated across three institutional maturity tiers. The results indicate that AI achieves high detection accuracies in digitally mature economies, yet effectiveness is contingent on data quality, governance capacity, and organizational readiness. Developing countries face structural asymmetries, infrastructural deficits, and human capital gaps that constrain algorithmic performance even where technical sophistication is high. The AAITCF treats context as constitutive of intervention effectiveness and identifies underexplored areas regarding causal pathways from AI deployment to long-term institutional change, taxpayer trust, and equitable fiscal governance.

Journal of Risk and Financial Management doi: 10.3390/jrfm19070512

Authors: Ahmad Shatnawi Hanady Bataineh Eyad Abdel Halym Hyasat Adel Dhaher Atqaa Alresheedi

This study aims to examine the moderating effect of board gender diversity in the relationship between the overconfidence of the CEO and corporate tax avoidance of listed firms in Jordan. Based on upper echelons theory, agency theory, and resource dependence theory, it examines the potential influence of female board representation on the tax implications of managerial overconfidence in an emerging-market context. The study uses panel data of 70 industrial and service enterprises listed on the Amman Stock Exchange (ASE) for the period (2019–2024), yielding 420 firm-year observations. To measure corporate tax avoidance, we use the effective tax rate (ETR) and cash flow effective tax rate (CFETR), and CEO overconfidence is measured by a composite index of observable executive characteristics. The level of board gender diversity is computed as the percentage of female directors, and the hypotheses are tested with panel regression models that include relevant firm-level control variables. The results indicate that CEO overconfidence is negatively and significantly related to ETR, suggesting that the overconfident CEO is more likely to engage in tax avoidance. The moderating results also indicate that the relationship between board gender diversity and tax avoidance is reshaped by enhancing accrual-based tax avoidance and curbing cash-based tax avoidance. The results contribute to the literature on executive traits, corporate governance, and tax behavior by providing evidence from Jordan and by applying a practical measure of CEO overconfidence suitable for contexts with limited data availability.

Journal of Risk and Financial Management doi: 10.3390/jrfm19070511

Authors: Abdulaziz Mohammed A. Almohannadi Ali Malik

This paper examines the association between two Financial Technology (FinTech)-related policy windows and the profitability of Qatari commercial banks over a twenty-year horizon (2005–2024). The analysis is anchored by two structural breaks: in 2017, the Qatar Central Bank (QCB) established its FinTech task force and lifted restrictions on the implementation of a regulatory sandbox and centralised electronic know your customer (e-KYC) framework; and during the digital-acceleration period in 2020 in response to the COVID-19 pandemic and the issuance of digital banking licences. FinTech adoption is not measured directly at the bank level; the two policy windows are used as intent-to-treat proxies. Using return on assets (ROA) and return on equity (ROE), bank performance is measured and influenced by bank size (log of total assets), bank age and type (Islamic and conventional). Multiple diagnostics of Hausman and Breusch–Pagan support the use of fixed-effects (FE) panel regressions with cluster-robust standard errors on an unbalanced panel of 125 bank–year observations. The results show a positive coefficient on the post-2017 dummy in the ROE model (β = 0.0306, p = 0.054, cluster-robust) and no detectable change in ROA (β = −0.00058, p = 0.868). For the post-2020 phase, both coefficients are positive but do not reach conventional significance (ROA: β = 0.00218, p = 0.539; ROE: β = 0.0224, p = 0.150). There is no systematic difference between Islamic and conventional banks that is offered by the interaction terms in either phase. Given the small sample (nine banks, eight effective clusters after the FE singleton drop; 125 observations) and the use of policy-window proxies rather than direct bank-level FinTech measures, the design cannot isolate the effect of the FinTech-related reforms from concurrent macroeconomic, sectorial or pandemic-related developments, and the cluster-robust p-values should be read as approximate. The results are therefore presented as preliminary and indicative. Read in light of these design constraints, the results are consistent with incremental rather than transformative change around the FinTech-related policy windows in Qatar, with results influenced more by timing, scale economies, and regulatory saturation than by bank type. The country-specific empirical findings can help restore context to the literature on the Gulf Cooperation Council (GCC) average, and provide measured guidance for bank managers and regulators working toward the Qatar National Vision 2030 digital aspiration.

Journal of Risk and Financial Management doi: 10.3390/jrfm19070510

Authors: Akrom A. Omonov Boburjon B. Izbosarov Erlane K. Ghani

This study investigates the impact of liquidity risk on asset quality and financial stability in Uzbekistan’s commercial banking sector. Using quarterly time-series data from 2016 to 2024, the study employs Ordinary Least Squares (OLS) regression with quadratic specifications to capture potential non-linear effects of liquidity. Two models are estimated to examine (i) the relationship between liquidity risk and asset quality, and (ii) the impact of liquidity risk on financial stability, proxied by net profit. The results indicate that liquidity risk does not have a statistically significant effect on asset quality, suggesting that credit performance is primarily driven by structural and macroeconomic factors rather than liquidity conditions. In contrast, the financial stability model demonstrates high explanatory power (R2 = 0.883), although individual coefficients are statistically insignificant due to severe multicollinearity among banking sector variables. The findings do not support the conventional liquidity–profitability trade-off hypothesis, as no evidence of a linear or non-linear relationship between liquidity and profitability is observed. Regulatory capital emerges as the most influential variable, indicating the importance of capital strength in supporting banking stability. This study contributes to the literature by providing novel empirical evidence from a transition economy, highlighting the limitations of isolating liquidity effects in rapidly expanding banking systems. The results suggest that in reform-oriented financial environments, banking stability is shaped more by structural growth and capital adequacy than by liquidity trade-offs, offering important implications for macroprudential policy design.

Journal of Risk and Financial Management doi: 10.3390/jrfm19070509

Authors: Elena Grinza Parisa Madhooshiarzanagh Consuelo Rubina Nava

Physical climate hazards—floods, storms, heatwaves, and wildfires—are increasingly disrupting banking operations and generating growing litigation and legal-risk exposures, yet operational risk remains one of the least studied channels through which climate change may affect financial institutions. This paper provides a critical review of the emerging literature on climate-related operational risk in banking, covering both physical disruptions and the legal risk dimension explicitly recognised within the Basel operational risk framework. We map the empirical evidence, critically evaluate the methodological toolkit—event studies, fixed-effects regressions, difference-in-differences, dynamic panel estimators, and logit models—and assess their suitability for a domain characterised by data scarcity, rare events, and non-linearity. Building on this assessment, we outline a conceptual methodological roadmap intended to guide future research, organised around three stages: (i) machine learning-based variable selection and anomaly detection applied to operational loss and climate databases; (ii) econometric modelling of climate-related operational events with explicit identification strategies; and (iii) agent-based modelling to simulate system-wide propagation of climate shocks. Each stage can be conceptually related to elements of the Basel operational risk framework, offering a structured research programme for academics and a diagnostic toolkit for supervisors and risk managers.

Journal of Risk and Financial Management doi: 10.3390/jrfm19070508

Authors: Hung-Hsi Huang Chia-Min Sun Ching-Ping Wang

This study investigates whether volatility-related variables and traditional financial predictors can explain and forecast Taiwan stock returns. Using the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) and eight major Taiwan industry indices, we examine the predictive ability of long-term adjusted volatility (LVadj), short-term volatility (SV), the volatility index (VIX), idiosyncratic volatility (IVOL), the earnings-to-price ratio (EP), the book-to-market ratio (BM), and the turnover ratio (TURN). The sample period spans from January 2007 to December 2025. Univariate and bivariate predictive regression models are estimated using ordinary least squares (OLS) and exponentially weighted least squares (EWLS). The empirical results show that SV exhibits the strongest in-sample explanatory power for TAIEX returns, whereas TURN plays a more important role in explaining industry-level returns. Out-of-sample forecasting performance varies considerably across industries. For TAIEX returns, the combination of LVadj and TURN provides the strongest forecasting performance, while models incorporating SV and VIX perform relatively well in several industry sectors. Overall, the results suggest that volatility-related variables provide useful predictive information under certain model specifications and industry sectors, with EWLS generally outperforming conventional OLS estimation.

Journal of Risk and Financial Management doi: 10.3390/jrfm19070507

Authors: Jolta Kacani Ervis Zeqiraj Ingrid Shuli

With the growing relevance of sustainable accounting reports, new ways of evaluating the accountability of firms have come into practice, particularly in sectors that are environmentally sensitive, such as the oil industry. However, doubts have emerged over whether sustainable reporting based on ethical principles actually reflects the sustainability performance of firms. This question becomes even more relevant in the context of transition economies, where weak institutional capacity leaves room for corporate manipulation. This paper attempts to determine whether sustainability reporting based on ethical principles indicates actual financial performance in transition economies or merely reflects the symbolic compliance trend among firms. In this regard, a financial ratio–based benchmarking methodology was applied to assess whether there is a link between financial and sustainability indicators of oil firms in transition economies like Albania, Bosnia and Herzegovina, Kosovo, Montenegro, North Macedonia, and Serbia. The paper expands on financial ratio benchmarking approaches by incorporating sustainable accounting into its methodology and presents an empirical analysis based on data from transition economies. Benchmarking methods in this research consist of mixed approaches, including secondary data analysis by using publicly available reports, financial statements, sustainability reports, and databases related to firms, alongside primary data collection based on the structured survey conducted among managers and sustainability specialists of the sampled of oil firms. The sampling includes 30 oil firms active in six countries of the Western Balkans for a five-year period. As opposed to previous research studies that used sustainability indicators based on multiple measures, the current paper uses benchmarking methods with the help of accounting-based financial ratios. The results indicate that sustainability performance reported by oil firms in transition economies can be described as more symbolic than real, as measured in terms of financial performance in environmental and cost-related aspects. The findings provide practical implications for regulators, investors, and policymakers seeking to strengthen sustainability governance, disclosure quality, and environmental accountability in transition economies.

Journal of Risk and Financial Management doi: 10.3390/jrfm19070506

Authors: Andrey Koshkin

The classical monetary trilemma implies a binding trade-off among exchange rate stability, capital mobility, and monetary autonomy. Yet, emerging market economies increasingly operate hybrid policy configurations that depart systematically from the trilemma’s corner solutions. This paper proposes a continuous, time-varying measure of such departures—the Hybridity of Regime Index (HRI)—extracted via a dynamic factor model from sub-indices capturing exchange rate hybridity, capital account segmentation, and effective monetary autonomy for a balanced panel of thirty emerging markets over the period 2005–2024. The analysis yields four principal findings. First, a secular increase in average regime hybridity is observed, with a marked acceleration following the financial fragmentation shocks of 2022. Second, moderate hybridity is associated with attenuated output and inflation volatility, and local projections show that high-HRI economies experience milder output contractions in the immediate aftermath of global financial shocks. Third, panel threshold regressions identify an endogenous HRI level beyond which the stabilizing effect reverses: further hybridity amplifies macroeconomic volatility and erodes reserve adequacy. Fourth, the post-2022 geopolitical fragmentation of the international monetary system has amplified the pre-existing trend toward hybridity, with sanction-affected economies exhibiting discontinuous jumps in HRI that push them into the high-vulnerability regime. This paper characterizes this non-linear pattern as a resilience–vulnerability nexus and discusses its implications for early warning indicators and for the assessment of policy responses to the fragmentation of the international monetary system.

Journal of Risk and Financial Management doi: 10.3390/jrfm19070505

Authors: Mohammed Khalaf Alshammari

This study examines the influence of board attributes on the financial reporting quality (FRQ) of Saudi commercial banks using panel data covering the period 2013–2025 via pooled OLS, fixed-effects, and random-effects models. The findings show that board independence and board meeting frequency are positively associated with FRQ, highlighting the importance of active board oversight in promoting reporting transparency. In contrast, board size and board diversity exhibit negative associations with FRQ. The negative effect of board size may reflect coordination and communication challenges, while the influence of board diversity appears limited within the Saudi banking context. Unlike most previous studies that rely on accrual-based proxies, this study assesses FRQ through qualitative characteristics, providing new evidence from the Saudi banking sector.

Journal of Risk and Financial Management doi: 10.3390/jrfm19070504

Authors: Duong Thi Anh Tien Anh Tuan Nguyen

This study investigates the causal relationship between profit efficiency and bank risk in Southeast Asian commercial banks using a Panel Vector Autoregression framework. The banking data is unbalanced panel data collected from BankFocus from 2007 to 2022 from the data of financial institutions in 11 Southeast Asian countries. The author excluded commercial bank data from three countries, including Brunei, East Timor, and Myanmar, due to their lack of financial reports. Therefore, the number of commercial banks obtained is 118 banks from eight countries including Cambodia, Indonesia, Laos, Malaysia, the Philippines, Singapore, Thailand, and Vietnam. Profit efficiency is measured by ROA and ROE, and bank risk is proxied by Z-score. The results reveal a bidirectional causal relationship between profit efficiency and bank risk. Bank risk positively affects ROA at a 10% significance level, while ROA has a negative effect on bank risk at a 1% level. In contrast, bank risk exerts a negative and significant impact on ROE at a 1% level, whereas changes in ROE do not significantly influence bank risk. These findings imply that Vietnamese commercial banks need to maintain a balance between traditional operations and diversification strategies. Simultaneously, evidence of a causal relationship between profitability and risk supports hypotheses of poor management and austere behavior, thereby highlighting the need to strengthen governance capacity, improve operational quality, and implement appropriate development strategies to optimize efficiency and ensure sustainable risk control.

Journal of Risk and Financial Management doi: 10.3390/jrfm19070503

Authors: Karime Chahuán-Jiménez

Understanding the relationship between green bond issuance and environmental performance is critical as governments, financial institutions, and investors seek to accelerate the transition toward a low-carbon economy. This study analyzes the relationship between green bond issuance and carbon intensity across 165 countries from 2015 to 2022. Two-way fixed-effects models reveal a negative and statistically significant association between green bond issuance and carbon intensity (GDP- and energy-based measures). Dynamic system GMM estimations confirm this relationship after accounting for persistence and endogeneity, with coefficients remaining negative and significant, while carbon intensity displays strong inertia (autoregressive coefficients: 0.864–0.928). Robustness checks—including the exclusion of the five largest issuers and the use of alternative dependent variables—sustain these findings, indicating a moderate, gradual impact of green bond markets on lowering carbon intensity.

Journal of Risk and Financial Management doi: 10.3390/jrfm19070502

Authors: Junming Yang Jingbo Cai Li He Jiya Hu Xiaoyu Ma

Against the backdrop of a new wave of scientific and technological revolution and industrial transformation, artificial intelligence has emerged as a pivotal technology for fostering new quality productive forces and advancing high-quality development, and is profoundly reshaping firms’ production organization and governance structures. Using data on Chinese A-share listed companies from 2016 to 2024, this study empirically examines the impact of AI on corporate internal control quality and its underlying mechanisms. The results indicate that AI significantly improves corporate internal control quality, mainly by enhancing firms’ human capital and reducing agency costs. Further heterogeneity analysis shows that the positive effect of AI on internal control quality is more pronounced among manufacturing firms, firms with higher levels of digital infrastructure, and firms with greater information transparency. From the perspective of internal corporate governance, this study extends the literature on the economic consequences of AI and provides empirical evidence on how AI, as embedded in a complex socio-technical system, empowers high-quality corporate development through institutional governance mechanisms. The findings also offer useful implications for governments seeking to refine AI-related policies and for firms aiming to promote the coordinated upgrading of intelligent transformation and internal control systems.