Journal of Risk and Financial Management doi: 10.3390/jrfm19110899

Authors: Antonija Matić Ivana Bestvina Bukvić Dražen Novaković

This paper examines the organizational and financial capacity of Croatian cultural-sector organizations to access and manage European Union (EU) project funding. An explanatory sequential mixed-methods design was applied, combining a survey of 81 organizations with semi-structured interviews with 11 managers of public cultural institutions, conducted immediately before and during the earliest phase of the COVID-19 pandemic. Civil society organizations reported significantly more submitted project applications and successfully implemented EU-funded projects than public cultural institutions. Exploratory regression models indicated that project-related knowledge was associated with application activity, whereas organizational revenue was associated with the number of successfully implemented projects. The interviews suggest that limited human resources, insufficient project-related knowledge, administrative complexity, and difficulties in securing co-financing and pre-financing constrain the participation of public cultural institutions. From a sustainable finance perspective, these constraints may increase financial and implementation risks and limit funding diversification. The findings point to the potential value of targeted capacity building, simplified administrative procedures, and predictable co-financing and pre-financing mechanisms, which could strengthen the financial resilience of cultural institutions and reduce their dependence on a single source of public funding, which can potentially strengthen their long-term financial sustainability.

Journal of Risk and Financial Management doi: 10.3390/jrfm19110898

Authors: Imad Nassim Salma Nassim Abdelkarim Moussa

This study examines whether Moroccan agricultural small- and medium-sized enterprises (SMEs) adjust their leverage toward a target capital structure and estimates the speed of adjustment. Drawing on the dynamic trade-off perspective and the partial-adjustment framework, this study uses a balanced panel of 54 Moroccan agricultural SMEs observed from 2017 to 2022, yielding 324 firm-year observations. The empirical analysis employs the Arellano–Bond Difference Generalized Method of Moments (GMMs) estimator to account for the dynamic nature of leverage and potential endogeneity. The results reveal significant leverage persistence, with the coefficient on lagged total leverage estimated at 0.6155 (p < 0.01). The implied annual speed of adjustment is 38.45%, suggesting that firms correct approximately 38% of the deviation between observed and target leverage over one year. The corresponding model-implied half-life is approximately 1.43 years. These findings provide evidence consistent with partial adjustment toward target leverage among Moroccan agricultural SMEs and offer insights into the dynamics of their financing decisions.

Journal of Risk and Financial Management doi: 10.3390/jrfm19110897

Authors: Radosveta Krasteva-Hristova Rayna Petrova Mariya Vrachovska

The 2023 amendments to IAS 7 and IFRS 7 require buyers to disclose the liabilities covered by supplier finance arrangements, the part already paid to suppliers by finance providers, and payment-term ranges. Taking the disclosure objective of IAS 7.44F as the benchmark, this study asks whether these mandated quantities, as reported, are sufficient for like-for-like comparison across companies without further reconciliation by the analyst. Using the 2024–2025 consolidated reports of five European consumer groups (Carlsberg, Haleon, Heineken, Nestlé and Unilever) as critical cases, it identifies four comparability failure modes. First, payment-term formats differ: Heineken reports an identical 7–180-day range inside and outside its arrangements alongside an 8–11-day gap in weighted averages, and Nestlé’s monetary buckets show about 53 percentage points more balances beyond 120 days inside the arrangement than outside it. Second, the choice of numerator matters: scaling covered rather than paid liabilities by trade payables reverses the Heineken–Unilever ranking in both years, the only reversal among the ten possible pairs. Third, the scope of the denominator is not disclosed: for Nestlé, three defensible payables bases put the paid-financing ratio between 23.1% and 35.6% and make Nestlé’s first place on coverage depend on the assumed payables base. Fourth, whole-million rounding leaves the smallest programme impossible to rank. The study proposes a diagnostic sequence for analysts and identifies disclosures that would remove the ambiguity; it shows that these failures can occur, not how frequent they are.

Journal of Risk and Financial Management doi: 10.3390/jrfm19110896

Authors: Petya Petrova

The growing importance of identifying environmental, social and governance (ESG) risks is driven by increasing regulatory requirements and stakeholder expectations. However, information asymmetry, disclosure–performance gaps and potential greenwashing may limit the reliability of sustainability disclosures. This study examines the potential of financial statements as an additional source for screening ESG risk exposures and proposes an ESG Proxy Mapping Approach based on financial statement indicators. The empirical analysis covers companies listed on the Bulgarian Stock Exchange (68 in total), combining content analysis of publicly available financial and non-financial information with quantitative proxy indicators, comparative analysis and cross-report verification. The findings indicate that the feasibility of identifying potential signals varies across indicators, companies, and sectors. Proxies based on conventional accounting items tend to be more observable than those that require disclosing ESG-specific amounts. However, the mere existence of an observable accounting item or computable ratio does not establish an ESG-related cause. Missing disclosures also constrain the assessment. The findings suggest that, when combined with contextual disclosure analysis, financial-statement-based proxy mapping can support preliminary ESG risk screening, but cannot independently confirm the existence or magnitude of ESG risks.

Journal of Risk and Financial Management doi: 10.3390/jrfm19110895

Authors: Ivelina Shtereva Daniel Yordanov Nikolay Sterev

Static risk registers compiled at financial close do not follow the market conditions that determine the cash flows of merchant-exposed renewable projects. This study asks how far public day-ahead market data can support a phase-adaptive decision-support and monitoring framework for market-to-credit risk in solar-dominated renewable projects in Bulgaria. It uses 1096 daily observations (13 July 2023–12 July 2026) of day-ahead prices, renewable generation, and interpolated installed capacity. Across thirteen price specifications, the capacity coefficient is a conditional association that cannot be separated from time trends and has the sign opposite to the merit-order hypothesis, and same-day generation shows no stable negative association with the daily average price. In 730 rolling-origin forecasts, the capacity-and-trend model improves on a naive forecast by 5.9%, but not on an AR(1) benchmark, whereas calendar terms reduce the root-mean-squared error by about 25%. Days with a negative minimum price rise from 15 to 44 per sample year. Candidate monitoring indicators do not outperform a seasonal benchmark in the test sample and are not validated early-warning signals. A rolling debt service coverage ratio (DSCR) analysis of a stylized 1 MW PV asset rests on assumed capture rates, generation profiles, and financing terms, not on observed project data; under the base structure, no 12-month window breaches a 1.20× reference value at a capture rate of 0.70, and breaches appear only at lower capture rates or under tighter structures. These scenario results do not generalize, and the framework requires project-level validation.

Journal of Risk and Financial Management doi: 10.3390/jrfm19110894

Authors: Kolade Sunday Adesina Daniela Vandone

The COVID-19 pandemic depressed household income and firm revenue, transmitting a broad real shock to bank loan portfolios. This study asks whether the composition of bank assets conditioned that transmission. Using data for 344 commercial banks in 47 African countries over 2010–2021, we estimate a difference-in-differences model comparing banks that entered the pandemic with above-median asset diversification against their less diversified peers. Unlike existing work, which relies on non-interest income, we measure diversification from the composition of the balance sheet, since several components of non-interest income are themselves generated by lending. Credit risk rose during the pandemic, and more diversified banks experienced a smaller increase in both non-performing loans and loan loss reserves. The result is robust to propensity-score matching and a placebo test; for loan loss reserves it also survives country-by-year fixed effects, pre-trend corrections and a continuous treatment, whereas for non-performing loans it loses precision once country-level shocks are absorbed. The effect does not reflect larger securities holdings or a flight to safety. The evidence is consistent with asset diversification acting as a buffer under a sectorally uneven real shock, and suggests that supervisors in bank-based emerging economies may usefully treat balance-sheet composition as a dimension of resilience.

Journal of Risk and Financial Management doi: 10.3390/jrfm19110892

Authors: Mohammad Rubyet Islam David Pere

Alpha generation is increasingly shaped by large language models (LLMs) and agentic AI, yet much of the literature still treats alpha primarily as a predictive output rather than the result of sequential investment decisions under constraints. This paper presents a PRISMA-ScR-aligned scoping review and evidence synthesis of 165 studies published from 2023–2026. The review examines how LLM-assisted and agentic approaches contribute to forecasting, information interpretation, portfolio construction, execution, and coordination. The evidence shows that LLM-assisted systems substantially outnumber agentic systems. Predictive, interpretive, and execution-oriented applications are represented at broadly similar levels, while coordination-oriented systems form a smaller but distinct class. Validation remains dominated by historical and backtest-based evidence, with live or forward evaluation comparatively rare. Building on these findings, the paper proposes a five-stage decision capability spectrum, an evidence-guided alpha typology distinguishing predictive, interpretive, execution, and coordination roles, and a decision-centric evaluation framework spanning performance, decision quality, robustness, explainability, and governance. Together, these contributions provide a structured basis for evaluating adaptive, auditable, and constraint-aware intelligent alpha systems.

Journal of Risk and Financial Management doi: 10.3390/jrfm19110893

Authors: Javier Gamboa-Cruzado Aaron Mina-Rios Kevin Barbaron-Paucar Augusto Hidalgo Sánchez Johnny Astete-Llerena Ivar Farfán Muñoz Angel Nuñez Meza

The digital transformation of banking and finance intensifies exposure to cyber threats and the need to understand how scientific knowledge on Information Security is evolving in this sector. This study systematically and bibliometrically analyzes the current state of research on Information Security applied to Banking and Finance. A systematic literature review, complemented by systematic mapping and bibliometric analysis, was conducted following PRISMA. Searches of EBSCOhost, IEEE Xplore, ScienceDirect, Scopus, and ARDI, last conducted on 1 August 2025, identified 19,389 records; after applying the exclusion criteria and quality assessment, 62 studies published between 2019 and 2025 were retained. The results show that Java and Python are the most frequently reported programming languages in the analyzed solutions. Scientific production is concentrated in Q1 and Q2 journals, although quartile rankings do not correspond linearly to citation intensity. The thematic analysis identifies Banking Security as the only Motor theme and Service Logistics as a Basic theme, whereas AI Finance and Banking Cybersecurity occupy Marginal positions despite their greater citation visibility. These findings reveal a thematic structure in which cohesive traditional security concepts coexist with AI-related research exhibiting comparatively lower structural connectivity and cohesion. These findings highlight the strategic role of Information Security in supporting FinTech governance, risk management, and digital financial services.

Journal of Risk and Financial Management doi: 10.3390/jrfm19110891

Authors: Hector O. Zapata Raghav Goyal

This article compares ARMA-GARCH models under alternative statistical distributions to identify the best fit for risk assessment in selected U.S. agricultural commodity markets. The Kupiec proportion-of-failures test assesses whether actual losses exceed expected losses from each model for rice, soybeans, wheat, corn, cotton, orange juice and live cattle in daily futures prices from 1994 to 2026. In sample, ARMA-eGARCH models with skewed Student-t innovations have the lowest AIC and give correct 5% Value-at-Risk coverage for all seven commodities, whereas models with normal innovations are rejected for five of the seven. Out of sample (March 2020 to June 2026), the selected models pass the Kupiec test in 26 of 28 cases, while a normal GARCH(1,1) produces too many violations at the 1% level. A GARCH(1,1) with skewed Student-t innovations forecasts Value-at-Risk about as well as the selected models, so the distributional assumption, rather than the complexity of the volatility equation, accounts for most of the gain in accuracy. The study finds, similarly to other studies, that the skewed Student-t distribution works best in fitting kurtosis and skewness for risk analysis purposes.

Journal of Risk and Financial Management doi: 10.3390/jrfm19110890

Authors: Lan Sun Rajibur Reza Sairoong Lourigan Jerry Buckland

Financial wellbeing generally refers to people’s perception of current financial stability, their readiness to handle emergencies, and their confidence to cope financially in the future. However, financial wellbeing studies in Australia have commonly focused on urban population centres rather than regional areas. Given the far-reaching impact of financial wellbeing on people’s financial health, a clearer understanding of financial wellbeing and its influencing factors is pivotal in enhancing people’s financial wellbeing level in the regional areas as well as in the urban ones. Given this need, this study aims to identify influencing factors that will impact the state of financial wellbeing in rural Queensland—the North Burnett Region. Using the CFPB Financial Well-Being Scale and the survey method, this study finds that older people tend to enjoy a higher level of financial wellbeing as compared to younger people; financial wellbeing level improves when moving from unemployed status to full-time employed status; higher incomes are associated with higher financial wellbeing levels; investment property owners tend to have higher financial wellbeing than non-property owners; and financial wellbeing is higher for people who are satisfied with government and non-government financial support. This study also finds lower financial wellbeing levels amongst Indigenous families. The findings will have implications for the policymakers and financial advisors who assist Australian regional communities, especially young generations, Indigenous people, low-income households, and unemployed people, in sustaining their finances.

Journal of Risk and Financial Management doi: 10.3390/jrfm19110889

Authors: Farida Farida Maya Syafriana Effendi Mery Wanialisa

This study examines financial performance before and after mergers and acquisitions (M&A) among Indonesian listed firms. The sample comprises 134 M&A events involving 91 companies and 481 event-horizon observations. Twelve financial indicators are assessed one year before the transaction (t − 1) and one, three, and five years afterward (t + 1, t + 3, and t + 5). Population-averaged generalized estimating equations (GEE), using winsorized indicators and robust event-clustered standard errors, estimate differences relative to the pre-M&A benchmark. The analysis applies Holm adjustment to 36 prespecified horizon comparisons and examines sensitivity to raw data, sample composition, firm-level dependence, and calendar-year effects. Tobin’s Q declines at t + 1 and t + 3 relative to t − 1, with both differences remaining statistically significant after Holm adjustment in the primary and calendar-year fixed-effects specifications. Evidence for other indicators is less consistent, and no t + 5 comparison survives the primary Holm correction. These findings suggest that market valuation responds differently from accounting and liquidity measures across post-transaction horizons. Because the study lacks a matched non-acquiring comparison group and comprehensive transaction-level controls, the estimates describe longitudinal associations rather than causal M&A effects.

Journal of Risk and Financial Management doi: 10.3390/jrfm19110888

Authors: Laura Aquiño Perales Willian Flores Sotelo Abraham Bonilla Migo Arístides Peláez Santi Jorge Miguel Chávez Díaz

Financial inclusion is expected to raise long-run income, yet the banks that deliver it in Latin America carry recurrent asset-quality problems. This study asks whether the usage of formal financial services is associated with higher income per capita in the region and whether banking fragility alters that association. A usage-based financial inclusion index was built from deposit accounts, loan accounts and private credit for 12 Latin American economies over 2004–2024 and externally compared with Global Findex account ownership. Dynamic fixed effects (DFE), mean group and common correlated effects estimators were compared. The panel cointegration tests do not reject the null of no cointegration, and the significance of the error-correction term under standard critical values does not overturn that result, so the long-run estimates are read as conditional level associations. The index has positive DFE coefficients between 0.0059 and 0.0097 per index point, but the association is not robust: it is insignificant in most mean group estimates, vanishes once common time effects are absorbed, and income Granger-causes inclusion rather than the reverse. The non-performing loan ratio is negative in most estimates but significant only in some specifications, and the moderating role of fragility is not supported.

Journal of Risk and Financial Management doi: 10.3390/jrfm19110887

Authors: Eranda Abeysinghe Padmi Nagirikandalage Yan Wang Arnaz Binsardi

Biodiversity loss is emerging as a material governance and accountability concern, yet biodiversity reporting often remains fragmented from species protection and ecological recovery. This paper reviews the development of accounting and accountability for biodiversity protection and examines how emerging technologies can reshape biodiversity measurement, corporate governance disclosure and CSR accountability. Drawing on a literature review informed by structured searching, the paper identifies three generations of biodiversity accounting: accounting of biodiversity, focused on measurement and disclosure; accounting for biodiversity and species, focused on responsibility and non-human visibility; and accounting for biodiversity recovery, focused on intervention, restoration and rewilding. The review argues that artificial intelligence, big data analytics, blockchain, and digital platforms can strengthen the visibility, timeliness, traceability and assurance of biodiversity information, but can also introduce new risks of opacity, algorithmic bias, data fragmentation and technology-enabled greenwashing. We develop a technology-enabled accountability framework that links visibility, verification, responsibility, intervention, and recovery. The paper contributes to the emerging literature on technology-enabled corporate governance disclosure and CSR by showing how digital infrastructures can move biodiversity accounting beyond narrative disclosure towards more verifiable and outcome-oriented accountability.

Journal of Risk and Financial Management doi: 10.3390/jrfm19110886

Authors: Umar Daraz Štefan Bojnec

Access to appropriate finance is central to smallholder agricultural adaptation, but the relationships among digital financial access, financial inclusion, and financial literacy remain insufficiently examined. This study examined the association between FinTech access and climate-resilient agricultural investment, assessing financial inclusion as a mediator and financial literacy as a moderator. A quantitative cross-sectional survey collected data from 384 smallholder farmers in Mardan, Charsadda, and Nowshera districts of Khyber Pakhtunkhwa, Pakistan. Respondents were predominantly male (91.4%); therefore, the findings primarily reflect male farmers’ experiences and cannot be generalized to female-headed smallholder operations without dedicated gender-disaggregated sampling. Multiple regression, bootstrapped mediation, and interaction analyses tested hypothesized relationships, controlling for socioeconomic and farming characteristics. FinTech access was positively associated with both investment and financial inclusion. Financial inclusion retained a positive association with investment after adjustment and partially mediated the access–investment relationship, with an indirect effect of 0.222 (95% bootstrap confidence interval: 0.158–0.292), accounting for approximately 57.5% of the total association. Financial literacy positively moderates the association between FinTech access and investment (B = 0.137, p = 0.001), with stronger associations observed at higher literacy levels. These findings support the financial capability perspective, highlighting the distinct roles of financial participation and financial knowledge in climate-resilient agricultural investment. The results provide an empirical basis for evaluating integrated initiatives combining digital financial access, appropriate financial products, financial education, and agricultural extension support.

Journal of Risk and Financial Management doi: 10.3390/jrfm19110885

Authors: Osama H. Mahmoud Hassan Eman A. Eldessouky

Antimicrobial resistance (AMR) is an emerging trend risk for health insurers. This paper develops a multi-year actuarial framework of the kind used in an own risk and solvency assessment (ORSA) to assess how an unpriced AMR trend affects capital adequacy and how a potential new antimicrobial agent would change it. Resistance prevalence follows a logit random walk, claim numbers are mixed Poisson, premiums are repriced with a one-year lag, and solvency is measured by the first passage of own funds below a stylized 99.5% capital benchmark, distinguished from classical ruin. A patent-informed candidate compound serves only as a secondary illustration; its in vitro activity sets one scenario parameter. We derive the expected cost and break-even price under two payment arrangements, the conditional and predictive variance of annual claims, and the repricing-lag loss. In a hypothetical portfolio, the 15-year breach probability is 0.332 without the agent and 0.279 conditional on its success, equivalent to an additional capital buffer of about 7% of the requirement; with a market-entry probability of 0.03, the effect is negligible. About two-fifths of the benefit disappears when premiums anticipate the trend, so the agent acts largely as a hedge against unpriced trend risk. All portfolio parameters are hypothetical.

Journal of Risk and Financial Management doi: 10.3390/jrfm19110884

Authors: Majed S. Balalaa

Digital media has become a central information channel for investors and policymakers, yet the information it provides on foreign direct investment (FDI) is often noisy, uncertain, and fragmented. This study examines the role and impact of digital media on FDI in the Gulf Cooperation Council (GCC) region using a GCC-wide media corpus with Saudi Arabia as the anchor case. Using digital media articles published between 2018 and 2024, we construct a words-by-year contingency table and apply a wavelet-denoising procedure before correspondence analysis. A Daubechies Db12 wavelet at decomposition level 3 is used to separate signal from noise, after which factorial correspondence analysis is performed on the denoised contingency matrix. The results show that wavelet denoising substantially improves the interpretability of the factorial structure: the first two axes explain 97.34% of the total inertia, compared with approximately 70% explained by three axes in the raw data. The denoised analysis reveals distinct temporal patterns, including the initial opening of GCC markets to non-petroleum FDI in 2018, disruption during the COVID-19 pandemic period in 2019–2020, and recovery and expansion in 2021–2024 in sectors such as digital media, finance, tourism, technology, and energy. These findings highlight the value of wavelet-based textual analysis for extracting reliable information from digital media and for supporting FDI risk assessment, market monitoring, and financial decision-making in GCC markets.

Journal of Risk and Financial Management doi: 10.3390/jrfm19110883

Authors: Zafer Kardeş Tolga Buyuktanir Taner Toraman Tuğrul Kandemir

This study aims to identify financial and non-financial variables associated with independent audit opinions by using predictive models interpreted through explainable artificial intelligence (XAI) techniques, thereby improving transparency in audit-opinion prediction. In this context, 2415 firm-year observations from 238 manufacturing firms listed on Borsa Istanbul for the period 2010–2024 were used. In the study, the independent audit opinion was used as the dependent variable, while 18 financial and non-financial attributes were employed as independent variables. Descriptive statistics and a correlation heatmap analysis were conducted. To examine which variables contribute most strongly to audit-opinion predictions across different model specifications, predictive models were developed using logistic regression, decision tree, random forest, XGBoost, LightGBM, and CatBoost. Three complementary interpretability analyses were performed: built-in feature importance, Shapley Additive Explanations (SHAP) global importance, and SHAP beeswarm analysis. The built-in feature importance heatmap and the SHAP feature importance heatmap for all models were presented comparatively. A descriptive cross-model synthesis was then constructed by averaging normalized built-in feature importance and SHAP importance values. According to this descriptive synthesis, the type of audit opinion in the previous year (X17) received the highest average importance score (0.78), followed by asset turnover ratio (X7) with 0.57, operating sector (X18) with 0.54, current ratio (X1) with 0.54, acid-test ratio (X2) with 0.51, and the natural logarithm of total assets (X10) with 0.50. These results indicate that both financial and non-financial variables contribute to model predictions, although their relative importance varies across algorithms and interpretation methods. The findings contribute to the audit-opinion prediction literature by providing a comparative and interpretable assessment of predictive importance across multiple model classes and may support audit-risk assessment and related decision-support processes.

Journal of Risk and Financial Management doi: 10.3390/jrfm19110881

Authors: Rama Dabbas Fadi Alkaraan

This study examines whether cross-sectional return-dispersion patterns on the Qatar Stock Exchange (QSE) differ during Ramadan and whether those patterns vary across sectors and market conditions. Using daily stock-price data from 2016 through 30 July 2026 and the Cross-Sectional Absolute Deviation (CSAD) framework, we estimate Ramadan-interacted sector models with point-in-time constituent sets and Newey–West heteroskedasticity- and autocorrelation-consistent inference. The point-in-time-estimation does not support the earlier claim of uniform Ramadan herding across all seven sectors. After Benjamini–Hochberg false-discovery-rate adjustment, none of the negative Ramadan quadratic coefficients are significant at the 5% level; statistically significant positive coefficients in Consumer Goods & Services and Insurance indicate greater, rather than lower, dispersion and therefore do not constitute herding. Results are also sensitive to subperiod, dispersion measure, and sector size, while the Ramadan coefficient in a QSE-index return regression is negative and statistically insignificant. Accordingly, the study documents specification-sensitive Ramadan-associated dispersion patterns rather than a universal herding effect or a causal sentiment mechanism. The findings also highlight the inferential limitations created by very small sector cross-sections, particularly Telecoms, Transportation, and Real Estate.

Journal of Risk and Financial Management doi: 10.3390/jrfm19110882

Authors: Khwanchol Hasayotin

Thai households faced a sharp external energy price shock in 2026 against high pre-existing indebtedness. This conceptual article develops a framework for analyzing whether energy price shocks, fuel subsidy financing, credit constraints, and overlapping uncertainty could shorten the interval over which households can form and implement financial plans—termed financial-horizon compression—without any change in underlying time preferences. The framework is built from a structured synthesis of research on time preference, the propensity to plan, liquidity constraints, scarcity, and uncertainty-based postponement, and is anchored in a dated chronology of verified official records. These records show that retail diesel rose from about THB 30/L at the end of February 2026 to a peak near THB 50/L in April after a temporary cap; that headline inflation reached 2.89% year on year in April and 2.79% in May; and that the Oil Fuel Fund’s estimated net position fell to THB −95.9 billion by 27 September 2026. No household data are analyzed, and horizon compression is not observed. The article contributes (i) an operational definition that separates horizon compression from preference change, liquidity stress, precautionary saving, and option-value delay; (ii) an account of the informational conditions under which fuel-fund financing could enter household expectations; and (iii) five directional propositions, each paired with a competing prediction and an observation that would refute it. A source register, contextual dataset, verification register, reproduction code, and measurement protocol accompany the article.

Journal of Risk and Financial Management doi: 10.3390/jrfm19110879

Authors: Hamza El Kaddouri Aisha Saif Al-Marri

This study tests whether conventional firm-level and macroeconomic determinants explain dividend payout on the Qatar Stock Exchange (QSE), a concentrated, state-influenced and hydrocarbon-dependent market. Using 245 firm-years for 28 listed firms (2008–2024) from Refinitiv and Federal Reserve Economic Data (FRED), we estimate pooled, random-effects and fixed-effects models with clustered and wild cluster bootstrap inference that recognises the 17 annual observations behind macroeconomic effects, complemented by robust, quantile, Tobit, fractional and two-part models, a dividends-to-equity measure and Lintner-type dynamics. Profitability, size, liquidity (non-financial firms), sector, four crisis episodes, oil prices and US interest rates explain little of the variation in payout ratios (adjusted R2 below 6%). The strongly negative raw correlation between profitability and the payout ratio largely reflects five extreme firm-years; thereafter, the association is zero to mildly negative, and evidence that profitable firms are more likely to pay is only suggestive. Payout ratios are persistent (ρ = 0.32) and firm-specific, while dividend levels adjust fairly quickly to current earnings, a pattern more consistent with firm-specific target payout ratios than with slow Lintner-type smoothing. Macroeconomic effects are imprecisely estimated, and ownership-based explanations remain untested.

Journal of Risk and Financial Management doi: 10.3390/jrfm19110880

Authors: Fatmira Kola Marin Meksi

This study examines the determinants of non-performing loans (NPLs) in Albania’s corporate lending market and explores the association between credit information sharing and loan performance. The analysis uses 11,874 corporate loans of at least €0.5 million, or the equivalent in another currency, granted between 2004 and 2016 by banks connected to the Bank of Albania’s Credit Information Registry (CIR). Established to facilitate the exchange of borrower credit information among participating lenders, the CIR is examined through a post-2008 period indicator, which coincides with, but is not limited to, the introduction of the registry. The empirical analysis combines the CREDITRISK+ framework to assess portfolio default risk and sectorial concentration with a random-effects Probit model to examine the relationship between loan characteristics, economic sectors, the CIR period indicator, and the probability of a loan being classified as non-performing. The results indicate that higher interest rates are associated with a greater probability of NPL classification. Conventional loans and overdrafts exhibit higher probabilities than leasing, letters of credit, and guarantees. Sectorial differences are also evident, particularly in health and social activities, hotels and restaurants, and construction. The post-2008 indicator is negatively associated with NPL probability, although this relationship cannot be attributed exclusively to the CIR. The findings highlight the importance of borrower screening, prudent loan pricing, sector-specific risk monitoring, and credit information sharing in corporate credit risk management.

Journal of Risk and Financial Management doi: 10.3390/jrfm19110875

Authors: Sawssen Araichi Aya Saket

Bitcoin price forecasting often uses broad information sets that combine market, on-chain, macroeconomic, financial, energy, and technical variables. This study examines whether predictor reduction improves one-day-ahead Bitcoin price forecasts within learning models and whether this effect persists across successive out-of-sample periods. Daily data from January 2015 to May 2024 are analyzed. A nine-variable specification is first selected from an eleven-variable information set using Drop-Column ranking and nested Top-K selection. The selected specification is evaluated with Random Forest (RF), XGBoost, and Long Short-Term Memory (LSTM) models using a fixed out-of-sample evaluation and ten successive 60-day walk-forward windows. Under observation-date alignment, the selected specification reduces RMSE in the fixed evaluation by 0.75% for RF, 2.59% for XGBoost, and 45.21% for LSTM. Across the walk-forward windows, it produces lower RMSE in seven of ten periods for RF and XGBoost and in nine of ten periods for LSTM. HAC-adjusted predictive-loss tests show statistically significant improvements for XGBoost and LSTM, while the RF differences are not statistically significant at the reference bandwidths. A time-ordered selection check further selects market_cap alone, indicating that the strongest reduction effect is concentrated in a highly parsimonious specification. With a publication-date and vintage-aligned information set, eight predictors (instead of nine) are selected and predictor reduction again lowers aggregate forecast errors for all three models. Predictor reduction therefore improves forecast accuracy in most comparisons within the tested learning models, although the random-walk benchmark produces lower absolute forecast errors than all learning-model specifications.

Journal of Risk and Financial Management doi: 10.3390/jrfm19110878

Authors: Mahmoud Nassar Ahmed Hassanein

This study examines the relationship between ownership structure and firm performance in Jordan and investigates the m role of audit committee presence in these relationships. Using a balanced panel of non-financial firms listed on the Amman Stock Exchange over the period 2012–2021 (40 firms and 400 firm-year observations), the study considers three dimensions of ownership structure: managerial ownership, family ownership, and ownership concentration. Firm performance is assessed using both accounting- and market-based measures, namely return on assets (ROA), return on equity (ROE), and Tobin’s Q. Fixed- or random-effects panel regression models, selected using the Hausman test and estimated with firm-clustered standard errors, are employed, and the moderating role of audit committees is tested using interaction terms. The findings reveal heterogeneous effects of ownership structure on firm performance. Managerial ownership is negatively associated with ROA but positively associated with Tobin’s Q, indicating a potential trade-off between managerial entrenchment and incentive alignment. Family ownership exhibits a positive and significant association with all three performance measures, consistent with the stewardship perspective and the long-term orientation of family-controlled firms. Ownership concentration is positively associated with ROE and Tobin’s Q, consistent with the monitoring role of large shareholders. The positive associations of family ownership and ownership concentration with all three performance measures, and the positive association between managerial ownership and Tobin’s Q, are significantly stronger in firm-years in which an active audit committee is present. In contrast, the attenuation of the negative association between managerial ownership and ROA in such firm-years is only marginally significant. These findings suggest that the performance implications of ownership structure may depend not only on the identity and concentration of shareholders but also on the presence of internal monitoring mechanisms. The study contributes to the corporate governance literature by providing evidence from Jordan, an emerging market characterized by concentrated and family ownership.

Journal of Risk and Financial Management doi: 10.3390/jrfm19110877

Authors: Keunyoung Kim

This study examines whether CEO political leaning, as a managerial attribute, is associated with diversity, equity, and inclusion (DEI) disclosure in mandatory corporate filings and whether DEI-related language changed during the first filing period following a conservative-leaning federal policy shift. Using 188 firm-year observations from 48 highly reputable U.S. public firms over the 2022–2025 period, this study measures DEI disclosure intensity using natural language processing (NLP)-assisted dictionary-based textual analysis of Form 10-K filings. CEO political leaning is measured using the Campaign Finance score from the Database on Ideology, Money in Politics, and Elections (DIME). The results show that firms led by liberal CEOs disclose more DEI-related language than those led by conservative CEOs. DEI disclosure also declines in 2025, consistent with a broad response to the changing political and legal environment. However, the interaction between CEO political leaning and the post-policy indicator is not statistically significant, providing no evidence that conservative-led firms reduced DEI disclosure more sharply than liberal-led firms. Overall, the findings suggest that both managerial characteristics and the external policy environment are associated with corporate disclosure choices: CEO political leaning is associated with baseline differences in DEI disclosure; however, the 2025 decline is observed in both CEO groups, without statistically significant evidence that its magnitude varies with CEO political leaning.

Journal of Risk and Financial Management doi: 10.3390/jrfm19110876

Authors: Mamta Gaur Mohammad Asif Rajat Kumar Sant

Closing the global sustainable-development financing gap increasingly depends on two pillars working together: green finance and technological innovation. Yet the literature on green finance, financial technology (fintech), and environmental, social, and governance (ESG) investment has largely grown in parallel, examined through siloed corporate, sectoral, or single-country lenses rather than as a single connected system. This conceptual paper synthesises that literature into an integrative model, the Technology-Enabled Green-Finance Transmission (TEGFT) Framework, which maps how technological tools, including artificial intelligence, blockchain, big-data analytics, the Internet of Things, and regulatory technology (RegTech), convert green financial instruments into measurable ESG and sustainable-development outcomes, moderated by institutional quality and regional context. Drawing on information asymmetry and symbiosis theory, the paper positions fintech as a structural transmission mechanism rather than a peripheral facilitator. It also introduces the Green–Fintech Maturity Matrix (GFMM), an original typology that classifies countries along the framework’s institutional-quality and technology-maturity dimensions to identify the binding constraint on green–fintech policy in a given market. Five testable propositions follow, with policy and managerial implications for regulators, financial institutions, and firms in developed and emerging markets. The paper concludes with a research agenda to empirically validate the proposed framework.

Journal of Risk and Financial Management doi: 10.3390/jrfm19110874

Authors: Foued Badr Gabsi Mohamed Ali Mahmoudi Bochra Mannai

This study investigates the relationship between financial inclusion (FI), energy poverty (EP), and food security (FS) in Sub-Saharan Africa (SSA), with particular attention to the nonlinear role of energy conditions in shaping the effect of FI on FS. Using a panel of 30 SSA countries over the period 2010–2024, the study constructs multidimensional indices of FI and EP using Principal Component Analysis (PCA). To account for nonlinearities and complex interactions, five machine learning algorithms are considered, namely Support Vector Regression, XGBoost, Gradient Boosting, Random Forest, and Artificial Neural Networks. The results show that XGBoost provides the highest predictive performance, explaining approximately 62.49% of the variation in FS. The SHAP analysis further indicates that FI and energy conditions are among the most influential determinants of FS, although the contribution of FI is heterogeneous across observations. The XGBoost-based mediation analysis reveals a significant indirect effect of FI on FS through energy conditions, while the direct and total effects remain statistically non-significant. More importantly, the threshold analysis identifies a critical EP level of 4.044. Below this threshold, FI is associated with an improvement in FS, whereas beyond the threshold, its estimated effect becomes negative. These findings indicate that the contribution of FI to FS depends critically on prevailing energy conditions. The study therefore highlights the importance of integrating FI policies with interventions aimed at expanding access to modern energy services, particularly electricity and clean cooking technologies, in order to strengthen FS in SSA.

Journal of Risk and Financial Management doi: 10.3390/jrfm19110872

Authors: Haibo Wang Jun Huang Lutfu S. Sua Jaime Ortiz Jinshyang Roan Bahram Alidaee

This study analyzes daily bank stock returns and their dynamic connectedness for nine American banks between 18 March 2022 and 15 March 2023. It examines the interrelationships between bank stock returns and three economy-wide variables: market sentiment, monetary conditions represented by the Secured Overnight Financing Rate (SOFR), and economic policy uncertainty (EPU). The analysis employs a framework that combines correlation, cointegration, and connectedness (3C), using a time-varying parameter vector autoregression (TVP-VAR) model to examine evolving forecast-error spillovers. Using a 12-variable TVP-VAR model with generalized forecast error variance decomposition, the full-system total connectedness index is 75.4%. SOFR emerges as the primary average net transmitter (+17.6 pp), reflecting funding-cost repricing amid aggressive policy tightening, whereas EPU (−28.1 pp) and VIX (−17.4 pp) are net receivers. The findings provide descriptive evidence of time-varying stress transmission across regional lenders without establishing causal predictive links to bank insolvency.

Journal of Risk and Financial Management doi: 10.3390/jrfm19110873

Authors: Kholod Fahad Alsahali

This study examines whether the associations between corporate governance attributes and sustainability assurance differ across major global regions. Specifically, it investigates how board characteristics, audit committee characteristics, sustainability committee presence, and ownership attributes are associated with firms’ decisions to obtain external sustainability assurance and to choose a Big-4 assurance provider across regions. Using a global sample of 16,457 firm-year observations from 2010 to 2020, this study employs logistic regression models to examine the associations between corporate governance attributes and sustainability assurance and the choice of Big-4 assurance provider across Africa, Asia & Pacific, Europe, North America, and South/Latin America. The analysis is grounded in institutional theory to interpret regional differences in the governance-assurance relationship. The results reveal substantial cross-regional variation in the associations between corporate governance attributes and sustainability assurance and the choice of Big-4 assurance provider. The Wald tests indicate significant regional differences in the associations between governance attributes and firms’ decision to obtain external sustainability assurance for board size, board meeting frequency, board gender diversity, board independence, institutional ownership, and block ownership. Across the regional analyses, board gender diversity and sustainability committees emerge as among the most consistent governance characteristics associated with the decision to obtain external sustainability assurance. The findings for Big-4 assurance provider choice also indicate significant regional differences in the associations for board size, CEO separation, board gender diversity, board independence, audit committee expertise, audit committee independence, institutional ownership, and block ownership. Overall, the findings suggest that the associations between governance mechanisms and sustainability assurance vary across the institutional environment in which firms operate. This study extends the sustainability assurance literature by providing comprehensive cross-regional examinations of how corporate governance is associated with both the decision to obtain sustainability assurance and the selection of Big-4 assurance providers. The study provides evidence that regional institutional contexts are associated with differences in the governance-assurance relationship and highlights the importance of examining the decision to obtain external sustainability assurance and provider choice as related but distinct decisions.

Journal of Risk and Financial Management doi: 10.3390/jrfm19110871

Authors: Karima Esmail Ahmed Esmail Sally Mahmoud Hashem Shams Sarah Mahmoud Hashem Shams Khaled Hussainey

Geopolitical risk (GPR) has gained increasing prominence in financial markets, given the significant influence of political events on corporate decision-making and investor behavior. This study examines the effect of GPR on financial opacity (FO) and investigates the moderating role of financial constraints in this relationship. This study uses a sample of firms listed on the Egyptian Stock Exchange from 2011 to 2025. Moreover, empirical evidence investigates the moderating role of financial constraints on such relationships. The study employs the Instrumental Variables-Generalized Method of Moments (IV-GMM) regression technique. Furthermore, robustness tests are used to validate the findings by employing another method, the Instrumental Variables-Two-Stage Least Squares (IV-2SLS) estimator, and using alternative measures of GPR and FO. The results show that geopolitical risk has a significant positive effect on financial opacity. Moreover, the findings indicate that financial constraints moderate this relationship by weakening it. Furthermore, the results of the additional analysis demonstrated the robustness and accuracy of the results of the baseline analysis. The study contributes to the literature on GPR and financial opacity by providing, to the best of the researchers’ knowledge, the first study that examines the relationship between GPR and financial opacity in an emerging market like Egypt. Furthermore, the study contributes to a deeper understanding of the role of the financial conditions of Egyptian companies in influencing the relationship between GPR and financial opacity. Our findings suggest that investors, creditors, and regulators should pay closer attention to firms’ financial transparency during periods of heightened GPR.

Journal of Risk and Financial Management doi: 10.3390/jrfm19110870

Authors: Natcha Saramas Pennapa Chaowana Supasuta Tuncharo

Digital Services Taxes (DSTs) tax the revenue that large digital platforms earn in a country, and critics warn that they slow economic growth. This study tests that claim using data for nine countries that adopted a DST and seven that did not over 2015–2024, with pooled OLS, two-way fixed effects and the Callaway–Sant’Anna staggered difference-in-differences estimator. The results show no evidence that DSTs harmed growth. On the contrary, annual GDP growth in adopting countries was 1.57 percentage points higher after adoption, and the Callaway–Sant’Anna estimate is similar (1.74 points). The positive association was strongest in highly connected economies, reaching 1.72 points where 90% of the population uses the internet. These findings challenge the view that DSTs act as growth-damaging sales taxes and suggest that digital platforms can be taxed at rates of 1.5–7.5% without a detectable growth cost. Because the higher growth is an association rather than a proven effect of the tax, governments should judge DSTs mainly on their revenue, fairness and trade merits. The study provides the first cross-country evidence on DSTs and growth and recommends that adopting countries publish DST revenue data so that each levy can be evaluated.

Journal of Risk and Financial Management doi: 10.3390/jrfm19110869

Authors: Fahd Alkhdaer Mohammed Alzarah

Tokenized real-world-asset (RWA) markets, in which blockchain tokens grant claims on off-chain loans, Treasury portfolios, and listed securities, reached USD 9.26 billion in May 2026 across twelve public-chain protocols. Using 9081 protocol-day observations from DefiLlama and FRED (December 2020–June 2026), we ask how closely weekly changes in total value locked (TVL), our proxy for net flows, track traditional credit conditions. The evidence is suggestive. A one-percentage-point widening of the Moody’s Baa default spread is associated with a 0.36 log-point weekly contraction in TVL, stable in sign across stress measures, TVL floors, and leave-one-protocol-out samples, but imprecise with twelve clusters (wild cluster bootstrap p = 0.15) and smaller under outlier treatment. The association is concentrated in the five vehicles holding uncollateralized real-economy loans (−0.62; five clusters, bootstrap p = 0.10–0.12) and small among tokenized-Treasury vehicles, but a formal interaction test places the difference at the margin of significance and finds none within the 2023–2026 window in which both groups coexist. Event studies around four stress episodes show economically large but statistically insignificant contractions after Terra/Luna and FTX and none after the 2024 yen-carry unwind. We offer market segmentation as one interpretation and draw cautious implications for supervision.

Journal of Risk and Financial Management doi: 10.3390/jrfm19110868

Authors: Parisa Jindaluang Somnuk Aujirapongpan

This study examines how ESG ratings and SG&A cost adjustment are associated with equity market value and whether gross margin provides an exploratory operating-performance pathway. Using a balanced panel of 1512 firm-year observations from 504 companies over 2023–2025, with 2022 financial information used only to construct lagged change measures, the study estimates two-way fixed-effects models with firm-clustered standard errors. Equity market value is measured as log market capitalization, while SG&A cost adjustment is defined as the negative change in the logarithm of the SG&A-to-sales ratio. Gross margin is examined as an exploratory intermediary rather than a predetermined causal mechanism. The results indicate a negative and statistically significant within-firm association between the ordinal ESG rating measure and equity market value (β = −0.211, p = 0.004). Alternative ESG codings show that this association is concentrated in entry into the rated universe: the rating-level coefficient is not statistically significant among rated firm-years or when a separate unrated indicator is included. SG&A cost adjustment is not statistically significant in the fully controlled specification that includes ROA, whereas the estimate becomes positive and statistically significant when ROA is excluded, highlighting specification sensitivity. The exploratory gross-margin pathway and the ESG–SCA interaction provide no statistically supported evidence of an additional pathway or conditional association. The findings indicate that the estimated valuation associations are sensitive to profitability conditioning and measurement choices and should not be interpreted as causal effects. The study contributes by distinguishing ESG positioning, observable operating-cost adjustment, gross-margin performance, and equity valuation within a firm-level panel framework. The results also show why the interpretation of sustainability-related valuation associations should account explicitly for profitability controls, rating participation, and within-firm identification.

Journal of Risk and Financial Management doi: 10.3390/jrfm19110867

Authors: Tapas R. Dash Siphat Lim

This study examines asymmetric exchange-rate pass-through to consumer prices and evaluates whether currency depreciation and appreciation exert differential effects on inflation. A nonlinear autoregressive distributed lag (NARDL) framework is employed to distinguish between positive and negative exchange-rate movements while controlling for broad money and international oil prices. The Akaike Information Criterion identifies a NARDL(2, 0, 1, 1, 1) specification, and diagnostic and stability tests indicate no evidence of serial correlation, heteroskedasticity, residual non-normality, or parameter instability. The results reveal a stable long-run relationship among consumer prices, exchange rates, broad money, and oil prices. The error-correction term is negative and statistically significant (−0.053, p < 0.001), indicating that approximately 5.3% of disequilibrium is corrected each month. The long-run estimates provide strong evidence of asymmetric exchange-rate pass-through: a 1% currency depreciation is associated with an approximately 3.55% increase in consumer prices, whereas a 1% appreciation is associated with only a 0.18% decline. The Wald test rejects long-run symmetry (F = 16.791, p < 0.001). Significant short-run exchange-rate effects are also observed, while broad money and oil prices exert positive inflationary effects in both the short and long run. These findings demonstrate that exchange-rate pass-through is nonlinear and direction dependent, with depreciation generating substantially stronger inflationary effects than appreciation. The results underscore the importance of incorporating asymmetric exchange-rate dynamics, monetary conditions, and external cost pressures into inflation assessment and macroeconomic policy formulation.

Journal of Risk and Financial Management doi: 10.3390/jrfm19110866

Authors: Ketut Gede Sri Diwya I Gede Agus Nova Pratama Putra I Made Marco Mahotama Christopher Natanael

Retail investors increasingly discuss investments with conversational artificial intelligence (AI), yet how this practice relates to cryptocurrency investment intention is unclear. Informed by a dual-process interpretation, this study tests belief-based (return expectation) and socially driven (herding behaviour) pathways, and whether perceived financial literacy conditions the herding pathway. Cross-sectional survey data from 400 Indonesian investors (89.25% male; 83.50% aged 18–30 years) were analysed with PLS-SEM (5000 bootstrap subsamples). AI investment discussion is strongly associated with return expectation (β = 0.756) and herding behaviour (β = 0.751); both mediate its association with intention (indirect effects 0.253 and 0.202, not significantly different) alongside a smaller direct effect (β = 0.184). Perceived financial literacy positively moderates the herding pathway (β = 0.149; slopes 0.120 at −1 SD and 0.418 at +1 SD). Because literacy was self-assessed and the sample is predominantly young and male, the moderation may partly reflect confidence rather than objective knowledge and may not generalise to other investor groups. Results are robust across several checks, although a Gaussian copula test indicates possible endogeneity in the AI investment discussion–herding path. The model explains 55.8% of intention variance. Education and AI-design responses addressing crowd-following are proposed for further evaluation.

Journal of Risk and Financial Management doi: 10.3390/jrfm19110865

Authors: Nadia Farjallah Wiem Ben Romdhane Mohamed Essaied Hamrita

Accurate forecasting of systemic risk is crucial for financial stability and regulatory oversight. Traditional parametric models often fail to capture the complex, nonlinear dependence structures in financial markets, particularly during periods of distress. This paper proposes a semi-parametric dynamic conditional correlation (DCC) framework that integrates nonparametric estimation of marginal distributions with a parametric DCC specification to improve the forecasting of systemic risk measures—Conditional Value-at-Risk (CoVaR) and Marginal Expected Shortfall (MES). We employ a kernel-based quantile regression approach for marginal modeling and combine it with a DCC-GARCH structure for capturing time-varying conditional correlations. Empirical results using daily equity returns for a panel of 20 large U.S. financial institutions over the period January 2000 to December 2023, with emphasis on three stress episodes—the 2007–2009 global financial crisis, the 2011 European sovereign debt crisis spillover, and the March 2020 COVID-19 market disruption—demonstrate that the suggested semi-parametric DCC framework yields more accurate and robust one-day-ahead forecasts of CoVaR and MES compared to fully parametric alternatives, with the advantage widening during stress periods. During the 2007–2009 crisis sub-period, the SP-DCC model reduces the Fissler–Ziegel loss for CoVaR by 19.0% relative to the Gaussian-DCC benchmark and by 6.5% relative to the strongest parametric benchmark (skewed-t DCC). Importantly, the SP-DCC specification is the only model in our comparison set for which the null hypotheses of correct unconditional and conditional coverage are not rejected at the 1% significance level during stress periods, whereas the Gaussian-DCC benchmark significantly over-rejects. The model also exhibits improved performance in identifying systemically important institutions and tail interdependencies during stress episodes, with rankings that align more closely with official G-SIB designations and SRISK-based rankings than those implied by Gaussian-DCC benchmarks. Our findings underscore the importance of flexible marginal modeling in systemic risk measurement and provide policymakers with an enhanced toolkit for macroprudential surveillance.

Journal of Risk and Financial Management doi: 10.3390/jrfm19110864

Authors: Silviya Chukanska Rayna Petrova

Moving from external treatment to internal recovery of a meat processing enterprise’s own animal by-products (ABPs) has interconnected production, economic, and environmental implications. This study develops and applies an extended sustainable strategic costing (SSC) model to assess these implications. It integrates activity-based costing, material flow cost accounting, cost of quality, and life cycle costing with joint cost allocation, investment appraisal, and a limited operational emissions assessment. A shared material balance, distinct analytical scopes, and rules for preventing double counting of financial effects connect the methods. Recorded production and accounting data are supplemented with calculated values and explicit assumptions. Revenue from meat and bone meal and animal fat covers 99.76% of their full cost. Over ten years at 7%, the ex ante appraisal yields a standalone net present value of EUR 3.23 million and a comparative value of EUR 14.42 million, including avoided cash outflows for external treatment. Comparative investment advantage persists across the tested parameter changes. The conditional external-treatment emissions threshold is 0.034264 t CO2e/t of own input, with differences in system boundaries considered. The model provides traceable resource, economic, and emissions information for assessing management decisions on circular ABP recovery.

Journal of Risk and Financial Management doi: 10.3390/jrfm19110862

Authors: Robert Zenzerović Ksenija Černe

Background and purpose: Financial ratios assess non-financial companies’ financial sustainability, but evidence on their predictive power across EU Member States remains fragmented and heterogeneous. To the authors’ knowledge, this is among the first integrated EU-focused reviews and meta-analyses covering seven ratio groups. Methodology: A structured research identified 29 studies, yielding 230 poolable effect sizes from 22 articles. Effects were standardized as Fisher’s z-transformed partial correlations and pooled using a random-effects DerSimonian–Laird model. Dependence was addressed through robust variance estimation (RVE) and a three-level model. Results: The analysis reveals that the overall pooled effect is small and, once effect-size dependence is accounted for, estimated with far less precision, although still statistically significant. Liquidity and profitability ratios show the largest pooled effects, followed by solvency ratios, whereas cash-flow and activity ratios show only small associations. Under dependence-robust inference, solvency and cash-flow ratios remain significant in both dependence-aware models, liquidity and profitability ratios in only one of them, and activity ratios in neither. At the indicator level, equity-to-assets, EBIT/total assets, and working capital/total assets show the largest and most consistent baseline effects, but each rest on one to six studies. Pooled effects are smaller at longer prediction horizons and larger in large-firm samples, but neither moderator is statistically significant once dependence is accounted for; only the type of outcome measure (continuous versus binary) moderates the effects significantly. Conclusions: Financial sustainability in EU non-financial companies is best captured by a core set of liquidity, profitability and solvency ratios, whereas the larger effects observed at short horizons and in large-firm samples are suggestive rather than conclusive. Associations differ sharply by outcome type, weak for binary bankruptcy and larger for distress scores, so individual ratios are weak and highly context-dependent rather than uniformly predictive.

Journal of Risk and Financial Management doi: 10.3390/jrfm19110863

Authors: M. Roby Yuliansyah Noer Azam Achsani Linda Karlina Sari Lukytawati Anggraeni

Listed oil palm firms combine long-lived biological and processing assets with financing needs that consolidated company ratios may not represent accurately. Using a balanced panel of 53 listed firms (19 Indonesian and 34 Malaysian) and 424 firm-year observations for 2017–2024, this study constructs activity-aligned measures from audited segment disclosures and estimates leverage persistence. The prespecified benchmark is a full two-step System GMM model with Windmeijer-corrected standard errors, collapsed instruments, ex ante regressor classifications, and year indicators. All four Malaysian debt-to-assets-ratio (DAR) models satisfy the available diagnostic criteria and imply annual adjustment speeds of 12.56–19.35%. Liquidity is negatively associated with DAR; profitability is insignificant. The Indonesian models are inadmissible because 27 instruments exceed 19 firms, with additional persistence or bias failures; their coefficients are not interpreted. Dynamic fixed-effects and split-panel-jackknife estimates show positive DAR persistence in both countries but different adjustment magnitudes and country rankings. Pooled DAR interactions are also inadmissible. Debt-to-equity estimates differ substantially, indicating denominator-related parameter instability. The results support cautious evidence of dynamic leverage adjustment while showing that adjustment speed, country ordering, and determinant inference depend on estimator and measurement choice.

Journal of Risk and Financial Management doi: 10.3390/jrfm19110860

Authors: Huifang Miao Danai Tanamee Suppanunta Romprasert

This paper examines whether Hang Seng Index (HSI) futures-market depth is associated with the volatility of Hong Kong’s real economy under the Linked Exchange Rate System (LERS). Using quarterly data from 2000Q1 to 2024Q4, futures-market depth is measured by the logarithm of quarterly average main-contract open interest (Depth), while macroeconomic volatility is measured by the rolling standard deviation of real gross domestic product (GDP) growth. The preferred three-lag vector autoregression (VAR(3)) provides only modest full-sample evidence of a macroeconomic role for futures-market depth. Lagged Depth does not significantly predict GDP-growth volatility (p = 0.2253), and the baseline impulse response turns negative after the initial quarters but remains statistically indistinguishable from zero at the 90% and 95% levels. At a twelve-quarter horizon, Depth innovations explain 1.22% of GDP-volatility forecast-error variance, compared with 38.16% for Chicago Board Options Exchange Volatility Index (VIX) innovations and 57.90% for GDP-volatility’s own innovations. The sensitivity analysis reveals differences in predictive evidence and response significance across specifications. Predictive evidence strengthens under a twelve-quarter volatility measure, reversing the contemporaneous ordering of Depth and GDP volatility produces a short-run negative response significant at the 90% level, and controlling for major crisis-period level shifts generates a negative response significant over several horizons at the 95% level. The ordering-free generalised response remains negative over much of the horizon but statistically indistinguishable from zero. Overall, HSI futures-market depth carries a modest average macroeconomic signal, with inference sensitive to the volatility window, identification, and crisis controls, while global risk remains the dominant external source of GDP-growth volatility in the estimated system.

Journal of Risk and Financial Management doi: 10.3390/jrfm19110861

Authors: Ketut Gede Sri Diwya Naomi Aurelia Johnny Alberta Komang Andika Prasetya I Kadek Mahesa Parwata Gandhi

Artificial intelligence (AI) features in FinTech-enabled digital banking support financial decisions but also introduce technological risks, such as black-box reasoning and erroneous recommendations. Studies often treat customer-facing AI as a single construct, leaving it unclear whether customers respond to explanation consistently across features. Drawing on the Stimulus–Organism–Response framework, this study models perceived AI explainability as a stimulus related to continuance intention through algorithmic trust (a cognitive pathway) and AI anxiety (an affective pathway) and compares chatbot and robo-advisor users. Survey data from 300 Indonesian digital banking users (160 chatbot users; 140 users of self-reported financial recommendation or automation features, labeled robo-advisor users) were analyzed with partial least squares structural equation modeling and multi-group analysis. Explainability is associated with higher continuance intention directly and through stronger trust and lower anxiety; technological risk serves as the theoretical context, and because neither actual risk nor perceived risk was measured, the findings do not show that explanation reduces risk. Neither indirect association differs significantly between self-selected groups. At the path level, anxiety is more strongly associated with lower continuance among robo-advisor users, and explainability with lower anxiety among chatbot users, the latter not being robust to multiple-comparison adjustment. The findings inform customer-side research on AI in a technological risk context and motivate, but do not test, a common explanation standard complemented by human oversight for financial recommendation features.

Journal of Risk and Financial Management doi: 10.3390/jrfm19110859

Authors: Khaled Hutaibat Krayyem Al-Hajaya Ahmed Eltweri Abdussalam Mahmoud Abu Tapanjeh

Financial capabilities may support effective financial management behaviour, but their relationship with behaviour may depend on individuals’ trust in financial institutions. This study examines whether institutional trust moderates the relationships between financial literacy, financial education, access to financial information, and financial management behaviour in Jordan. Drawing on institutional trust reasoning, the study analyses an online survey of 535 Jordanian adults collected in July 2026 through convenience and snowball sampling, using partial least squares structural equation modelling. Financial literacy (β = 0.218, p = 0.001), financial education (β = 0.231, p = 0.001), and access to financial information (β = 0.298, p = 0.001) were positively associated with financial management behaviour, with information access showing the largest reported direct coefficient. The moderation effect results indicate that these relationships were stronger at higher levels of institutional trust. Positive interactions were reported for access to financial information (β = 0.157, p < 0.001) and financial literacy (β = 0.134, p = 0.002). The interaction involving financial education was smaller and borderline significant (β = 0.109, p = 0.048), warranting cautious interpretation. The model explained 38.9% of the variance in financial management behaviour. The study contributes by examining institutional trust as a boundary condition linking financial capabilities with financial behaviour in the surveyed sample. The findings suggest that financial capability initiatives should consider institutional credibility and the perceived reliability of financial services and information. However, non-probability sampling design utilized may limit generalizability of the findings.

Journal of Risk and Financial Management doi: 10.3390/jrfm19110856

Authors: Nusrat Khan Shahid Alam Asra Inkesar Asif Hasan Farrukh Rafiq Ahmad Syed Khusro Chishty

This study examines associations among perceived political ideology, local stakeholder reactions, liability of foreignness, and perceived foreign direct investment (FDI) risk in India. Drawing on survey responses from 312 managers and executives, the study employs covariance-based structural equation modelling to assess theoretically specified direct and indirect relationships. The reported structural estimates indicate positive associations between perceived political hostility toward foreign investment and stakeholder resistance, liability of foreignness, and perceived FDI risk. Liability of foreignness is positively associated with perceived risk, whereas stakeholder resistance exhibits an unexpected negative association, contrary to the hypothesized direction. The findings highlight the relevance of managerial perceptions of political conditions and foreignness-related disadvantages to perceived investment risk. The cross-sectional, single-source design limits causal interpretation, while the negative stakeholder association requires further investigation.

Journal of Risk and Financial Management doi: 10.3390/jrfm19110858

Authors: Nouhaila El Hajjaji Zakariae BelMkaddem Mohammed Amine Hajjaj Hicham Es-saadi

This study examines the short- and long-run relationships between fiscal capacity, digital connectivity, and economic growth in Morocco over the period 2003–2023, using an Autoregressive Distributed Lag (ARDL) bounds testing approach. Tax revenue as a share of GDP proxies the state’s fiscal capacity, while Internet usage captures the scale of digital connectivity; results are discussed in light of Morocco’s ongoing digital fiscal reforms, including the 2024 Finance Act. The F-Bounds test confirms a stable long-run cointegrating relationship among all variables (F = 11.838, p < 0.01). In the short run, gross fixed capital formation is positively associated with GDP per capita, while Internet usage is negatively associated with GDP per capita; tax revenue enters only through the long-run relationship, and public expenditure shows no significant short-run effect. In the long run, gross fixed capital formation retains a significant positive effect, and Internet usage remains negatively and significantly associated with GDP per capita, while tax revenue and public expenditure remain statistically insignificant at both horizons. These results suggest that, over this period, capital investment has been a more reliable driver of Morocco’s GDP per capita than digital connectivity. We propose a reinvested digital taxation framework as a forward-looking policy direction worth exploring, rather than as a conclusion directly established by these results.

Journal of Risk and Financial Management doi: 10.3390/jrfm19110857

Authors: Nihel Halouani

This study examines the conditional association between recognized intangible asset intensity (INTANG) and external audit fees in a selected sample of U.S.-listed companies. The author-designated study period is 2016–2025; the available estimation sample comprises 1383 firm-year observations from an initial frame of 503 firms. The fiscal-year assignment of individual observations has not yet been independently verified against the row-level dataset. Pooled ordinary least squares (OLS) models control for firm size, financial leverage, profitability, loss status, market-to-book ratio, equity beta, TRBC sector indicators, and relative-fiscal-period indicators. In the reported HC1 benchmark, INTANG is positively correlated with logged audit fees (Pearson r = 0.112), yet carries a negative conditional coefficient (β = −0.300; HC1 SE = 0.121). A 10-percentage-point difference in INTANG corresponds to approximately 3.0% lower predicted fees; a one-standard-deviation difference corresponds to approximately 3.2% lower fees. The adjusted R2 rises from 0.7770 to 0.7780 when INTANG is introduced. These estimates provide a limited descriptive benchmark, not a panel-robust or causal finding: firm-clustered standard errors, actual fiscal-year effects, selection diagnostics, and account-level complexity controls require re-estimation from the underlying firm-year records. The findings motivate more precise measurement of intangible components and audit-engagement complexity, without claiming that lower physical-verification costs, audit-program economies, or knowledge spillovers have been directly identified.

Journal of Risk and Financial Management doi: 10.3390/jrfm19110855

Authors: Simon A. Broda Gianluca De Nard Patrick Walker

We consider an asset allocation problem where funds must be allocated to six broad classes of assets—equity, corporate bonds, government bonds, listed real estate, gold, and commodities—each represented by a liquid, predominantly US public-market index that can be tracked with ETFs. We use a parsimonious reinforcement learning model to solve it, capitalizing on the time separability of the problem and the fact that for a price-taking investor, the state of the world is independent of her prior actions; these two features collapse the multi-period problem into a sequence of one-period direct policy optimizations, so that the policy network can be trained like a supervised model, without value functions or policy-gradient rollouts. In a walk-forward backtest from 20 October 2011 through 5 June 2026, the four-asset strategy earns a gross total return of 374% and a Sharpe ratio of 0.77, against 235% and 0.62 for Markowitz tangency portfolio and 222% and 0.68 for an equally weighted portfolio. Net of trading costs of 10 basis points, these are 342%, 228%, and 220%, with Sharpe ratios of 0.73, 0.61, and 0.68. In the six-asset case, which adds government and corporate bonds, our total return is 367% gross and 336% net, against 203% and 139% gross (199% and 138% net) for the two benchmarks. The Sharpe ratio, 0.79 gross and 0.75 net, exceeds equal weighting but not the tangency portfolio (0.91 and 0.89), which concentrates in low-volatility bonds. The sign of the return advantage is the same in four of five pre-defined monetary-policy regimes, although the individual regime-level differences are not statistically significant; the advantage survives a broad set of further benchmarks (including mean-variance rules with the same risk aversion, mean–expected-shortfall and risk-parity portfolios), and disappears only at trading costs above 60 basis points per unit traded (72 against the tangency portfolio).

Journal of Risk and Financial Management doi: 10.3390/jrfm19110854

Authors: Arusaya Thamaree Simon Zaby

This study examines whether the share and number of women on corporate boards are associated with the financial performance of Thai listed firms. Using a hand-collected 2012–2021 panel of 584 firms listed on the Stock Exchange of Thailand, excluding financial institutions (5475 firm-year observations), we estimate models with firm and year fixed effects, with Tobin’s Q and return on assets (ROA) as performance measures. Critical mass is examined with three classifications from the literature: Kanter’s typology, a 30% threshold, and the number of female directors. With firm and year fixed effects, neither the share of women nor any critical mass category is significantly associated with Tobin’s Q. The ROA estimates are negative but imprecise, and they disappear when the pandemic years 2020 and 2021 are excluded. Confidence intervals rule out large effects in either direction. Models with firm fixed effects alone show significant negative associations, but these reflect common trends: Female representation rose during the period while the performance of Thai listed firms declined. The study adds evidence from an emerging market without a gender quota and with widespread family control, and it shows that omitting time effects can make common trends appear as an effect of board composition.

Journal of Risk and Financial Management doi: 10.3390/jrfm19110853

Authors: Banchongsan Charoensook Lukas Timbate Sing Ong Yu

We investigate the speed and permanence of geopolitical shock pricing in a frontier market by examining the 2025 Thai–Cambodian border conflict on the Cambodia Securities Exchange (CSX). To capture both the immediate onset and subsequent medium-horizon stabilization of geopolitical risk, we deploy a dual-horizon econometric architecture. Short-term, to account for the informational inefficiency inherent to nascent exchanges, we utilize an event-conditioned state-space AR(1) model via a Kalman filter. We find that high-visibility physical conflicts trigger immediate, statistically significant downward price adjustments, whereas low-visibility informational shocks exhibit gradual price incorporation. Medium-term, we evaluate post-conflict valuation recovery across an eight-month horizon in 2026 (January–August, T = 164 trading sessions) by constructing a within-market constant-mean counterfactual evaluated against an empirical distribution of historical placebo windows (2021–2024). We document a distinct behavioral contrast: despite acute short-term adjustments during the conflict, the aggregate CSX Index generated a substantially positive +34.93% Buy-and-Hold Abnormal Return (BHAR) during the 2026 recovery horizon, ranking at the 100th percentile of comparable pre-conflict historical horizons with zero observed exceedances across both rolling windows (fupper = 0/824 = 0.00%) and strictly non-overlapping 164-day blocks (0/6). This descriptive benchmark outperformance is preserved (+30.85%, 100th percentile, fupper = 0/583 = 0.00% rolling windows and 0/4 non-overlapping blocks) under a restricted 2022–2024 baseline. While our three-episode short-run comparison is necessarily exploratory and our aggregate medium-horizon design does not econometrically isolate specific causal mechanisms from contemporaneous macroeconomic covariates (such as monetary conditions or bilateral negotiations), we note that this rapid equity recovery is congruent with independent evidence from spatial development economics, trade analyses, and macroeconomic reporting documenting Cambodia’s swift post-conflict economic adaptation and the realization of a ‘peace dividend’ across formal economic pillars.

Journal of Risk and Financial Management doi: 10.3390/jrfm19110852

Authors: Deshui Yu Fuying Wei Shengyao Wang

This study examines the effect of artificial intelligence (AI) innovation on corporate ESG performance in Chinese listed firms. Using 42,996 firm-year observations of 4792 A-share listed firms from 2009 to 2023, we measure AI innovation by AI patent applications and ESG performance by Huazheng ESG ratings. Two-way fixed effects estimates indicate that a one-unit increase in ln(1 + AI patent applications) is associated with a 0.037-point increase in the ESG score, or about 5% of a standard deviation for a one-standard-deviation increase in AI. Double/Debiased Machine Learning (DML) estimates with firm-level cross-fitting are similar (0.026–0.038). The relationship is robust to lagged AI, predetermined controls, ordinal and rank-based outcomes, and an event-study design that shows no differential ESG trends before firms’ first AI patent application; however, it is driven by the intensity of AI patenting rather than by the extensive margin and is imprecisely estimated within the pre- and post-2017 disclosure regimes. Mechanism tests suggest efficiency and internal-control channels, whereas the management expense ratio does not mediate the effect. The effect is stronger in larger firms and in firms with greater green innovation capability and tends to be larger in more market-oriented regions (significant at the 10% level), while the difference between heavily polluting and other firms is not statistically significant. Under a conditional exogeneity assumption, these results are consistent with a causal effect of AI innovation on ESG performance.

Journal of Risk and Financial Management doi: 10.3390/jrfm19110851

Authors: Ana Lorena Jiménez-Preciado José Carlos Trejo-García Miguel Ángel Martínez-García Francisco Venegas-Martínez

Reputational risk is hard to quantify, and more so for public institutions in emerging markets. We build a daily index of media-derived reputational pressure for INFONAVIT, Mexico’s largest public housing lender, from 138,789 news articles published by 9495 outlets between November 2019 and July 2024. Transformer-based sentiment classification, neural topic modeling, emotion detection and time-series anomaly analysis are combined into a Reputational Risk Score (RRS), bounded between 0 and 100, that measures media pressure, not reputational losses. Agreement between the BERT classifier and the monitoring service’s human coders is negligible (Cohen’s kappa = −0.0019), so absolute sentiment levels cannot be read; replacing the classifier with two Spanish-language models leaves the index almost unchanged (r = 0.941 and 0.952). Rolling z-scores flag 63 crisis days in 53 episodes over 1223 days with at least 30 articles, and the highest peaks correspond to fraud investigations, operational changes affecting borrowers and incidents at institutional offices. The fraud-related detections survive all 48 detection settings, and the main detections are robust to alternative weights and coverage thresholds, but they require the tone components, and media volume carries 78% of the variance of the composite. Because episodes are interpreted ex post, we present an auditable record of media pressure rather than external validation.

Journal of Risk and Financial Management doi: 10.3390/jrfm19110850

Authors: Ivan Todorov Gancho Ganchev

This research estimates the impact of fiscal policy (approximated by the ratio of public investment to government consumption and the shares of direct and indirect tax revenues in GDP) on economic growth and income inequality in Portugal during 1995–2025. The study uses annual data and autoregressive distributed lag (ARDL) models that account for trade openness, corruption control, and tourism. The empirical results indicate that, in the long term, Portugal’s growth is affected by corruption control and tourism, while in the short term, it is affected by the ratio of government investment to government consumption. The results also show that income distribution in Portugal is influenced by corruption control, indirect tax revenues, tourism, and trade openness in the long run, and by direct tax revenues, tourism, and trade openness in the short run. Interestingly, while Portugal’s economic growth depends on the structure of government expenditures in the short term, income inequality depends on direct taxes in the short run and on indirect taxes in the long run. This suggests that foreign trade, corruption control, and tourism are key determinants of economic growth and income distribution in Portugal, underscoring the importance of economic freedom and institutional quality for economic well-being and social resilience.

Journal of Risk and Financial Management doi: 10.3390/jrfm19110849

Authors: Fatima Zahrae Souleimani Amine Maktoub Mimoun Benali

Impact investing requires the assessment of social and environmental outcomes alongside financial returns, yet impact measurement remains methodologically fragmented and heterogeneous in purpose and use. This systematic review synthesizes the scholarly literature treating impact measurement as a central object of study within impact investing. Following PRISMA 2020 guidelines, records were identified through Scopus and Web of Science, screened at title/abstract and full-text stages, and appraised using design-specific quality criteria. The final corpus comprised 69 studies published between 2012 and 2026. Findings were synthesized narratively and complemented by descriptive bibliometric analysis. Four recurring themes emerged: persistent standardization efforts alongside methodological fragmentation; the growing role of ex-ante assessment; the organizational and relational functions of measurement; and contextual, institutional and organizational variation. The review further shows that impact measurement involves not only technical choices but also causal reasoning. Remaining evidence gaps concern the validation of ex-ante approaches, the use of impact information in actual investment decisions, external validation of measurement frameworks, and comparative evidence across institutional contexts.

Journal of Risk and Financial Management doi: 10.3390/jrfm19110848

Authors: Thi Thu Thuy Do Ngoc Hung Tran

Computer-assisted audit tools and techniques (CAATs) are central to audit digitalization, yet it remains unclear which system-quality attributes make their use successful, particularly in audit markets where spreadsheets still dominate. Drawing on the DeLone and McLean information systems (IS) success model and operationalizing system quality through the ISO/IEC 25010 standard, this study examines how usability, compatibility, reliability, and security shape successful CAATs use (auditors’ use of and satisfaction with CAATs) and, through it, audit benefits. Survey data from 289 auditors in Vietnamese audit firms were analyzed using partial least squares structural equation modeling. Usability and reliability are the only attributes with unique effects on successful use, whereas compatibility and security add no explanatory power beyond them. Successful use strongly increases audit benefits and transmits the effects of usability and reliability; usability also improves benefits directly. Theoretically, the findings show that system-quality attributes are not equally consequential: in a spreadsheet-dominated audit setting, some act as differentiating drivers of IS success, whereas others function as baseline requirements. Practically, audit firms, especially small firms staffed largely by junior auditors, should select easy-to-learn, dependable tools and pair technology investment with hands-on training and standardized CAAT procedures.

Journal of Risk and Financial Management doi: 10.3390/jrfm19110847

Authors: Olusegun Kayode Agbesuyi Samuel Ojogbo Babatunde Olufemi Oke

Climate finance in Sub-Saharan Africa has grown, yet the region’s ecological footprint continues to exceed its biocapacity. Most studies treat climate finance as a single aggregate and estimate average effects. This study asks whether adaptation and mitigation finance affect ecological pressure differently and whether these associations vary across the conditional footprint distribution. Using a panel of 48 Sub-Saharan African countries over 2010 to 2022, it measures adaptation and mitigation finance as disbursements per capita from the OECD Climate-Related Development Finance database and ecological footprint per capita from the National Footprint and Biocapacity Accounts. After tests for cross-sectional dependence, slope heterogeneity, unit roots and cointegration, the study applies the Method of Moments Quantile Regression, tests the equality of the two finance coefficients at each quantile, and separates the carbon and land-based footprint components. Mitigation finance is associated with a lower footprint, more strongly at the upper conditional quantiles, whereas adaptation finance is associated with a lower footprint mainly at the lower and middle conditional quantiles. The two coefficients differ significantly at the tails, and the pattern holds with year fixed effects and, broadly, in first differences. Implied elasticities vary with disbursement levels. Allocation of each stream should reflect national footprint structure.

Journal of Risk and Financial Management doi: 10.3390/jrfm19110846

Authors: Diana Patricia Franco Campos Milton J. Rueda Varón Alex Jiménez Díaz

Voluntary carbon markets are often described as diversified because they contain thousands of projects, technologies, and jurisdictions. This study examines whether that visible variety corresponds to effective diversification once credit volumes and vintage exposure are considered. Using the Voluntary Registry Offsets Database, version 2026-06, we analyze 11,468 projects and 44,106 nonzero project-year records covering 2.655 billion tonnes of carbon dioxide equivalent (tCO2e) issued across six registries. The database includes 10,785 voluntary-only projects and 683 projects linked to regulatory programs. The empirical strategy combines market shares, raw and normalized Herfindahl–Hirschman indices, effective numbers of categories, vintage-weighted inventory measures, non-parametric tests, and fractional logit models with heteroskedasticity-consistent (HC3) robust inference. Registry concentration was high for issuance and positive remaining inventory, whereas detailed project-type concentration was low. The three largest registries accounted for 88.6% of issuance, and 46.8% of positive remaining inventory was more than five years old. Project age since first issuance was positively associated with the accumulated retirement share, and a quadratic specification suggested a diminishing association, with limited evidence at advanced ages. The findings identify institutional and temporal dependencies that can inform accounting controls and financial due diligence. They do not independently establish liquidity, fair value, registry performance, or environmental integrity.

Journal of Risk and Financial Management doi: 10.3390/jrfm19110845

Authors: Thomas Sumarsan Goh Erika Erika Albert Albert Henry Henry

This study investigates the volatility dynamics of Bitcoin (BTC), Ethereum (ETH), and Binance Coin (BNB) from 1 January 2021 to 31 December 2025. Using daily return data, the study applies Generalized Autoregressive Conditional Heteroskedasticity (GARCH)(1,2), Exponential GARCH (EGARCH)(1,2), and Power ARCH (PARCH)(1,2) models to examine conditional volatility, persistence, and asymmetric responses to shocks. The three cryptocurrencies differ substantially in distributional characteristics: BNB exhibits the highest variability, positive skewness, and excess kurtosis, indicating more extreme returns. The Augmented Dickey–Fuller test confirms that all three series are stationary, while the ARCH Lagrange Multiplier (LM) test indicates significant ARCH effects, supporting GARCH-family models. The estimated models show that volatility depends on recent shocks and past conditional volatility, is highly persistent in all three markets, and differs across the cryptocurrencies. The estimated asymmetry coefficients differ in sign and size across assets, but their significance cannot be established from the reported estimates. Diagnostic tests indicate no significant residual autocorrelation or remaining ARCH effects at the 5% level. Forecast evaluation using Root Mean Square (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and the Theil coefficient over 10-, 30-, and 60-period horizons shows similar performance across models, varying by cryptocurrency, horizon, and measure, with no model consistently superior. Overall, the findings highlight the heterogeneity of cryptocurrency volatility and the importance of comparing alternative models.

Journal of Risk and Financial Management doi: 10.3390/jrfm19110844

Authors: Ismail Merchich Kaoutar El Abidi Amine

This study examines the association between documented corporate social responsibility (CSR) recognition and accounting performance in eleven Moroccan banks during 2014–2023. Reconciliation of the original 110 bank-year records identified measurement inconsistencies and led to an unbalanced estimation sample of 108 observations, or 103 for the risk burden ratio. Profitability is measured separately by net income-based returns on assets and equity and the net banking margin. Pooled and fixed effects regressions are complemented by bank and year fixed effects, bank-clustered inference, and an exhaustive restricted wild cluster bootstrap. None of the CSR coefficients in the bank and year fixed effects models is statistically significant at the 5% level. Confidence intervals remain wide, however, and only five banks change recorded recognition status. The findings provide limited associational evidence about formal recognition, rather than a causal assessment of substantive CSR practices. Remaining accounting perimeter differences and incomplete recognition records constrain interpretation and generalization.

Journal of Risk and Financial Management doi: 10.3390/jrfm19110843

Authors: Aida Smaoui

This study examines the relationship between artificial intelligence (AI) disclosure and firm value and investigates the moderating role of board independence among Saudi listed non-financial firms over 2019–2025. A multidimensional Artificial Intelligence Disclosure Intensity Index (AIDI) is developed to capture the breadth and substantive depth of AI-related corporate reporting across five dimensions. Using an unbalanced panel of 145 firms and 930 firm-year observations (785 in the estimation sample), the study employs panel regression and dynamic System GMM, with Tobin’s Q as the primary measure of firm value. The findings show a positive association between AI disclosure intensity and firm value, conditional on the included controls. We also find positive associations using alternative disclosure measures, accounting-based performance outcomes, firm fixed effects, and System GMM; however, the one-year lagged AIDI coefficient is statistically insignificant. The study finds no statistically significant evidence of moderation by board independence. The study contributes to the emerging AI disclosure literature by providing a multidimensional disclosure measure and new evidence from the Saudi capital market. The results provide evidence of an association between reported AI disclosure and firm valuation, without establishing causality or investors’ responses to report publication.

Journal of Risk and Financial Management doi: 10.3390/jrfm19110842

Authors: Kishan Kumar Goel Ritu Sapra Rajdeep Singh Jyoti Chahal Sumanjeet Singh

Firms make substantial investments in environmental, social, and governance (ESG) initiatives, but there is limited understanding of whether such investments actually safeguard them from financial distress. In emerging economies, this is particularly relevant because unforeseen events such as crisis, regulatory changes, and industry dynamics can drive companies towards failure. Addressing these issues, this study analyses a panel dataset of 566 listed Indian companies from 2012 to 2024 and finds that sustainability performance consistently mitigates financial distress risk across the full distribution of firms, as validated through a method-of-moments quantile regression analysis. Context-based analyses further reveal that the favorable ESG effect remains stable during the COVID-19 crisis period and varies by industry affiliation. Simulation-based mediation analysis identifies firm leverage as a key transmission channel of the ESG-distress link. Lending support to risk management, trade-off, and strategic management theories, this work underscores the ‘insurance-like’ role of sustainability and deepens the conceptual understanding of how ESG can act as a strategic hedge against financial risks.

Journal of Risk and Financial Management doi: 10.3390/jrfm19110841

Authors: Mingze (Marcellus) Li Thanasis Stengos Yiguo Sun

Extensive research has delved into the interplay between energy consumption and economic development. Building upon this rich literature, this paper revisits this nexus from a spatial perspective that emphasizes cross-country interdependencies and spatial dynamics. We utilize a dataset of 103 countries spanning 1990–2015, treating this period as a historically coherent benchmark preceding the major shift in the international climate-policy environment associated with the Paris Agreement and the United Nations 2030 Agenda. Our empirical findings reveal a robust and positive relationship between renewable energy consumption, non-renewable energy consumption, and GDP per capita within a country, consistent with findings in the existing literature. Furthermore, employing a unified spatial model, our study uncovers cross-country spillover effects through geographical proximity, energy consumption similarity, and economic linkages. Notably, the results reveal negative spatial spillover associations between energy consumption in neighboring countries and the home country’s GDP per capita. Potential explanations for these negative spillover effects include increased emissions from neighboring countries, which may impose health and economic burdens on the home country, and the possibility that Renewable Energy Technology Innovation (RETI) promotes local green industrial advancement while limiting similar progress in neighboring countries.

Journal of Risk and Financial Management doi: 10.3390/jrfm19110840

Authors: Angelo Leogrande Mauro Di Molfetta Valeria Notarnicola Maria Giovanna Trotta Antonio Volpe Plantamura

Operating structure, not financing, is the main determinant of profitability in small firms. This study reaches that conclusion by comparing, in the same model and on the same scale, two explanations offered for why some small firms earn more than others how they are financed and how they organise their costs. Existing studies focus almost entirely on financing and treat costs as a background control. The evidence comes from a large panel of unlisted Italian firms observed between 2014 and 2025, including innovative start-ups, innovative SMEs and conventional firms, and each result is checked with three independent types of analysis. Firms that devote a larger share of the value they create to wages earn systematically less, in every type of firm. Part of this link reflects how profits are recorded in the accounts. The link between equity and profitability, by contrast, is weak and its direction cannot be established, because profitable firms accumulate equity and cause and effect cannot be separated. Profitability is also determined differently across groups of firms fall into distinct operating profiles. Support schemes for innovative firms that focus on access to finance may therefore be addressing the wrong constraint.

Journal of Risk and Financial Management doi: 10.3390/jrfm19110839

Authors: Levent Bulut Sami Keskek Abdullah Kumas

Analyst consensus forecasts are widely used in investment decisions and as proxies for market expectations, yet they typically assign equal weight to component forecasts despite predictable differences in quality. We develop a constrained machine-learning framework that predicts each forecast’s relative quality from 29 ex ante analyst, forecast, broker, firm, and consensus characteristics and converts those predictions into positive, normalized weights. Over 21 sequential out-of-sample test years (2005–2025), the quality-weighted consensus reduces the late-horizon mean absolute error by 11.5% relative to the conventional mean and outperforms it in 62.9% of firm-years and every test year. Features locating a forecast within the contemporaneous distribution provide the largest incremental signal, while analyst track records, revisions, and broker attributes add further information. Improvements are larger when analyst coverage and forecast dispersion are high. The results show that forecast quality is inherently relative and that constrained machine learning can materially improve the consensus forecast accuracy by assigning context-dependent weights to individual forecasts based on their characteristics and the composition of the forecast set.

Journal of Risk and Financial Management doi: 10.3390/jrfm19110838

Authors: Nguyen Huu Dang Loc Dong Truong Le Minh Hao Khuu Thi Phuong Dong

This study investigates the effect of ownership structure on the real earning management (REM) of food and beverage (F&B) companies listed on the Vietnam stock market, with particular emphasis on the moderating role of auditing quality. The data employed in this study includes 138 listed F&B companies covering the period from 2022 to 2024. Using a feasible generalized least squares (FGLS) approach, the empirical findings confirm that foreign ownership and managerial ownership have significantly negative effects on REM of the F&B companies. However, this study does not find any evidence on the effect of the state ownership on the REM. Additionally, the results indicate that higher audit quality, as proxied by Big 4 auditors, is associated with lower REM. Especially, the findings show positive moderating effects of the Big 4 auditors on the relationship between ownership structure and REM, meaning that the presence of a Big 4 auditor weakens the marginal impact of ownership structure on the REM. This evidence supports the view that governance mechanisms can act as substitutes rather than complements in constraining real earning management, contributing to the broader literature on corporate governance in emerging markets. This study provides the first empirical evidence on how ownership structure influences REM within Vietnamese F&B companies. Moreover, the study offers insights into the moderating role of audit quality in the relationship between ownership structure and REM, thereby enhancing understanding of whether governance mechanisms act as substitutes or complements in constraining opportunistic behavior within transitional economies.

Journal of Risk and Financial Management doi: 10.3390/jrfm19110837

Authors: Anna Knezevic

There was an error in the original publication (Knezevic, 2026) [...]

Journal of Risk and Financial Management doi: 10.3390/jrfm19100836

Authors: Thanh Huong Pham Ngoc Hoang Thi Bich

Environmental, social, and governance disclosure has become increasingly important as stakeholders pay greater attention to sustainable and ethical business practices. This study examines the effect of ESG disclosure on financial performance (FP) of 46 pharmaceutical companies in the Vietnamese stock market over the period 2015–2024. ESG disclosure scores are measured using content analysis of annual reports based on an industry-specific framework comprising 22 criteria across the environmental, social, and governance pillars. The regression results show that the ESG composite score (ScoreESG) and the individual pillar scores (ScoreE, ScoreS, ScoreG) are all positively and significantly associated with ROA. When ROA is replaced by ROE, the ScoreESG, ScoreE, and ScoreS remain positive and statistically significant, whereas the ScoreG remains positive but is no longer statistically significant. The lagged specification further shows that ESG disclosure in the preceding period remains positively associated with FP in the subsequent period. This study provides further empirical evidence to the ongoing debate on the relationship between ESG disclosure and corporate FP and provides implications for pharmaceutical companies in an emerging economy seeking to improve the quality of ESG disclosure, support FP, and promote sustainable development.

Journal of Risk and Financial Management doi: 10.3390/jrfm19100835

Authors: Jorge Anibal Restrepo-Morales Emerson Andrés Giraldo Betancur Eduar Antonio Rodríguez Flores Freddy Zea Restrepo

This article asks how United States asset prices respond to shocks in the federal deficit and whether the response is stable. We estimate a recursive VAR on 265 monthly observations, 2002–2024. A deficit shock of one standard deviation is followed on average over 12 months by rises of 0.55% in house prices and 1.97% in the S&P 500 and a narrower corporate spread; gold’s response is not significant. The equity average is not stable: a sup-F test places a break in the coefficients on the deficit in March 2009 (p = 0.002), the local projection response is not significant in the full sample (p = 0.153) and does not differ significantly between recessions and expansions, and it is imprecise without the crisis windows. House prices respond positively in local projections, but not in a VAR from 2009 alone. The fiscal dominance ratio is above one in nine of ten combinations of specification and metric, but housing accounts for most of the variance ratios. The deficit ratio is associated with the top 1% wealth share within the quarter, not cumulatively. The estimates are conditional associations; the deficit shock is not free of reactions to markets.

Journal of Risk and Financial Management doi: 10.3390/jrfm19100834

Authors: Ali Haji Tanvir Mahmoud Hussein Wadeeah Marhoon Vinoth Raman

This study examines whether volatility in Brent crude oil prices spills over to the Bahrain Stock Market Index (BSMI), and whether that spillover differs by regime, over 2865 trading days from 25 May 2010 to 28 February 2025. Bahrain is a distinctive case among Gulf Cooperation Council (GCC) economies in that its stock exchange has been dominated historically by the banking and financial services sector and that Bahrain’s equity valuation is exposed to crude oil price uncertainty mainly through the intermediate role of financial institutions and through fiscal transmission mechanisms rather than through direct sectoral linkage. An integrated multivariate GARCH framework combining AR (1)-GJR-GARCH (1,1), DCC-GARCH (1,1), and full BEKK-GARCH (1,1) is used together with the Diebold–Yilmaz (DY) connectedness methodology to examine volatility asymmetry, time-varying conditional correlations, directional volatility transmission, and regime-dependent connectedness. The GJR-GARCH estimates indicate significant volatility clustering, high persistence, and statistically significant asymmetric leverage effects, particularly in the oil market. The DCC-GARCH results show that the conditional correlation between oil prices and the BSMI is time-varying and highly persistent (i.e., a + b ≈ 0.71). The correlation rose sharply during episodes of heightened global uncertainty such as the COVID-19 pandemic. The BEKK-GARCH estimates identify unidirectional volatility transmission from global oil prices to BSMI-listed equities, with no statistical or economic evidence of transmission in the opposite direction, consistent with the status of Bahrain as a small open economy that is influenced by, but has limited influence on, global oil price formation. The full-sample Diebold–Yilmaz Total Connectedness Index is 8.37%, rising to nearly 50% during the COVID-19 crisis—a regime shift confirmed by a Newey–West HAC robust test. Overall, these findings have implications for portfolio risk management, hedging strategy design, and macroprudential surveillance in oil-exporting small open economies.

Journal of Risk and Financial Management doi: 10.3390/jrfm19100833

Authors: Nikolay Sterev Nadezhda Minkova

Insurers increasingly seek to replace self-reported underwriting data with verified data, from satellite and IoT monitoring in agriculture to digital-identity credentials in property insurance. Distributed ledger technology (DLT, “blockchain”) is often proposed as the natural verification layer, but this proposition is rarely tested in economic terms. This paper develops a Net Advantage of Verification (NAV) and Blockchain Net Value (BNV) framework. It decomposes a verified-data architecture into four value components, net of five cost components. It then compares a status quo (S0), the best feasible non-DLT architecture (S1) and a DLT architecture (S2). The framework is applied to two Bulgarian cases: a behavior-based agricultural product (Pay-as-you-Farm) and EUDI-Wallet-enabled property insurance. All monetary inputs are illustrative assumptions, not observed data. Under this illustrative parameterization, BNV is negative in both cases at the base case and in most simulated scenarios. Within the stated ranges, a well-governed non-DLT architecture is therefore more economical. The result demonstrates the method and is not an empirical estimate for the Bulgarian market. The paper derives the conditions under which the sign could reverse, proposes a validation protocol and discusses implications for EU regulators and insurers. The framework transfers to other markets, whereas the numerical results require recalibration.

Journal of Risk and Financial Management doi: 10.3390/jrfm19100832

Authors: Ahesha Perera Ahsan Habib

Contemporary organisations face increasing uncertainty from events such as geopolitical tensions, natural disasters, and pandemics that are beyond their control. These events create both financial and non-financial risks, including threats to organisational legitimacy. Drawing on legitimacy theory, this study examines how New Zealand healthcare companies use corporate reporting to manage the legitimacy risk posed by the COVID-19 pandemic. The sample comprises the entire population of 15 healthcare companies listed on the New Zealand Stock Exchange (NZX). We collected data from the annual reports of health companies between 2020 and 2022, a period characterised by significant disruption due to the COVID-19 pandemic. We also obtained additional data from media reports published during the same period. We analysed the data using thematic analysis. The findings reveal that health companies, rather than adopting a one-size-fits-all approach, shifted their reporting across different phases of the crisis to manage legitimacy risk. In the initial phase, when uncertainty and stakeholder concerns heightened legitimacy risks, health companies adopted an extensive reporting approach and disclosed both positive and negative information. As the crisis escalated, reporting shifted to a bolstering approach, focusing largely on positive information. In the recovery phase, reporting moved toward a normalising approach, signalling a return to the pre-pandemic status quo. The findings suggest that health companies use corporate reporting strategically to manage legitimacy risk during crises.

Journal of Risk and Financial Management doi: 10.3390/jrfm19100831

Authors: Tarek Sadraoui Sameh Zarai

The study of regime-dependent predictability of cryptocurrency market distress is examined in this paper using the dynamics of cross-exchange spread signals as an innovative early-warning tool. The research offers a three-stage econometric framework comprising: a Markov regime-switching VAR to infer endogenous bull, transition, and bear regimes in the BTC, ETH, BNB, and SOL markets; a Panel Smooth Transition Regression (PSTR) model for the regime-dependent spread-distress predictive relationship; and quantile regression providing the complete profile of the distress distribution. The full sample spans January 2020–December 2025 (2191 daily observations); the January 2020–December 2022 sub-period (1095 observations) is used for baseline in-sample estimation, while January 2023–December 2025 is reserved for out-of-sample validation. The results show that cross-exchange spread dynamics contribute to the occurrence of distress regardless of the market state and confirm that bear markets predict distress better than bull markets (with AUROC values changing from 0.848 to 0.963, respectively). In addition, the results of the PSTR function suggest that the predictive role of the spread increases in stressful periods (from 0.152 to 0.588), as supported by LM tests yielding results with p-values less than 0.001. The suggested model outperforms seven competing models, including MS-VAR, GARCH-M, linear logit, LSTM, and XGBoost, on all evaluation measures (AUROC, Brier score, Diebold–Mariano) across every regime. The results remain stable across different exchange-pair configurations and COVID-19-related sub-samples, and when varying the threshold definition.

Journal of Risk and Financial Management doi: 10.3390/jrfm19100830

Authors: René Berg

Double materiality assessment has become a central mechanism of sustainability reporting under the European Sustainability Reporting Standards, which link material sustainability matters to strategy, the business model, and management responses. Existing research has examined reporting obligations, implementation practices, and regulatory discretion but has paid considerably less attention to the methodological integration of materiality assessment into management control. The far-reaching simplification of the ESRS reduces disclosure obligations without resolving this integration problem. As a conceptual design paper, this study examines how regulatory discretion can be structured so that double materiality assessment generates decision-useful information for sustainability strategy, corporate planning, and enterprise risk management. It connects the ESRS with the literature on Sustainability Management Control, corporate planning, and risk management. The resulting four-level model structures the assessment as a principle-based sequence of methodologically grounded managerial judgments across analytically distinct but feedback-linked levels: identification and measurement, reference frame selection, interpretation and validation, and aggregation and materiality judgment. A common information base produces a regulatory reporting portfolio for period t and a broader management control portfolio for period t + 1. The sustainability statement thereby becomes the regulatory output of a broader management control process. By specifying the methodological transitions from materiality assessment to managerial use, the paper contributes an integrated conceptual architecture and derives an empirical research agenda.

Journal of Risk and Financial Management doi: 10.3390/jrfm19100829

Authors: Thi Minh Ngoc Nguyen Ngoc Hung Tran

We ask whether an increase in the salience of climate-transition risk changes corporate liquidity policy. Using the December 2015 Paris Agreement as a global shock, we estimate a difference-in-differences model using 1745 listed non-financial firms from 55 countries over 2010–2025. Firms in the top quartile of pre-treatment carbon intensity (CO2 emissions scaled by revenue in 2015) are classified as treated. In our preferred specification, which includes firm fixed effects and industry-by-year fixed effects with standard errors two-way clustered by firm and year, the cash-to-net-assets ratio of treated firms rises by 2.17 percentage points relative to less carbon-intensive peers after 2015. This effect is equivalent to approximately 14.9% of the full-sample mean and is statistically significant at the 10% level. In the event study, the five pre-treatment coefficients for 2010–2014 are small and individually insignificant, but their joint test rejects the null under two-way clustering, so the evidence on parallel pre-trends is sensitive to the inference procedure; the post-treatment coefficients turn positive around 2019 and remain positive through 2025. In a 500-replication randomisation-inference placebo test, the actual estimate lies toward the upper tail of the placebo distribution (empirical two-sided p = 0.0978). The coefficient remains positive under propensity-score matching, country-by-year fixed effects, alternative sample windows, an alternative scaling of the cash variable, a 50th-percentile treatment cut-off, and the exclusion of potential post-treatment controls, although several of these estimates are only marginally significant. The level of cash does not increase, so the result concerns the cash ratio rather than cash accumulation. Exploratory analysis using the Whited–Wu index is consistent with, but does not establish, a financing-constraints channel. Because the design cannot separate the Agreement itself from subsequent implementation and other post-2015 developments, we interpret the result as an association between the post-Paris transition-policy environment and a relative rise in the cash ratios of carbon-intensive firms, which we regard as suggestive rather than conclusive evidence of a precautionary response to climate-policy uncertainty.

Journal of Risk and Financial Management doi: 10.3390/jrfm19100828

Authors: Kelvin F. B. Biiranee Babatunde O. Oke Samuel Ojogbo

This study examines the relationship between environmental, social and governance (ESG) disclosure and the financial stability of quoted deposit money banks in Nigeria, and whether governance structure moderates that relationship. Using panel data hand-collected from the audited annual and sustainability reports of twelve banks listed on the Nigerian Exchange Group over 2015–2025, this study relates an 18-item content-analysis ESG disclosure index and its environmental, social and governance sub-indices to five bank-level stability indicators: capital adequacy, liquidity, asset quality (non-performing loans), profitability (return on assets) and insolvency risk (Z-score). Bank fixed-effects models with Driscoll–Kraay standard errors form the baseline, and fixed-effects two-stage least squares (FE-2SLS) is used as a robustness check for potential endogeneity. The composite ESG index is positively and significantly associated with profitability, a result that persists in the FE-2SLS estimates, but its associations with liquidity, non-performing loans and the Z-score are insignificant and its association with capital adequacy is negative; the associations between disclosure and stability are therefore heterogeneous across its dimensions rather than uniformly positive. Governance structure, entered as a separate regressor, is directly associated with higher capital adequacy and profitability and with lower non-performing loans, whereas its interaction with disclosure is statistically significant only for capital adequacy and liquidity and is sensitive to the exclusion of individual banks, so that the moderation evidence is treated as fragile. Among the disclosure dimensions, governance disclosure is most consistently associated with stability, environmental disclosure is associated with lower capital adequacy and social disclosure is associated with higher non-performing loans. The near-saturation of the disclosure index among these large banks limits within-bank variation and counsels caution in interpretation. The findings suggest that regulators should prioritise the credibility and quality of disclosure, and the governance mechanisms that underpin it, rather than its quantity.

Journal of Risk and Financial Management doi: 10.3390/jrfm19100827

Authors: Ahmed Eid Mohamed Abo El Matey Ahmed Hassanein Yara Ibrahim Mohamed Moshreh Ali Ahmed

This study investigates how board characteristics shape the financial performance of Egyptian banks. It also examines whether ESG disclosure alters the strength and direction of these relationships. Drawing on a balanced panel of the 20 largest banks operating in Egypt between 2016 and 2024, the study analyses four board attributes: board size, gender diversity, board activity, and board independence. Financial performance is captured by return on assets (ROA) and return on equity (ROE), while ESG transparency is evaluated through a 35-item disclosure index encompassing environmental, social, governance, and economic dimensions. The empirical analysis employs bank fixed-effects models with year fixed effects and bank-clustered standard errors. Given the relatively small number of bank clusters, statistical inference is further assessed using a wild cluster bootstrap procedure with 9999 replications. The primary fixed-effects results indicate that board activity is positively and significantly associated with ROA. In contrast, board size, gender diversity, and board independence are not statistically significantly associated with ROA. For ROE, board size, gender diversity, and board activity also show no statistically significant associations. Board independence, however, is negatively associated with ROE. Overall, the findings provide limited evidence that board characteristics are systematically associated with bank financial performance, with the clearest evidence being the positive association between board activity and ROA. Moreover, the moderation analysis further shows that none of the interaction terms between ESG disclosure and the examined board characteristics is statistically significant for either ROA or ROE, providing no statistically reliable evidence that ESG disclosure moderates these relationships. By providing evidence from Egypt, an emerging economy undergoing substantial governance and sustainability reforms, this study extends the literature at the intersection of corporate governance, ESG transparency, and bank performance. It further contributes by providing empirical evidence on whether ESG disclosure operates as a boundary condition in the relationship between board governance and financial performance in the Egyptian banking sector.

Journal of Risk and Financial Management doi: 10.3390/jrfm19100826

Authors: Antonio Lopo Martinez Arquimedes de Jesus Moraes Alfredo Sarlo Neto

Brazilian listed firms report material and persistent tax disputes in a setting that requires recurring judgment about recognition and disclosure and includes an observable premium listing regime. Using 3544 firm-year observations for 254 non-financial firms listed on B3 from 2009 to 2024, we examine whether accrual-based earnings management (AEM), measured as the absolute residual from a pooled Jones-family accrual model augmented with lagged return on assets, is associated with disclosed tax dispute exposure (DTE), measured as recognized tax provisions plus disclosed contingent tax liabilities scaled by total assets, and whether the association differs by Novo Mercado status. The performance control reduces the risk of classifying normal profitability-related accruals as abnormal, while the absolute value captures deviations in either direction; AEM remains a reporting-outcome proxy rather than a direct measure of internal-control effectiveness. In the primary fully interacted specification, the AEM slopes are 0.023 (SE 0.266) among firms outside and 0.093 (SE 0.068) among Novo Mercado firms; their difference is 0.070 (SE 0.274). The 95% confidence interval for the outside-segment slope [−0.498, 0.545] includes estimates from restricted models, indicating limited precision rather than evidence of no association. Sensitivity analyses using winsorized DTE and ln(1 + DTE) yield modest positive slopes in both groups but no precisely estimated group difference; restricting the sample to DTE ≤ 1 yields a positive outside-segment slope but an imprecise difference. Secondary between-firm and quantile models yield negative interactions, whereas the within-firm slope among Novo Mercado firms is positive. These estimator-specific patterns show that the AEM–DTE relation depends on the estimand and model restrictions and do not support a uniform governance-moderation, causal, or out-of-sample predictive interpretation.

Journal of Risk and Financial Management doi: 10.3390/jrfm19100825

Authors: Paolo Maccarrone Renato Della Cioppa

The relationship between ESG performance and firm value is contested and appears to depend on context. This study asks whether that relationship differs between the United States and the European Union, two mature markets that differ primarily in the extent to which sustainability disclosure is mandated. Firm fixed-effects panel regressions are estimated on LSEG (Refinitiv) data for 2016–2024, covering 874 firms and 5848 firm-year observations, with firm value measured by winsorized Tobin’s Q in levels. Among U.S. firms, the estimated association is indistinguishable from zero until 2022 and positive and significant in 2023; among EU firms, it is not significant in any year. The joint hypothesis that the regional differentials are zero in every year is rejected and remains rejected under every robustness check, including the logarithm of Enterprise Value, a lagged ESG score and alternative control sets; the test concerns the period as a whole and does not identify individual years. Functional-form criteria retain linearity, and the turning point implied by a quadratic specification cannot be located on the ESG scale. A direct test of equality across sectors is weak. Institutional context is proxied by region; regulatory stringency and market sentiment are not measured, so the mechanism is not tested.

Journal of Risk and Financial Management doi: 10.3390/jrfm19100824

Authors: Ziyao Wang Svetlozar T. Rachev Frank J. Fabozzi

Language models can translate mathematical prose into formal statements and produce machine-checked proofs, and they are increasingly used in finance. Such a proof, however, certifies the model as written, not the model intended. In dynamic finance the difference concerns information: a position must be chosen using only what is known when it is taken, and an exercise decision cannot depend on prices not yet observed. A formalization that allows more information than the model specifies can be proved correct and still be financially inadmissible. We show that auditing realized outcomes cannot detect such errors: one realized history is consistent with exponentially many patterns of look-ahead, while two scenarios suffice to expose one. We build the model’s information structure into the formalization procedure, so that every intermediate quantity provably uses only information available at its date, and verify the main theorems in Lean 4. On 2960 synthetic instances, removing only the information rules lowers certified success by 19.8 percentage points, and the procedure then declares 91.5% of look-ahead errors valid. When the model is read from prose, the main residual error is a timing convention the text never states; in an exploratory experiment, requiring a verbatim quotation for each convention eliminated it.

Journal of Risk and Financial Management doi: 10.3390/jrfm19100823

Authors: Meriem Youssef Sabrine Ben Ammar

This study examines the relationship between environmental, social, and governance (ESG) performance and firm financial performance while considering the moderating role of institutional quality and the sensitivity of the results to control-variable selection. The analysis uses 68,244 firm-year observations for 6204 listed firms across developed and developing economies between 2011 and 2021. Firm performance is measured using Tobin’s Q, return on assets (ROA), and return on equity (ROE), while ESG is examined through its aggregate score and its environmental, social, and governance pillars. We use Random Forest to rank candidate controls, and we combine its rankings with multicollinearity diagnostics. Conventional full-control and Random Forest-informed specifications are then compared using OLS and quantile regressions. The results show that control selection materially affects the magnitude, direction, and statistical significance of the estimated ESG coefficients. Although there are clear relationships between ESG and performance variables in the baseline model, they become statistically insignificant when using the firm-fixed effects models, implying that the findings arise mainly from the persistent difference between firms rather than within-firm ESG performance. In addition, market responses are more selective: the Social pillar is positively associated with Tobin’s Q, whereas the Environmental pillar is negatively valued in several specifications. Institutional moderation also varies across ESG pillars, governance dimensions, performance measures, and quantiles. Government Effectiveness and Rule of Law provide the most consistent evidence of positive moderation for the aggregate ESG–Tobin’s Q relationship, while the evidence is weaker or less stable for ROA and ROE. Our findings demonstrate that the financial relevance of ESG is context-dependent and sensitive to empirical model specification.

Journal of Risk and Financial Management doi: 10.3390/jrfm19100822

Authors: Jong-Min Kim

Accurate out-of-sample yield-curve forecasting requires models that capture complex non-linear temporal dynamics while respecting the cross-sectional economic structure of the term structure. This paper introduces an economically structured deep sequential learning framework for multi-horizon Treasury yield forecasting that unifies multi-head self-attention, temporal convolutions, bidirectional recurrent states, and latent affine term-structure factors. To address the severe optimization degradation caused by strong temporal autocorrelation during gradient descent, we introduce adaptive experience replay dynamics—specifically comparing uniform, prediction-error-based prioritized, and structural yield-entropy sampling regimes—under strict chronological out-of-sample evaluation protocols. Empirical results demonstrate that experience replay mechanisms significantly outperform traditional chronological training regimes. Structurally, entropy-based sampling achieves optimal performance in aggregate yield forecast accuracy and latent factor estimation, while prioritized experience replay excels in absolute point error minimization and conditional volatility tracking. Crucially, incorporating cross-sectional no-arbitrage constraints prevents structural yield-curve inversions and monotonicity violations without compromising predictive precision. Statistically superior point forecasts translate directly into enhanced economic tracking fidelity for duration-based bond portfolio pricing. Overall, the findings demonstrate that unifying structural term-structure constraints with non-uniform historical memory sampling provides a powerful, economically coherent methodology for fixed-income modeling and financial risk management.

Journal of Risk and Financial Management doi: 10.3390/jrfm19100821

Authors: Pathompong Kookkaew Pitan Sanpakdee

Environmental, social, and governance (ESG) disclosure may have different valuation relevance across ESG pillars and governance contexts. This study examines the associations between environmental, social, and governance disclosure and firm value and whether board accounting expertise (BAE) and female board representation (FBR) moderate these associations. Using a balanced panel of 88 Thai listed non-financial companies from 2021 to 2025 (440 firm-year observations), the study employs random-effects regression with firm-clustered robust inference, supplemented by alternative panel, temporal, functional-form, and dynamic sensitivity analyses. The primary results show no significant direct associations between the three ESG disclosure pillars, BAE, or FBR and firm value. BAE does not significantly moderate any ESG disclosure–firm value association, while FBR does not moderate the environmental or governance disclosure associations. However, the SOC × FBR interaction is negative and significant in the primary contemporaneous specification. Marginal-effect analysis indicates that the SOC–firm value association declines as FBR increases. This interaction is specification-sensitive and is not confirmed in lagged or dynamic analyses. Overall, ESG disclosure value relevance may be pillar-specific and governance-contingent, while the focal interaction should not be interpreted as establishing a stable predictive or causal relationship.

Journal of Risk and Financial Management doi: 10.3390/jrfm19100820

Authors: Le Ngoc Lan Bui Thi Thanh Tinh

This study examines the effects of CO2 emissions and ownership structure on green credit across 21 Vietnamese commercial banks over the period 2013–2025. SGMM results indicate that CO2 emissions affect green credit differently across emission sources. Specifically, energy-related emissions increase the green credit ratio and decrease the non-performing green loan ratio, while industrial emissions increase the latter. Overall CO2 emissions have an unstable effect on the green credit ratio but increase the non-performing green loan ratio. Board ownership promotes green credit, while foreign ownership reduces the non-performing green loan ratio. In contrast, state ownership tends to reduce the green credit ratio. The inclusion of ownership structure also alters the effects of CO2 emissions on green credit. These findings clarify the roles of CO2 emissions and ownership structure in shaping green credit in an emerging market and provide insight into green credit development and Vietnam’s commitment to net-zero emissions by 2050.

Journal of Risk and Financial Management doi: 10.3390/jrfm19100819

Authors: Wasim Al-Shattarat Khalaf Alhaifi Yara Ibrahim

This study examines the development and intellectual structure of artificial intelligence (AI)-based financial fraud detection through a bibliometric analysis of 2200 Scopus- and Web of Science-indexed journal articles published between 1992 and 2026. The literature has expanded rapidly, with 89.6% of publications appearing since 2018 and an annual growth rate of 29.4%. Research is concentrated in Computer Science and Engineering, whereas Business and Economics account for 9.2% of classified publications. Credit card and payment fraud constitutes the largest research domain (32.0%), followed by banking, loan, and transaction fraud (14.3%), insurance fraud (12.2%), and accounting and financial statement fraud (9.9%). Neural networks and deep learning, ensemble and boosting methods, and classical supervised classifiers are the dominant methodological families. The field has progressively shifted from traditional data-mining and classical machine-learning approaches toward deep learning, ensemble methods, graph neural networks, and explainable AI. Science mapping shows that the intellectual structure remains strongly centered on payment fraud, while accounting and insurance fraud form more distinct research fronts. Author productivity is highly dispersed, with 88.9% of authors contributing a single publication, while international collaboration varies considerably across leading countries. By integrating performance analysis, science mapping, fraud-domain classification, bibliometric laws, and publication-growth analysis, the study provides a comprehensive assessment of the field’s development, intellectual structure, and emerging research directions.

Journal of Risk and Financial Management doi: 10.3390/jrfm19100818

Authors: Shynara Sarkambayeva Aliya Turegeldinova Marat Sultangaziyev Yerkezhan Mussabayeva Satyanand Singh

Traditional consumer credit scoring models frequently fail to identify rising default risks in transition economies because they rely almost exclusively on formal macroeconomic indicators and unchanging demographic data. This paper offers a preliminary empirical assessment of digital gambling participation and utility payment arrears as stand-in risk indicators in Kazakhstani consumer credit scoring. Using a unique survey-derived dataset from Kazakhstan (N = 41 survey respondents, yielding a finalized modeling cohort of N = 233 active borrowers), we develop, tune, and evaluate five predictive models—Logistic Regression (LR), Decision Trees, Artificial Neural Networks (ANNs), Random Forests, and Extreme Gradient Boosting (XGBoost). Models were optimized using stratified 5-fold cross-validation on the training partition (N ≈ 163) and benchmarked across both a SMOTE-balanced evaluation pool (Deval, N = 473) and an empirical 30% holdout test set (Demp, N ≈ 70). The results provide localized baseline evidence on incorporating non-traditional behavioral data into credit evaluation frameworks in data-constrained emerging economies. While the raw empirical cohort (𝒟emp) exhibits a dominant majority distribution with an 88.84% default/late-payment rate, a balanced resampled evaluation pool (𝒟eval, N = 473) was constructed to ensure unbiased model optimization. The empirical results show that tree-based ensemble structures considerably outperform conventional methods, establishing a clear performance hierarchy. With an accuracy of 83.1% and an Area Under the Receiver Operating Characteristic Curve (AUROC) of 0.854, the classical LR baseline has the lowest predictive power and is unable to map complex, interactive behavioral hazards. On the other hand, the best architecture is the improved XGBoost ensemble engine, which achieves an AUROC of 0.976 on the balanced evaluation pool (𝒟eval). The XGBoost framework successfully reduces institutional asymmetric loss constraints, according to a micro-structural audit of the underlying confusion matrix. Evaluated on 𝒟eval, the model achieves a specificity of 97.4% (TN = 228) and a sensitivity of 96.2% (TP = 230), reducing catastrophic FNR underwriting errors while preserving high loan onboarding efficiency. Furthermore, comparative testing directly on the unadjusted empirical cohort (𝒟emp) confirms strong diagnostic robustness, demonstrating that high class-separation performance generalizes effectively to skewed real-world credit distributions. These findings demonstrate that incorporating real-time behavioral abnormalities into regularized ensemble pipelines offers a very reliable method of reducing risk. In conclusion, this research provides emerging market financial institutions and regulators with an empirical guide to enhance credit underwriting accuracy, strike a compromise between inclusive lending and capital preservation, and shield the retail banking portfolio from localized default contagion. Although the size of the available dataset does not allow us to attain a definitive conclusion, these results point to a promising direction to further investigate with the same methods proposed in this work and larger datasets. Indeed, they suggest that behavioral data has the potential to enhance traditional credit-scoring techniques when properly vetted and integrated with machine-learning techniques.

Journal of Risk and Financial Management doi: 10.3390/jrfm19100817

Authors: Carlos Alberto Echeverría Mayorga Celso Rodríguez Echenique Carlos Roberto Monroy Alfaro Isidro Cipriano Andaluz Lobo

Financial-account access and digital merchant-payment use are distinct dimensions of inclusion. Combining Global Findex historical subgroup series with the 2024 cross-section, we prioritize a common-sample analysis of 62,480 phone-owning adults across 74 economies. Weighted economy-fixed-effects logistic models show positive associations of smartphone ownership (OR 1.25), recent internet use (OR 1.77), basic messaging capability (OR 1.89), and secure device control (OR 1.93) with digital merchant-payment use. A three-category device-security specification distinguishes device locking from autonomous credential management; the latter is associated with greater merchant-payment use than locking without autonomy (OR 1.55, 95% economy-cluster bootstrap percentile interval 1.35–1.77; standardized difference 5.71 percentage points, 95% interval 3.87–7.51; 1999 resamples). Positive internet, messaging, and security associations persist for a narrower digitally paid online-purchase outcome, with module-routed samples of 40,678 and 42,529. Results for all four digital predictors remain positive across four weighting specifications; exploratory device-security interactions with gender and Latin America and the Caribbean survive Benjamini–Hochberg adjustment. Historical changes in gender and income disparities vary across balanced-economy sets and weighting assumptions; economy-resampling intervals do not establish universal faster convergence of account access than payment use. The observational design, questionnaire routing, and nonrandom module coverage prohibit causal and global-population interpretations.

Journal of Risk and Financial Management doi: 10.3390/jrfm19100816

Authors: Khalid Mehmood Natalja Lace Karine Oganisjana

Islamic banking (IB) and conventional banking (CB) coexist in many financial systems, yet limited research explains how their complementary capabilities can be integrated into hybrid financial products while preserving Shariah requirements and institutional boundaries. This study proposes a seven-stage, problem-driven framework for hybrid IB-CB product development and demonstrates its application through three concepts: Tawazun SME capital, Baraka Green Finance, and Taaleem Fund, addressing SME financing, sustainable investment, and education financing, respectively. Expert evaluations from banking professionals and IB-CB specialists indicate that all three concepts were generally viewed positively, although assessments of collaboration potential were more cautious than evaluations of the concepts themselves. Among the proposed products, Tawazun SME capital exhibited the most balanced stakeholder value proposition, while Baraka Green Finance and Taaleem Fund were perceived as delivering stronger customer-oriented benefits. Qualitative findings highlight regulatory compatibility, independent Shariah governance, stakeholder alignment, operational readiness, and market awareness as critical conditions for successful implementation. The study contributes to the literature by providing a structured framework for transforming IB and CB complementarities into assessable hybrid product concepts. The findings offer practical insights for financial institutions seeking to expand innovation, promote financial inclusion, and facilitate collaboration across banking systems while preserving their distinct operational and regulatory characteristics.

Journal of Risk and Financial Management doi: 10.3390/jrfm19100815

Authors: Yan Luo

Event studies of liquidation cascades locate their event zero using a rule applied to the data, usually its peak day and hour. On two Hyperliquid cascades, a zero-transaction-hash heuristic, which in fact tags time-weighted average price (TWAP) executions, feeds that rule and places the zero five hours late in one event and a day early in the other. In the mislocated windows, a two-way fixed-effects liquidation × event interaction is +1.44 and +0.48, both significant under Driscoll–Kraay errors; correctly located, on the same rows, it is a null and a negative estimate resting on one coin-minute. Because the coefficient is a difference of slopes, the peak hour, carrying 78–93% of the window’s liquidations at a low slope, pulls down whichever side contains it: every placebo window containing the peak returns a negative interaction and most excluding it a positive one. These counts are nested views of two price paths, not independent evidence. Rebuilt panels, a high–low spread proxy, and a wild cluster bootstrap over nine coins preserve the sign pattern but not every significance level. The event zero must therefore be verified against a native label before estimation; window sweeps and peak deletion do not substitute for that check.

Journal of Risk and Financial Management doi: 10.3390/jrfm19100814

Authors: Angelo Leogrande Mauro Di Molfetta Valeria Notarnicola Maria Giovanna Trotta Antonio Volpe Plantamura

Debt maturity, the split of a firm’s borrowing between short- and long-term debt, determines its exposure to refinancing risk, yet for small unlisted firms, it has rarely been studied as an outcome. This paper asks whether the debt maturity of such firms is associated with their asset composition in the way the maturity-matching principle predicts, with long-lived assets matched by long-term debt, and whether this association holds within firms and not only across firms. We use 73,662 annual balance sheets of 11,265 Italian unlisted firms (innovative start-ups, innovative SMEs and ordinary SMEs) over 2016–2024. The core analysis is a panel model that compares each firm with itself over time; dynamic and instrumental-variable estimates serve as robustness checks, an exploratory clustering describes heterogeneity, and a machine learning comparison diagnoses functional form. Within the same firm, a ten-percentage-point rise in the share of fixed assets is associated with about 2.5 percentage points less short-term debt, against 3.8 points across firms, and the evidence suggests gradual adjustment, although its long-run size depends on the dynamic model. The association is weaker among innovative firms; for innovative SMEs, the difference is largely accounted for by the intangible composition of their fixed assets, and non-linear models add little to the linear specification. The negative within-firm association predicted by maturity matching is, thus, robust among unlisted firms, and its strength varies with the type of assets firms hold. All results are associations rather than causal effects, and the mechanisms behind the differences across groups are not tested.

Journal of Risk and Financial Management doi: 10.3390/jrfm19100813

Authors: Rym Belgaroui Karama Saadaoui Salah Ben Hamad Amani Abbas

This study examines the association between fintech development and corporate financing and investment policies among non-financial listed firms in ten MENA countries over 2010–2024. Using firm-level financial and market data together with country-year measures of fintech development, the analysis evaluates whether digital financial development is associated with firms’ external financing and investment activity. The baseline estimates indicate positive and statistically significant associations between fintech development and both external financing and corporate investment. Additional analyses examine heterogeneity related to information asymmetry, corporate governance, financial constraints, and ownership structure, as well as associations with the cost of debt, investment efficiency, and firm value. Robustness tests employ alternative fintech proxies and complementary specifications. The findings contribute to the literature by providing firm-level evidence from the MENA region and by documenting conditions under which the association between digital financial development and corporate financial policies varies. The results are interpreted as conditional associations rather than causal effects.

Journal of Risk and Financial Management doi: 10.3390/jrfm19100812

Authors: Cristian Garcia-Pumagualle Katherin Carrera-Silva Diego Haro Ávalos Patricio Xavier Moreno-Vallejo Angel Gerardo Castelo Salazar Victor Gabriel Avalos Peñafiel

The supervision of inclusive financial institutions in emerging economies represents a critical challenge for financial stability. While cooperatives and mutualists are pillars of financial inclusion, there is a lack of explainable early warning models adapted to their structural risk profiles. This study evaluates an explainable machine learning framework for early supervision, utilizing a longitudinal dataset covering 584 unique financial institutions (cooperatives and mutualists) with 24,232 monthly observations across the 2018–2024 predictor observation window (yielding 17,799 healthy transition candidate observations and 74 evaluated distress episodes: 68 in the training set and 6 in the out-of-time validation set). Adhering to ISO/IEC 25012 data quality standards and a six-month forecast horizon (T + 6), the performance of an XGBoost-based model was evaluated against traditional logistic regression benchmarks. Given the extreme class imbalance (0.1556% event rate in the out-of-time test set), model utility was assessed primarily through the precision–recall AUC (PR-AUC). The results demonstrate that XGBoost achieved a higher PR-AUC point estimate (0.2466) than the logistic regression benchmark (0.0931) in the 2024 out-of-time validation sample. The use of SHAP explainability revealed distinct risk drivers for cooperatives and mutualists, providing exploratory support for segmented supervisory monitoring. This study proposes an interpretable decision support framework that enables proactive, risk-based prioritization in a dollarized emerging economy.

Journal of Risk and Financial Management doi: 10.3390/jrfm19100811

Authors: Saeed AlMarri Kristof Juhasz Mathieu Ravaut Gautier Marti Hamdan Al Ahbabi

Zero-shot large language models (LLMs) offer a low-barrier route to credit-risk prediction, but regulated use requires evidence on discrimination, probability reliability, and auditable decision patterns. We compare four open-source LLMs (Gemma-2-9B, Llama-3.2-3B, Mistral-7B-v0.3, and Qwen2.5-7B) with LightGBM on 396,030 U.S. LendingClub loans issued from 2007 to 2016. Treating loans as independent and identically distributed observations from this pooled cross-sectional population, we evaluate every model on the same random 20% holdout. All models receive the same 21 structured predictors and output repayment probabilities. We audit empirical decision patterns with SHapley Additive exPlanations (SHAP); separately, each LLM supplies one context-free positive, negative, or neutral direction per feature, which we treat as its feature-level self-explanation. LightGBM achieved ROC-AUC 0.724, PR-AUC 0.907, and Brier score 0.141, compared with 0.669, 0.880, and 0.204 for the best-discriminating LLM, Gemma-2-9B. An equal-weight LightGBM–Gemma ensemble did not improve any metric. In an eight-cycle SHAP audit, each LLM shared six or seven of LightGBM’s ten most important features; these self-explanations agreed with empirical SHAP directions for only 33–67% of assessable top features, revealing self-explanation unfaithfulness under this directional criterion. A nested four-cycle estimate reproduced every LLM’s top-10 set and nearly identical importance ranks, although small magnitude and distribution shifts were statistically detectable. These results caution against standalone zero-shot LLM use in credit risk and support calibration checks, explainability audits, interpretable benchmarks, and human oversight before deployment.

Journal of Risk and Financial Management doi: 10.3390/jrfm19100810

Authors: Tian Yang Teh Boon Heng Kwan Jing Hui

This study examines how green initiatives are associated with financial performance through a statistical indirect pathway involving environmental, social, and governance (ESG) assessment and whether that pathway varies across life-cycle stages. Using an unbalanced panel of 6579 firm-year observations from 1114 Chinese A-share firms in heavily polluting industries during 2015–2024, the analysis compares green investment, green innovation, green subsidy, and ISO 14001 certification within a common framework. Firm and year fixed-effects models with firm-clustered inference are combined with 5000 firm-cluster bootstrap replications and Benjamini–Hochberg false-discovery-rate adjustment. All four initiatives are positively associated with Huazheng ESG ratings. ESG ratings are positively associated with ROA and ROE, whereas a positive association with Tobin’s Q is not established. Each initiative exhibits a positive statistical indirect association with ROA through ESG, although total financial associations remain heterogeneous; green subsidy shows a negative contemporaneous total association with ROA. The mediated effects are economically modest: a one within-firm standard-deviation increase in each initiative corresponds to about 0.20–0.34% of a within-firm standard deviation of ROA through ESG. By contrast, a one within-firm standard-deviation increase in green subsidy is associated with a −0.133 percentage-point total ROA association. Corporate life cycle selectively conditions the pathway: ESG-mediated associations of green investment and green subsidy are significantly stronger in growth-stage firms than in mature or declining firms, while comparable stage differences are not established for green innovation or certification. The evidence distinguishes external sustainability assessment from immediate profitability and locates life-cycle dependence in resource-deployment-intensive green actions.

Journal of Risk and Financial Management doi: 10.3390/jrfm19100809

Authors: Manpriya Singh Navjit Singh Vikas Sharma Ramona Rupeika-Apoga

The growing use of artificial intelligence (AI) to evaluate Environmental, Social, Governance (ESG) scoring from corporate disclosures raises questions about alignment with conventional lexicon-based scores. Such alignment can be problematic as a lexicon-based approach quantifies the textual density using an ESG dictionary, while LLM-based scores are able to assess the thematic coverage of ESG themes. It remains unclear whether the two approaches produce comparable or distinct results when applied to the same corporate information. This paper examines the construct alignment between LLM-generated and lexicon-based ESG scores using 625 Indian IPO prospectuses issued between 2018 and 2024. A rebalanced dictionary of 630 terms measures disclosure intensity, while contextual scores are obtained by prompting a generative AI model. Both sets of scores are harmonised into quantile categories and compared using confusion matrices, correlation analysis, and chance-corrected agreement. The methods converge for the environmental dimension (EMR = 46.1%; weighted κ = 0.56) but diverge for social (28.2%) and governance (23.0%) disclosure, with only limited alignment overall (31.7%). The findings suggest the two approaches operationalise ESG differently, with implications for construct validity and transparency in ESG analytics.

Journal of Risk and Financial Management doi: 10.3390/jrfm19100808

Authors: Sumaiya Saud Humaid Al-Adhoobi Suzaida Bakar Mahyudin Ahmad

The increasing adoption of blockchain technologies raises scholars’ and managers’ concerns over how digital resources, intellectual capital and corporate governance jointly affect firm performance. However, there has been conflicting evidence on blockchain adoption’s performance implications, while studies on intellectual capital efficiency and ownership concentration are either scattered or context-specific. Furthermore, existing research rarely investigates these factors together, leaving little knowledge about their combined effects on firm performance. To fill this gap, we examine the association of blockchain digital assets and the Value-Added Intellectual Coefficient (VAIC™) with firm performance, and we explore the potential moderating role of one corporate governance characteristic, ownership concentration. Drawing on Agency Theory and the Resource-Based View and Transaction Cost Theory, the study examines whether blockchain digital assets are associated with firm performance. The prior literature suggests that blockchain may enhance organizational efficiency and reduce information asymmetry; however, these mechanisms are not directly tested in the present study. The study also treats intellectual capital as a strategic organizational asset whose contribution to firm performance may be strengthened or constrained by governance mechanisms such as ownership concentration. Using a panel of 94 blockchain-adopting public firms from various industries over 2013–2022, the study employs the System Generalized Method of Moments (SGMM) to control for endogeneity, unobserved heterogeneity and the dynamic nature of firm performance. The results indicate that blockchain digital assets are positively associated with firm performance. This association is most robust for market-based performance (Tobin’s Q); the association with accounting-based performance (Return on Assets, ROA, and Return on Equity, ROE) is weaker and sensitive to the choice of instruments and to industry and country effects. The direct effect of VAIC™ is generally insignificant and inconsistent across the estimated models. Ownership concentration does not strengthen the relationship between firm performance and blockchain digital assets. However, in the main specification, it is positive and strengthens the relationship between intellectual capital efficiency and accounting-based firm performance: the interaction between VAIC™ and ownership concentration is positive and significant for ROA and ROE, although this result is not confirmed in models with a reduced number of instruments. There is no such effect in the relationship between intellectual capital and Tobin’s Q. The study contributes to the literature a framework that integrates blockchain digital assets, intellectual capital efficiency and ownership concentration. It expands on Agency Theory by examining whether the observed relationships are consistent with theoretical arguments concerning information asymmetry, enriches the Resource-Based View by conceptualizing blockchain digital assets as strategic resources, and relates the findings to Transaction Cost Theory. However, the underlying mechanisms are not directly examined. In practice, these findings can guide managers, investors and policymakers in aligning digital transformation, intellectual capital and governance for improved firm performance.

Journal of Risk and Financial Management doi: 10.3390/jrfm19100807

Authors: Windhu Putra Irfan Mahdi Sri Wulani Rezeki Elida

Behavioral finance research on public utilities remains limited, particularly studies that use archival organizational data to infer persistent decision patterns. This study examines whether revealed status quo bias (RSQB), defined as the persistence of indicators that were initially weak and failed to improve, is associated with financial decision quality and financial performance in Indonesian public water utilities. The dataset covers 110 utilities in Java and a 2021–2023 panel of 330 utility-year observations. RSQB is constructed from five managerially relevant indicators, whereas the Financial Decision Quality Index (FDQI) summarizes tariff adequacy, collection effectiveness, operating-cost control, and solvency. The main models use 107 complete observations, OLS with HC3 standard errors, province fixed effects, and controls for firm size, service coverage, and non-revenue water. RSQB is negatively associated with FDQI (p < 0.001), while FDQI is positively associated with ROE in the baseline linear model (p < 0.001). A 5000-resample bootstrap identifies a negative indirect statistical association through FDQI of −2.284 percentage points of ROE (95% CI [−3.719, −1.284]). A centered quadratic sensitivity model also identifies a positive FDQI-squared term (p = 0.023), indicating that the fitted FDQI-ROE association varies with the level of FDQI. The results remain broadly consistent across robustness checks, including a full-sample sensitivity analysis using all 110 utilities. The findings position financial decision quality as an important intermediate financial-decision index in the observed association between persistent non-improvement and financial performance, without implying a psychological diagnosis or definitive causal identification.

Journal of Risk and Financial Management doi: 10.3390/jrfm19100806

Authors: Abdulrazak Balilah

This study investigates the relationship between chief executive officer (CEO) overconfidence and earnings management in non-financial companies listed on the Saudi Exchange (Tadawul) from 2018 to 2024. Using 686 firm-year observations from 98 firms in markets where measures of executive stock options are unavailable or inappropriate for researchers, we create a formative principal component composite (OC_COMPOSITE) comprising an excess investment residual, a relative compensation ratio, and a 3-year acquisition frequency index. The composite meets pre-specified validation criteria for pairwise correlations, variance inflation factors, eigenvalues, and factor loadings. CEO overconfidence is positively associated with absolute discretionary accruals and aggregate real earnings management in both firm- and year-fixed-effects regressions and System Generalised Method of Moments (GMM). Based on the preferred System GMM specifications, the estimated coefficients associated with a one-standard-deviation increase in OC_COMPOSITE are equivalent to approximately 38% of the sample mean of absolute discretionary accruals and 44% of the sample mean of aggregate real earnings management; these are mean-relative effect-size benchmarks, not literal percentage changes in the dependent variables. Big Four audit engagement attenuates the overconfidence–accrual association but does not significantly moderate the real-activities relationship, whereas ownership concentration strengthens the association across both earnings-management channels, particularly for real activities. The gender interaction is interpreted as suggestive because female-led firm-years are rare in the sample. A pre-/post-2021 comparison further indicates attenuation of the overconfidence–earnings management association in the later period, although the observational design and gradual temporal pattern preclude causal attribution to the 2021 reforms. The study extends behavioural corporate finance and upper echelons theory to a concentrated-ownership emerging-market setting and develops a transferable three-proxy approach to measuring CEO overconfidence in institutional environments where conventional option-based measures are unavailable.

Journal of Risk and Financial Management doi: 10.3390/jrfm19100805

Authors: Leo H. Chan

This paper provides a comprehensive analysis of leveraged Exchange-Traded Funds (ETFs) and Exchange-Traded Notes (ETNs), financial instruments that have grown significantly in popularity and market presence since their introduction in the early 2000s. Using data from 2020–2024, we examine the performance characteristics, risk profiles, and potential market impacts of these complex investment vehicles across different asset categories and market conditions. Our analysis reveals significant volatility drag and tracking errors that increase with holding period length and underlying asset volatility. We find that leveraged ETFs tracking technology and semiconductor indices experience the most extreme performance patterns, while fixed-income leveraged ETFs show more moderate but still significant decay effects. The paper demonstrates that these products generally fail to deliver their stated multiple of underlying index returns over periods longer than their daily rebalancing horizon, with the divergence increasing during periods of high market volatility. These findings have important implications for individual investors, financial advisors, and regulators concerned with market stability and investor protection.

Journal of Risk and Financial Management doi: 10.3390/jrfm19100804

Authors: Mohamed Essaied Hamrita Anouar Ben Mabrouk Tawfeeq Mohammed Alanazi

Multifractality and market volatility are important features of financial time series and may provide useful information for understanding complex market dynamics. This paper proposes a diagnostic framework combining wavelet multifractal analysis and Dynamic Mode Decomposition (DMD) to investigate stock price and trading volume series associated with major Saudi brands. The multifractal analysis reveals heterogeneous results across brands: statistically significant multifractality is detected for only five of the twelve brands (B1, B6, B7, B8, and B9), while for the remaining seven brands the null hypothesis of a monofractal process cannot be rejected. This indicates that multifractal behavior is not a universal feature of the sample. DMD is applied as a data-driven decomposition tool to identify dominant temporal modes and characteristic frequencies in both stock prices and trading volume series. Forecasting results indicate that the predictive value of DMD is limited and brand-specific, with performance improvements observed only for selected brands. We also compare DMD characteristics with conventional risk measures, including Value-at-Risk (VaR) and Expected Shortfall (ES). The relationship between DMD modes and risk indicators is exploratory and suggestive rather than formally demonstrated. Overall, the proposed framework provides a descriptive and diagnostic perspective on market dynamics rather than a direct risk measurement or forecasting tool.

Journal of Risk and Financial Management doi: 10.3390/jrfm19100803

Authors: Kaoutar Lahrari Siham Sahbani

In spite of a considerable amount of attention being paid to the interaction between tax avoidance and corporate governance across the globe, this study aims to provide a detailed bibliometric evaluation concerning the productivity, performance, and intellectual structure of scientific production on tax avoidance and corporate governance. All data used in this study were gathered from the Scopus database using documents published from 2006 to 2025, which made the total number of documents reach 295. In this study, both VOSviewer and Biblioshiny were used to conduct a bibliometric analysis of this corpus. The findings of this research revealed a remarkable annual increase in publication rate equal to 25.77%, showing an increasing trend in academic research concerning the role of corporate governance in shaping corporate tax avoidance behavior. The most productive journals that have been identified as the main sources are Accounting and Finance, Journal of Corporate Finance, Journal of risk and financial management and Cogent Business and Management. Moreover, Richardson G. and Taylor G. have been found to be the most productive authors in terms of the number of documents published in this field, whereas China, Indonesia, and the USA are the leading countries in terms of the number of affiliated documents. There are seven major clusters discovered based on co-occurrence analysis, among which corporate governance and tax avoidance emerge as the most powerful driving forces. The trending-topics analysis further highlighted that board-gender diversity, CEO compensation and agency problems are the keywords emerging most recently in the field.

Journal of Risk and Financial Management doi: 10.3390/jrfm19100801

Authors: Nasser Minaei Vitaliy Milke Man Fai Leung Cristina Luca

Intelligent decision systems assume a stable relationship between observations and outcomes. Financial markets violate that assumption, since shifts in volatility, trend and liquidity erode models trained on historical data. This study proposes an unsupervised decision system that restricts trading according to market conditions. Features from multiple time horizons are partitioned by walk-forward clustering into distinct market states, and execution is permitted only when the state satisfies predefined conditions, so that clustering governs when predictions are acted upon rather than how they are formed. The framework is evaluated on gold, Bitcoin, the S&P 500, Nasdaq 100, Dow Jones and the US dollar–Japanese yen exchange rate between January 2024 and December 2025, against a non-gated baseline with identical signals, risk controls and position sizing. Maximum drawdown falls on every asset, by half in several cases, whilst trade frequency declines by a third to two thirds; on the S&P 500 the Sortino ratio rises from 2.57 to 6.63 as maximum drawdown falls from 42.27% to 22.51%. A random-drop ablation shows the gain is not explained by trading less often, and bootstrap resampling indicates higher Sharpe ratios across all six markets, with a combined p-value of 0.0074. Results are gross of transaction costs.

Journal of Risk and Financial Management doi: 10.3390/jrfm19100802

Authors: Florian Richard

Did Congress trade ahead of the 2026 Iran War? We examine 135 disclosed transactions made by members of Congress in ten geopolitically exposed industries during the escalation. Members traded heavily in industries that subsequently rose, and congressional trading volume peaked roughly two months before public attention, but they earned no first-mover advantage. We compare portfolios that track congressional transactions at trade dates against portfolios entering at public disclosure dates. Entering positions at transaction dates offers no tangible edge: Under member-clustered inference, the disclosure delay advantage, trade timing, and any committee premium are indistinguishable from zero. Members of the Armed Services and Intelligence committees show no return edge over other members. The Senate result reduces to four trades by three senators, of which three timely oil purchases supply the bulk of the advantage. The two exceptions are purchases in positively exposed industries, which earned a 7.28% abnormal return over the disclosure window across seven members, and marginally significant sales in the negatively exposed industries. Neither can be separated from informed reading of public escalation signals. Despite intense media scrutiny of congressional trading during the escalation, we find no evidence sufficient to attribute these returns to private information.

Journal of Risk and Financial Management doi: 10.3390/jrfm19100800

Authors: Douae Youbi Abdessamad Ouchen El Mehdi Steli

Artificial intelligence (AI) and sustainable finance are increasingly intersecting, yet research at their intersection remains fragmented and theoretically underdeveloped. This study combines bibliometric analysis with the Theory–Context–Characteristics–Methodology (TCCM) framework to examine 61 PRISMA-screened articles retrieved from Web of Science. No publication-year restriction was applied; the included corpus spans 2021 to July 2025. Publication output within the corpus increased from seven articles in 2023 to fifteen in 2024, while the 2025 count covers only the period up to the July search date. China accounts for the largest share based on the corresponding author’s country. Stakeholder Theory is the most frequently applied theoretical lens (10 studies), followed by the Resource-Based View (7 studies) and Institutional Theory (5 studies). Within the thematic map, ESG and artificial intelligence occupy the motor-theme quadrant, whereas green finance lies in the emerging-or-declining quadrant. Building on the TCCM synthesis, the study develops a dual-level conceptual framework grounded in the Resource-Based View and Institutional Theory. The framework proposes that AI-related capabilities may contribute to sustainability outcomes through absorptive capacity, green-innovation processes, and supply-chain efficiency, and positions institutional quality, environmental uncertainty, ownership type, and industry competitiveness as boundary conditions. Overall, the study provides a structured theoretical synthesis and a theory-informed agenda for future research at the AI-sustainable finance nexus.