Journal of Risk and Financial Management doi: 10.3390/jrfm19100781

Authors: Ibrahim Alhanaya

This study examines whether the internal composition of an environmental, social and governance (ESG) rating contains incremental information about one-year accounting-loss onset after the overall ESG level is held constant. Bloomberg environmental (E), social (S) and governance (G) pillar scores are reparameterised as ESGLevel = (E + S + G)/3, GovernanceHalo = G − (E + S)/2, and ESContrast = E − S. The FY2020–FY2024 panel contains 13,745 firm-years for 2751 relatively large listed firms; the principal model uses 8029 eligible transitions and 472 loss-onset events. A one-standard-deviation increase in GovernanceHalo has an average marginal effect of −0.02 percentage points (95% CI −0.87 to 0.83). Firm-blocked cross-validation provides essentially no incremental one-year predictive gain. Reviewer-driven sensitivity analyses using within-year standardised pillars, a firm fixed-effects linear probability model, Firth penalised logit, stabilised inverse-probability weighting for complete-case selection, and dynamic pre-trend controls do not reveal a stable loss-onset association. A separate 30% annual market-capitalisation contraction outcome is negatively associated with GovernanceHalo and remains significant after false-discovery-rate adjustment (q = 0.024), but it is a valuation-contraction proxy rather than an accounting-loss or crash-risk measure. The previously observed E/S score build-out is no longer evident after controls for prior score changes, current E/S level and historical score volatility. The evidence therefore rules out large average one-year loss-onset effects in this Bloomberg-covered sample while leaving open smaller, heterogeneous, longer-horizon and provider-specific effects.

Journal of Risk and Financial Management doi: 10.3390/jrfm19100780

Authors: Rania Loubaris Al Mahdi Koraich

This study investigates whether the directional accuracy of commonly used technical indicators changes with market volatility and whether combining several indicators can improve signal reliability. The analysis draws on 500 daily financial observations and considers moving averages, the Relative Strength Index (RSI), the Moving Average Convergence Divergence (MACD), the stochastic oscillator and Bollinger Bands. Conditional volatility is estimated with a GARCH(1,1) model. Observations are then divided into stable and high-volatility regimes depending on whether estimated conditional variance falls below or above its sample mean, resulting in 260 stable and 240 high-volatility observations. Indicator accuracy is measured by comparing conventional buy and sell signals with the direction of the following day’s price movement. The two regimes display noticeably different volatility patterns. Return volatility rises from 1.213% in stable conditions to 2.489% in the high-volatility regime, while estimated volatility persistence (α + β) increases from 0.890 to 0.983. At the aggregate level, individual indicators generally achieve directional accuracy close to 50%. Combining indicators produces higher observed accuracy, although this improvement comes with a substantial reduction in the number of signals. The combination of RSI, Bollinger Bands and MACD records the highest observed accuracy at 66.7%, corresponding to 10 correct predictions out of only 15 signals. Given this limited number of observations, the result should be viewed cautiously and does not establish robust predictive superiority. The study adds to the existing literature by examining technical-signal reliability explicitly in relation to volatility conditions and by comparing individual indicators with confirmation-based strategies within the same empirical setting. The findings indicate that prevailing volatility conditions matter when interpreting technical signals and that fixed technical rules may not perform consistently across market environments. Since the analysis concerns directional accuracy rather than realised returns, the findings should not be interpreted as evidence of trading profitability. Further research using out-of-sample validation is needed to assess the economic relevance and stability of these results.

Journal of Risk and Financial Management doi: 10.3390/jrfm19100779

Authors: Fabricio Miguel Moreno-Menéndez José Francisco Vía y Rada-Vittes Nataly Gabriela Solis-Tapia José Antonio Cuadros-Espinoza Elizabeth Beatriz Barzola-Capcha Manuel Silva-Infantes Mercedes Rosario Canchan-Casas Vicente González-Prida Sara Ricardina Zacarías-Vallejos

Self-report financial surveys require auditable scoring as well as evidence that their items measure the intended constructs. This study combines a Peruvian workplace archive of 50 records with an independently reconstructed Monte Carlo experiment. The audit distinguishes archived categories, direct sums, a historical partial key and correction of all four explicit constraint items. Full correction changes the overall correlation from −0.081 to −0.331 (record-level permutation p = 0.01930), while perceived-financial-freedom alpha falls from 0.708 to 0.255. The archive contains only 20 distinct response patterns; a pattern-cluster sensitivity interval includes zero. Archived categories are not deterministic functions of the stored sums. The simulation evaluates 82,500 synthetic datasets across a 27-cell baseline and 28 targeted extension cells. It separates measurement-related displacement, protocol distortion and finite-sample error. At N = 150 and a latent correlation of 0.40, the empirical key-map scenario yields a joint correlation change of −0.158 and a paired interaction of −0.041 (MCSE 0.0020). Mean observed-target baseline coverage is 0.949 for corrected sums and 0.948 for direct categories; latent-target inclusion is a different diagnostic. The contribution is an operational audit combining software invariants, item and provenance checks, and conditional robustness assessment. Scoring repair and synthetic evidence do not validate the source instrument or establish a substantive financial-capability effect.

Journal of Risk and Financial Management doi: 10.3390/jrfm19100778

Authors: Gee-Jung Kwon

From fiscal year 2020, Korea has required a listed company that has kept the same auditor for six consecutive years to accept an auditor chosen by the securities regulator and keep it for three years. This study estimates what the rule costs and what it delivers. Using 17,890 firm-years from 2258 listed companies over 2015 to 2024, with designation identified from the auditor-change pattern the statute dictates, it estimates firm and year fixed-effects regressions against voluntary switchers. Designation raises the audit fee by 37.5 per cent and contracted audit hours by 14.8 per cent, so the average fee per audit hour rises by 19.7 per cent. Hours actually worked, disclosed separately by the auditor, give the same answer, and the composition of the engagement team does not change. Net of a voluntary change, the fee still rises by 0.1475 log points and hours by 0.0866. Absolute discretionary accruals do not move, and the interval excludes any improvement larger than a tenth of a standard deviation. Every effect appears in the designation year and disappears when the term expires. The costs of the regime are high and precisely estimated; no consistent improvement is observed in the accrual-based measure examined.

Journal of Risk and Financial Management doi: 10.3390/jrfm19100777

Authors: Amedu B. Oche Syed Moudud-Ul-Huq Yongsheng Guo

This study examines perceived anti-money laundering (AML) effectiveness in the context of cryptocurrency usage in Nigeria, with particular emphasis on perceived regulatory awareness and institutional experience. As cryptocurrency adoption continues to expand in developing economies, concerns have emerged regarding the capacity of existing regulatory frameworks to effectively monitor and control illicit financial activities. A quantitative research design was adopted using structured questionnaires administered to regulatory stakeholders, including personnel from the Nigeria Police Force (NPF) and the Economic and Financial Crimes Commission (EFCC). Data were analysed using statistical techniques, including analysis of variance and regression analysis, to assess differences in perceived AML effectiveness and its association with awareness and experience. The findings indicate that perceived regulatory awareness and institutional experience were not significantly associated with perceived AML effectiveness. This suggests that perceived regulatory awareness and institutional experience alone may have limited explanatory value for differences in perceived AML effectiveness in the context of cryptocurrency-related financial crime. The results indicate that perceived regulatory awareness and institutional experience explain only a small proportion of the variation in perceived AML effectiveness. The study contributes to the literature by providing evidence on regulatory stakeholders’ perceptions of AML effectiveness and the roles of perceived regulatory awareness and institutional experience within Nigeria’s cryptocurrency context.

Journal of Risk and Financial Management doi: 10.3390/jrfm19100776

Authors: Nurdana Zhaishylyk Parida Issakhova Mahfuzur Rahman Raushan Sadykova Assiya Issakhova

This study examines regional agricultural credit allocation and intensity in Kazakhstan and their relevance to financial inclusion. Data cover 17 harmonised units in 2014–2023. Two-way fixed-effects models use 153 observations for 2015–2023 (T = 9), after lagging output. Outcomes comprise total real credit, credit per agricultural worker, and credit per hectare. Credit remains concentrated: the 2014–2023 Spearman rank correlation is 0.93, and 13 of 17 units remain in the same quartile. The modest decline in the top-three share is not statistically distinguishable from zero. A 1% increase in lagged agricultural output is associated with 0.63% higher current credit. Subsidies are positively associated with credit, with moderate evidence of a stronger association in initially high-credit regions. The interaction has weaker bootstrap support (p = 0.061; 95% CI [−0.01, 0.30]), so the distributional evidence is suggestive rather than definitive. Separate and joint models support a positive branch-density association; ATM-density estimates are imprecise. Regional differences and the main associations persist in the normalised outcomes. These non-causal associations concern geographic financial access and cannot establish borrower-level exclusion. Aggregate credit growth alone does not establish geographically inclusive agricultural finance.

Journal of Risk and Financial Management doi: 10.3390/jrfm19100775

Authors: Alejandro Acevedo Amorocho Duwamg Alexis Prada Marín Gladys Elena Rueda Barrios José Fernando Martínez Lozano Henry Fernández Pinto

This article examines volatility, tail risk, temporal dependence, scaling behavior, and simulation-based uncertainty in daily coffee, Brent oil, and gold futures series. The sample spans 4 January 2016 to 31 December 2025 for coffee (2515 logarithmic returns) and Brent oil (2581 returns), and 4 January 2016 to 30 December 2025 for gold (2187 returns). The analysis combines historical Value at Risk (VaR) and Expected Shortfall (ES), rolling volatility, dependence diagnostics, common-date robustness, detrended fluctuation analysis (DFA), multifractal diagnostics, and bootstrap-based Monte Carlo scenarios. The vendor-supplied histories were obtained from Investing.com; observable contract-family, symbol, and quotation metadata were cross-checked against official ICE and CME specifications, while the unavailable vendor rollover algorithm is treated explicitly as a measurement limitation. A synchronized sample of 2162 return dates preserves the principal risk ranking: Brent oil remains the most volatile and records the deepest 1% ES, coffee occupies an intermediate position, and gold remains the least dispersed. DFA estimates are H = 0.460 (95% block-bootstrap CI [0.430, 0.537]) for coffee, H = 0.492 [0.380, 0.569] for Brent oil, and H = 0.436 [0.423, 0.539] for gold. Because all intervals include values near 0.5 and shuffled/AAFT-surrogate benchmarks do not support robust persistence, the evidence does not justify a strong long-memory claim. Multifractal-spectrum results show greater width for Brent oil and gold than for coffee, but shuffled-series comparisons require cautious interpretation. The contribution lies in comparing agricultural, energy and precious-metal futures within a common, reproducible and robustness-tested risk-management framework rather than in proposing a new estimator.

Journal of Risk and Financial Management doi: 10.3390/jrfm19100774

Authors: Julian Alexander Klöcker Frank Daumann

Professional football clubs increasingly use blockchain-based fan tokens to strengthen fan engagement, though little is known about how these digital assets respond to club-specific information. Our paper investigates whether official player transfer announcements are value-relevant events for football club fan tokens. Employing an event study framework, complemented by GARCH(1,1) estimations, we find no evidence of a systematic aggregate abnormal-return response to official transfer announcements. However, several individual transfer events are associated with statistically significant abnormal returns, including both positive and negative reactions. Thus, transfer announcements appear to be value-relevant in selected cases, but the evidence does not support a systematic market-wide effect. The study contributes to the emerging literature on blockchain-based sports assets by providing new evidence on information efficiency and price formation in tokenized sports markets.

Journal of Risk and Financial Management doi: 10.3390/jrfm19100773

Authors: Ibrahim Alnohoud Sajead Mowafaq Alshdaifat Safaa M. Ahmad Mushtaq Yousif Alhasnawi Ahmad Ali Atieh

This study investigates how sustainability reporting disclosure relates to earnings management and whether this relationship changes depending on board gender diversity and the effectiveness of the risk committee. Using 590 firm-year observations drawn from Jordanian listed companies, the study estimates the main relationships through pooled ordinary least squares (pooled OLS) regression. The findings indicate a significant negative association between sustainability reporting disclosure and accrual-based earnings management. This suggests that greater sustainability reporting disclosure is associated with less managerial discretion in financial reporting. However, the analysis does not provide statistically significant evidence that board gender diversity or risk committee effectiveness moderates this relationship. The results are robust to alternative earnings management proxies, as similar conclusions are obtained using Kothari-adjusted discretionary accruals and measures of real earnings management. To overcome potential endogeneity and selection issues, additional analyses with the two-step system GMM model yield similar main findings. By focusing on an emerging-market context, the study extends prior research on sustainability and corporate governance and shows that sustainability reporting may serve an important accountability role in strengthening the quality of financial reporting.

Journal of Risk and Financial Management doi: 10.3390/jrfm19100772

Authors: Mehmet Şuayb Yıldırım Ismail Onur Baycan

This study asks whether energy price shocks reach banking-sector credit risk differently across borrower types, combining three elements the literature has used separately: disaggregation by borrower, asymmetric modelling of energy prices, and a formal test of whether the asymmetries differ. Using monthly data for Türkiye, 2005–2026, we estimate nonlinear ARDL (NARDL) models for total, household and commercial non-performing loan (NPL) ratios across seven energy price indicators. The two portfolios respond asymmetrically in opposite directions: a 1 per cent cumulative rise in real consumer energy prices is associated with a 1.86 per cent higher household NPL ratio in the long run—about 0.6 percentage points for a typical year—while declines bring no measurable relief, whereas for commercial loans a 1 per cent cumulative decline is associated with a 3.54 per cent lower ratio and increases with no change. Joint estimation rejects the equality of the two asymmetry gaps (asymptotic p < 0.001; block-bootstrap p = 0.007), and the commercial response is carried by consumer rather than producer energy prices, pointing to a household-demand channel. The long-run elasticities are conditional on a level relationship the bounds test supports only weakly; the asymmetry tests do not depend on it, and the household result is the more securely established. Symmetry cannot be rejected for total NPLs, consistent with the two asymmetries offsetting: aggregate ratios can mask borrower-specific responses, and consumer energy price stability matters for banking-sector asset quality.

Journal of Risk and Financial Management doi: 10.3390/jrfm19100771

Authors: Jens H. Fischer Peter Garde Mogens Steffensen

We study how past consumption and health history affect optimal consumption, investment, and insurance in a multi-state life-insurance model. Two one-dimensional indices encode these distinct forms of history. The Habit-index is controlled by consumption and enters preferences, whereas the Health-index is uncontrolled and may also determine biometric transition intensities; state duration is obtained as a special case. Under power utility, both formulations admit explicit feedback controls and Feynman–Kac representations but lead respectively to systems of ordinary and partial differential equations. We give a verification theorem for the augmented-state control problem. A stylised three-state disability example isolates the mechanism through which additive habit formation can generate a consumption hump.

Journal of Risk and Financial Management doi: 10.3390/jrfm19100770

Authors: Ketut Gede Sri Diwya I Kadek Mahesa Parwata Gandhi

Financial influencers (“finfluencers”) have become central information intermediaries in retail capital markets, above all among Generation Z. Drawing on Dual-Process Theory, this study argues that finfluencer exposure functions as a double-edged sword, activating deliberative (System 2) and intuitive (System 1) cognition through two competing mediating pathways: financial literacy and perceived herd behaviour. Using data from 300 Generation-Z respondents in Bali, Indonesia, collected through an online questionnaire and analysed with Partial Least Squares–Structural Equation Modelling (PLS-SEM) in SmartPLS 4, we test a parallel mediation model. Bootstrapping with 5000 subsamples confirms all five hypothesised relationships (β range 0.255 to 0.812; all p < 0.001). The two mediating pathways operate at comparable magnitudes (indirect effects: 0.249 for literacy and 0.256 for herding), producing a total indirect effect (0.505) that far exceeds the direct effect (0.255), indicative of complementary partial parallel mediation. R2 values indicate moderate explanatory power (R2 for investment intention = 0.660) and satisfactory predictive relevance (Q2predict > 0 for all endogenous constructs). Our findings advance the dual-process literature in behavioural finance by empirically distinguishing cognitive from social-behavioural mechanisms through which digital information sources shape retail investment intention. The results underscore the need for content-based regulation of finfluencers, targeted digital financial literacy programmes, and platform design interventions that dampen social-proof cues in financial content.

Journal of Risk and Financial Management doi: 10.3390/jrfm19100769

Authors: Ting-Jung Lee Abootaleb Shirvani Farzana Afroz Svetlozar T. Rachev Frank J. Fabozzi

Taiwan’s central role in global semiconductor manufacturing exposes Taiwan-related ETFs to technology concentration, geopolitical uncertainty, and supply-chain disruptions, resulting in return distributions characterized by heavy tails, volatility clustering, and asymmetric responses to negative shocks. This study analyzed thirty U.S.-listed ETFs with Taiwan exposure from February 2015 to February 2025 using tail-risk diagnostics, asymmetric volatility modeling, and portfolio optimization under mean–variance and conditional value-at-risk (CVaR) criteria. Hill tail-index estimates documented heavy-tailed behavior across the ETF universe. Although the ETFs exhibited broadly similar asymptotic tail-decay behavior, semiconductor-focused ETFs produced substantially larger VaR and CVaR estimates than diversified benchmarks, indicating that cross-sectional differences in extreme downside risk were driven primarily by differences in return scale rather than tail-index estimates. GJR–GARCH estimates revealed persistent and asymmetric volatility. The apparent long memory in squared returns may therefore largely reflect conditional heteroskedasticity rather than genuine fractional integration, as GJR–GARCH filtering substantially reduced the evidence of long-range dependence. This result was robust across bandwidth choices for EWT, although some bandwidth sensitivity remained for EWP. CVaR optimization produced substantially more concentrated allocations than mean–variance optimization, with the CVaR tangent portfolio allocating a large weight to SMH; this concentration was most pronounced during the post-COVID, AI-driven semiconductor boom and should be interpreted as a feature of this sample period rather than a persistent structural property of semiconductor ETFs. Portfolio rankings depended on the performance measure: the Sharpe ratio and STARR measure favored the equally weighted portfolio, whereas the Rachev ratio favored CVaR-based portfolios. Overall, the results suggested that variance-based frameworks alone provided an incomplete characterization of risk in technology-concentrated investment environments and that variance-based and tail-sensitive performance measures could favor different portfolios over the same sample period.

Journal of Risk and Financial Management doi: 10.3390/jrfm19100768

Authors: Mazin Alahmadi Mohammed Basingab

This systematic review investigates the development of testing procedures for weak-form market efficiency under the Efficient Market Hypothesis (EMH) from 2000 to 2025. We organize the literature into three categories of approaches: traditional linear statistical tests (e.g., Variance Ratio, runs and unit root tests), nonlinear and fractal methods (e.g., multifractal detrended fluctuation analysis, wavelet transforms, entropy measures), and emerging hybrid techniques incorporating artificial intelligence (AI). Using PRISMA guidelines, we identified 72 peer-reviewed empirical studies through Scopus, Web of Science, JSTOR, and Google Scholar. Over the past two decades, research on weak-form efficiency has shifted from simple linear tests to more complex nonlinear and AI-based frameworks, reflecting the growing complexity of financial markets amid volatility, crises, and technological disruptions. Fractal and entropy-based models have uncovered long-memory effects and market anomalies with greater robustness, while AI methods have improved predictive power at the cost of interpretability. Persistent weaknesses remain, including inadequate handling of structural breaks and limited analysis of specialized contexts such as Islamic finance and Environmental, Social, and Governance (ESG) investing. Our findings highlight the need for integrated models that combine statistical rigor with interpretability and adaptability to modern market dynamics

Journal of Risk and Financial Management doi: 10.3390/jrfm19100767

Authors: Bukola Bose Lawal-Adedoyin Mofoluwaso Iyabode Ojedele Temitope Mariam Worimegbe

Corporate governance guidelines often emphasize board independence to minimize agency costs. Yet, in highly concentrated emerging markets, strict compliance with outside-monitoring mandates can be associated with structural bottlenecks and decision friction during economic shocks. We evaluate how board independence and corporate social responsibility (CSR) relate to bank valuations during systemic disruptions, using data from the 10 systemically important commercial banks listed on the Nigerian Exchange (NGX) from 2013 to 2024. This panel captures 120 bank-year observations, representing 87.4% of total commercial banking industry assets. Long-run parameters are estimated using a Pooled Mean Group (PMG) Panel ARDL framework with robust Driscoll–Kraay standard errors, supplemented by a non-parametric Random Forest machine learning feature importance diagnostic. The machine learning model identifies CSR expenditure (LTCSR) as the most important predictor of bank value restoration, outranking traditional balance-sheet controls like equity book value and asset scale. The parametric estimations reveal a significant long-run independence discount (β = −0.342, p < 0.05), where a 10 percentage point increase in outside directors is associated with an absolute 0.034 unit market valuation penalty, a trend theoretically consistent with crisis-driven decision friction. However, the underlying banking network shows high recovery elasticity, absorbing 69% of external valuation shocks within a single annual cycle (φ = −0.690, p < 0.01). Finally, a quadratic inflection point (translating to an actual annual monetary expenditure threshold of approximately ₦1.67 billion Naira) paired with asymmetric quantile distributions indicates that CSR serves as a plausible emergency reputational shield for lower-quantile institutions (q25) but acts as a strategic asset for market leaders (q90). These findings suggest that macroprudential supervisors should consider shifting from rigid, headcount-based compliance toward functional capability thresholds.

Journal of Risk and Financial Management doi: 10.3390/jrfm19100766

Authors: Alessio Faccia

Machine-readable reporting standards provide shared data infrastructure, while firm-side reporting associations may vary with technological disclosure. The study examines the US Inline XBRL phase-in using 1450 Form 10-K firm years from 250 CIKs over fiscal years 2014–2024. SEC filing metadata, XBRL facts, amendments, accounting controls and full-primary-document text form the analytical panel. Preferred models date exposure from the first periodic report whose SEC metadata identify Inline XBRL use and include firm and year fixed effects with firm-clustered inference. Observed entry is non-random, 41 firms are left-censored for event-time analysis, 82 firms lack observed entry, and later periods offer limited untreated support. The design therefore estimates conditional associations, not a mandate effect. None of the four reporting indicators support H1 after Holm adjustment at the 5 per cent level. Dictionary-defined AI-related disclosure breadth, described as disclosed AI orientation, yields no preferred moderation evidence, and the intangible-intensity and innovation-disclosure tests also remain insignificant after adjustment. A fully specified fixed pre-entry moderator produces two suggestive interactions with Holm-adjusted p = 0.080, without rejection at 5 per cent. Extending asset-threshold and control-omission tests across the main outcomes does not overturn that threshold-based inference. Custom-tag leads reject a flat pre-entry path. The findings distinguish disclosure breadth from verified operational capability and quantify remaining uncertainties rather than establish exact zero.

Journal of Risk and Financial Management doi: 10.3390/jrfm19100765

Authors: Fatma Zehri Laila Mohamed Alshawadfy Aladwey Raghad Alsudays

In the original publication (Zehri et al [...]

Journal of Risk and Financial Management doi: 10.3390/jrfm19100764

Authors: Kiril Luchkov Kiril Anguelov

This study examines the relationship between ESG integration and sustainable construction practices in the Bulgarian construction sector. The analysis is based on 143 valid responses from representatives of construction companies operating in Bulgaria. The study employed descriptive statistics and comparative analysis based on enterprise size, as well as correlation and regression analysis. The average index for ESG integration is 3.44, and for sustainable construction practices, it is 3.40. Management commitment, social policies, safety, energy efficiency, and waste management are more developed. Reporting, circular practices, green certification, and carbon control remain less institutionalized. A positive correlation was found between the two indices (r = 0.615; 95% CI [0.501; 0.708]; p < 0.001). The results obtained do not allow for the establishment of a cause-and-effect relationship between the two.

Journal of Risk and Financial Management doi: 10.3390/jrfm19100763

Authors: Seline Loewel Winston Khogres Jonathan Edwards Telaumbanua Shakira Sudjono Benedictus Rahardjo Evaristus Didik Madyatmadja

Periods of presidential transition in emerging markets may coincide with heightened political and economic uncertainty. This study examines whether Twitter-based political sentiment during Indonesia’s 2024 presidential transition is associated with short-term USD/IDR movements and year-on-year headline inflation. IndoBERT and IndoELECTRA were adapted to Indonesian political discourse using approximately 150,000 unlabeled tweets and evaluated on 10,000 manually labeled tweets using stratified five-fold cross-validation. Weekly USD/IDR log returns were analyzed using OLS regressions with lagged political sentiment, Brent crude oil prices, the Effective Federal Funds Rate, and Bank Indonesia’s BI-Rate, with Newey–West inference and additional diagnostic and sensitivity checks. Monthly Pearson correlations were used separately to examine contemporaneous associations between political-discussion measures, the monthly average USD/IDR exchange rate, and inflation. Both sentiment models achieved accuracy of approximately 0.84. Lagged sentiment was not statistically significantly associated with weekly USD/IDR returns, and the monthly exchange-rate correlations were also weak and statistically imprecise. Monthly inflation correlations were positive but uncertain, resembled the association observed for total tweet activity, and weakened when the partial October observation was excluded. Overall, Twitter-based political sentiment appears more useful as a contextual measure of political discussion than as a stand-alone signal of short-term macro-financial movements.

Journal of Risk and Financial Management doi: 10.3390/jrfm19100762

Authors: Félix Casares-Conforme Ángel Maridueña-Larrea Rocío González-Reyes Deysi Medina-Hinojosa

Over the past decade, the microcredit portfolio extended to women has grown strongly across Latin America, yet the empirical literature has yet to deliver a consistent verdict on its aggregate effect. Macroeconomic studies typically rely on linear specifications that do not allow for dependence on the state of the business cycle. For Ecuador, moreover, there is no systematic empirical evidence on this link. This study provides the first monthly territorial evidence for a dollarised economy by estimating the dynamic response of provincial sales to changes in the female share of microcredit. We employ a panel local projections framework with a logistic smooth transition function, which identifies contractionary and expansionary regimes without resorting to discrete thresholds. The sample covers the country’s 24 provinces from January 2018 to July 2025, with 2136 effective observations. The results document a sign reversal that depends on the state of the cycle. When the cycle is in contraction, a one-percentage-point increase in the female share is associated with a cumulative fall in sales of up to 0.7 percentage points at 12 months. When the cycle is in expansion, it is associated with a cumulative increase of up to 0.5 percentage points over the same horizon. These findings indicate that the transmission channel is state-dependent in sign as well as in strength: the response reflects the cyclical conditions and the productive structure into which the credit is placed, rather than any intrinsic attribute of lending to women. In low-activity phases, gender-focused financial inclusion could be coordinated with countercyclical public policy so that its transmission to real activity operates in an expansionary direction.

Journal of Risk and Financial Management doi: 10.3390/jrfm19100761

Authors: Fadi Al-Asfour Aliaa Elfedawy

Environmental, social, and governance (ESG) disclosure has become increasingly important in corporate reporting and accountability; however, evidence regarding its implications for audit pricing remains mixed and is particularly limited in emerging markets, where ESG reporting and assurance practices are still evolving. This study investigates the association between ESG disclosure and audit fees and examines whether board gender diversity moderates this relationship in the Egyptian institutional context. Using a panel dataset of 80 non-financial firms listed on the Egyptian Exchange (EGX100) from 2018 to 2023, comprising 480 firm-year observations, the study applies pooled ordinary least squares and fixed-effects regression models. Sensitivity analyses and instrumental variable estimation are employed as additional robustness and endogeneity checks. The findings indicate a significant positive association between ESG disclosure and audit fees, consistent with the argument that ESG reporting may entail additional audit complexity and risk assessment. Board gender diversity is negatively associated with audit fees and significantly attenuates the positive association between ESG disclosure and audit fees. This moderating pattern is consistent with the view that gender-diverse boards may strengthen governance and the credibility of corporate disclosure, although the underlying auditor-risk and audit-effort mechanisms are not directly tested. The study contributes to the audit-pricing and ESG literature by identifying board gender diversity as an important governance contingency in the association between ESG disclosure and audit fees within an emerging-market setting.

Journal of Risk and Financial Management doi: 10.3390/jrfm19100760

Authors: Ivana Miklošević Anica Vukašinović Željko Sudarić

Almost every year, a single global climate policy shock reaches every market, yet its financial footprint is uneven. The present paper examines whether a country’s own climate vulnerability intensifies the way climate policy uncertainty is transmitted into equity market risk. The present study compiled a monthly panel of twenty countries comprising developed and emerging equity markets over the period from 2010 to 2025. Further, the present paper combines the climate policy uncertainty index with the vulnerability score of the Notre Dame Global Adaptation Initiative. As the countries are strongly interconnected, we relied on second-generation panel methods. The present paper employed the cross-sectional dependence test and the CIPS panel unit root test. The results confirmed that dependence is pervasive and the series are stationary. The core specification modifies climate policy uncertainty with vulnerability within a fixed-effects panel along with a panel quantile regression that examines the effect across the conditional distribution of the rolling volatility measure. The interaction is positive and highly significant. Markets that are more vulnerable are associated with a larger volatility response to the same climate policy shock, and the intensification is concentrated in the upper part of the conditional distribution rather than rising monotonically across all quantiles. A connectedness analysis showed that the developed economies are net transmitters while the more vulnerable emerging markets act as net receivers. The results survived the exclusion of China. The direct effect of climate policy uncertainty also carries over to sovereign spreads, although the vulnerability interaction on spreads is not statistically significant. The current study also provides investors and policymakers with a vulnerability-based ranking of resilience to climate policy shocks.

Journal of Risk and Financial Management doi: 10.3390/jrfm19100759

Authors: Jesús Gil Ruiz Rafael Muñoz Gil Diego Rubén Rodríguez Regadera

Can a token’s collapse be predicted from design parameters fixed at launch, before it has market history? We compile on-chain data for 155 tokens (79 survived, 76 collapsed) and benchmark seven classifiers under leave-one-out cross-validation. Random Forest attains the highest area under the curve (AUC; 0.909; nested cross-validation 0.906), though no pairwise test separates the leading models. The result is not a category prior: out-of-fold AUC stays high within categories (decentralized finance 0.943 on 64 tokens; layer-1/layer-2 0.923 on two collapsed tokens, hence unstable), and excluding the two categories whose labels are near-definitional costs 0.014. Leave-one-category-out validation yields a mean AUC of 0.764 but fails for memecoins (0.571), which bounds the claim. Using only four unambiguously pre-launch features costs 0.038; restricting to long-exposure tokens does not degrade the model (0.921). A 13-token prospective test under distributional shift, with three collapsed cases, is merely indicative. Shapley-value attributions, permutation importance, and Sobol sensitivity agree that inflation rate dominates; team allocation is second by the first two and by standardized logistic coefficients, whereas Sobol ranks whale concentration second. The Safe Operating Envelope, counterfactuals, and fragility screen are in-sample and illustrative. Collapse risk is substantially predictable from design parameters within the categories represented here.

Journal of Risk and Financial Management doi: 10.3390/jrfm19100758

Authors: Rihem Soussi Fathallah Hamza Nizar Houssam Bouzgarrou Naila Amara Ahmed Hassanein

This paper investigates the association between board expertise and firm profitability. Using an international sample of 42,623 firm-year observations from 2002 to 2021, we find that firms with greater industry and financial expertise on their boards exhibit significantly higher profitability. This finding suggests that board expertise function as an important internal governance mechanism through which firms strengthen managerial oversight, improve strategic decision-making, and ultimately create shareholder value. We further show that the effectiveness of board expertise depends on the external governance environment. Specifically, the profitability-enhancing effect of board expertise is more pronounced in competitive product markets, consistent with a complementary relationship between market discipline and board-level governance. Product market competition appears to enhance the effectiveness of skilled directors in monitoring management and making strategic decisions that create shareholder value. Conversely, the positive relationship between board expertise and firm profitability is less pronounced among firms operating in countries with stronger institutional quality, consistent with a substitution view whereby effective legal systems, regulatory frameworks, investor protection, and enforcement reduce the incremental governance benefits of skilled boards.

Journal of Risk and Financial Management doi: 10.3390/jrfm19100757

Authors: Muteb Ahmed Almihbash

Corporate financial planning and analysis (FP&A) is being reorganised around machine learning, yet the supporting evidence consists largely of vendor case reports using undefined accuracy measures. This study decomposes the machine-learning advantage into components that prior work bundles together—information recency versus model re-estimation, and pooled estimation versus learner flexibility—measured on corporate income-statement data rather than retail or equity series. An open benchmark from SEC XBRL data covering 2599 non-financial US filers and 118,406 firm-quarters, from 2011 to 2025, evaluates five pre-registered hypotheses under rolling-origin validation with clustered inference. First, the advantage of continuous re-forecasting over annual budgeting derives from information recency rather than parameter updating: supplying current quarterly actuals to a model estimated once at fiscal year start recovers 27 to 58 percent of forecast error, whereas quarterly re-estimation produces no detectable improvement (p = 0.82), a result that holds across the COVID-19 and 2022 inflation regimes. Second, the advantage is an interaction rather than a main effect: a pooled linear model modestly outperforms a Theta benchmark, a flexible learner fitted per firm underperforms it, and a foundation model pooled across unrelated domains underperforms further; only pooling across comparable firms combined with a flexible learner realises the full gain. Third, model ranking reverses under asymmetric budget-variance cost.

Journal of Risk and Financial Management doi: 10.3390/jrfm19100756

Authors: Anas Alqudah Doha Alshlool

Digital payment adoption is widely believed to broaden the visible tax base, and prior work links cashless payments to smaller value-added tax (VAT) compliance gaps. Whether this effect is large enough to shift a country’s tax mix—the share of revenue from personal income tax (PIT), corporate income tax (CIT), and VAT—rather than raising each tax proportionately, remains untested. We examine this using a panel of 38 OECD countries in 2000–2022, instrumenting digital payment adoption with two time-invariant infrastructure-legacy proxies—broadband rollout timing and submarine cable distance—via two-stage least squares, benchmarked against naive OLS and two-way fixed effects. Naive OLS shows digital payment adoption significantly lowering the CIT and VAT shares of revenue, opposite to the base-broadening intuition; these associations vanish under instrumentation and fixed effects for every outcome. Weak-instrument-robust tests corroborate the null for three of the four outcomes; for VAT, where our overidentification and placebo evidence raise the most doubt about instrument validity, the weak-instrument-robust confidence set excludes zero, a result we treat as inconclusive given those same validity concerns rather than as either confirming an effect or corroborating the null. A cluster bootstrap and lagged and extended-control specifications corroborate the null throughout, while a placebo test shows that our instruments correlate with pre-sample tax composition, a genuine limitation we report in full. The two-way fixed-effects results, which do not rely on these instruments and are therefore not exposed to that specific confounder (though, like any fixed-effects design, it cannot rule out time-varying confounding), corroborate the same null and are weighted as the most credible evidence. We interpret the results as evidence against an economically meaningful, robust association between digital payment adoption and OECD tax composition, rather than as a causally identified null effect—a pattern at least consistent with the possibility that mature tax administrations had already captured most realizable enforcement gains from digitalization before 2000.

Journal of Risk and Financial Management doi: 10.3390/jrfm19100755

Authors: Jiacheng Liu

Financial regulation separates the size of a position from the probability of an adverse event and the loss it would generate. No comparable separation exists for the labour market and spatial consequences of artificial intelligence (AI), which are analysed through one quantity: the occupational AI exposure index, weighted by local employment shares to yield place-level risk indicators. Borrowing the hazard, exposure, and vulnerability decomposition from disaster risk assessment, this review shows that the leading indices measure hazard, are read as exposure, and are used as vulnerability. Five measures, selected under stated criteria, are compared; their agreement partly reflects a shared occupational substrate, and the exposure–wage relationship is unstable across technological generations. The step to place-level risk requires five conditions, four of which clearly fail against current evidence. The paper sets out an accounting decomposition of expected local loss, states the unit, range, and timing of every term, and gives an estimating equation in which the weights on local characteristics are fitted rather than assigned. With published data, conventional exposure varies 1.3-fold across the 27 European Union member states and an adoption proxy 8-fold. Within 13 member states, the proxy accounts for most regional variation and, in 12, widens the lead of capital regions.

Journal of Risk and Financial Management doi: 10.3390/jrfm19100754

Authors: Kaoutar Benslama Ghyzlane El Alaoui Hanane Ben Hsayen Hajar Ahadi Lalla Nezha Lakmiti Abdellah Oulakhmis

This study investigates whether, and to what extent, the COVID-19 pandemic disrupted the financial performance of publicly listed non-financial and financial firms in Morocco, and whether any disruption proved transitory or persistent. Using a balanced panel of 76 firms listed on the Casablanca Stock Exchange observed over six fiscal years (2018–2023, 456 firm-year observations), the paper combines a theoretical framework grounded in the Resource-Based View, dynamic capabilities theory, and capital structure theory with an empirical strategy that partitions the sample into a pre-pandemic period (2018–2019), a pandemic-shock period (2020), and a post-pandemic recovery period (2021–2023). Paired-sample comparisons and firm-fixed-effects panel regressions show that Return on Assets (ROA) fell by approximately 1.1 to 1.2 percentage points during 2020 relative to the pre-pandemic baseline (p < 0.05), while Return on Equity (ROE) exhibited a similar but noisier pattern. Profitability in the post-pandemic years is statistically indistinguishable from the pre-pandemic baseline, consistent with a V-shaped, transitory shock rather than permanent scarring. Firm size, proxied by the logarithm of revenue, is positively and significantly associated with profitability throughout, lending support to a resource-based resilience argument. The paper proposes a testable conceptual model and four hypotheses that can guide future extensions of this research, including the introduction of leverage, liquidity, and ESG/social-and-environmental performance indicators once such data become available.

Journal of Risk and Financial Management doi: 10.3390/jrfm19100753

Authors: Hamza Sadad Lachheb Ikram Benomar

Financial sustainability has become a central concern in contemporary banking, particularly with the growing integration of ESG principles and regulatory reforms. Although both Islamic and conventional banking systems are frequently examined in relation to sustainability, the intellectual structure and comparative evolution of this research field remain insufficiently clarified. Existing studies often focus on specific dimensions such as governance, performance, or green finance without systematically mapping how sustainability-related knowledge has developed across banking models. This study addresses this gap through a bibliometric comparative analysis of 629 Scopus-indexed publications published between 2010 and 2025. Using VOSviewer version 1.6.20, we analysed publication trends, influential contributors, and keyword co-occurrence networks to identify dominant research themes in financial sustainability within Islamic and conventional banking literature. The results reveal five major thematic clusters and highlight differences in research emphasis between the two models, particularly regarding ethical finance, ESG, and financial stability. The model-specific networks further show that conventional banking research is broader and more thematically differentiated, whereas Islamic and participative banking research forms a smaller but denser network centered on Shariah-compliant instruments, social finance, financial inclusion, and ethical sustainability. By mapping the intellectual structure of this field, the study provides a clearer understanding of how sustainability research is evolving across banking systems and identifies directions for future research.

Journal of Risk and Financial Management doi: 10.3390/jrfm19100752

Authors: Mohsen Imeni Seyyed Ahmad Edalatpanah

This study examines the contemporaneous association between earnings management (EM) and corporate social responsibility (CSR) disclosure breadth in an emerging-market setting. Using a balanced panel of 136 manufacturing firms listed on the Tehran Stock Exchange across eight annual panel periods (2017–2024; 1088 firm-year observations), CSR disclosure is measured with a 33-item binary content-analysis index with explicit coding boundaries. The primary Kothari-style performance-adjusted signed abnormal-accrual measure is positively associated with disclosure breadth (β = 0.198, p = 0.031). A secondary modified Dechow–Dichev-style signed accrual–cash-flow residual sensitivity is positive but marginal (β = 0.285, p = 0.060), and real earnings management is positive but marginal in the beta-regression specification (β = 0.254, p = 0.071). In firm- and year-fixed-effects OLS, AEM-K remains positive and significant, AEM-DD remains marginal, and REM is not statistically significant. The evidence is associational rather than causal and is consistent with—but does not establish—an opportunistic-reporting interpretation in which financial-reporting opportunism can coexist with broader CSR disclosure.

Journal of Risk and Financial Management doi: 10.3390/jrfm19100751

Authors: Fang Du Diana Hancock

Capital standards and loan loss reporting standards are deeply interconnected for U.S. domestic and internationally active banking organizations, as both regulate a banking organization’s ability to absorb losses and to maintain solvency. This paper empirically considers their joint effectiveness utilizing different regimes for each standard, different timings for the respective regime changes, and, most importantly, changes in bank behavior over the business cycle in response to such changes. We employ data for (i) large bank holding companies with total assets greater than $10 billion and (ii) U.S. holding companies subject to the Federal Reserve’s tailoring regime to demonstrate that the Current Expected Credit Losses (CECL) standard increased loan loss reserves in normal economic conditions, thereby alleviating the “too little too late” problem associated with the previous loan loss provision standard, but it appears to have not have mitigated the “pro-cyclicality” problem compared to the Incurred Loss standards for its adopters during the COVID pandemic-related recession in 2020. For banking organizations with the strongest loan loss reserve rates, loan loss reserve rates and capital ratios play complementary roles; a one percent increase in the capital ratio makes it less likely for a banking organization to be among those with the strongest loan loss reserve rates. However, for banking organizations with the weakest reserves, more capital may be needed to cover future loan losses; a one percent increase in the capital ratio makes it more, not less, likely for a banking organization to be among those with the weakest loan loss reserve rates. Our consideration of reported loan loss reserve rates in opposite tails of the loan loss rate distributions is consistent with the view that higher capital requirements need not provide higher overall buffers, which include both loan loss reserves and capital, throughout the business cycle to cover loan losses.

Journal of Risk and Financial Management doi: 10.3390/jrfm19100750

Authors: Rameshwari Kothapalli Pradeep Sai Bokka Saiteja Puppala Eugene Pinsky

Investors and financial media routinely treat last year’s best-performing mutual fund as a signal of manager skill that is worth following, yet the academic evidence on whether past rank predicts future rank is decidedly mixed. This paper addresses that gap directly: is it profitable to invest in the best-performing large-cap mutual fund identified from the previous year’s annual report, or does a different rank position offer a more reliable combination of return and risk? This paper analyzes ten large-cap mutual funds in each of four categories—growth, income, value, and balanced—over 22 years (2003–2024). The ten funds per category are the strongest full-sample performers against the S&P 500 benchmark, identified via a cumulative-sum (CUSUM) screen, so the sample reflects a curated set of long-run outperformers rather than the broader fund universe. Within this sample, we test whether chasing the single best-performing fund from the prior year is effective and identify which past-performance rank, if any, best predicts strong subsequent results. The findings are mixed but instructive: in two of the four categories (income and balanced), a mid-ranked fund—rather than the top-ranked fund—delivers the strongest cumulative returns over the following year, while top-ranked funds show greater volatility and a tendency toward mean reversion. Growth and value are exceptions, where the top-ranked fund and a rotation strategy built on it remain the strongest long-term performers. Across three of the four categories (growth, income, and balanced), the reported average maximum drawdown figures worsen as a fund’s prior-year rank falls, a directional pattern that is broadly consistent across categories; in value, this relationship is present but much weaker, and the single worst average drawdown occurs at a middle-to-low rank rather than the very bottom. The lowest-ranked funds in every category show weak and inconsistent recovery relative to their mid-ranked peers. When mutual funds and rotation strategies in all four categories are compared against passive buy-and-hold of a matched benchmark index, the benchmark outperforms every active strategy that is considered in three of the four categories—growth, income, and balanced—often by a wide margin; value is a narrow exception, where the strongest rotation strategy edges out its benchmark by only about two percent in cumulative terminal value over the full sample. For investors who are choosing among funds with an established long-run record, the takeaway is that a fund’s rank last year is a poor guide to next year’s winner: the best- and worst-ranked funds are, on average, less reliable than funds in the middle of the pack, with growth and value again the notable exceptions.

Journal of Risk and Financial Management doi: 10.3390/jrfm19100749

Authors: Luís Lima Santos Conceição Gomes Lucília Cardoso

Artificial intelligence (AI), business intelligence (BI) and analytics are increasingly used in hotel accounting, financial reporting and performance management. Existing research, however, has concentrated mainly on prediction, automation and operational optimisation. Their integration with the critical interpretation of accounting information and human judgement remains less clearly developed. This study provides a structured review and bibliometric mapping of research on AI-supported accounting and financial reporting in the hospitality industry. It adopts a structured bibliometric review combining science mapping with qualitative content-oriented interpretation. The review comprises three complementary search streams addressing: (i) AI, BI and analytics in hotel accounting; (ii) AI-supported reporting, dashboards and decision-support systems; and (iii) the broader contextual literature on human judgement and the interpretation of accounting information. The results identify machine learning, revenue management, forecasting and dynamic pricing as the most prominent and structurally influential areas within the retrieved literature. Research on reporting interpretation, dashboard-based judgement, anomaly detection and human validation is more fragmented and remains weakly integrated across the mapped literature. The principal research gap therefore concerns the limited connection between AI-supported technologies, reporting outputs and human interpretive judgement, rather than the absence of relevant research in these areas. The study proposes a human-centred perspective on AI in hotel accounting and identifies future research directions in which AI supports critical interpretation, professional judgement and responsible managerial decision-making.

Journal of Risk and Financial Management doi: 10.3390/jrfm19100748

Authors: Mohammad Sarwar Jahan Rekabder FJ Abu Mohaimen Iftear Ahmed Chowdhury Hasan A. Mamun Jobaida Tasnim Chowdhury

This study examines whether pro-climate lobbying intensity is associated with corporate default risk. Using a panel of 4176 firm-year observations from U.S.-listed firms, we measure financial stability using distance-to-default and pro-climate lobbying intensity as annual pro-climate lobbying expenditure scaled by total assets. Fixed-effects estimates show that pro-climate lobbying intensity is positively and significantly associated with distance-to-default, indicating lower default risk. Economically, a one-standard-deviation increase in lobbying intensity corresponds to an approximately 0.084-unit increase in distance-to-default, equivalent to 1.39% of its sample mean. The evidence is consistent with signaling theory, as costly climate engagement may signal transition preparedness, and with stakeholder theory, as alignment with climate-conscious stakeholders may lower regulatory, reputational, and financing risks. The relationship remains evident after entropy balancing, controlling for lagged distance-to-default in a dynamic specification, and replacing distance-to-default with the Altman Z-score. It is also qualitatively robust to replace the comprehensive lobbying measure with a narrower text-based proxy that identifies pro-climate lobbying through explicit climate-related keywords. Split-sample analyses show a stronger association among firms with at-or-above-median environmental and social performance and among firms with at-or-above-median cash-flow and earnings volatility, suggesting that climate-policy engagement is most informative under greater operating uncertainty; these patterns remain descriptive pending formal coefficient-comparison tests. Overall, the study contributes to the corporate political activity, climate-finance, and credit-risk bodies of literature by showing that pro-climate lobbying carries information relevant to financial resilience and that its relevance varies with firms’ sustainability performance and operating uncertainty.

Journal of Risk and Financial Management doi: 10.3390/jrfm19100747

Authors: Sarthak Pattnaik Chhayank Jain Eugene Pinsky

The concentration of market capitalisation inside the S&P 500 rose sharply between 2016 and 2025, and it rose far faster than wealth concentration among American households. We establish this by treating index constituents as a population whose income is market capitalisation and applying the measurement apparatus of the income-distribution literature to annual cross-sections. Two construction choices do most of the work and are the paper’s first contribution: year-end membership is reconstructed from a dated historical constituent file rather than projected backwards from the current index, and prices and share counts are reconciled onto a common split basis. The two corrections do not work in the same direction. A roster projected backwards from today’s index produces a rising concentration trend whether or not concentration rose, while the split reconciliation raises the measured 2016 concentration rather than lowering it. A corrections waterfall quantifies each. Across 4443 firm–years, the share of index market capitalisation held by the five largest constituents rose from 13.4% to 30.0%, the Gini coefficient from 0.586 to 0.719, and the effective number of independent positions implied by the Herfindahl–Hirschman index fell from 112 to 43. The top-share trends survive imputation of every missing delisted firm at three plausible sizes, a balanced panel, and every specification of the inference we tried, including a bootstrap that resamples constituent firms rather than years at p<0.0001 throughout. The Gini trend is significant under every imputation at p≤0.0016, although, under the most adverse, its slope falls from 0.0133 to 0.0073 per year. The plug-in Gini understates inequality in fat-tailed samples, and the size of that correction depends on the distribution that is assumed to generate the data: it steepens the trend under a Pareto model, leaves it unchanged under a lognormal, and leaves it insignificant under a Pareto fitted at the Clauset–Shalizi–Newman exponent, so no corrected slope is quoted as the paper’s estimate. Over the same decade the top 1% share of US household net worth moved 0.65 percentage points, so firm concentration moved 24 to 30 times as far depending on the fixed-count basis used. An additive decomposition attributes 32% of the decade’s rise in the Theil index and 46% of the rise in the mean log deviation to the widening gap between digital and non-digital firms; the rest is dispersion within the two groups, so the movement is disproportionately but not wholly digital. Pareto tail indices are reported throughout as a diagnostic of tail shape rather than a headline result because the Hill and Clauset–Shalizi–Newman estimators of the same parameter disagree, a bootstrap goodness-of-fit test rejects the power law at the 5% level in seven of ten years, and the fitted trend does not survive a bootstrap that resamples firms rather than years. The second contribution is that negative finding, which bears on any study estimating tail indices from cross-sections of a few hundred firms.

Journal of Risk and Financial Management doi: 10.3390/jrfm19100746

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

An implied volatility surface is typically rebuilt each day from that day’s quotes alone, even though the quotes missing on a given day were usually traded the previous day. We treat a panel of daily surfaces as frames over moneyness, maturity and calendar time—a video-completion view—and reconstruct a target day’s missing region from the surrounding days. The model propagates along the three axes separately, mixes them by a gate reading which nodes are absent, and learns only a correction to a classical single-day fit. Using S&P 500 index options from January 2010 to August 2025, we score against strictly held-out market quotes. When an entire wing is withheld, the reconstruction beats a cross-sectional benchmark by 27.1%; holding the architecture fixed and varying only the input window shows that nine days of context rather than one accounts for a further 15.0% of the remaining error. Under random withholding, the method does not help, and under an interior expiry withholding it slightly hurts. Temporal information is thus valuable for structured outages, wing outages above all. The completed surface violates static no-arbitrage conditions more often than the benchmark, however, and a projection removing those violations removes most of the accuracy advantage, so it is not yet ready for pricing or risk use.

Journal of Risk and Financial Management doi: 10.3390/jrfm19100745

Authors: Nadezhda Blagoeva Vanya Georgieva

Environmental taxation is an important instrument in the transition to a low-carbon economy, yet empirical findings on its relationship with economic growth remain inconclusive. This study examines whether public debt moderates the relationship between environmental tax revenue and real GDP growth in the EU-27. It uses a balanced panel of 297 observations covering 2014–2024. The baseline specification is a two-way fixed-effects model with Driscoll–Kraay standard errors, supplemented by marginal-effects analysis and robustness checks. At the mean level of the other interacting variable, neither environmental tax revenue nor public debt exhibits a statistically significant conditional association with economic growth. In the baseline two-way fixed-effects specification, the interaction coefficient is negative and statistically significant, indicating that the estimated relationship between environmental tax revenue and growth becomes less favourable as public debt increases. The marginal effect is positive and statistically significant at low debt levels, statistically insignificant at the mean debt level, and negative but statistically insignificant at high debt levels. Robustness checks using alternative covariance estimators and dynamic System GMM specifications indicate that the statistical significance and precise magnitude of the interaction effect are sensitive to the estimation method employed. Therefore, the results provide limited, specification-dependent evidence for the moderating role of public debt, without establishing a causal effect or a universal debt threshold.

Journal of Risk and Financial Management doi: 10.3390/jrfm19100744

Authors: Evanthia K. Zervoudi Elena Athanasiou Apostolos G. Christopoulos

Money laundering remains a persistent challenge to financial integrity and effective economic and institutional governance, while its concealed nature and heterogeneous measurement methods limit precise estimates of its scale. This study examines money laundering and anti-money-laundering (AML) governance through a structured integrative review and comparative policy analysis of the United States and the European Union. The review synthesizes peer-reviewed research, legal and regulatory materials, official statistics, institutional assessments, enforcement evidence, and selected specialist evidence, prioritizing official and peer-reviewed sources for quantitative and institutional claims. The comparison distinguishes formal regulatory development from operational effectiveness and applies six common analytical dimensions: supervision and regulatory implementation, financial-intelligence capacity and use, investigation and enforcement, confiscation and asset recovery, beneficial-ownership transparency, and domestic and cross-border institutional coordination. The findings show substantial overlap in the principal laundering vulnerabilities affecting both systems, including financial intermediation, beneficial-ownership opacity, trade-based money laundering, real estate, professional facilitation, and virtual assets. The principal comparative difference lies in governance architecture: the United States operates through a predominantly federal AML system, whereas the European Union combines supranational rules and institutions with national supervisory, financial-intelligence, law-enforcement, and judicial authorities, creating an additional multi-level coordination challenge. Available indicators do not support a simple quantitative ranking of the two systems because reporting, enforcement, and asset-recovery measures differ in definition, institutional meaning, and comparability. Instead, the evidence identifies a recurrent divergence between the extensive development of formal AML frameworks and the more uneven evidence available concerning operational outcomes. The study concludes that AML effectiveness should be assessed through the capacity to translate regulation, ownership information, and financial intelligence into coordinated supervision, investigation, enforcement, and asset-recovery outcomes. Priority areas are beneficial-ownership transparency and information quality, the intelligence-to-enforcement and asset-recovery chain, and cross-border and cross-sector institutional coordination.

Journal of Risk and Financial Management doi: 10.3390/jrfm19100743

Authors: Jesús Alexander Pinillos-Villamizar Hugo Macías Luis Castrillon Rolando Eslava Jerson Ortega

This study examines the association between the debt tax shield (DTS) and corporate leverage in 61 listed non-financial firms from Chile, Colombia, Mexico, and Peru during 2013–2023, using a perfectly balanced panel of 671 firm-year observations obtained from Bloomberg. The DTS is measured as interest expense multiplied by the effective income tax rate and scaled by total assets. Fixed-effects and random-effects panel models are estimated and complemented by a correlated random-effects (Mundlak) specification, a liquidity-augmented model, and several robustness checks. The contemporaneous DTS is positively associated with leverage, although the magnitude and statistical significance of the coefficient are sensitive to model specification. In particular, the relationship becomes statistically insignificant when the DTS is lagged and increases substantially after winsorization, while remaining positive and statistically significant when the DTS is reconstructed using statutory corporate income tax rates and positive at marginal significance when a non-debt tax shield control is included. The Mundlak decomposition further shows that the association is driven primarily by persistent between-firm differences rather than by within-firm changes over time. These findings indicate that the DTS–leverage relationship should be interpreted as a contemporaneous association rather than as evidence of a causal financing effect. The study contributes to the limited empirical evidence on debt-related tax incentives and capital structure in Latin American listed firms and highlights the importance of distinguishing persistent cross-sectional heterogeneity from dynamic financing behavior.

Journal of Risk and Financial Management doi: 10.3390/jrfm19100742

Authors: Afşin Şahin

This study examines whether correlation dynamics among the CSI 300, BIST 100, and S&P 500 improve hedging performance under alternative currency denominations and market–clock alignments, including during the geopolitical event window beginning on 28 February 2026. Market-specific GARCH models and CCC, DCC, and asymmetric DCC (aDCC) models with multivariate Student-t innovations are estimated using 1199 daily return observations on common trading days from 12 March 2021 to 17 July 2026, expressed in local currencies and US dollars. Among the dynamic models, symmetric DCC is preferred to aDCC. However, the Engle–Sheppard tests do not reject constant conditional correlation in either the local-currency specification (p = 0.7457) or the USD-denominated specification (p = 0.7519); the DCC news coefficient is also statistically insignificant, and CCC has the lower BIC under both currency denominations. Mean correlations are weakly positive and are highest for the BIST 100–S&P 500 pair. After Holm correction within each currency basis, structural breaks are supported for both CSI-related pairs in local-currency terms, but only for the CSI 300–BIST 100 pair in USD terms; no break is supported for the BIST 100–S&P 500 pair under either currency basis. Over the 85-day out-of-sample period, DCC produces the lowest mean QLIKE loss under both currency denominations. Relative to CCC, the DCC advantage remains significant after Holm correction at the 5% level in USD terms, but not in local-currency terms. Nevertheless, the 90% Model Confidence Set retains DCC, CCC, and the static covariance model under both currency denominations. DCC also provides the highest gross hedging effectiveness in all six pair–currency comparisons, whereas the static benchmark records a higher realized cumulative net hedge P&L in five of the six comparisons under the illustrative base-cost profile; however, this non-risk-adjusted 85-day measure is sensitive to realized sample drift and does not establish general static-strategy superiority. Overall, the evidence indicates limited and denomination-dependent differences in performance and does not establish the unconditional superiority of DCC.

Journal of Risk and Financial Management doi: 10.3390/jrfm19100741

Authors: Inna Romānova Marina Kudinska Irina Solovjova Olga Grigorenko Simon Grima Valērijs Mihailovs Dace Raipale

Despite extensive regulatory harmonisation under the European Capital Markets Union (CMU), equity market development in several peripheral EU economies remains critically weak. While institutional infrastructure exists, issuer participation in public markets remains limited. This study aims to determine the capital market potential by assessing issuer-side financial readiness, with a specific focus on Latvia as a representative small EU, bank-dominated capital market. The study adopts a mixed-methods research design. Quantitatively, fuzzy c-means clustering is applied to firm-level financial statement data for 4070 Latvian non-financial companies for the period 2022–2023 to identify latent groups of potential equity issuers based on turnover, EBIT, equity, and employment size. Qualitatively, an expert-peer forum and a PESTEL framework are used to analyse institutional, political, economic, and behavioural barriers to IPO participation. The clustering analysis identifies 49 firms with comparatively strong financial and organisational characteristics consistent with potential issuer readiness; the analysis does not assess their willingness or complete organisational preparedness to undertake an IPO. Despite this latent issuer potential, Latvia recorded only two IPOs in 2024. The results show that Latvia has a critical mass of financially viable potential issuers, while IPO inactivity is driven by several factors. Qualitative findings reveal that issuer participation is constrained primarily by entrenched bank-financing dominance, disclosure aversion, weak political market signalling, low financial literacy, and insufficient market liquidity rather than by a lack of financially capable firms. The findings highlight several policy implications for increasing capital market potential, including targeted support to improve firms’ readiness for public issuance, selective listings of state-owned enterprises to strengthen market signalling, and initiatives to improve financial literacy and enhance equity investment culture.

Journal of Risk and Financial Management doi: 10.3390/jrfm19090740

Authors: Gulmira Yessengeldievna Kassenova Bakhytkul Faridullaevna Karimova Azhar Zeynullayevna Nurmagambetova Aizhan Sarsenovna Assilova Gaukhar Bodesovna Uvakbayeva

Stock market volatility forecasting is important for risk management, portfolio allocation, and investment decision-making. This study provides a bibliometric and methodological review of stock market volatility measurement and forecasting research published between 2016 and 2025. A model-neutral search of the Web of Science Core Collection, conducted on 18 August 2026, identified 440 records. Following title, abstract, full-text, and document-type screening, 177 eligible journal articles were retained. To assess search-term sensitivity, a supplementary search conducted on 4 September 2026 using alternative volatility terminology identified 33 additional eligible studies, yielding a final corpus of 210 studies. Bibliometrix/Biblioshiny and structured methodological classification were used to examine the field. Econometric approaches remained dominant (168 studies; 80.0%), followed by Machine Learning (25; 11.9%), Deep Learning (8; 3.8%), and Hybrid approaches (9; 4.3%). The evidence reveals substantial methodological diversification beyond conventional GARCH models and increasing use of realized and implied volatility, high-frequency information, sentiment, macroeconomic variables, and uncertainty indicators. No methodological family demonstrates universal forecasting superiority, as performance depends on markets, horizons, information sets, benchmarks, and evaluation criteria. Overall, the literature reflects methodological diversification, information enrichment, and selective integration rather than replacement of econometric models by artificial intelligence. Although the review is limited to the Web of Science Core Collection, the sensitivity analysis demonstrates the importance of alternative terminology in identifying relevant studies.

Journal of Risk and Financial Management doi: 10.3390/jrfm19090739

Authors: Marcus Paulo de Oliveira Gonçalves Philipp Ehrl

Home insurance plays a key role in mitigating the economic and social impacts of natural disasters. This paper investigates whether the occurrence of a severe natural disaster affected the demand for home insurance in Brazil. We collect weekly sales data from the dominant insurance company with national presence, and exploit the May 2024 flood—the most severe natural disaster in the federal state of Rio Grande do Sul’s recent history—as an exogenous shock which we evaluate using Bayesian structural time-series methods (CausalImpact). We estimate that home insurance sales in Rio Grande do Sul exceeded their counterfactual by roughly 184 policies, or about 95%, over the first eight weeks following the event, while the difference is indistinguishable from zero thereafter; averaged over the full 35-week observation window, the excess is 19.6%. The timing of the response, together with additional evidence on the mechanism, is consistent with a temporary shift in the weight attached to low-probability losses rather than a lasting revision of beliefs.

Journal of Risk and Financial Management doi: 10.3390/jrfm19090738

Authors: Vilija Bite Fominiene Edmundas Jasinskas Arturas Simanavicius Antanas Usas Arturas Rutkevicius

The transition toward a digital, circular, and human-centric (Industry 5.0) economy is as much a financial and economic transformation as a technological one. Firms that build the innovation capacity to redesign how they create and capture value depend on managers and investors who allocate scarce capital under uncertainty. Yet, whether the financial support that employees perceive actually accompanies innovative behavior, or whether the human and organizational conditions surrounding it matter more, remains underexamined at the firm level, particularly in service industries. This study examines how employees’ perceptions of their organization’s financial capability and willingness to support innovation relate to innovative work behavior (IWB) and how those perceptions operate alongside perceived organizational climate (OC), using the sports economy—a large and innovation-dependent service sector—as a test case. A quantitative cross-sectional survey was conducted among 181 coaches employed in for-profit sports organizations in Lithuania. Data were analyzed using correlation and hierarchical multiple regression with demographic controls, a test of the climate–finance interaction, and diagnostic checks for common-method bias and multicollinearity. OC was positively associated with both IWB and perceived financial support for innovation. Perceived financial capability and willingness correlated with IWB at the bivariate level but added no significant variance once OC and the controls entered the model (ΔR2 = 0.013, p = 0.230). The climate–finance interaction was likewise non-significant. OC remained the strongest correlate, accounting on its own for approximately 24% of the variance in IWB and for an additional 19 percentage points beyond the demographic controls. Because all measures were self-reported at a single point in time, these results are interpreted as associations rather than causal effects, and the pattern is consistent with—though does not establish—an interpretation in which perceived financial support accompanies innovative behavior only where the organizational climate already supports it. The study contributes to research on innovative work behavior, human resource management, and the human-centric premise of Industry 5.0, suggesting to managers and funders that innovation budgets are unlikely to translate into innovative behavior unless paired with motivation, learning opportunities, leadership support, and psychological safety.

Journal of Risk and Financial Management doi: 10.3390/jrfm19090737

Authors: Mirjana Jeleč Raguž Elenica Pjero Beqiraj Ariana Ergović

This study examines the relationship between economic growth and CO2 emissions in Croatia and Albania from 1995 to 2024, using the EU27 as a benchmark. The analysis combines carbon intensity trends, Mann–Kendall tests, Sen’s slope estimates, Tapio decoupling elasticities, and a supplementary LMDI decomposition. Contiguous intervals and rolling five-year windows, together with threshold, near-zero GDP, and alternative emissions-source checks, assess temporal consistency and robustness. Carbon intensity declined significantly in all three cases, but decoupling outcomes differed across time horizons. Favorable annual decoupling occurred in 44.8% of observations in Croatia, 55.2% in Albania, and 69.0% in the EU27, while the corresponding descriptive shares across the overlapping five-year windows were 64.0%, 56.0%, and 92.0%. The differences in favorable annual decoupling shares were not statistically significant. The LMDI results indicate that improved energy intensity offset part of the emissions pressure associated with economic activity in Croatia and Albania, while Albania’s carbon-intensity-of-energy effect was close to zero. Annual decoupling results were more sensitive to the emissions source than the medium-term comparison. CBAM-covered products accounted for 14.02% of EU27 imports from Albania in 2024. Overall, the results show that assessments of low-carbon transition progress can differ with the time horizon considered and that domestic decoupling may coexist with trade exposure to carbon regulation.

Journal of Risk and Financial Management doi: 10.3390/jrfm19090736

Authors: Thomas Zimmermann García Manuel Monjas Barroso Fernando Gallardo Olmedo

The Discounted Cash Flow (DCF) methodology is widely regarded as one of the most theoretically well-established approaches to firm valuation. The most critical issue within this methodology is the reliable and well-grounded estimation of those cash flows, particularly the systematic incorporation of qualitative enterprise risks into future cash-flow projections. The objective of this study is to develop a systematic and standardized methodology for estimating risk-adjusted cash flows based on a set of parameters derived from the Internal Control System (ICS) of the firm under valuation, operationalized through a rating system focused on the qualitative dimensions of the company. The proposed framework systematically translates qualitative risk assessments into explicit adjustments of the economic drivers underlying projected cash flows, thereby establishing a structured link between Internal Control Systems (ICS), Enterprise Risk Management (ERM), and Discounted Cash Flow (DCF) valuation. The methodology is applied to a baseline financial scenario representing the central financial projection of the firm, which is subsequently adjusted for enterprise risks derived from the Internal Control System. Naturally, quantitative factors must also be considered; however, this issue has already been extensively examined in the literature and is firmly established in corporate finance textbooks. The contribution of this paper lies in providing the academic and professional communities with a systematic framework designed to improve the transparency, consistency, and traceability of firm valuation while supporting a more structured incorporation of qualitative risk factors into projected cash flows. The proposed framework is methodological in nature and is intended to provide a conceptual foundation for future empirical calibration and validation using real-world corporate data.

Journal of Risk and Financial Management doi: 10.3390/jrfm19090735

Authors: Jéssica Karina Saavedra Vásconez Alexander Fernando Haro Sarango Eymmy Jimena Grados Lazaro Estrella Divina Lopez Pantoja Monica Jhanyra Gamarra Pacaya Silvia Mabel Cachay Salcedo Thelma Madian Lazo Pilco

This study examines how accounting professionals’ self-reported forensic-auditing capabilities relate to their attitudinal readiness to recognize suspicious anti-money-laundering (AML) indicators, using a transparent hybrid design that combines classical psychometrics, structural equation modeling (SEM), and leakage-controlled explainable machine learning (ML). A cross-sectional survey collected 700 valid responses from public accountants in Lima, Peru, with a 30-item, five-point Likert instrument (19 forensic-auditing items in three dimensions and 11 AML-recognition items). Reliability was high (Cronbach’s α = 0.935; McDonald’s ω = 0.935), but average variance extracted was below 0.50 in every block (0.329–0.449), and Fornell–Larcker testing showed that skills-and-knowledge and AML recognition were not discriminantly distinct (r = 0.673 > √AVE = 0.651/0.649). Responses showed a pronounced ceiling (51% of answers were the maximum), and 86 respondents (12.3%) answered all 30 items identically; removing them lowered the forensic-auditing–AML association from r = 0.731 to 0.630 and explained variance from 53.8% to 40.3%. Skills-and-knowledge remained the strongest predictor in SEM and HC3-robust regression (β = 0.478 and 0.425). Under a leakage-free protocol, ensemble models reached ROC-AUC ≈ 0.86 on held-out data, but threshold tuning did not improve F1 test, and item-level attributions were unstable (Spearman ρ = 0.28). Forensic-auditing capabilities are positively associated with declared AML-recognition readiness, driven by applied skills and knowledge; the evidence is attitudinal and correlational, and should not be read as real detection capability. Because professional experience, seniority, sector, and prior AML training were not measured, the reported associations may be partly confounded by unobserved professional background, and the dominance of skills-and-knowledge is therefore advanced as tentative, pending resolution of the skills-and-knowledge/AML-recognition discriminant-validity overlap. A second, procedural contribution is that the study reports the data-quality screening, the failed validity tests, and the explanation-stability diagnostics that survey-based forensic-accounting research rarely makes visible.

Journal of Risk and Financial Management doi: 10.3390/jrfm19090734

Authors: Ahmad A. Alwaked Anas Alqudah

Purpose: This paper examines what predicts digital financial inclusion among already high-income, well-governed countries, reversing the usual causal framing that treats inclusion as a driver rather than an outcome of institutional quality, financial sector development, and digital infrastructure legacy. Design/methodology/approach: Using a panel of 38 OECD countries (2000–2022, 874 country-years), account ownership and digital payment use are modeled as fractional response variables decomposed into within- and between-country components, with Tobit and Worldwide Governance Indicator cross-sectional robustness checks. Findings: Government effectiveness predicts inclusion almost entirely through persistent between-country differences, not within-country governance change. Early broadband rollout and submarine cable proximity independently predict higher digital payment use. This infrastructure-legacy result is weaker for account ownership and, per the actual-survey-year robustness, less robust for digital payment use as well. A conventional two-way fixed-effects specification, which cannot separate within- from between-country variation, finds no institutional relationship at all. Rule of law and regulatory quality carry most of this institutional effect, though political stability is also independently significant; voice and accountability is the weakest and least consistent dimension. Research limitations/implications: The decomposition establishes association, not causation; the governance-dimension check is cross-sectional rather than a full panel. The central institutional-quality result is robust to restricting the panel to actual, non-interpolated Findex survey-wave years (government effectiveness remains significant at p = 0.038 for account ownership and p = 0.011 for digital payment use, versus p = 0.026 and p = 0.001 on the full interpolated panel); however, one secondary infrastructure finding is not. Practical implications: The evidence here, which is associational rather than causal, is consistent with prioritizing digital payment infrastructure over governance reform as a short-run inclusion strategy, while continuing to invest in the rule of law for its longer-run structural payoff. Originality/value: The paper reverses the standard causal framing in the digital financial inclusion literature and combines five data sources within a single OECD panel not previously analyzed together.

Journal of Risk and Financial Management doi: 10.3390/jrfm19090733

Authors: Veraphong Chutipat Peerapat Wattanasin Tanpat Kraiwanit

Traditional risk parity approaches rely largely on volatility measures, which may not fully capture asymmetric risk profiles. This study examines a dynamic allocation approach that minimizes portfolio-level Conditional Value-at-Risk (CVaR). The CVaR-Minimizing Dynamic Allocation (CVaR-DA) approach is intended to manage tail-risk events more effectively than traditional variance-based methods. We conducted an out-of-sample rolling-window simulation for the dynamically re-estimated strategies, covering different market conditions from 2015 to early 2025. Two investment universes were examined: a concentrated equity–gold portfolio and a multi-asset portfolio comprising global equities, sovereign bonds, commodities, and gold. Variance-based DRP generated higher Sharpe ratios than Static Risk Parity in both universes while maintaining low portfolio turnover. The CVaR-DA approach provided better downside protection, particularly in the multi-asset universe, but produced higher turnover. Bootstrap inference yielded positive mean differences in Sharpe ratios between DRP and Static Risk Parity. However, the confidence intervals included zero, indicating that the differences were not statistically significant at conventional levels. The favorable drawdown results nevertheless suggest that dynamic risk allocation may improve portfolio resilience when risk conditions change. Moving from static, volatility-based allocation toward adaptive strategies that account for tail risk may therefore support capital preservation for institutional investors and fund managers.

Journal of Risk and Financial Management doi: 10.3390/jrfm19090732

Authors: Qian Ruby Shen Rafiqul Bhuyan Shehzad Ahmed Kaniz Sakina

Bangladesh is widely cited as a success story for closing the financial inclusion gender gap through mobile money. We use a November 2021 nationwide survey of 3121 adults, a sample that over-represents men (72.8%) and is, therefore, validated throughout against nationally representative Global Findex microdata, and we combine logistic and ordinal regression, machine learning classification, clustering, and a channel-level decomposition of access. The gender gap in access is almost entirely a mobile money gap. Once education and income are held constant, men and women are equally likely to hold and use formal bank accounts, while men have roughly twice the odds of holding and using a mobile wallet. The pattern replicates in the Findex data, where the mobile money gap holds and the banking gap, present among the least educated, closes with education. A Blinder–Oaxaca decomposition attributes about two-thirds of the composite gap to women’s lower education and income, leaving a residual concentrated in mobile money. Gender appears weak as an aggregate predictor precisely because its effect is channel-specific. Exclusion is sharply concentrated in a segment of women with below-secondary education and no income, but the concentration is additive rather than multiplicative: a main effects model reproduces even the most excluded cell. For policy, closing the gender gap means closing the gap in mobile money adoption, the gap that neither banking-side progress nor education closes.

Journal of Risk and Financial Management doi: 10.3390/jrfm19090731

Authors: Tanattrin Bunnag

This study examines the dynamic transmission of geopolitical risk across Brent crude oil, gold, the U.S. Dollar Index, and the Thai stock market using a Bayesian time-varying coefficient vector autoregressive (TVC-VAR) framework. Using monthly data from January 1990 to December 2025, the analysis combines time-varying impulse responses, generalized forecast error variance decomposition, dynamic connectedness measures, and network analysis. The results show substantial time variation in spillover intensity and direction. On average, Brent crude oil is the strongest net transmitter, gold has a smaller positive net position, and the U.S. Dollar Index and the Thai stock market are net receivers. Episode-specific point estimates indicate changes in transmitter-receiver roles and bilateral channels, but bootstrap sensitivity analysis shows that several apparent role changes are not statistically distinguishable once uncertainty is considered. The evidence therefore supports a dynamic, reconfigurable connectedness structure while cautioning against causal, safe-haven, or portfolio-performance interpretations that are not directly tested in this study. The findings are relevant to financial-risk monitoring and macro-financial surveillance under changing geopolitical conditions.

Journal of Risk and Financial Management doi: 10.3390/jrfm19090730

Authors: Biser Krastev Radosveta Krasteva-Hristova

This study examines whether the annual green-bond share of relevant corporate and government bond issuance is associated with circular material use and resource productivity in the EU-27. The accounting problem is the traceability gap between labelled issuance, allocated expenditure, and realised material outcomes. A balanced 2014–2024 panel contains 297 country-year observations. Two-way fixed-effects models with six controls use 270, 243, and 216 observations for contemporaneous, one-year-lagged and two-year-lagged specifications, respectively. Inference uses country-clustered standard errors and restricted wild cluster bootstrap-t tests with 9999 repetitions, with Driscoll–Kraay sensitivity estimates. None of the six baseline specifications, 18 additional robustness specifications, or 54 leave-one-country-out re-estimations has a wild-bootstrap p-value below 0.10. Circular material use coefficients are negative but imprecise; resource-productivity coefficients change sign. Neither directional hypothesis is supported. These conditional associations do not identify causal circular finance additionality, instrument ineffectiveness, or reporting failures. Incomplete allocation traceability, issuer composition, and implementation delays are possible mechanisms requiring disaggregated evidence. This study distinguishes issuance penetration from market size and links its findings to the need for comparable allocation reporting and verifiable circular outcomes.

Journal of Risk and Financial Management doi: 10.3390/jrfm19090729

Authors: Manish Kumar Gupta Kundan M. Patel Chitra Saruparia Sangeet Rudra Atwe

Average IPO returns hide what most investors earn. This study examines 197 Indian main-board IPOs listed between 2016 and 2022, with daily prices to 2026, giving every issue a complete 780-trading-day record. Buy-and-hold abnormal returns are measured against the NIFTY 50 and the NIFTY 500 over seven horizons from 20 to 780 trading days. Mean abnormal return reaches 26.39 per cent at 780 days, and the median over the same window is −31.24 per cent. Only 41.1 per cent of IPOs beat the market. Returns are concentrated, with a Gini coefficient of 0.557, and the top decile of positive performers earn 42.9 per cent of all positive abnormal returns. Removing ten firms from 197 turns the mean negative. Calendar-time portfolios produce no significant alpha at any window, and a placebo test using random start dates yields higher abnormal returns than the actual post-listing windows. Search attention is associated with returns over the first sixty days. IPO volume becomes negatively associated with returns from 240 days onward, and hot-market timing is significant at 20 days and again from 240 days onward. Mean-based evidence overstates what a typical IPO investor earns, and the distribution matters more than the average.

Journal of Risk and Financial Management doi: 10.3390/jrfm19090728

Authors: Birsel Sabuncu

This study investigates the structural characteristics, annual trends, and qualitative rationales underlying modified audit opinions issued within the independent audit reports of Turkish banks between 2022 and 2025. Utilizing a formal content analysis of 248 bank-year independent audit reports based on a complete census approach, the study evaluates the incidence and persistence of modifications under ISA 705 and IAS 37. The empirical findings reveal a recurring pattern of qualified opinions associated with unmandated discretionary provisioning, with all qualified opinions in the sample issued by Big Four audit firms and recurring across multiple reporting periods. Interpreted through institutional decoupling and signaling perspectives, the findings are consistent with the possibility that these qualifications may reflect interactions between accounting requirements, regulatory conditions, and provisioning practices rather than providing direct evidence of financial distress or managerial intent. The study contributes to the auditing and banking literature by examining the accounting bases and longitudinal patterns underlying modified audit opinions and by discussing their implications within an institutional framework relevant to emerging economies.

Journal of Risk and Financial Management doi: 10.3390/jrfm19090727

Authors: Hai Van Tran Lan Thi Tran

This study examines the effects of income diversification on the stability of Vietnamese commercial banks, with particular attention to the roles of credit risk, profitability, and capital adequacy. Using panel data from 406 bank-year observations covering the period 2010–2024, the study employs the two-step system Generalized Method of Moments (System GMM) estimator to address endogeneity and dynamic effects. The results indicate that income diversification has no significant direct impact on bank stability, credit risk, or profitability. In contrast, capital adequacy exhibits a U-shaped nonlinear relationship with both bank stability and profitability. The findings suggest that the positive effects of capital adequacy emerge only after threshold levels of approximately 7.6% for bank stability and 8.1% for profitability. The interaction between income diversification and capital adequacy is statistically insignificant, indicating no moderating effect. These findings highlight the importance of maintaining adequate capital buffers rather than relying solely on income diversification to enhance banking resilience. The study provides new evidence from an emerging economy and offers practical implications for bank managers and policymakers seeking to strengthen financial stability.

Journal of Risk and Financial Management doi: 10.3390/jrfm19090726

Authors: Ahmed Hashim Abbas Maliki Amin Rostami Alireza Rahrovi Dastjerdi

This study develops a context-sensitive framework for prioritizing the indicators of financial resilience for banking stability in Iraqi banks operating in a fragile emerging-market environment. Drawing on the financial resilience literature and the institutional, economic, and regulatory characteristics of Iraq, the study identifies the main dimensions, components, and indicators that shape banks’ capacity to absorb shocks, maintain essential functions, and sustain institutional continuity under financial and operational disruption. Data were collected in 2025 from 33 academic and professional experts in Iraq’s banking sector, including university professors, banking specialists, and senior bank executives. To operationalize the framework, the study employs an integrated multi-criteria decision-making approach, using the Analytic Hierarchy Process (AHP) to determine the relative weights of the main dimensions and components and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to rank the indicators. The expert-based prioritization indicates that micro-level banking management received a higher relative weight than macro-level banking management, with weights of 0.5674 and 0.4326, respectively. Among the components, the financial component receives the highest global priority weight, followed by corporate governance, Supervision, policies and controls, and laws and regulations. At the indicator level, forward-looking supervision and forecasting, anti-money-laundering regulatory compliance, installment-loan share, prevention and control of corruption, credit risk, and liquidity management received the highest global priority weights in the expert-based framework. The study contributes to the literature by providing an integrated and context-sensitive framework for prioritizing resilience-related factors in Iraqi banks and by highlighting the relevance of internal management capacity, governance quality, regulatory discipline, and risk control for resilience assessment and improvement in fragile institutional settings. The findings also offer practical implications for bank managers, regulators, and policymakers seeking to enhance the continuity, resilience, and stability of financial institutions in emerging economies. The reported weights represent expert-informed priorities for resilience assessment and improvement; they do not constitute direct estimates of realized bank-level resilience or causal effects on banking stability.

Journal of Risk and Financial Management doi: 10.3390/jrfm19090725

Authors: Sheng-Yuan Wang San-Pui Lam

As ESG disclosure and climate governance requirements become increasingly institutionalized, understanding how firms respond to external sustainability pressures through specific governance mechanisms and how these responses relate to carbon performance has become an important research issue. Moving beyond aggregate ESG ratings, this study examines the informational value of specific ESG-related factors for corporate carbon management, including energy management systems, innovation capability, human capital, market valuation, and board governance. Using data on Taiwanese listed and over-the-counter companies from the ESG database of the Financial Supervisory Commission, the Taiwan Economic Journal, and the Leadership ISO Survey, this study employs a time-lagged design linking 2023 firm characteristics to 2024 carbon emission intensity. Multiple regression analysis is the primary method, with firm size, leverage, capital intensity, profitability, and firm age included as firm-level controls in extended models; exploratory data analysis (EDA) serves as a supplementary diagnostic for data distribution, nonlinearity, and variable operationalization. Cross-year, alternative dependent-variable, and supplementary analyses are used to assess the stability of the estimates. The results show that ISO 50001 certification is positively associated with subsequent carbon emission intensity, whereas the structural characteristics of R&D investment and independent director governance show more stable negative associations. Average salary, female director representation, and Tobin’s Q yield inconsistent results. Proportion-based board measures outperform director-count and threshold-based measures, but no clear critical-mass threshold is supported. Results also vary under alternative carbon-performance measures, suggesting that carbon intensity and absolute emissions capture different dimensions of environmental performance. These findings indicate conditional statistical associations rather than causal effects.

Journal of Risk and Financial Management doi: 10.3390/jrfm19090724

Authors: Vanya Georgieva Nadezhda Blagoeva

The European Union aims to double circular material use by 2030, yet the rate reached only 12.2% in 2024, and environmental tax revenue remains dominated by energy taxes. This article examines whether the composition and timing of environmental taxation and agricultural specialisation are associated with the circular transition. Using an unbalanced EU-27 panel for 2010–2024 (CMUR and recycling) and 2005–2023 (economic indicators), this study estimates two-way fixed-effects lagged and interaction models with Eurostat data. The aggregate environmental tax revenue share shows no systematic association with the five circular outcomes. Disaggregated results show that energy tax revenue is associated with lower private investment in circular sectors, transport tax revenue with higher municipal waste recycling, and pollution tax revenue with a weak increase in circular sector gross value added. Resource tax coefficients are statistically insignificant and imprecisely estimated. Lagged models reveal different patterns across outcomes but no common transmission horizon. The associations of the aggregate tax revenue share with recycling and circular employment weaken as agricultural specialisation increases. However, the associations lose statistical significance when country-specific linear trends are included. The findings suggest that tax composition and economic context are more informative than the aggregate tax burden, while the observational design and sensitivity of the results require cautious policy interpretation.

Journal of Risk and Financial Management doi: 10.3390/jrfm19090722

Authors: Mohamed Bal Mohamed Benadi Abdellah Ait Oufkir

Digital transformation is reshaping management accounting practices worldwide, yet empirical evidence on how digitalization translates into improved financial performance, particularly in emerging economies, remains fragmented and contested. Drawing on institutional theory, dynamic capabilities theory and contingency theory, this study investigates whether the digitalization of management control processes directly improves financial performance and whether artificial intelligence (AI) and data quality sequentially mediate this relationship. A sequential mixed-methods design was employed: an exploratory qualitative phase involving semi-structured interviews with 18 management controllers and finance professionals in the Souss-Massa region of Morocco (analyzed via NVivo 15) was followed by a confirmatory quantitative phase administering a structured questionnaire to 68 professionals, analyzed using partial least squares structural equation modeling (PLS-SEM, SmartPLS 4). Results reveal that digitalization alone does not significantly improve financial performance (H1 rejected; β = 0.154, p = 0.293), and AI in isolation does not sufficiently mediate this relationship (H2 rejected; β = 0.169, p = 0.100). However, the complete serial mediation chain, digitalization to AI to data quality to financial performance, is statistically significant and robust (H3 confirmed; β = 0.179, p = 0.020). These findings challenge naive technological determinism in management accounting transformation and demonstrate that AI-driven performance gains are conditional on coherent data governance.

Journal of Risk and Financial Management doi: 10.3390/jrfm19090723

Authors: Malak Khreis Hadi Harb Soha Dia

Tax administrations increasingly use data-driven risk models to prioritize collection resources, yet machine learning applications to personal income tax (PIT) arrears remain limited. This study introduces TaxMind (v1.0), an interpretable risk management framework for predicting whether PIT obligations will progress to mandatory collection and for integrating debtor- and debt-related information into risk-based segmentation. Administrative records from the Lebanese Tax Administration were analyzed; after removing 5385 exact duplicates from 16,010 records, the final dataset contained 10,625 obligations across 9264 taxpayers. Five tuned classifiers were evaluated using a taxpayer-grouped train/test design, with SHAP used for model interpretation. XGBoost achieved the highest observed discrimination (ROC-AUC = 0.784; accuracy = 0.708; F1-score = 0.704), closely followed by Random Forest (ROC-AUC = 0.780); the leading tree-based models substantially outperformed logistic regression benchmarks. SHAP identified Total Tax Amount, Tax Category 2, and Age as the three leading individual encoded features, while the Age contribution was nonlinear and varied across Tax Categories. The principal SHAP ranking was highly consistent under Random Forest. Top-decile ranking by predicted probability captured only 0.4% of the monetary exposure of realized mandatory collection cases, versus 92.7% under probability-weighted exposure ranking. These findings show that debt-related information remains central, but selected debtor characteristics add predictive value, supporting TaxMind as a model-agnostic early-warning framework for preventive tax debt management.

Journal of Risk and Financial Management doi: 10.3390/jrfm19090721

Authors: Nuno J. P. Rodrigues Ricardo J. P. Campelos Francisco Santos

This paper examines whether volatility management enhances the risk-return profile of factor investing strategies based on the momentum factor and the five-factor model across major developed equity markets. The study uses daily and monthly data from the Kenneth R. French database for the United States, Europe, and Developed Markets, covering long samples through December 2023. Volatility-managed factor returns are constructed by scaling original factor returns by functions of their realised volatility and are then evaluated against the corresponding unmanaged series. Performance is assessed by changes in average returns, volatility, and the Sharpe ratio, comparing volatility-managed factors with their unmanaged counterparts across regions and volatility-estimation horizons. The analysis evaluates whether the effectiveness of volatility management is consistent across factors, markets and estimation windows. Results show that volatility management can materially improve risk-adjusted performance for some factors and regions, particularly for momentum and profitability, although benefits are not uniform across all factors or geographies. These findings provide updated evidence on volatility management in factor investing and offer insights into the behaviour of volatility-managed factor strategies, although implementation costs and trading frictions are not incorporated in the reported results.

Journal of Risk and Financial Management doi: 10.3390/jrfm19090720

Authors: Dmytro Zherlitsyn Mykhailo Kuzheliev Volodymyr Mandra Nataliia Mandra

Cryptocurrency holdings in conventional portfolios challenge the empirical adequacy of standard tail-risk estimators. This study identifies a calibration mechanism that brings feature-based machine learning to supervisory-grade value at risk (VaR) coverage, improves its joint VaR and expected shortfall (ES) record relative to volatility filtering, and measures the value of tail-oriented allocation. Ten risk models are evaluated on equity, cryptocurrency and mixed portfolios across 1397 out-of-sample trading days, covering several distinct market phases. Three of these models are variants of a single learner, sharing the same feature set and estimation protocol, and differing only in how the predicted quantile is placed. Uncalibrated gradient boosting understates the tail in every portfolio, yielding violation rates as high as 10.81% against a 5% nominal level, and volatility filtering does not correct the shortfall. Split-conformal calibration keeps forecasts in the green zone of the generalised traffic-light criterion throughout and under every initialisation, yet 47 of the 49 significant loss comparisons still favour a classical benchmark. Separation is only modestly stronger on the cryptocurrency book, at 19 significant comparisons against 15 for each of the other two portfolios. Minimum conditional value at risk (CVaR) allocation reduces realised tail loss by 65% and maximum drawdown by 64% without improving risk-adjusted return. Thus, the evaluated machine learning models require calibration to achieve adequate coverage, whereas the econometric benchmarks retain an advantage in predictive accuracy.

Journal of Risk and Financial Management doi: 10.3390/jrfm19090717

Authors: Zlatko Čehulić Rajka Hrbić

Macroeconomic indicators of European Union member states are essential for analyzing patterns of comparative economic development, identifying structural differences, and designing policies aimed at reducing regional disparities. This study applies cluster and discriminant analysis to group countries according to selected indicators and to determine the variables that contribute most to differentiating more developed and less developed member states. Principal component analysis (PCA) was employed to reduce data dimensionality and identify latent structures among countries, while factor analysis provides exploratory evidence consistent with the role of selected variables in shaping economic profiles. The results reveal the existence of a stable core of countries with high GDP and favourable fiscal positions, whereas Southern and Eastern European countries exhibit greater internal differences, particularly in inflation rates, unemployment, and public debt. Cluster and factor patterns indicate the consistency of country typologies across both periods, and PCA highlights more complex latent structures and potential convergence processes among certain country groups. These findings provide a solid foundation for understanding the dynamics of macroeconomic processes and planning future comparative analyses and policies, including guidance for targeted economic interventions and strategies to reduce regional inequalities, for both academic research and policy formulation. Measured as an annual rate rather than a price-level index, inflation is shown to diverge markedly after 2020, whereas real convergence in GDP per capita and unemployment continues; endogenous structural-break tests support a break around 2020, and internal validation indices support a stable three-cluster typology.

Journal of Risk and Financial Management doi: 10.3390/jrfm19090719

Authors: Antanas Usas Dalia Streimikiene

Cultural festivals increasingly depend on digital channels to reach audiences, which makes the allocation of limited promotional resources a recurring management problem. This study examines how digital engagement shapes visitor satisfaction and revisit intention at a cultural tourism event, using the Vilnius Pink Soup Festival as a case. Five factors (social media, public website quality, electronic word of mouth (eWOM), festival expectations, and ICT usability) are tested as predictors of visitor satisfaction and revisit intention. The decision to model visitor-level rather than adoption-level outcomes follows the human-centric premise of Industry 5.0, which motivates the choice of dependent variables but is not itself operationalized or tested. Five factors (social media, public website quality, electronic word of mouth (eWOM), festival expectations, and ICT usability) are tested as predictors of visitor satisfaction and revisit intention. A quantitative survey of 384 attendees was analyzed in IBM SPSS Statistics 29 by ordinary least squares regression on composite construct scores, with mediation tested through the PROCESS macro (Model 4) using 5000 bootstrap resamples. Four of the five factors significantly influence satisfaction, with festival expectations exerting the strongest effect, followed by social media, public website quality and electronic word of mouth; ICT usability was not significant once website quality was accounted for. Satisfaction did not mediate the relationships between these antecedents and revisit intention; with the antecedents controlled, they predicted revisit intention directly, explaining 57.5% of its variance. No ticket revenue, visitor expenditure, marketing expenditure or sponsorship income was collected, and no financial outcome is tested here. The findings are therefore reported as evidence on visitors’ response, and their financial relevance—which channels organizers might prioritize when allocating promotional budgets—is developed as a managerial implication rather than as an empirical result.

Journal of Risk and Financial Management doi: 10.3390/jrfm19090718

Authors: Malak Khreis Hadi Harb Soha Dia

Innovations in artificial intelligence are reshaping how tax administrations approach compliance and audit planning, yet existing AI-based fraud detection studies largely treat the taxpayer population as homogeneous or remain conceptual frameworks awaiting empirical validation. This gap is consequential because audit resources are limited, evasion tactics are increasingly sophisticated, and misallocating scarce audit capacity carries a direct fiscal cost. To address it, this study presents VERITAS, a machine learning-based decision support system operationalizing a segment- and sector-aware architecture for corporate income tax audit planning: a single-layer model for Large Taxpayer case selection, and a novel two-layered model for small and medium enterprises (SMEs) that filters evasion-suspect cases before prioritizing them by expected tax-recovery yield against a target threshold. Ten classification algorithms were compared across 4063 SME and 1903 Large Taxpayer financial statements, with correlation-ranked feature selection subsequently applied to each. Random Forest consistently outperformed all alternatives across every segment, sector, and task examined; feature selection improved performance in every case; sector-specific modeling outperformed a generic classifier in two of four SME sectors, matched it in a third, and was marginally outperformed in the fourth; and a novel business-activity-code feature was retained in most analyses. These findings position VERITAS as a practical innovation in tax audit practice: an architecture for AI-driven audit planning that is internally validated against verified audit outcomes within the historical Lebanese dataset examined, built entirely from data tax administrations already collect, and potentially transferable to comparable jurisdictions, though not yet operationally deployed.

Journal of Risk and Financial Management doi: 10.3390/jrfm19090716

Authors: Javier André Ferro Pérez María Andrea Arias-Serna Jhon Jair Quiza-Montealegre

Credit default prediction is a standard risk-management task, and large language models (LLMs) have been proposed as prompt-based alternatives, without task-specific parameter updating, for institutions that cannot deploy full machine learning (ML) pipelines. This study evaluates the Informed GPT on Colombian solidarity-sector cooperative lending data, benchmarking gpt-4o-mini against five tuned tree ensemble and gradient boosting classifiers on native imbalanced data (17.3% default rate, 12,861 loans). Six additions relative to the seminal reference are reported: (i) a new empirical domain (Colombian solidarity-sector cooperatives regulated by the SES); (ii) a multi-model benchmark rather than a logistic-regression-only baseline; (iii) a leakage-mitigation prompt design that excludes supervised-analysis-derived hints, causal directions, and target-distribution disclosures; (iv) a calibration analysis using Brier score, log loss, expected calibration error (ECE), reliability diagrams, and calibration slope and intercept; (v) bootstrap 95% confidence intervals, DeLong tests, and McNemar tests for paired significance; and (vi) matched label-budget learning curves for logistic regression, XGBoost, and LightGBM. Tuned ML models attain AUC ≈0.96 (bootstrap CI [0.94,0.98]), while the LLM operates in the AUC 0.67–0.74 range across few-shot sizes N∈{0,10,20,40,80}. Under matched budgets, the LLM outperforms logistic regression at every N but is surpassed by gradient boosting once training samples reach approximately 40–80 observations. LLM probabilities are miscalibrated (ECE 0.11–0.21 vs. ≈0.03 for ML) and over-predict default (mean predicted probability 0.28–0.38 vs. observed base rate 0.17); threshold optimisation and post hoc calibration (Platt scaling, isotonic regression) are required for operational use. The findings qualify earlier claims about LLM auditability and position the approach as an assessment tool for cooperatives with fewer than ≈100 labelled defaults, rather than as a substitute for a well-resourced ML pipeline.

Journal of Risk and Financial Management doi: 10.3390/jrfm19090715

Authors: Christos G. Kampouris

This paper explores the effects of COVID-19 pandemic-related uncertainty on the European tourism subsectors, including airlines, hotels, and restaurants. Daily returns connectedness quantifies risk-sharing patterns in the tourism and hospitality industry and links them to measures of COVID-19-related uncertainty. The results suggest that while COVID-19 affects the hospitality and tourism industry return connectedness of companies within their respective sectors, those convergent effects are stronger during the relatively normal market regimes of the pandemic period. Further, airlines seem to be affected the most across all market regimes, while restaurants seem to be less affected due to their ability to adjust their operations during the COVID-19 crisis. Supply shocks related to oil prices are associated with higher return convergence among companies within the hotel and airline sectors under the euphoric market regimes. Supply shocks related to economic policy-related uncertainty seem to be associated with increased return divergence within the companies of each tourism sector across all market regimes. These observations could guide policymakers and investors in shaping more informed and timely decisions.

Journal of Risk and Financial Management doi: 10.3390/jrfm19090714

Authors: José Campos Amorim Diogo Miguel Barbosa dos Santos Catarina Cepeda

This study analyses the typology, legal framework and tax implications of investment funds in the European context, with particular emphasis on the distinction between Undertakings for Collective Investment in Transferable Securities (UCITS) and Alternative Investment Funds (AIFs). It examines the structural, regulatory and tax differences between these categories, as well as the challenges of tax neutrality and cross-border tax coordination arising from the fragmentation of national tax systems within the European Union. The research adopts a qualitative and interpretative approach based on legal-dogmatic analysis and a review of the relevant literature, with particular consideration of Directive 2009/65/EC (UCITS Directive), Directive 2011/61/EU (AIFMD), OECD principles and recent scholarship on international taxation. The analysis shows that the European investment fund regime is characterized by a high degree of prudential harmonization, in contrast to persistent fiscal fragmentation among Member States. By applying a common comparison matrix to Portugal, Luxembourg and Ireland, the study demonstrates that formal adherence to fund-level tax neutrality coexists with materially different vehicle-level tax mechanisms: Portugal applies a partial exclusion regime, Luxembourg operates a subscription tax, while Ireland provides a full fund-level exemption. These differences demonstrate that regulatory harmonization under UCITS and AIFMD has not been matched by equivalent convergence in the technical design of vehicle-level tax rules and may therefore produce different investor-level tax outcomes in cross-border contexts. The distinction between UCITS and AIFs remains central to the European regulatory architecture, reflecting different levels of investor protection, liquidity requirements and strategic flexibility, while AIFs are more heterogeneous and may involve greater exposure to illiquidity, leverage and management costs. The findings further indicate that differences in fund-level taxation, withholding mechanisms, treaty access and exit-tax or deemed-disposal rules continue to affect the location and structuring of investment funds, creating practical challenges for fund managers, tax authorities and institutional investors. Although the case law of the Court of Justice of the European Union and international initiatives such as the OECD’s BEPS project have contributed to reducing explicit discrimination and abusive tax practices, prudential harmonization has not eliminated the tax asymmetries that constrain full economic neutrality. The study concludes that the consolidation of a genuine internal market for investment funds requires not only continued prudential harmonization but also stronger tax coordination, particularly through more consistent cross-border tax reporting and financial disclosure requirements.

Journal of Risk and Financial Management doi: 10.3390/jrfm19090713

Authors: Ali H. I. Aljadba Abdallah A. S. Fayad Khaled O. Alotaibi Ahmad F. Almutairi

Given the limited environmental, social, and governance disclosure (ESGD) among Palestinian firms, this study’s purpose is to explore potential connections between diversity of board gender and nationality, as well as representation of non-executive directors, environmental, social, and governance factors and aggregate ESG disclosure. The study focuses on industrial companies listed on the Palestine Exchange (PEX), employing random-effects panel-data regression with firm-clustered robust standard errors on a balanced panel of 11 industrial companies, all listed on PEX from 2018 to 2024. The results show that ESGD is positively associated with female directors, foreign directors, and non-executive directors and provide support for agency theory, resource dependence theory, and stakeholder theory. This study furthers the literature via empirical evidence (from a conflict-affected emerging market) on the impact of board gender diversity, nationality diversity, and non-executive directors on ESGD. The study recommends that policymakers and stakeholders promote board independence and diversity of gender and nationality, with a view to enhancing ESG disclosure in Palestine.

Journal of Risk and Financial Management doi: 10.3390/jrfm19090712

Authors: Amaia Jone Betzuen Álvarez Amancio Betzuen Zalbidegoitia

Population ageing and the growing importance of private savings in financing retirement have increased interest in identifying investment strategies that enhance portfolio sustainability and risk-adjusted performance during the retirement decumulation phase. This study examines whether threshold-based rebalancing improves portfolio sustainability relative to a buy-and-hold strategy. Using historical equity and government bond returns for Spain, the United States, and Japan, the analysis considers alternative asset allocations, sustainable withdrawal rates (SWRs), and rebalancing thresholds over a 25-year retirement horizon. Strategy performance is evaluated using two complementary indicators: the average number of years of portfolio sustainability and a risk–return ratio. The results show that intermediate rebalancing thresholds generally provide the most favourable balance between sustainability, return, and risk, although the effectiveness of rebalancing depends on the SWR, portfolio allocation, and the characteristics of the market under consideration. The study provides new empirical evidence on the effectiveness of threshold-based rebalancing during the retirement decumulation phase and offers practical insights for portfolio management and retirement financial planning. The findings are also relevant for public policymakers, providing evidence that may support the design of strategies aimed at helping individuals maintain an adequate standard of living throughout retirement decumulation phase.

Journal of Risk and Financial Management doi: 10.3390/jrfm19090711

Authors: Joseph Serghani Hussein Trabulsi

Financial crises severely constrain entrepreneurs’ access to traditional financing, increasing the need for alternative funding mechanisms. Venture capital (VC) represents one such financing alternative and may also provide strategic and managerial support. However, evidence concerning how entrepreneurs perceive VC financing in crisis-affected economies remains limited. This study examines the associations between perceptions of equity-based VC financing, entrepreneurs’ willingness to launch new ventures, and perceived firm financial performance while assessing the moderating roles of risk aversion and perceived VC coaching and mentoring. This study draws on cross-sectional survey data from 392 Lebanese entrepreneurs and entrepreneurially oriented individuals. The findings indicate that perceptions of equity-based VC financing are positively associated with entrepreneurs’ willingness to launch new ventures, with this association becoming stronger at higher levels of respondent risk aversion. Perceptions of equity-based VC financing are also positively associated with perceived firm financial performance, with this association being stronger at higher levels of perceived VC coaching and mentoring. These findings represent perception-based associations and should not be interpreted as evidence of causal effects, temporal progression, or objectively measured financial performance.

Journal of Risk and Financial Management doi: 10.3390/jrfm19090710

Authors: Junming Yang Zelei Lin Li He Aiping Wang

Against the backdrop of the expanding digital economy, artificial intelligence, as a key technology underpinning corporate digital transformation, is increasingly influencing corporate governance practices and firms’ financial behavior. Using data from Chinese A-share listed firms from 2016 to 2024, this study applies a double machine learning approach to investigate the effect of AI on corporate accounting information quality and to identify the mechanisms underlying this relationship. The empirical results indicate that greater AI application significantly improves accounting information quality. This effect operates primarily through three channels: reducing operational risk, easing financing constraints, and mitigating agency costs. Further analysis reveals that the improvement in accounting information quality associated with AI is stronger for firms located in regions with higher levels of marketization and more developed digital infrastructure, as well as for firms facing less intense market competition. By examining accounting information quality as an important economic consequence of AI adoption, this study broadens the existing literature on the governance effects of AI and provides additional empirical evidence on the channels through which AI contributes to higher-quality accounting information. The findings also provide practical implications for policymakers seeking to improve the implementation of the “AI Plus” initiative and for firms pursuing digital transformation alongside improvements in intelligent governance.

Journal of Risk and Financial Management doi: 10.3390/jrfm19090709

Authors: Mejda Tebessi Heni Boubaker

This paper examines whether the adoption of Supervisory Technology (SupTech) is associated with a reduction in greenwashing in the European banking sector over the period 2014–2025. Using a progressive empirical framework combining the Synthetic Control Method (SCM), a split-sample SCM, an OLS regression of the SCM-estimated treatment effect, and a Causal Random Forest (CRL) via T-Learner applied to a panel of European banks, we provide evidence consistent with a meaningful reduction in greenwashing associated with SupTech adoption, which is robust across multiple identification and validation strategies. The split-sample SCM and OLS analyses reveal that this effect is amplified by higher capital adequacy, genuine ESG engagement, and stricter regulatory environments, while larger banks exhibit a systematically attenuated response. Contrary to the complementarity hypothesis, RegTech does not reinforce SupTech’s disciplining effect; instead, the evidence points to a substitution mechanism whereby banks with developed internal compliance infrastructure derive limited marginal benefit from external supervisory technology. The Causal Random Forest analysis provides evidence of a statistically significant and stable average treatment effect and indicates that bank digital maturity and FinTech adoption are the most consistent drivers of SupTech’s effectiveness. Policy simulations show that improving digital maturity, rather than RegTech endowment, yields the largest additional greenwashing-reduction gains. These findings suggest that SupTech acts as a credibilization mechanism whose effectiveness depends on the stringency of the external regulatory architecture and the digital absorptive capacity of supervised institutions. External validity to less harmonized regulatory environments remains an open empirical question.

Journal of Risk and Financial Management doi: 10.3390/jrfm19090708

Authors: Imane El Imami Abdelkader El Alaoui Bassma Guermah Said Ouatik El Alaoui Miklos Vasarhelyi

Environmental, Social, and Governance (ESG) ratings increasingly shape capital allocation, corporate strategy, and regulatory oversight, yet their credibility is constrained by methodological opacity, rating divergence, and greenwashing risk. Prior reviews treat machine learning (ML) in ESG as a prediction problem. We identify an emerging research trajectory in which ML is increasingly used not only to consume ESG signals but also to verify their construction and credibility. Drawing on signaling theory, we conduct a PRISMA-guided systematic review of 127 peer-reviewed studies from Scopus and Web of Science to examine how machine learning (ML), deep learning (DL), Natural Language Processing (NLP), and Explainable AI (XAI) are transforming ESG rating analysis. We develop a tripartite framework classifying studies by the functional role of the ESG score: predicted (n = 29), used (n = 57), or assessed (n = 41). Our central contribution is the first synthesis of the methodological-assessment stream, organized into four clusters: XAI reverse-engineering of proprietary scoring functions, divergence reconciliation, greenwashing detection, and unsupervised industry-materiality clustering. The evidence assembled in this stream indicates that ESG ratings weight low-cost aspirational disclosure heavily relative to costly performance evidence, suggesting that greater reliance on aspirational disclosure relative to performance evidence may increase greenwashing risk, consistent with signaling-theory concerns. A study-level validation appraisal further shows that the most extreme fit statistics often arise in target-proximal reconstruction or non-temporal validation settings, cautioning against interpreting high R2 as evidence of transferable out-of-time forecasting.

Journal of Risk and Financial Management doi: 10.3390/jrfm19090707

Authors: Huda Aldhahi

Although cryptocurrency markets trade continuously, the intraday distribution of Bitcoin’s volatility has migrated toward United States trading hours as the asset has institutionalized. Using ten years of hourly Kraken XBT/USD data (2016–2025; 87,672 observations), I document this migration and tie its timing to the U.S. trading calendar with two natural experiments. When U.S. clocks change, the intraday volatility peak shifts by one hour in UTC, tracking the displaced equity open; the shift appears only in the institutionalized period. On weekday NYSE holidays, when the U.S. cash market is closed while most other markets trade, the U.S.-hours share of realized variance falls by 13.9 percentage points relative to matched weekdays, close to the uniform benchmark of 0.375 (the share expected if variance were distributed evenly across the 24 h day), and this effect is also absent before 2019. Neither result is consistent with an explanation fixed in UTC. A window-free circular index of intraday concentration rises by more than 40% over the decade, with a structural break in November 2021, and no local break at the 2017 futures launch or the 2024 spot-ETF approval. A placebo simulation shows that naive whole-sample event contrasts on this trending series are significant for 100% of random pseudo-event dates. The microstructure of a nominally 24/7 market increasingly bears the imprint of the U.S. equity calendar.

Journal of Risk and Financial Management doi: 10.3390/jrfm19090706

Authors: Gee-Jung Kwon

Korean firms record the cost of entertaining customers and counterparties in a separate account and report it on its own where they judge it material. The COVID-19 pandemic interrupted the activity that account pays for. This study asks whether the stock market’s valuation of that spending changed around the interruption. Using 19,334 firm-years on 2331 Korean listed firms from 2016 to 2025, Tobin’s Q is regressed on reported entertainment expenditure scaled by sales and interacted with indicators for the pandemic years 2020 to 2021 and the years that followed, with firm and year fixed effects and standard errors clustered by firm. Entertainment intensity was positively associated with firm value before the pandemic, although that association is carried by the heaviest spenders and does not survive their removal. The pandemic interaction is imprecise, running from −20.81 to 11.66 against a benchmark of 21.916. After the pandemic, the association was eliminated: the interaction is −29.655, and it holds across nine measurement and deflator variants. The association changes sign around 2022; a test that does not impose the date locates the change there, but a discrete break and a decline that steepens cannot be separated. Advertising and research intensity attenuate at least as much, so the change belongs to discretionary expenditure as a class rather than to entertainment alone.

Journal of Risk and Financial Management doi: 10.3390/jrfm19090705

Authors: William Ng I-Cheng Yeh

Expected stock returns reflect cash distributions, fundamental growth, and market valuation, yet these sources are often examined separately. This study develops a Theoretical Rate of Return (TRR) framework that organizes them within a common multiplicative representation. The theoretical foundation is a one-period ex post decomposition of realized total shareholder return into dividend yield, internal growth, and valuation adjustment. Its empirical implementation instead uses Growth and Value characteristics observable at portfolio formation as predictive proxies; these variables are neither individually novel nor treated as exact equivalents of subsequently realized components. Using quarterly Chinese A-share data, the study evaluates the joint Growth–Value return relation through within-quarter sequential double sorting, log-linear ordinary least squares, interaction and centered quadratic specifications, and threshold regression. The full-sample results indicate that Growth generally provides the stronger first-order relation, whereas the standalone Value coefficient is less consistent across horizons. The interaction term is not robust to multiple-testing adjustment, while evidence of curvature is confined to selected specifications and horizons; threshold evidence is likewise limited and requires cautious interpretation. Quarter-by-quarter cross-sectional regressions and a purged four-period out-of-sample design further show that the quadratic model is not systematically more stable or predictively superior to the parsimonious linear benchmark. Supplementary evidence from Hong Kong and the United States indicates that the relative importance of Growth and Value varies across market environments. The contribution is therefore not a new pricing factor or econometric method. Rather, TRR provides a common economic organization for established characteristics and a disciplined framework for distinguishing full-sample functional-form evidence from temporal stability and out-of-sample predictability.

Journal of Risk and Financial Management doi: 10.3390/jrfm19090704

Authors: Rony Romdany Tettet Fitrijanti Dini Rosdini Zubir Azhar

IFRS 17/PSAK 117 fundamentally transforms the risk information architecture of insurance companies by mandating estimation-based measurement, granular disclosure of onerous contracts, and cross-functional governance of actuarial assumptions. This study investigates how accountant–actuary collaboration, regulatory enforcement, and IT capability shape accounting quality and governance outcomes in the implementation of IFRS 17/PSAK 117 in Indonesian insurance companies. Using an exploratory multi-case qualitative design, the study draws on semi-structured interviews with ten participants across insurance entities and regulatory bodies, analyzed through NVivo-supported thematic analysis. The findings reveal three processual mechanisms: collaboration functions as a “logic translation” process through which actuarial and accounting frameworks are reconciled; enforcement operates as an interpretive stabilizer that reduces variation in implementation practices; and IT capability introduces socio-technical frictions at the human–automation boundary even as it enables audit trail infrastructure. Together, these mechanisms constitute an interdependent configuration that shapes accounting quality, which in turn strengthens governance through improved monitoring, reduced information asymmetry, and enhanced accountability. The study extends IFRS 17 implementation literature by offering a process-based, configurative explanation of how three interdependent mechanisms jointly produce governance-enhancing accounting quality in an emerging market context.

Journal of Risk and Financial Management doi: 10.3390/jrfm19090703

Authors: Zuocheng Li Chenxu Ling Yifan Ye

Financial returns have heavy tails and nonzero skewness. Machine learning risk models typically return isolated quantiles or rest on thin-tailed laws. We introduce a skewed, heavy-tailed distribution that is as easy to use as the normal and that converts any machine learning forecast of conditional moments into a full density. The law splices the left half of one logistic density onto the right half of another. A prescribed mean, variance, and skewness map into its three parameters by elementary algebra. Value at Risk (VaR), Expected Shortfall (ES), optimal holdings, and risk premia then have closed-form expressions. The attainable third-moment interval is wider than that of the smooth half-normal law and even a small departure from symmetry already moves the implied tails away from the Gaussian benchmark. The logistic base has a kurtosis of 4.2 and above, so tail-risk estimates are more conservative than those of thin-tailed alternatives. Gradient-boosted trees predict the conditional mean, volatility, and skewness that enter the closed-form formulas. The resulting one-day-ahead VaR and ES forecasts are well calibrated and pass standard coverage tests. Unlike quantile-based machine learning forecasts, they deliver the entire conditional density in analytic form. Exponentially weighted moving average moments, fed through the same formulas, already give accurate ES forecasts. An application to stock-index, commodity, and foreign-exchange returns shows that the distribution tracks sample asymmetry and tail behavior. A three-moment calibration matches mean, variance, and skewness. The implied kurtosis is that of the logistic base and is not a free parameter.

Journal of Risk and Financial Management doi: 10.3390/jrfm19090702

Authors: Antanas Usas Edmundas Jasinskas Arturas Simanavicius Dalia Streimikiene

This study takes a demand-side perspective on sustainable digital government, examining how citizens perceive and value the sustainability of public digital services. The transition to a digital and circular economy places public institutions, no less than firms, under pressure to deliver digital transformation in ways that are technologically robust, socially inclusive, and environmentally responsible; whether citizens register those efforts, as well as in what order, is an open question. Framed by the human-centric logic of Industry 5.0, this study examines how three perceived sustainability dimensions of public digital services (technological, social, and environmental) shape the value citizens derive from e-government services and how that perceived value converts into satisfaction. Using a quantitative design, we surveyed 412 Lithuanian citizens who actively use e-government platforms and tested the model through correlation, multiple regression, and mediation analysis. Perceived technological sustainability showed the strongest association with perceived value and satisfaction, followed by perceived social sustainability and then perceived environmental sustainability, whose contribution is the smallest of the three and materializes chiefly where citizens recognize tangible benefits. Each dimension was measured as a single reflective composite of three self-reported items; the attribute labels used in the separate ranking exercise are not identical to these constructs and the two sets of results should not be read interchangeably. Perceived value emerges as the critical link between perceived sustainability and citizen satisfaction, and citizens rank usability, security, and reliability as their top priorities. The findings indicate a clear hierarchy in citizens’ priorities: value is generated first where systems are perceived as reliable and secure, and only then where inclusiveness and ecological benefits are visible to users. Because all constructs are perceptual, the results describe how citizens rank the sustainability attributes of public digital services, not the fiscal returns of any particular spending decision; linking these perceptions to budgetary data remains a task for future research.

Journal of Risk and Financial Management doi: 10.3390/jrfm19090701

Authors: Hanae Idari Hajar Bouladasse Said El Ganich Taoufiq Yahyaoui Mohamed Oudgou

Green taxation has evolved from a fiscal instrument for correcting environmental externalities into a strategic policy mechanism for promoting sustainable development and supporting the transition toward low-carbon economies. As research on this topic has expanded, the literature has become increasingly fragmented, characterized by diverse research streams, limited interdisciplinary integration, and an incomplete understanding of its intellectual and conceptual development. This study provides a comprehensive bibliometric analysis of green taxation research to examine its scientific evolution, map its knowledge structure, and identify emerging research frontiers and knowledge gaps. Conceptually, the literature on green taxation extends beyond conventional Pigouvian foundations to encompass ecological, institutional, and broader heterodox perspectives. The analysis is based on 2952 publications indexed in the Scopus database between 1990 and 2025. Biblioshiny (Bibliometrix in R) and VOSviewer were employed to examine publication trends, co-citation networks, keyword co-occurrence, and international scientific collaboration. The results reveal sustained growth in scientific production, reaching its highest level in 2024, and a heterogeneous research landscape structured around major themes, including the double dividend, innovation for sustainable development, environmental policy related to pollution and welfare, circular economy and sustainability transitions, as well as green investment linked to technological change. Scientific output remains highly concentrated in a limited number of countries, with China emerging as the leading contributor, while international collaboration remains comparatively limited. The analysis also highlights significant geographical disparities, particularly the underrepresentation of Africa and the MENA region, and reveals that the field remains only partially integrated despite its rapid expansion. These findings provide an integrated understanding of the evolution of green taxation research and identify key priorities for future empirical and comparative studies to support more effective and inclusive environmental fiscal policies.

Journal of Risk and Financial Management doi: 10.3390/jrfm19090700

Authors: Francesco Rania

Environmental, social, and governance (ESG) quality cannot be directly observed because substantial disagreement across rating providers contaminates the observed ESG scores with measurement error. This paper addresses this problem by modelling the true ESG state as a latent, vector-valued Itô diffusion defined on a filtered probability space whose information set is progressively enlarged by FinTech signals. We establish the well-posedness of the latent ESG process, prove the existence of an equivalent martingale measure under an explicit exponential-moment condition, and solve an ESG-constrained portfolio problem under a wealth-scaled sustainability constraint through a Hamilton–Jacobi–Bellman verification theorem. Computationally, raw sustainability information is extracted from SEC Form 10-K filings using a FinBERT transformer architecture and incorporated into a linear Gaussian state-space model, where the latent ESG state is recovered via Kalman filtering. Theoretical results are then linked to asset pricing, portfolio allocation, and systemic risk networks through a common filtered ESG factor. Using an unbalanced panel of 1086 U.S. listed firms over 2011–2023 and ESG information from MSCI, Refinitiv, and Sustainalytics, we document substantial provider disagreement and show that the observed ESG ratings contain significant transitory measurement noise. The filtered ESG state exhibits higher reliability, lower noise, and greater persistence than individual provider scores. In asset pricing tests, the latent ESG state predicts future excess returns, whereas a composite provider-based ESG measure does not; a one-standard-deviation increase in the latent ESG state is associated with approximately 0.35 percentage points higher monthly excess returns (about 4.3% annualised). When both measures are included simultaneously, only the filtered ESG state retains explanatory power. Out-of-sample portfolio tests show that a latent ESG strategy achieves a Sharpe ratio of 0.72, significantly exceeding both an unconstrained benchmark (0.59) and a composite ESG screen strategy (0.55). At the network level, ESG-adjusted weighting attenuates systemic fragility by reducing the spectral abscissa from 0.34 to 0.21, with the mitigating effect remaining significant under permutation-based placebo tests. Overall, the evidence supports the central hypothesis that ESG measurement error attenuates the observed pricing effects and that FinTech-enabled filtering recovers economically meaningful sustainability information relevant for asset pricing, portfolio construction, and systemic risk assessment.

Journal of Risk and Financial Management doi: 10.3390/jrfm19090699

Authors: Kamel Alanazi Alison Gray

Forecasting Gulf Cooperation Council (GCC) country stock market returns is challenging because these markets exhibit volatility clustering, heavy tails, oil-price sensitivity, and asymmetric responses to shocks. This study evaluates whether ARIMA–GARCH-family hybrid models improve one-step-ahead forecasting of daily GCC stock index returns relative to single econometric specifications and whether time-series cross-validation provides a more robust model-selection framework than relying solely on information criteria such as AIC. Daily index data for Saudi Arabia, Kuwait, Bahrain, Qatar, Oman, Abu Dhabi, and Dubai from 3 October 2012 to 3 November 2022 are modelled using ARIMA, GARCH, TGARCH, APARCH, and hybrid ARIMA–GARCH-family models with Student-t innovations. Forecasting performance is assessed using RMSE, MAE, and MAPE measures under holdout testing, block cross-validation, walk-forward cross-validation, and rolling-window cross-validation. Model differences are evaluated using Friedman and Diebold–Mariano tests. The results show that single ARIMA specifications do not adequately capture the conditional heteroscedasticity and heavy-tailed behaviour of GCC stock index returns. Hybrid ARIMA–GARCH-family models provide more stable forecasting performance, with ARIMA–TGARCH(1,1)-t specifications selected for most GCC markets. ARIMA(5,0,3)–APARCH(1,1)-t provides the strongest performance for the Saudi index. The findings show that time-series cross-validation can reduce the selection of potentially over-complex models indicated by the AIC criterion and provides a more reliable basis for selecting forecasting models in emerging, oil-dependent financial markets.

Journal of Risk and Financial Management doi: 10.3390/jrfm19090698

Authors: Michael Di Carlo Jordi Mondria Ilias Tsiakas

We propose a two-factor Capital Asset Pricing Model (CAPM), which includes two separate factors for the market capital gains and the market dividend yield. We find that the dividend yield factor carries a significant negative premium in the post-1978 period, which coincides with the persistent decline in the number and proportion of US dividend-paying firms. We motivate this finding by proposing a theoretical model, which shows that the predictive information of the dividend yield can be high if capital gains are vastly more volatile than the dividend yield and investors have a behavioral bias against dividends.

Journal of Risk and Financial Management doi: 10.3390/jrfm19090697

Authors: Ahmed Abdelsalam Nikiforos T. Laopodis

This study examines the dynamic connectedness and spillover transmission mechanisms among tourism-related exchange-traded funds (ETFs), clean energy markets, oil, geopolitical risks, and tourism transportation using daily percentage returns from 24 March 2017 to 16 March 2026. We employed a battery of econometric methodologies, including the R2-decomposed connectedness approach, a TVP-VAR framework to examine the extent and nature of connectedness and distinguish contemporaneous and lagged spillovers, and the DCC-GARCH specification to assess dynamic conditional correlations. The data exhibit substantial volatility and non-normality, supporting these dynamic econometric approaches. The results reveal that connectedness varies considerably over time and tends to intensify during periods of major economic and geopolitical uncertainty. PEJ emerges as the dominant overall net transmitter of shocks, primarily through lagged spillovers, while Transportation and Clean Energy act as contemporaneous transmitters but become net receivers in the lagged horizon. The decomposition further shows that contemporaneous linkages are relatively weak, while lagged effects are stronger, suggesting that shock transmission occurs gradually rather than instantaneously. The dynamic correlations are time-varying, and ETFs display asymmetric behavior across different market conditions. Overall, this study highlights the time-varying nature of interactions between tourism and clean energy assets, driven by energy market dynamics and geopolitical risks, and provides useful implications for portfolio diversification, risk management, and policy decisions in interconnected tourism and sustainable financial markets.

Journal of Risk and Financial Management doi: 10.3390/jrfm19090696

Authors: Angel Angelov Velichka Nikolova

Fiscal cyclicality occupies a central position in macroeconomic stabilization, but existing empirical studies have focused predominantly on determining whether fiscal behaviour is procyclical or countercyclical, with relatively limited attention being paid to the dynamic evolution and intensity of fiscal response over time. The purpose of this study is to develop a dynamic framework for assessing the direction, intensity and temporal evolution of fiscal cyclicality in the European Union. This research analyses 27 Member States during the period 2001–2025. The fiscal cyclicality coefficient is estimated using five-year rolling ordinary least squares (OLS) regressions of the budget balance on the output gap, allowing fiscal cyclicality to vary across countries and over time. In order to enhance the reliability of the estimates, the computed coefficients are winsorised and used to construct a continuous measure of fiscal cyclicality intensity and a normalized Score index. The findings reveal substantial heterogeneity in fiscal cyclicality across EU Member States and over time, indicating a predominant countercyclical behaviour during significant macroeconomic shocks, while also demonstrating significant differences in the strength and stability of fiscal responses. Countercyclical observations account for 83.07% of the rolling-window estimates, compared with 11.82% procyclical and 5.11% acyclical observations. The additional robustness tests indicate that the main structure of the estimated coefficient series is preserved under alternative treatments of extreme observations, the exclusion of individual countries and the use of an earlier information set for the output gap. Analysing fiscal cyclicality solely through its direction therefore provides an incomplete representation of fiscal behaviour. The proposed framework extends the existing literature by simultaneously considering the direction, intensity and dynamics of fiscal cyclicality over time, providing a more comprehensive basis for comparative analysis of fiscal policy and future assessments of fiscal sustainability. For policymakers, the resulting Score provides an additional quantitative indicator for monitoring changes in the intensity of fiscal behaviour, and when considered jointly with the estimated coefficient, changes in its direction may complement existing assessments of fiscal policy within the European Semester and the European economic governance framework.

Journal of Risk and Financial Management doi: 10.3390/jrfm19090695

Authors: Martin Sundberg George Giannopoulos Kazi Abul Bashar Muhammad Afzal Hossain

This study examines whether U.S. monetary tightening cycles and market uncertainty are associated with initial public offering (IPO) underpricing. Using a sample of 1750 U.S. IPOs issued between 2003 and 2024, the analysis investigates three Federal Reserve monetary tightening cycles and employs multivariate regression models to examine the relationships between monetary-policy conditions, market uncertainty, proxied by the CBOE Volatility Index (VIX), and first-day IPO returns. The findings provide no consistent evidence that monetary tightening cycles are associated with higher IPO underpricing. Although descriptive evidence indicates higher underpricing during the 2022–2023 tightening period, the tightening indicators are not statistically significant in the controlled multivariate regressions. In contrast, market uncertainty is negatively associated with IPO underpricing. This relationship is statistically significant in the equity-only sample for both contemporaneous and one-day lagged VIX measures, while the lagged VIX also remains statistically significant in the full sample. These findings contribute to the IPO literature by showing that monetary tightening and market-based uncertainty exhibit different relationships with IPO pricing. The study also provides practical implications for issuers, underwriters, investors, and policymakers seeking to understand IPO pricing behaviour under changing macro-financial conditions.

Journal of Risk and Financial Management doi: 10.3390/jrfm19090693

Authors: Alessio Faccia

Federal Reserve press conferences convey policy path information and uncertainty beyond formal decisions. This study tests whether transcript-derived risk language predicts the magnitude of financial market responses after policy surprises and event characteristics enter the model. The dataset contains 93 official press conference transcripts from April 2011 to June 2026, with 90 scheduled events in the primary sample. A Q&A lexical risk index combines standardised frequencies of negative, uncertainty, and weak modal terms from the Loughran–McDonald dictionary. The outcome is an equal weight composite of absolute S&P 500 returns, two- and ten-year Treasury yield changes, and US dollar returns during a 70 min press conference window. OLS models use HC3 standard errors, asset-specific regressions, influence analysis, permutation testing, and leave-one-out cross-validation. Q&A lexical risk does not predict larger responses. The full-model coefficient equals −0.063 (p = 0.444), incremental R2 equals 0.0034, and prediction error rises by 0.60% after adding the index. Policy surprise magnitude remains the strongest predictor. The 90-event sample limits precision for small effects, with an approximate 80% minimum detectable effect of 0.230 response index units. The estimates describe conditional association and incremental predictive content. They do not test acoustic delivery, realised intraday volatility, or a causal communication effect.

Journal of Risk and Financial Management doi: 10.3390/jrfm19090694

Authors: Abdelrazaq Farah Freihat Huthaifa Al-Hazaima Hashem Alshurafat Nihel Halouani

Drawing on Agency Theory, Institutional Theory, and the Diffusion of Innovation (DOI) framework, this study examines the relationship between mandatory adoption of the eXtensible Business Reporting Language (XBRL) and earnings management in an emerging market. Jordan introduced compulsory XBRL reporting for listed firms in 2020, providing a natural setting to evaluate its governance implications. The analysis is based on firm-level data for 40 industrial companies listed on the Amman Stock Exchange over 2016–2023 (320 firm-year observations). Accrual-based earnings management is measured by absolute discretionary accruals from the cross-sectional Modified Jones Model,. Firm fixed-effects regressions with firm-clustered standard errors, an event-study specification with year fixed effects, and an extensive robustness battery (performance-adjusted accruals, pooled estimation, balance-sheet accruals, exclusion of the pandemic years, and a placebo adoption date) consistently show no statistically detectable change in accrual-based earnings management after adoption. By contrast, absolute abnormal production costs increase significantly after the mandate, an effect that strengthens when the COVID-19 years are excluded and disappears under a placebo date, a pattern consistent with partial substitution from accrual-based towards real-activities manipulation. The findings suggest that digital reporting mandates alone do not discipline reporting behavior in environments with limited institutional enforcement and may redirect rather than reduce managerial opportunism. Implications for regulators, auditors, and standard setters are discussed.

Journal of Risk and Financial Management doi: 10.3390/jrfm19090692

Authors: Zhefan Wu Eugene Pinsky

Cryptocurrency markets trade continuously, but their return dynamics need not be uniform over the 24 h cycle. Using hourly Kraken prices from 2016 to 2025, we divide each day into complementary 12 h sessions and evaluate 25 ordered combinations of cash, long, short, momentum, and reversal positions across all 12 non-redundant hourly boundaries. The full-sample selection identifies Reversal/Reversal at an 08:00 UTC daytime start for Bitcoin (BTC) and Long/Reversal at a 05:00 UTC start for Ethereum (ETH). The return mechanisms differ: BTC is associated with conditional reversal in both sessions, whereas ETH combines positive overnight drift with daytime reversal. The selected rules generate higher realized terminal wealth and more favorable drawdown and Sharpe-ratio outcomes than buy-and-hold along the observed full-sample path. These realized differences, however, are not statistically significant in paired bootstrap tests, and Hansen’s Superior Predictive Ability test does not reject the null after accounting for the search over 300 cutoff–strategy combinations. A chronological holdout exercise, in which selection uses only 2016–2020 and evaluation uses 2021–2025, further shows that the ETH rule persists, but the BTC training-selected rule underperforms buy-and-hold. The findings should therefore be interpreted as evidence of asset-specific historical return structure, not as proof of a stable or readily implementable abnormal-profit opportunity. The 0–2 basis-point cost scenarios are illustrative and exclude slippage, market impact, borrowing, and funding costs.

Journal of Risk and Financial Management doi: 10.3390/jrfm19090691

Authors: Mohammed Abdulaziz M. Alhossini

Departing from the existing literature, this study disaggregates non-executive directors with industry expertise into co-opted directors (those appointed after the incumbent CEO assumes office) and non-co-opted directors, and examines their associations with corporate tax avoidance. It further examines whether these associations vary according to whether such directors serve in advisory or monitoring roles. Using a sample of UK non-financial firms over the period 2000–2020 and firm fixed-effects estimation, the results indicate that greater board representation of industry expert directors is associated with lower tax avoidance. This association is primarily driven by non-co-opted rather than co-opted industry expert directors. Among non-co-opted industry expert directors, the association is stronger when directors serve in monitoring rather than advisory roles. Additional analyses indicate that the documented associations are more pronounced among firms exhibiting aggressive tax positions relative to size- and industry-matched peers, those characterized by heightened operating risk, and those operating in more competitive industries. Overall, the results indicate that industry expertise is associated with more effective board oversight of tax-related managerial discretion, particularly when such expertise is less susceptible to CEO influence and is deployed through monitoring-oriented board functions.

Journal of Risk and Financial Management doi: 10.3390/jrfm19090690

Authors: Sara Chegdal Mustapha Kabil Abdeljalil Settar

Modern financial risk management increasingly relies on transformer-based anomaly detection, although the sensitivity of these methods to the underlying volatility model specifications remains unexplored. This study thoroughly compares four GARCH variants—symmetric GARCH and asymmetric specifications (EGARCH, GJR-GARCH, APARCH)—across two state-of-the-art transformer architectures (TranAD for point anomalies, VTT for regime detection) using S&P 500 returns spanning 1980 to 2026. The investigation addresses a fundamental question for practitioners integrating econometric and deep learning methods: does the additional complexity of asymmetric volatility modeling yield meaningfully different anomaly detection when passed through transformer architectures? The analysis reveals that the GARCH specification affects anomaly severity rankings rather than detection consensus, with high cross-model agreement (minimum Jaccard of 0.88 for TranAD and 0.76 for VTT) despite statistically significant differences in score distributions. Asymmetric models exhibit extended post-crisis sensitivity, driven by their stronger response to negative shocks—the leverage effect captured by the GJR-GARCH threshold term—rather than by greater persistence; indeed, the symmetric GARCH exhibits the longest half-life. This enables specification selection based on risk philosophy: conservative monitoring via GJR-GARCH or efficient normalization via symmetric specifications. The choice of detection method—point versus regime identification—proves more consequential for anomaly detection performance than volatility model specification.

Journal of Risk and Financial Management doi: 10.3390/jrfm19090689

Authors: Abraham Kisembe Wawire Christine Nanjala Simiyu Munene Laiboni Rogers Ochenge

Frontier financial markets face a diagnostic gap in forecasting volatility: linear and single-regime GARCH fails to capture breaks, spillovers, and regime transitions. Despite the importance of these markets, there is a gap in the literature: lack of a Kenya-specific, regime-sensitive Early Warning System (EWS) that can integrate long-memory filtering of volatility with nonlinear classification models. Therefore, policymakers lack signals to anticipate systemic stress. This study constructed a multi-stage pipeline using 6703 daily observations of the NSE 20 Share Index, USD/KES exchange rate, and Brent spot prices (1997–2024). K-Means clustering, FIGARCH filtering, LSTM–XGBoost ensemble classification, a logistic threshold model, and VaR/ES back-testing were used to generate alarm signals and validate risk-management performance. Across model estimation and evaluation phases, time horizons were assessed, showing that one-day signals were reactive and ten-day forecasts diluted precision, while five-day predictions achieved the strongest balance. Across model iterations, accuracy improved from 0.86 in the initial FIGARCH–LSTM pipeline, to 0.92 in the unbalanced classification model, and 0.94 in the weighted baseline, culminating in 0.98 with the final hybrid LSTM–XGBoost ensemble. The ensemble model delivered robust detection across Calm (F1 = 0.99), Moderate (F1 = 0.82), and Stress (F1 = 0.69) regimes. Stress thresholds were developed to translate regime signals into actionable alarms. This study provides an empirical application of a hybrid framework, combining long memory modelling of volatility with the adaptability of deep learning models to deliver Basel-compliant tail-risk alarms and a state-dependent policy matrix for regulators.

Journal of Risk and Financial Management doi: 10.3390/jrfm19090688

Authors: Natalia Acevedo-Prins Juan D. Velásquez

Accurate conditional volatility forecasting is essential for risk management, asset pricing, and portfolio optimization. Despite the widespread use of GARCH family models and the proliferation of deep learning architectures, a methodological gap persists: neural networks are rarely trained under criteria statistically coherent with the distributional properties of financial returns. This paper makes two contributions. First, we establish formal structural equivalences between classical heteroscedastic models and neural architectures, showing that ARCH(p) is equivalent to a single-layer linear MLP and GARCH(1,1) to a constrained LSTM, with an explicit parameter correspondence. Second, we propose LSTM-SSE-t-Student, a parsimonious LSTM trained with a hybrid loss that combines the sum of squared errors with the Student-t negative log-likelihood, penalizing errors in the tails of the return distribution. The model is evaluated on six daily series spanning three asset classes—Bitcoin, Ethereum, Gold, Oil, the DJIA, and the S&P 500—across diverse regimes, including the COVID-19 period, against a broad set of econometric and deep learning benchmarks. Relative to GARCH(1,1), it significantly improves point forecast accuracy and probabilistic calibration over naive, short-memory, and regime-switching specifications, while matching the strongest GARCH family and deep learning competitors; a sensitivity analysis shows that the likelihood term lowers the QLIKE loss for most series, with a market-dependent optimal weighting. Statistical significance is assessed via Diebold–Mariano tests with a correction for multiple comparisons, and Value-at-Risk and Expected Shortfall backtests confirm adequate tail calibration for the equity and cryptocurrency series. Interpretability is preserved through the GARCH-consistent structure, whose learned gate dynamics are stable across random seeds.

Journal of Risk and Financial Management doi: 10.3390/jrfm19090687

Authors: Radostin Vazov Zhelyo Hristozov

The Solvency II regulatory framework (Directive 2009/138/EC) defines the Solvency Capital Requirement (Solvency Capital Requirement, SCR) using a value-at-risk (VaR) approach, with a confidence level of 99.5% and a one-year time horizon. This structure makes regulatory capital extremely sensitive to the characteristics of the right tail of the loss distribution and, consequently, to the effectiveness of risk transfer mechanisms. This study analyzes the impact of the five main types of reinsurance contracts quota share, surplus, quota–surplus, excess-of-loss (XoL), and stop-loss on the transformation of the net loss distribution and the resulting dynamics of the SCR. The empirical analysis is based on a computational experiment using real data from a ten-year insurance portfolio covering the period 2016–2025. The results show that the use of reinsurance leads to a reduction in the total capital requirement in the range of 18.4–23.4% on an annual basis, with the effect exhibiting an approximately linear relationship with the size of the cession quota. The stratified comparative analysis conducted identifies significant differences in the effectiveness of individual contract structures with regard to the reduction of tail risk. In particular, XoL contracts demonstrate the strongest effect on the extreme quantiles of the loss distribution, with a reduction reaching −52.5% at the 99.5% VaR level and −68.6% at the 99.9% VaR level. In contrast, quota-share contracts result in a practically proportional scaling of risk, characterized by a symmetric reduction of approximately −40% across all confidence levels. The results further show that multi-tiered reinsurance programs combining quota share, excess, catastrophe XoL, and stop-loss components provide the highest degree of capital relief, reaching 48.8%, which indicates the presence of significant nonlinear diversification and complementarity effects among the individual risk transfer mechanisms. A waterfall decomposition is applied to identify the main factors determining the difference between the standard formula and the internal model. The analysis finds that the dominant drivers of the observed capital relief are the effect of precise risk calibration (on average −8.3%) and the effect of diversification (−4.7%). These results underscore the importance of adequately modeling the interdependencies among risk modules and the limitations of standardized regulatory parameterizations. In addition, a “wrong-way risk” stress scenario is developed, involving the simultaneous occurrence of a catastrophic risk and the insolvency of two key reinsurers. Under this scenario, the effectiveness of risk transfer is reduced to −27.3%, and the solvency ratio falls below the minimum capital requirement (12.5%). This result empirically confirms the cautious regulatory stance of the European Insurance and Occupational Pensions Authority regarding the limited recognition of capital reliefs that do not demonstrate resilience under extreme stress conditions. This study provides a quantitatively grounded framework for optimizing reinsurance programs under the Solvency II regime. The main conclusion is that capital efficiency is not a function of a single “optimal” reinsurance contract, but rather results from the strategic combination of various reinsurance mechanisms capable of simultaneously reducing tail risk, improving diversification, and limiting vulnerability to systemic stress events.

Journal of Risk and Financial Management doi: 10.3390/jrfm19090686

Authors: Phuong Thi Khanh Nguyen Thanh Thi Le Nguyen Anh Thi Lam Nguyen Linh Dieu Nguyen Tue Thi Minh Pham

Two arguments in the literature bear on how accounting information should be priced in emerging markets. The first holds that where enforcement of financial reporting is weak, investors favour book value over earnings because book value depends less on managerial judgement. The second holds that value relevance falls when macroeconomic uncertainty rises. Vietnam offers an informative setting for both because reporting enforcement is uneven and measurable, individual investors supply more than eighty per cent of trading value, and the sample period contains unusually wide macroeconomic variation. Using 631 non-financial listed firms over 2009 to 2024 and 7982 firm-year observations, we find no supporting evidence for either prediction. Earnings carry more than twice the economic weight of book value, and the earnings result withstands selection on unobservables more than four times that on observables, against approximately one for book value. Value relevance is no weaker among small firms, where full disclosure compliance is roughly 50 per cent, than among large firms, where it is roughly 85 per cent. Testing state dependence item by item through interaction terms rather than through aggregate explanatory power, the valuation weights on earnings and book value are statistically indistinguishable across favourable and adverse states under two independent definitions, while the weights on operating cash flow and firm size shift significantly. The results hold under lagged, dynamic and logarithmic specifications.

Journal of Risk and Financial Management doi: 10.3390/jrfm19090685

Authors: Yen Thi Hai Nguyen Hang Thi Thu Bui

Drawing on agency theory, resource dependence theory, and upper echelons theory, this study examines how board characteristics and ownership structure relate to digital transformation in 29 Vietnamese commercial banks from 2012–2024. Using 377 bank-year observations, the study develops a multidimensional Digital Transformation Index (DTI) comprising four dimensions. The baseline models use Prais–Winsten panel-corrected standard errors (PCSEs) and are complemented by nonlinear, post-2020, ownership-moderation, robustness, endogeneity, and alternative-measurement analyses. The results indicate that board size is negatively associated with digital transformation. In contrast, board independence is consistently and positively associated with it, and directors’ educational attainment is positively associated with selected dimensions. The nonlinear analysis further suggests that the negative association of board size becomes more pronounced beyond a moderate board size, without implying a universally optimal threshold. State ownership is negatively associated with the aggregate DTI and several of its dimensions, whereas foreign ownership shows no consistently positive association. We find no statistically significant evidence that state ownership systematically strengthens or weakens the relationships between board gender diversity or board independence and digital transformation. The post-2020 analysis further reveals temporal heterogeneity, particularly in the relationships involving board size, chief executive officer (CEO) board membership, and board independence. The principal findings remain broadly robust across alternative specifications and endogeneity analyses. Based on these findings, the study offers practical implications for bank managers and policymakers regarding board composition and expertise, ownership-related governance, and regulatory support for effective and sustainable bank digital transformation.

Journal of Risk and Financial Management doi: 10.3390/jrfm19090684

Authors: Mark A. Ritter

This study asks whether chief executive compensation at private nonprofit four-year institutions reflects financial performance or financial scale. The sample is 569 institutions in the 1000 to 4999 enrollment band, matched to IPEDS finance data for 2018–19 through 2023–24 and IRS Form 990 compensation data. Financial scale dominates: when entered jointly, the revenue coefficient is 0.295, enrollment is insignificant, and a Wald test rejects the equality of coefficients, although compensation remains positively associated with performance (0.088, p = 0.001). As a test of agency theory, institutions whose revenue and net tuition both declined are compensated about 14 percent below prediction; in an exploratory severity analysis, the discount reaches about 16 percent when each fell more than 20 percent in constant dollars. These are cross-sectional associations, not causal effects of board policy. Panel estimates are consistent with the discount developing over the window (the growth differential is significant at the 10 percent level). Adjustment is incomplete: about half of institutions in real decline are paid above prediction; the breakaway core label for this group is descriptive, not a finding of excess, and its larger enrollment is not distinguishable from the sector-wide pattern. Because the sector’s recovery is nominal rather than real, above-benchmark compensation occurs amid real contraction.

Journal of Risk and Financial Management doi: 10.3390/jrfm19090683

Authors: Kristina Rudžionienė Greta Keliuotytė-Staniulėnienė Rasa Kanapickienė Mantas Valukonis Rūta Klimaitienė

This study examines the association between ESG disclosure and corporate financial performance (CFP) and assesses whether this association differs across selected Western European (WE) and Central and Eastern European (CEE) countries. Using Bloomberg data for 4553 firms from 2013 to 2024, this study applies bivariate and multivariate pooled, fixed-effects, and random-effects panel regression models. The Bloomberg ESG disclosure score is used as the independent variable, while return on assets (ROA), return on equity (ROE), EBITDA margin, net profit margin, price-to-book ratio, and shareholder yield represent different dimensions of CFP. The results reveal that ESG disclosure is negatively associated with ROA and ROE, positively associated with shareholder yield, and not significantly associated with the remaining performance measures. Moreover, regional differences are observed only for selected accounting-based measures, with a stronger negative association in CEE countries. This study contributes to the literature by providing comparative evidence from WE and CEE firms and demonstrating that the association between ESG disclosure and CFP varies across performance measures and between the two regional groups.

Journal of Risk and Financial Management doi: 10.3390/jrfm19090682

Authors: Chaiyathad Phutthadet Ausawatap Akartwipart Chainarong Kaewmuangmoon

Low correlation alone does not establish an implementable diversification benefit. This study tests whether Bitcoin and Ethereum improved a fixed, retrospectively selected sample of 13 Thai REITs—not a point-in-time investable universe—for a Thai baht-based investor, using dependence-aware bootstrap inference (Bonferroni, Benjamini–Hochberg, and Romano–Wolf correction) across 1562 matched daily observations (16 January 2020–30 June 2026); modelled costs cover quarterly top-level reallocations only, not daily REIT-basket weight maintenance. The most robust finding is a cost, not a benefit: daily 95% conditional value-at-risk deteriorates significantly and consistently across all three correction methods for five of eight crypto-inclusive portfolios. By contrast, the seemingly compelling point-estimate pattern—all eight portfolios show higher Sharpe ratios and smaller maximum drawdowns—does not survive the same scrutiny: none of the eight ΔSharpe improvements is significant under Bonferroni or Benjamini–Hochberg, only the rolling strategy (P9) is significant under Romano–Wolf, and all unadjusted evidence disappears under sample-window sensitivity checks. Annual inclusion margins were driven largely by cryptocurrency performance, while counterfactual re-centering descriptively illustrated declining ΔSharpe as benchmark strength increased; none of the underlying comparisons survived multiplicity adjustment. The findings illustrate how seemingly robust point-estimate gains can fail to survive multiplicity-corrected inference, while a downside-risk cost remains statistically significant across all three multiplicity procedures in the principal specification—a cautionary result for practitioners and future diversification studies alike.