Journal of Risk and Financial Management

27 pages, 1214 KB

Open AccessArticle

Venture Capital Financing in a Crisis Economy: Entrepreneurial Risk, Venture Creation, and Perceived Firm Financial Performance

by Joseph Serghani and Hussein Trabulsi

J. Risk Financ. Manag. 2026, 19(9), 711; https://doi.org/10.3390/jrfm19090711 - 9 Sep 2026

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Section Business and Entrepreneurship)

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25 pages, 557 KB

Open AccessArticle

Artificial Intelligence and Accounting Information Quality: Causal Inference Based on Double Machine Learning

by Junming Yang, Zelei Lin, Li He and Aiping Wang

J. Risk Financ. Manag. 2026, 19(9), 710; https://doi.org/10.3390/jrfm19090710 - 9 Sep 2026

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Section Financial Technology and Innovation)

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34 pages, 1561 KB

Open AccessArticle

SupTech and Greenwashing in European Banking: A Causal and Nonlinear Heterogeneous Analysis Using Synthetic Control and Causal Random Forest

by Mejda Tebessi and Heni Boubaker

J. Risk Financ. Manag. 2026, 19(9), 709; https://doi.org/10.3390/jrfm19090709 - 8 Sep 2026

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue The Risks and Returns of “Greenwashing”)

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55 pages, 5137 KB

Open AccessSystematic Review

Predicting, Using, and Assessing ESG Signals: A Tripartite Systematic Review of Machine Learning in Sustainable Finance

by Imane El Imami, Abdelkader El Alaoui, Bassma Guermah, Said Ouatik El Alaoui and Miklos Vasarhelyi

J. Risk Financ. Manag. 2026, 19(9), 708; https://doi.org/10.3390/jrfm19090708 - 8 Sep 2026

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Finance, Governance, and Digital Accountability: AI, Fintech, and Sustainable Financial Systems)

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23 pages, 1556 KB

Open AccessArticle

Bitcoin on Wall Street Time: Natural Experiments on the Institutionalization of a 24/7 Market

by Huda Aldhahi

J. Risk Financ. Manag. 2026, 19(9), 707; https://doi.org/10.3390/jrfm19090707 - 8 Sep 2026

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Section Financial Technology and Innovation)

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31 pages, 1083 KB

Open AccessArticle

After the Pandemic, Not During It: Business Promotion Expenditure and Firm Value Across the COVID-19 Timeline

by Gee-Jung Kwon

J. Risk Financ. Manag. 2026, 19(9), 706; https://doi.org/10.3390/jrfm19090706 - 8 Sep 2026

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Section Business and Entrepreneurship)

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30 pages, 383 KB

Open AccessArticle

A Structural Representation of Expected Return Generation: Theory, Empirical Framework, and Cross-Market Evidence

by William Ng and I-Cheng Yeh

J. Risk Financ. Manag. 2026, 19(9), 705; https://doi.org/10.3390/jrfm19090705 - 8 Sep 2026

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Section Applied Economics and Finance)

21 pages, 1441 KB

Open AccessArticle

Accountant–Actuary Collaboration, Regulatory Enforcement, and IT Capability in IFRS 17/PSAK 117 Implementation: Qualitative Evidence on Accounting Quality and Governance in Indonesian Non-Life Insurers

by Rony Romdany, Tettet Fitrijanti, Dini Rosdini and Zubir Azhar

J. Risk Financ. Manag. 2026, 19(9), 704; https://doi.org/10.3390/jrfm19090704 - 7 Sep 2026

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Section Business and Entrepreneurship)

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47 pages, 911 KB

Open AccessArticle

Closed-Form Moment-to-Distribution Mapping for Machine Learning-Driven Dynamic Risk Forecasting: A Smooth Half-Logistic Distribution Approach

by Zuocheng Li, Chenxu Ling and Yifan Ye

J. Risk Financ. Manag. 2026, 19(9), 703; https://doi.org/10.3390/jrfm19090703 - 7 Sep 2026

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Collection AI and Data-Driven Quantitative Finance)

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26 pages, 546 KB

Open AccessArticle

Perceived Sustainability of E-Government and Citizen Satisfaction: A Demand-Side Perspective on Public Digital Investment Priorities

by Antanas Usas, Edmundas Jasinskas, Arturas Simanavicius and Dalia Streimikiene

J. Risk Financ. Manag. 2026, 19(9), 702; https://doi.org/10.3390/jrfm19090702 - 7 Sep 2026

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Financing the Sustainable Digital Economy: Investment, Risk, and Human-Centric Value in the Industry 5.0 Era)

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27 pages, 25028 KB

Open AccessReview

The Evolution of Green Taxation Research: A Bibliometric Analysis of Knowledge Structures, Thematic Trends, and Emerging Research Frontiers

by Hanae Idari, Hajar Bouladasse, Said El Ganich, Taoufiq Yahyaoui and Mohamed Oudgou

J. Risk Financ. Manag. 2026, 19(9), 701; https://doi.org/10.3390/jrfm19090701 - 7 Sep 2026

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Section Sustainability and Finance)

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30 pages, 466 KB

Open AccessArticle

Decoding ESG Contagion: FinTech Information Flows, FinBERT Filters, and Optimal Portfolios

by Francesco Rania

J. Risk Financ. Manag. 2026, 19(9), 700; https://doi.org/10.3390/jrfm19090700 - 7 Sep 2026

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Sustainable Finance: Navigating the Path to a Greener Future)

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35 pages, 16862 KB

Open AccessArticle

Enhancing Time Series Forecasting: Applying Cross-Validation Methods to Hybrid Models for Predicting GCC Stock Market Indices

by Kamel Alanazi and Alison Gray

J. Risk Financ. Manag. 2026, 19(9), 699; https://doi.org/10.3390/jrfm19090699 - 7 Sep 2026

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Machine Learning, Economic Forecasting, and Financial Markets)

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18 pages, 991 KB

Open AccessArticle

The Disconnect Between Market Capital Gains and the Dividend Yield in Asset Pricing

by Michael Di Carlo, Jordi Mondria and Ilias Tsiakas

J. Risk Financ. Manag. 2026, 19(9), 698; https://doi.org/10.3390/jrfm19090698 - 7 Sep 2026

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Behavioral Factors and Risk-Taking in Financial Markets)

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39 pages, 5059 KB

Open AccessArticle

Global Determinants and Dynamic Connectedness Among Tourism-Related ETFs, Clean Energy, and Geopolitical Risk

by Ahmed Abdelsalam and Nikiforos T. Laopodis

J. Risk Financ. Manag. 2026, 19(9), 697; https://doi.org/10.3390/jrfm19090697 - 7 Sep 2026

Abstract

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, [...] Read more.

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. Full article

(This article belongs to the Section Financial Markets)

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39 pages, 3307 KB

Open AccessArticle

Fiscal Cyclicality in EU Countries: A Rolling-Window Approach

by Angel Angelov and Velichka Nikolova

J. Risk Financ. Manag. 2026, 19(9), 696; https://doi.org/10.3390/jrfm19090696 - 6 Sep 2026

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Fiscal Policy, Tax Systems, and Financial Stability)

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23 pages, 345 KB

Open AccessArticle

US IPO Performance During Monetary Tightening Cycles

by Martin Sundberg, George Giannopoulos and Kazi Abul Bashar Muhammad Afzal Hossain

J. Risk Financ. Manag. 2026, 19(9), 695; https://doi.org/10.3390/jrfm19090695 - 6 Sep 2026

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Navigating Sustainable Development Goals (SDGs): Narrative Disclosure Approach)

20 pages, 360 KB

Open AccessArticle

XBRL and the Transparency Challenge: Evidence from Earnings Management in Jordan’s Industrial Sector

by Abdelrazaq Farah Freihat, Huthaifa Al-Hazaima, Hashem Alshurafat and Nihel Halouani

J. Risk Financ. Manag. 2026, 19(9), 694; https://doi.org/10.3390/jrfm19090694 - 6 Sep 2026

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Section Business and Entrepreneurship)

26 pages, 1335 KB

Open AccessArticle

by Alessio Faccia

J. Risk Financ. Manag. 2026, 19(9), 693; https://doi.org/10.3390/jrfm19090693 - 6 Sep 2026

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Section Financial Markets)

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