Journal Description
Journal of Risk and Financial Management
Journal of Risk and Financial Management
is an international, peer-reviewed, open access journal on risk and financial management, published monthly online by MDPI (since Volume 6, Issue 1 - 2013).
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, EconBiz, EconLit, RePEc, and other databases.
- Journal Rank: CiteScore - Q1 (Business, Management and Accounting (miscellaneous))
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 18.8 days after submission; acceptance to publication is undertaken in 5.5 days (median values for papers published in this journal in the second half of 2025).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
Latest Articles
Credit Risk Index as a Support Tool for the Financial Inclusion of Smallholder Coffee Producers
J. Risk Financial Manag. 2026, 19(1), 73; https://doi.org/10.3390/jrfm19010073 - 16 Jan 2026
Abstract
This study aimed to develop a credit risk index to classify coffee producers according to socioeconomic, agronomic, and financial performance variables, with the purpose of strengthening financial inclusion. We combined qualitative and quantitative methods to understand credit risk factors among smallholder coffee producers.
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This study aimed to develop a credit risk index to classify coffee producers according to socioeconomic, agronomic, and financial performance variables, with the purpose of strengthening financial inclusion. We combined qualitative and quantitative methods to understand credit risk factors among smallholder coffee producers. The study followed a descriptive-analytical approach structured in consecutive methodological phases. The systematic review, conducted following the Kitchenham protocol, identified theoretical factors associated with credit risk, while fieldwork with 300 producers provided the socioeconomic and productive contexts of coffee-growing households. Producer income, cost of living, and farm management expenses were modeled using regression, statistical, and machine learning methods. Subsequently, these variables were integrated to construct a financial risk index, which was normalized using expert scoring. The index was validated using data from 100 additional producers, for whom annual repayment capacity and maximum loan amounts were estimated according to their risk level. The results indicated that incorporating municipal-level economic variables, such as estimated average prices, income, and expenses, enhanced predictive accuracy and improved the rational allocation of loan amounts. The study concludes that credit risk analysis based on variables related to human, productive, and economic capital constitutes an effective strategy for improving access to finance in rural areas.
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(This article belongs to the Special Issue Lending, Credit Risk and Financial Management)
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Predicting Financial Contagion: A Deep Learning-Enhanced Actuarial Model for Systemic Risk Assessment
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Khalid Jeaab, Youness Saoudi, Smaaine Ouaharahe and Moulay El Mehdi Falloul
J. Risk Financial Manag. 2026, 19(1), 72; https://doi.org/10.3390/jrfm19010072 - 16 Jan 2026
Abstract
Financial crises increasingly exhibit complex, interconnected patterns that traditional risk models fail to capture. The 2008 global financial crisis, 2020 pandemic shock, and recent banking sector stress events demonstrate how systemic risks propagate through multiple channels simultaneously—e.g., network contagion, extreme co-movements, and information
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Financial crises increasingly exhibit complex, interconnected patterns that traditional risk models fail to capture. The 2008 global financial crisis, 2020 pandemic shock, and recent banking sector stress events demonstrate how systemic risks propagate through multiple channels simultaneously—e.g., network contagion, extreme co-movements, and information cascades—creating a multidimensional phenomenon that exceeds the capabilities of conventional actuarial or econometric approaches alone. This paper addresses the fundamental challenge of modeling this multidimensional systemic risk phenomenon by proposing a mathematically formalized three-tier integration framework that achieves 19.2% accuracy improvement over traditional models through the following: (1) dynamic network-copula coupling that captures 35% more tail dependencies than static approaches, (2) semantic-temporal alignment of textual signals with network evolution, and (3) economically optimized threshold calibration reducing false positives by 35% while maintaining 85% crisis detection sensitivity. Empirical validation on historical data (2000–2023) demonstrates significant improvements over traditional models: 19.2% increase in predictive accuracy (R2 from 0.68 to 0.87), 2.7 months earlier crisis detection compared to Basel III credit-to-GDP indicators, and 35% reduction in false positive rates while maintaining 85% crisis detection sensitivity. Case studies of the 2008 crisis and 2020 market turbulence illustrate the model’s ability to identify subtle precursor signals through integrated analysis of network structure evolution and semantic changes in regulatory communications. These advances provide financial regulators and institutions with enhanced tools for macroprudential supervision and countercyclical capital buffer calibration, strengthening financial system resilience against multifaceted systemic risks.
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(This article belongs to the Special Issue Financial Regulation and Risk Management amid Global Uncertainty)
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Aligning Inclusive Finance with the European Union’s Digital–Green Twin Transition
by
Massimo Preziuso
J. Risk Financial Manag. 2026, 19(1), 71; https://doi.org/10.3390/jrfm19010071 - 15 Jan 2026
Abstract
This study examines how inclusive finance organisations are adapting to the European Union (EU)’s digital–green twin transition and how regulatory design can reinforce this alignment. Drawing on qualitative insights from 26 institutions—including microfinance organisations, small and medium-sized enterprise finance providers and socially oriented
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This study examines how inclusive finance organisations are adapting to the European Union (EU)’s digital–green twin transition and how regulatory design can reinforce this alignment. Drawing on qualitative insights from 26 institutions—including microfinance organisations, small and medium-sized enterprise finance providers and socially oriented fintechs—across the EU and neighbouring countries, the analysis identifies how digitalisation, financial inclusion and environmental sustainability are being integrated into organisational strategies. The findings show that hybrid models, built on partnerships between nationally rooted microfinance institutions and cross-border fintech platforms, enable scalable, high-tech, high-touch ecosystems that align closely with sustainability objectives. The study argues that a coordinated EU-wide regulatory sandbox would advance inclusive, green financial innovation and build resilience across the inclusive finance ecosystem.
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(This article belongs to the Special Issue Financial Inclusion Strategies: Emerging Trends and Global Perspectives)
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Sector Rotation Strategies in the TSX 60: A Comprehensive Analysis of Risk-Adjusted Returns, Machine Learning Applications, and Out-of-Sample Validation (2000–2025)
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Gourav Salotra and Eugene Pinsky
J. Risk Financial Manag. 2026, 19(1), 70; https://doi.org/10.3390/jrfm19010070 - 15 Jan 2026
Abstract
We investigate the profitability of systematic sector rotation strategies in the Canadian equity market using TSX 60 constituents (2000–2025). Testing 72 distinct strategies across three theoretical frameworks—momentum, mean-reversion, and balanced approaches—with varying rebalancing frequencies, we identify that median-performer selection combined with quarterly rebalancing
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We investigate the profitability of systematic sector rotation strategies in the Canadian equity market using TSX 60 constituents (2000–2025). Testing 72 distinct strategies across three theoretical frameworks—momentum, mean-reversion, and balanced approaches—with varying rebalancing frequencies, we identify that median-performer selection combined with quarterly rebalancing generates statistically significant risk-adjusted returns (Sharpe ratio 0.922 versus 0.624 for equal-weighted buy-and-hold). Our primary contributions include rigorous out-of-sample validation, demonstrating performance persistence from 2020 to 2025, machine learning regime classification with 72.7% accuracy, and a comprehensive transaction cost analysis. Results support intermediate-horizon mean reversion in sector returns and challenge strict efficient market hypothesis interpretations in concentrated markets. Findings inform tactical asset allocation practices and contribute to the momentum-reversal literature by documenting conditions under which rotation strategies generate economically meaningful alpha.
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(This article belongs to the Special Issue Advances in Financial Modeling and Innovation)
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Performance and Risk Analytics of Asian Exchange-Traded Funds
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Bhathiya Divelgama, Nancy Asare Nyarko, Naa Sackley Dromo Aryee, Abootaleb Shirvani and Svetlozar T. Rachev
J. Risk Financial Manag. 2026, 19(1), 69; https://doi.org/10.3390/jrfm19010069 - 15 Jan 2026
Abstract
Exchange-traded funds (ETFs) provide low-cost, liquid access to broad equity and fixed-income exposures, including rapidly growing Asian and Asia-focused markets. Yet the academic evidence on Asian ETF portfolio construction remains fragmented, often limited to narrow country samples and centered on mean–variance trade-offs and
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Exchange-traded funds (ETFs) provide low-cost, liquid access to broad equity and fixed-income exposures, including rapidly growing Asian and Asia-focused markets. Yet the academic evidence on Asian ETF portfolio construction remains fragmented, often limited to narrow country samples and centered on mean–variance trade-offs and standard performance statistics, with comparatively less emphasis on downside tail risk and on implementable long-only versus long–short designs under leverage constraints. This study examines the performance and risk characteristics of 29 Asian and Asia-focused ETFs over 2014–2025 and evaluates whether optimization using variance-based and tail-sensitive risk measures improves portfolio outcomes relative to a simple, implementable benchmark. We construct Markowitz mean–variance and conditional value-at-risk (CVaR) efficient frontiers and implement six optimized portfolios at the 95% and 99% tail levels under long-only and long–short configurations with leverage up to 30%. Performance is evaluated relative to an equally weighted Asian ETF benchmark using the Sharpe ratio and tail-sensitive measures, including the Rachev ratio and the stable tail adjusted return (STARR), complemented by fat-tail diagnostics based on the Hill tail-index estimator. The empirical results show that optimization improves efficiency relative to equal weighting in risk-adjusted terms and that moderate leverage can increase returns but typically amplifies volatility, dispersion, and drawdowns. Taken together, the evidence indicates that risk-measure choice materially affects portfolio composition and realized outcomes, with tail-based optimization generally producing more robust allocations than mean–variance approaches when downside risk is a primary concern.
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(This article belongs to the Collection Quantitative Advances and Risks in Asian Financial Markets)
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ESG Risk Spillover Between Peers
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Lucas Walker and Shumi Akhtar
J. Risk Financial Manag. 2026, 19(1), 68; https://doi.org/10.3390/jrfm19010068 - 14 Jan 2026
Abstract
We investigate how environmental, social, and governance (ESG) risk can spread between peers and its impact on long-term firm performance. Using data across six geographically diverse countries over a fourteen-year period, we find a significant spillover of ESG risks among multinational firms, which
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We investigate how environmental, social, and governance (ESG) risk can spread between peers and its impact on long-term firm performance. Using data across six geographically diverse countries over a fourteen-year period, we find a significant spillover of ESG risks among multinational firms, which fails to yield a meaningful impact on the performance of affected firms. These findings place a spotlight on a critical gap in ESG risk management and echo an urgent signal for policy intervention, aligning with the United Nations’ faltering Sustainable Development Goals for 2030. This work is a clarion call for immediate academic and practical action in a world teetering on the brink of unsustainable practices. Our findings suggest that market-based mechanisms alone may be insufficient to discipline ESG risk, highlighting a potential role for regulatory oversight and policy attention.
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(This article belongs to the Special Issue Corporate Social Responsibility and Governance)
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Does Earning Management Matter for the Tax Avoidance and Investment Efficiency Nexus? Evidence from an Emerging Market
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Ingi Hassan Sharaf, Racha El-Moslemany, Tamer Elswah, Abdullah Almutairi and Samir Ibrahim Abdelazim
J. Risk Financial Manag. 2026, 19(1), 67; https://doi.org/10.3390/jrfm19010067 - 14 Jan 2026
Abstract
This study examines the impact of tax avoidance practices on investment efficiency in Egypt, with particular emphasis on the moderating role of earnings management by exploring whether these tactics reflect managerial opportunism or serve as a mechanism to ease financial constraints. We employ
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This study examines the impact of tax avoidance practices on investment efficiency in Egypt, with particular emphasis on the moderating role of earnings management by exploring whether these tactics reflect managerial opportunism or serve as a mechanism to ease financial constraints. We employ panel data regression to analyze a sample of 58 non-financial firms listed on the Egyptian Exchange (EGX) over the period 2017–2024, yielding 464 firm-year observations. Data are collected from official corporate websites, EGX, and Egypt for Information Dissemination (EGID). Grounded in agency theory, signaling theory, and pecking order theory, this study reveals how conflicts of interest and information asymmetry between managers and stakeholders lead to managerial opportunism. The findings show that tax avoidance undermines the investment efficiency in the Egyptian market. Earnings manipulation further intensified this effect due to the financial statements’ opacity. A closer examination reveals that earnings management exacerbates overinvestment by masking managerial decisions. Conversely, for financially constrained firms with a tendency to underinvest, tax avoidance and earnings management may contribute to improved efficiency by generating internal liquidity and alleviating external financing constraints. These results provide valuable insights for regulators, highlighting that policy should be directed against managerial opportunism and improving transparency, instead of focusing solely on curbing tax avoidance. From an investor perspective, they should closely monitor and understand the tax-planning strategies to ensure they enhance the firm’s value.
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(This article belongs to the Special Issue Tax Avoidance and Earnings Management)
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Strategic Risks and Financial Digitalization: Analyzing the Challenges and Opportunities for Fintech Firms and Neobanks
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Camila Betancourt, Viviana Aranda, Camilo García and Eduart Villanueva
J. Risk Financial Manag. 2026, 19(1), 66; https://doi.org/10.3390/jrfm19010066 - 14 Jan 2026
Abstract
This research aims to analyze strategic risks from financial digitalization, highlighting the disruptive role of Fintech firms and Neobanks, the associated challenges and opportunities, and how traditional banks can adapt to remain competitive and stable in a rapidly evolving financial ecosystem. A qualitative
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This research aims to analyze strategic risks from financial digitalization, highlighting the disruptive role of Fintech firms and Neobanks, the associated challenges and opportunities, and how traditional banks can adapt to remain competitive and stable in a rapidly evolving financial ecosystem. A qualitative methodology was employed, involving semi-structured interviews with 10 executives and risk management experts from the financial sector. The study employed a concurrence analysis to identify semantic relationships among categories. The unit of analysis was the paragraph, and concurrence was computed based on the frequency with which two categories appeared within the same segment. Key findings indicate that the most significant risks are linked to technological competition, regulatory shifts, cybersecurity, and consumer trust. Conversely, notable opportunities exist in technological modernization, enhanced regulatory compliance, collaboration with digital players, and the development of user-centric products and services. This study introduces the concept of a cultural gap in strategic adaptation, distinct from resistance to change, by emphasizing misalignment between organizational culture and the pace of digital transformation. This gap poses a strategic risk by delaying execution, increasing exposure to regulatory and technological risks, and reducing competitiveness.
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(This article belongs to the Special Issue Fintech, Digital Finance, and Socio-Cultural Factors)
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Corporate Reputation and Internal Control Quality: Evidence from Fortune 1000 Companies
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Haomiao (Holly) He, Fei Kang and Lijuan Zhao
J. Risk Financial Manag. 2026, 19(1), 65; https://doi.org/10.3390/jrfm19010065 - 14 Jan 2026
Abstract
This paper examines the association between company reputation and internal control quality. The prior literature suggests that reputation concerns reduce the range of risky choices by management. Building on this idea, we propose that reputation concerns drive high-reputation firms to uphold strong internal
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This paper examines the association between company reputation and internal control quality. The prior literature suggests that reputation concerns reduce the range of risky choices by management. Building on this idea, we propose that reputation concerns drive high-reputation firms to uphold strong internal control quality, leading to lower internal control risk as reflected by fewer material weaknesses in their internal controls. By analyzing Fortune 1000 companies, our study finds that high-reputation companies are motivated to safeguard their reputation, driven by their need to signal strong performance and by the monitoring pressure from high-quality auditors. As a result, these high-reputation companies are less likely to have internal control material weaknesses, reflecting lower internal control risk and higher internal control quality. Our study enhances the understanding of the role company reputation plays in corporate behavior and decision-making processes.
Full article
(This article belongs to the Special Issue Shaping the Future of Accounting)
Open AccessArticle
Socio-Cultural and Behavioral Determinants of FinTech Adoption and Credit Access Among Ecuadorian SMEs
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Reyner Pérez-Campdesuñer, Alexander Sánchez-Rodríguez, Rodobaldo Martínez-Vivar, Roberto Xavier Manciati-Alarcón, Margarita De Miguel-Guzmán and Gelmar García-Vidal
J. Risk Financial Manag. 2026, 19(1), 64; https://doi.org/10.3390/jrfm19010064 - 14 Jan 2026
Abstract
This study analyzes the socio-cultural and behavioral determinants of FinTech adoption and access to credit among Ecuadorian SMEs. A probabilistic sample of 600 firms, operating in the services, commerce, information and communication technologies (ICT), and industry sectors, was surveyed to ensure representation of
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This study analyzes the socio-cultural and behavioral determinants of FinTech adoption and access to credit among Ecuadorian SMEs. A probabilistic sample of 600 firms, operating in the services, commerce, information and communication technologies (ICT), and industry sectors, was surveyed to ensure representation of the country’s productive structure. The model integrates financial literacy, institutional trust, and perceived accessibility as key independent variables, with FinTech adoption as a digital behavioral factor and access to credit and credit conditions as the primary dependent outcomes. Using Partial Least Squares Structural Equation Modeling (PLS-SEM), complemented by multi-group invariance tests and cluster analysis, the study evaluates seven hypotheses linking cognitive, perceptual, and digital mechanisms to financing behavior and firm performance. Results show that financial literacy and institutional trust significantly improve access to formal credit, with perceived accessibility acting as a partial mediator. FinTech adoption enhances credit conditions but remains limited among micro and small firms. Based on these findings, the study recommends strengthening financial education programs, simplifying credit procedures to reduce perceived barriers, and developing trust-building regulatory frameworks for digital finance. The results highlight the importance of socio-cultural and behavioral factors in shaping SME financing decisions and contribute to the understanding of financial inclusion dynamics in emerging economies.
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(This article belongs to the Special Issue Fintech, Digital Finance, and Socio-Cultural Factors)
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Digital Asset Analytics for DeFi Protocol Valuation: An Explainable Optuna-Tuned Super Learner Ensemble Framework
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Gihan M. Ali
J. Risk Financial Manag. 2026, 19(1), 63; https://doi.org/10.3390/jrfm19010063 - 13 Jan 2026
Abstract
Decentralized Finance (DeFi) has become a major component of digital asset markets, yet accurately valuing protocol performance remains difficult due to high volatility, nonlinear pricing dynamics, and persistent disclosure gaps that amplify valuation risk. This study develops an Optuna-tuned Super Learner stacked ensemble
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Decentralized Finance (DeFi) has become a major component of digital asset markets, yet accurately valuing protocol performance remains difficult due to high volatility, nonlinear pricing dynamics, and persistent disclosure gaps that amplify valuation risk. This study develops an Optuna-tuned Super Learner stacked ensemble to improve risk-aware DeFi valuation, combining Extremely Randomized Trees (ETs), Support Vector Regression (SVR), and Categorical Boosting (CAT) as heterogeneous base learners, with a K-Nearest Neighbors (KNNs) meta-learner integrating their forecasts. Using an expanding-window panel time-series cross-validation design, the framework achieves significantly higher predictive accuracy than individual models, benchmark ensembles, and econometric baselines, obtaining RMSE = 0.085, MAE = 0.065, and R2 = 0.97—representing a 25–36% reduction in valuation error. Wilcoxon tests confirm that these gains are statistically significant (p < 0.01). SHAP-based interpretability analysis identifies Gross Merchandise Volume (GMV) as the primary valuation determinant, followed by Total Value Locked (TVL) and key protocol design features such as Decentralized Exchange (DEX) classification, while revenue variables and inflation contribute secondary effects. The findings demonstrate how explainable ensemble learning can strengthen valuation accuracy, reduce information-driven uncertainty, and support risk-informed decision-making for investors, analysts, developers, and policymakers operating within rapidly evolving blockchain-based digital asset environments.
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(This article belongs to the Section Financial Technology and Innovation)
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Optimal Investment and Consumption Problem with Stochastic Environments and Delay
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Stanley Jere, Danny Mukonda, Edwin Moyo and Samuel Asante Gyamerah
J. Risk Financial Manag. 2026, 19(1), 62; https://doi.org/10.3390/jrfm19010062 - 13 Jan 2026
Abstract
This paper examines an optimal investment–consumption problem in a setting where the financial environment is influenced by both stochastic factors and delayed effects. The investor, endowed with Constant Relative Risk Aversion (CRRA) preferences, allocates wealth between a risk-free asset and a single risky
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This paper examines an optimal investment–consumption problem in a setting where the financial environment is influenced by both stochastic factors and delayed effects. The investor, endowed with Constant Relative Risk Aversion (CRRA) preferences, allocates wealth between a risk-free asset and a single risky asset. The short rate follows a Vas ček-type term structure model, while the risky asset price dynamics are driven by a delayed Heston specification whose variance process evolves according to a Cox–Ingersoll–Ross (CIR) diffusion. Delayed dependence in the wealth dynamics is incorporated through two auxiliary variables that summarize past wealth trajectories, enabling us to recast the naturally infinite-dimensional delay problem into a finite-dimensional Markovian framework. Using Bellman’s dynamic programming principle, we derive the associated Hamilton–Jacobi–Bellman (HJB) partial differential equation and demonstrate that it generalizes the classical Merton formulation to simultaneously accommodate delay, stochastic interest rates, stochastic volatility, and consumption. Under CRRA utility, we obtain closed-form expressions for the value function and the optimal feedback controls. Numerical illustrations highlight how delay and market parameters impact optimal portfolio allocation and consumption policies.
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(This article belongs to the Special Issue Quantitative Methods for Financial Derivatives and Markets)
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Enforcing Good Deeds: Investment Efficiency of Indian Firms Going Through CSR Law
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Swati Kumaria Puri, Jiali Fang, Udomsak Wongchoti and Wei Hao
J. Risk Financial Manag. 2026, 19(1), 61; https://doi.org/10.3390/jrfm19010061 - 13 Jan 2026
Abstract
With the enactment of the 2013 government mandate, Indian corporations meeting specific criteria no longer have the discretion to forgo CSR expenditures. Previous studies have reported negative capital market reactions to this regulatory intervention. In contrast, our study offers a long-term perspective on
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With the enactment of the 2013 government mandate, Indian corporations meeting specific criteria no longer have the discretion to forgo CSR expenditures. Previous studies have reported negative capital market reactions to this regulatory intervention. In contrast, our study offers a long-term perspective on the impact of the CSR law on firms’ investment efficiency. Using a difference-in-differences framework, this study examines publicly listed Indian firms from 2011 to 2018, capturing a clean pre- and post-mandate window that isolates the structural impact of the CSR law while excluding confounding and shocks such as the COVID-19 crisis. Thus, the paper focuses on identifying the long-term institutional and structural effects of CSR rather than short-term cyclical fluctuations. We find that the CSR law leads to an increase in the investment efficiency of affected firms, driven primarily by reductions in agency conflicts and information asymmetry. This effect is more pronounced among firms with a strong presence of active monitoring groups, such as Hindu-owned promoters and institutional investors. Improved efficiency is also profound among firms located in areas with a lower Human Development Index (HDI) and Gender Diversity Index (GDI). Our findings demonstrate the positive impact of mandatory CSR law on capitalism and present insights for policymakers for regulators as ESG and CSR mandates are increasingly debated and adopted.
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(This article belongs to the Special Issue Corporate Finance and ESG: Shaping the Future of Sustainable Business)
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Open AccessCommunication
The GT-Score: A Robust Objective Function for Reducing Overfitting in Data-Driven Trading Strategies
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Alexander Pearson Sheppert
J. Risk Financial Manag. 2026, 19(1), 60; https://doi.org/10.3390/jrfm19010060 - 12 Jan 2026
Abstract
Overfitting remains a critical challenge in data-driven financial modelling, where machine learning (ML) systems learn spurious patterns in historical prices and fail out of sample and in deployment. This paper introduces the GT-Score, a composite objective function that integrates performance, statistical significance, consistency,
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Overfitting remains a critical challenge in data-driven financial modelling, where machine learning (ML) systems learn spurious patterns in historical prices and fail out of sample and in deployment. This paper introduces the GT-Score, a composite objective function that integrates performance, statistical significance, consistency, and downside risk to guide optimization toward more robust trading strategies. This approach directly addresses critical pitfalls in quantitative strategy development, specifically data snooping during optimization and the unreliability of statistical inference under non-normal return distributions. Using historical stock data for 50 S&P 500 companies spanning 2010–2024, we conduct an empirical evaluation that includes walk-forward validation with nine sequential time splits and a Monte Carlo study with 15 random seeds across three trading strategies. In walk-forward validation, GT-Score improves the generalization ratio (validation return divided by training return) by 98% relative to baseline objective functions. Paired statistical tests on Monte Carlo out-of-sample returns indicate statistically detectable differences between objective functions (p < 0.01 for comparisons with Sortino and Simple), with small effect sizes. These results suggest that embedding an anti-overfitting structure into the objective can improve the reliability of backtests in quantitative research.
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(This article belongs to the Special Issue Investment Strategies and Market Dynamics)
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Informed Trading Through the COVID-19 Pandemic: Evidence from the Bitcoin Market
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Timotheos Mavropoulos, Oguz Ersan and Ender Demir
J. Risk Financial Manag. 2026, 19(1), 59; https://doi.org/10.3390/jrfm19010059 - 10 Jan 2026
Abstract
We investigate informed trading in the Bitcoin market throughout the COVID-19 pandemic. Compared to the pre-pandemic period, we find that informed trading is significantly higher in the affective first stage of the pandemic, before reverting to its pre-COVID-19 level during the later stage
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We investigate informed trading in the Bitcoin market throughout the COVID-19 pandemic. Compared to the pre-pandemic period, we find that informed trading is significantly higher in the affective first stage of the pandemic, before reverting to its pre-COVID-19 level during the later stage of the pandemic. Furthermore, information asymmetry tends to increase in daily COVID-19-related news: confirmed cases and deaths. Our findings are robust to alternative parameters and model specifications. The main implication for traders is that they should be extra cautious in timing their trading decisions during such events, as these tend to encourage informed trading.
Full article
(This article belongs to the Special Issue Quantitative Finance in the Era of Big Data and AI)
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Open AccessReview
The Role of Artificial Intelligence in Enhancing ESG Disclosure Quality in Accounting
by
Jiacheng Liu, Ye Yuan and Zhelun Zhu
J. Risk Financial Manag. 2026, 19(1), 58; https://doi.org/10.3390/jrfm19010058 - 9 Jan 2026
Abstract
As corporate sustainability reporting evolves into a pivotal resource for investors, regulators, and stakeholders, the imperative to evaluate and elevate ESG disclosure quality intensifies amid persistent challenges like opacity, inconsistency, and greenwashing. This review synthesizes interdisciplinary insights from accounting, finance, and computational linguistics
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As corporate sustainability reporting evolves into a pivotal resource for investors, regulators, and stakeholders, the imperative to evaluate and elevate ESG disclosure quality intensifies amid persistent challenges like opacity, inconsistency, and greenwashing. This review synthesizes interdisciplinary insights from accounting, finance, and computational linguistics on artificial intelligence (AI), particularly natural language processing (NLP) and machine learning (ML), as a transformative force in this domain. We delineate ESG disclosure quality across four operational dimensions: readability, comparability, informativeness, and credibility. By integrating cutting-edge methodological innovations (e.g., transformer-based models for semantic analysis), empirical linkages between AI-extracted signals and market/governance outcomes, and normative discussions on AI’s auditing potential, we demonstrate AI’s efficacy in scaling measurement, harmonizing heterogeneous narratives, and prototyping greenwashing detection. Nonetheless, causal evidence linking managerial AI adoption to stakeholder-perceived enhancements remains limited, compounded by biases in multilingual applications and interpretability deficits. We propose a forward-looking agenda, prioritizing cross-lingual benchmarking, curated greenwashing datasets, AI-assurance pilots, and interpretability standards, to harness AI for substantive, equitable improvements in ESG reporting and accountability.
Full article
(This article belongs to the Special Issue Data and Technology: Shaping the Future of Finance, Accounting, and Business Systems Innovation)
Open AccessArticle
Determinants of Goodwill Impairment Recognition and Measurement: New Evidence from Moroccan Listed Firms
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Mounia Hamidi, Sara Khotbi and Youssef Bouazizi
J. Risk Financial Manag. 2026, 19(1), 57; https://doi.org/10.3390/jrfm19010057 - 8 Jan 2026
Abstract
This study examines the determinants of goodwill impairment recognition under IFRS 3 in the context of Moroccan listed firms. Using an unbalanced panel covering the period of 2006–2024 and comprising 862 firm-year observations, we employ a three-stage empirical strategy that integrates a Probit
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This study examines the determinants of goodwill impairment recognition under IFRS 3 in the context of Moroccan listed firms. Using an unbalanced panel covering the period of 2006–2024 and comprising 862 firm-year observations, we employ a three-stage empirical strategy that integrates a Probit model to estimate the likelihood of impairment, a Tobit model to assess the magnitude of the loss, and a Heckman two-step procedure to correct for potential self-selection. The results show that goodwill impairment reflects key economic and financial fundamentals, including revenue growth, book-to-market ratios, and operating performance. However, both real and accrual-based earnings management significantly influence the probability and intensity of impairment, particularly through abnormal cash flows and income-smoothing behavior. Discretionary accruals become significant only after correcting for selection bias, indicating that they do not drive the recognition decision but contribute to determining the size of the impairment once it has been recorded. The findings are robust across multiple specifications and contribute to the broader literature on financial reporting quality under IAS/IFRS, while enriching empirical evidence on managerial discretion and earnings management in emerging-market environments.
Full article
(This article belongs to the Special Issue Research on Corporate Governance and Financial Reporting)
Open AccessArticle
The Effects of Fintech Adoption on CEO Compensation: Evidence from JSE-Listed Banks
by
Rudo Rachel Marozva and Frans Maloa
J. Risk Financial Manag. 2026, 19(1), 56; https://doi.org/10.3390/jrfm19010056 - 8 Jan 2026
Abstract
Over the last decade, there has been a significant increase in banks’ investment in technology, alongside a substantial rise in CEO compensation. Research on executive compensation has primarily focused on traditional performance metrics, such as return on assets and return on equity, as
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Over the last decade, there has been a significant increase in banks’ investment in technology, alongside a substantial rise in CEO compensation. Research on executive compensation has primarily focused on traditional performance metrics, such as return on assets and return on equity, as well as governance factors. Investigating the nexus between fintech adoption and CEO compensation introduces a new perspective on the determinants of CEO pay and how technological transformation influences executive remuneration structures. This study investigated the relationship between Chief Executive remuneration and fintech adoption among banks listed on the Johannesburg Stock Exchange. There is a lack of literature on the impact of technology adoption on CEO compensation in developing and emerging economies. The quantitative longitudinal study, conducted over 15 years from 2010 to 2024, collected secondary data from the annual reports of six banks and the IRESS database. A panel data fixed effects regression analysis was employed to analyze the data. CEO compensation included both salary and total compensation. Fintech variables used for the study included automated teller machines, mobile banking, and internet banking. The findings revealed a positive relationship between CEO salary and the rollout of ATMs and mobile banking, while an inverse relationship was noted between salary and internet banking. Similarly, total compensation showed an inverse relationship with the adoption of ATMs and internet banking, whereas mobile banking had a positive effect on total compensation. Understanding how technology impacts CEO compensation can help remuneration committees ensure that CEO pay is linked to the value that infrastructure investments bring to an organization, rather than simply the number of innovations introduced. This understanding will also help solve the principal-agent problem, as it will ensure technology innovations that enhance firm performance are rewarded. In the context of emerging markets, the study’s findings suggest that organizations should recognize and formalize pay linked to digital transformation, rather than focusing solely on short-term financial metrics. This also suggests the need to develop guidelines for executive remuneration disclosure related to the technology sector. The close connection between fintech adoption and technological and regulatory risks highlights the need to balance incentive structures that reward innovation with risk-adjusted performance measures.
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(This article belongs to the Special Issue Emerging Trends and Innovations in Corporate Finance and Governance)
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Frugal Entrepreneurial Ecosystems and Alternative Finance in Emerging Economies: Pathways to Resilience and Performance and the Role of Incubators and Innovation Hubs
by
Badr Machkour and Ahmed Abriane
J. Risk Financial Manag. 2026, 19(1), 55; https://doi.org/10.3390/jrfm19010055 - 8 Jan 2026
Abstract
Between 2018 and 2025, alternative finance expanded while micro-, small- and medium-sized enterprises in emerging economies continued to face a substantial funding gap. This study examines how entrepreneurial frugality articulates frugal ecosystems, access to alternative finance, resilience and SME performance within a single
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Between 2018 and 2025, alternative finance expanded while micro-, small- and medium-sized enterprises in emerging economies continued to face a substantial funding gap. This study examines how entrepreneurial frugality articulates frugal ecosystems, access to alternative finance, resilience and SME performance within a single explanatory framework. Following PRISMA 2020 and PRISMA-S, we conduct a systematic review of Scopus, Web of Science and Cairn; out of 1483 records, 106 peer-reviewed studies are retained and assessed using the Mixed Methods Appraisal Tool and a narrative synthesis approach. The findings show that frugal ecosystems characterized by pooled assets, norms of repair and modularity, and lightweight digital tools reduce experimentation costs and develop frugal innovation as an organizational capability. This capability enhances access to alternative finance by generating readable quality signals, while non-bank channels provide a financial buffer that aligns liquidity with operating cycles and strengthens entrepreneurial resilience. The article proposes an operationalized conceptual model, measurement guidelines for future quantitative surveys, and public policy and managerial implications to support frugal and inclusive innovation trajectories in emerging contexts.
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(This article belongs to the Special Issue Entrepreneurship in Emerging Economies: Entrepreneurial Ecosystems, Resilience and Finance)
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Open AccessArticle
Regulation and Risk in Decentralised Finance: An Event Study of DeFi Tokens
by
Hai Yen Hoang
J. Risk Financial Manag. 2026, 19(1), 54; https://doi.org/10.3390/jrfm19010054 - 8 Jan 2026
Abstract
This study investigates the influence of major regulatory interventions on decentralised finance (DeFi) token markets by conducting an event study of six high-profile announcements issued between 2023 and 2025. The analysis reveals that these interventions primarily lead to risk-sensitive, token-specific price adjustments rather
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This study investigates the influence of major regulatory interventions on decentralised finance (DeFi) token markets by conducting an event study of six high-profile announcements issued between 2023 and 2025. The analysis reveals that these interventions primarily lead to risk-sensitive, token-specific price adjustments rather than systemic disruptions across the broader DeFi ecosystem. While enforcement actions trigger asymmetric and delayed volatility effects, legal clarity alone does not stabilise liquidity conditions. Notably, governance and decentralised exchange (DEX) tokens exhibit heightened sensitivity to enforcement actions and policy signals, underscoring the role of protocol function in regulatory risk transmission. These results contribute to the literature on market microstructure in decentralised ecosystems and offer practical insights into liquidity formation, volatility persistence, and differentiated risk management within emerging fintech infrastructures.
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(This article belongs to the Special Issue Market Liquidity, Fintech Innovation, and Risk Management Practices)
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