Journal of Risk and Financial Management

32 pages, 1339 KB

Open AccessArticle

Beyond Quotas: The Influence of Board Gender Diversity on Capital Structure in Firms from Latin America and the Caribbean

by Juan David González-Ruiz, Nini Johana Marín-Rodríguez and Camila Ospina-Patiño

J. Risk Financial Manag. 2025, 18(9), 505; https://doi.org/10.3390/jrfm18090505 - 11 Sep 2025

Abstract

Board gender diversity (BGD) has gained attention as a governance mechanism that may influence corporate financial decisions. However, empirical evidence from Latin America and the Caribbean (LAC) remains limited despite the region’s significant gender disparities in corporate leadership and distinct institutional characteristics. This [...] Read more.

Board gender diversity (BGD) has gained attention as a governance mechanism that may influence corporate financial decisions. However, empirical evidence from Latin America and the Caribbean (LAC) remains limited despite the region’s significant gender disparities in corporate leadership and distinct institutional characteristics. This study examines how BGD affects capital structure decisions in LAC firms, drawing on agency theory and resource dependency theory. We analyze a panel dataset of 403 firms from 2015 to 2022, sourced from the London Stock Exchange Group database, using fixed effects models with Driscoll–Kraay standard errors to control for firm heterogeneity and econometric concerns. Results show that BGD is significantly and negatively associated with leverage ratios, with a one percentage point increase in female board representation corresponding to a 0.15 to 0.25 percentage point decrease in debt-to-capital ratios. This relationship is robust across multiple specifications and exhibits threshold effects, with stronger impacts when female representation reaches 20% or higher. The negative association is more pronounced for larger firms, consistent with enhanced governance benefits in complex organizations. Our findings suggest that gender-diverse boards exercise more effective oversight of financial decisions, leading to more conservative capital structures in emerging markets where governance mechanisms are particularly important for firm credibility and stakeholder confidence. Full article

(This article belongs to the Special Issue Emerging Trends and Innovations in Corporate Finance and Governance)

► Show Figures

Figure 1

14 pages, 601 KB

Open AccessArticle

The Effect of Currency Misalignment on Income Inequality

by Sarah R. Crane, Uyen T. Le and Scott A. Miller

J. Risk Financial Manag. 2025, 18(9), 504; https://doi.org/10.3390/jrfm18090504 - 11 Sep 2025

Abstract

This paper examines the relationship between currency misalignment and income inequality across 70 countries from 1998 to 2021. Currency misalignment occurs when the actual exchange rate diverges significantly from the equilibrium exchange rate. Using fixed-effects and random-effects regressions, we find that currency overvaluation [...] Read more.

This paper examines the relationship between currency misalignment and income inequality across 70 countries from 1998 to 2021. Currency misalignment occurs when the actual exchange rate diverges significantly from the equilibrium exchange rate. Using fixed-effects and random-effects regressions, we find that currency overvaluation is associated with higher income inequality, while undervaluation is linked to lower income inequality. These findings are strongest in emerging markets and upper-middle-income countries, where undervalued currencies may be associated with stronger tradable-sector activity and narrower income gaps. In contrast, lower-income countries experience increasing levels of inequality during the early stages of development, even with growth, which is consistent with the Kuznets hypothesis. For advanced markets and higher-income nations, currency misalignment is not statistically related to income inequality, which is likely due to the presence of stronger financial systems and more stable institutions that reduce the effects of currency misalignment. The results are robust across the two grouping methods—development level (IMF) and income level (World Bank). Overall, the study highlights that while undervaluation may be associated with equitable growth in emerging markets, its benefits likely depend on a country’s development stage and are more likely when accompanied by appropriate social and economic policies to mitigate potential risks. Full article

(This article belongs to the Special Issue Emerging Topics in Business Risk)

► Show Figures

Figure 1

34 pages, 880 KB

Open AccessArticle

ESG Strategy and Tax Avoidance: Insights from a Meta-Regression Analysis

by Maria Mitroulia, Evangelos Chytis, Thomas Kitsantas, Michalis Skordoulis and Petros Kalantonis

J. Risk Financial Manag. 2025, 18(9), 503; https://doi.org/10.3390/jrfm18090503 - 11 Sep 2025

Abstract

This research examines the relationship between environmental, social, and governance (ESG) criteria and tax behavior, with a particular focus on tax avoidance (TA). Despite the extensive literature on ESG and tax behavior, there remains a research gap concerning their interaction in the financial [...] Read more.

This research examines the relationship between environmental, social, and governance (ESG) criteria and tax behavior, with a particular focus on tax avoidance (TA). Despite the extensive literature on ESG and tax behavior, there remains a research gap concerning their interaction in the financial sector. The study is based on a dataset of 125 observations from 33 articles covering the period 2012–2023. The results of the meta-regression suggest that both ESG and TA indicators account for the different findings of the primary studies. Part of the observed heterogeneity can also be explained by the diversity of data samples and econometric approaches. Using the results of the meta-regression, we attempt to predict the association between ESG and TA in hypothetical and plausible study designs. The findings show no or small-to-moderate association between the two, suggesting that companies tend to separate ESG strategies from TA and underscoring the need for more consistent measurement practices. Notably, the link between the main variables appears to be strengthened in environments with extreme behaviors, both in terms of ESG and tax strategy. Distinct from prior meta-studies that centered on CSR and taxation, our analysis isolates the ESG/TA nexus by accounting for measurement heterogeneity (different ESG and TA proxies) and demonstrates that extreme behaviors largely drive the observed association. By examining the determinants of the heterogeneity of primary research into the ESG/TA relationship, this meta-analysis provides valuable insights that can guide future research, practical implementation, and regulatory policies. In particular, researchers should rely on long-run measures of TA (e.g., multi-year ETRs) and harmonized ESG indicators to reduce bias and enhance comparability across studies, thereby providing policymakers with more robust and consistent evidence. Full article

(This article belongs to the Special Issue AI and Sustainable Growth in Economics and Finance: Global Trends and Challenges)

► Show Figures

Figure 1

25 pages, 1662 KB

Open AccessSystematic Review

Tax Fraud Detection Using Artificial Intelligence-Based Technologies: Trends and Implications

by Rida Belahouaoui and James Alm

J. Risk Financial Manag. 2025, 18(9), 502; https://doi.org/10.3390/jrfm18090502 - 11 Sep 2025

Abstract

This study examines the role of artificial intelligence (AI) tools in enhancing tax fraud detection within the ambit of the OECD Tax Administration 3.0, focusing on how these technologies streamline the detection process through a new “Adaptive AI Tax Oversight” (AATO) framework. Through [...] Read more.

This study examines the role of artificial intelligence (AI) tools in enhancing tax fraud detection within the ambit of the OECD Tax Administration 3.0, focusing on how these technologies streamline the detection process through a new “Adaptive AI Tax Oversight” (AATO) framework. Through a textometric systematic review covering the period from 2014 to 2024, the integration of AI in tax fraud detection is explored. The methodology emphasizes the evaluation of AI’s predictive, analytical, and procedural benefits in identifying and combating tax fraud. The research underscores AI’s significant impact on increasing detection accuracy, predictive capabilities, and operational efficiency in tax administrations. Key findings reveal the ways by which the development and application of the AATO framework improves the tax fraud detection process. The implications highlight not only the governance benefits and ethical challenges that arise, but also provide practical guidance for tax authorities worldwide in leveraging AI to reduce compliance costs and strengthen regulatory frameworks. Finally, the study offers recommendations for future research, particularly in refining AI methodologies, differentiating policy implications across high-income and low- and middle-income countries, and addressing governance and ethical issues to ensure equitable and sustainable tax administration practices. Full article

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

► Show Figures

Figure 1

17 pages, 1480 KB

Open AccessArticle

Banking and Cooperatives in Ecuador: Comparative Evidence of Technical Efficiency and Financial Resilience

by Byron Eraso Cisneros, Cristina Pérez-Rico and José L. Gallizo Larranz

J. Risk Financial Manag. 2025, 18(9), 501; https://doi.org/10.3390/jrfm18090501 - 10 Sep 2025

Abstract

In Ecuador’s financial system, private banks and savings and credit cooperatives coexist, both playing a key role in financial intermediation and the economic inclusion of traditionally underserved sectors. During the COVID-19 pandemic, these institutions faced unprecedented challenges that tested their adaptability and operational [...] Read more.

In Ecuador’s financial system, private banks and savings and credit cooperatives coexist, both playing a key role in financial intermediation and the economic inclusion of traditionally underserved sectors. During the COVID-19 pandemic, these institutions faced unprecedented challenges that tested their adaptability and operational efficiency. In this context, the present study evaluates the technical efficiency of banks and cooperatives in Ecuador over the 2015–2023 period, using a combined approach involving Data Envelopment Analysis (DEA) and mixed linear models (MLMs). A longitudinal and comparative methodology is adopted, allowing for the analysis of efficiency trends over time and the identification of their main structural determinants. The results show that cooperatives exhibit a higher average technical efficiency than banks, as well as greater resilience during the health crisis. The analysis reveals that operating expenses negatively impact efficiency, while equity and social capital show no significant effects. By combining DEA and MLMs, the study offers a more comprehensive and nuanced understanding of the factors influencing efficiency, underscoring the importance of tailored policies and institutional strategies focused on resource optimization and continuous improvement. The study concludes that efficiency does not rely solely on size or asset volume, but rather on managerial capacity and organizational adaptability in complex and changing environments. Full article

(This article belongs to the Section Financial Markets)

► Show Figures

Figure 1

34 pages, 426 KB

Open AccessArticle

Monitoring Mechanisms and Budget Variances: Evidence from the 50 Largest US Cities

by Dongkuk Lim

J. Risk Financial Manag. 2025, 18(9), 500; https://doi.org/10.3390/jrfm18090500 - 10 Sep 2025

Abstract

I examine how the association between the current period’s budget variance and the subsequent period’s budget is affected by various governmental monitoring mechanisms. Specifically, I consider the following governance and monitoring mechanisms: governance structure, state/city budget-limiting regulations, and voter-initiated monitoring. I find that [...] Read more.

I examine how the association between the current period’s budget variance and the subsequent period’s budget is affected by various governmental monitoring mechanisms. Specifically, I consider the following governance and monitoring mechanisms: governance structure, state/city budget-limiting regulations, and voter-initiated monitoring. I find that city budgets ratchet in the top 50 populous cities in the US. I also document evidence of asymmetric ratcheting—the current period’s favorable budget variances result in budget increases in the following year that are larger than the decreases associated with unfavorable variances of the same magnitude. Consistent with the political budget cycle hypothesis that budget pattern alters during pre-election periods, I find the asymmetric ratcheting pattern becomes invisible in times of election, particularly when an incumbent runs for re-election. Given this evidence of the opportunistic budgetary pattern, I hypothesize and find that some monitoring mechanisms mitigate the sensitivity of the subsequent period’s budget with respect to the current period’s budget variance. Full article

(This article belongs to the Special Issue Politics and Financial Markets)

21 pages, 1088 KB

Open AccessArticle

Corporate Governance and Shareholders’ Value: The Mediating Role of Internal Audit Performance—Empirical Evidence from Listed Companies in Ghana

by Dawuda Abudu and Syed Ahmed Salman

J. Risk Financial Manag. 2025, 18(9), 499; https://doi.org/10.3390/jrfm18090499 - 8 Sep 2025

Abstract

The relationship between corporate governance and shareholder value remains a subject of contention, with studies reporting positive, weak, or context-dependent effects. Drawing on multiple theoretical perspectives, this study examines how and when corporate governance influences shareholder value, with a focus on the mediating [...] Read more.

The relationship between corporate governance and shareholder value remains a subject of contention, with studies reporting positive, weak, or context-dependent effects. Drawing on multiple theoretical perspectives, this study examines how and when corporate governance influences shareholder value, with a focus on the mediating role of internal audit performance (IAP) among listed companies on the Ghana Stock Exchange. Using an explanatory design and a sample of 300 respondents (74.3% response rate), we employed partial least squares structural equation modelling (PLS-SEM) to test the relationships. The findings show that corporate governance significantly enhances internal audit performance, which in turn improves shareholder value. In contrast, the direct impact of corporate governance on shareholder value is insignificant. Bootstrapped tests confirm a near-full mediation effect, positioning internal audit performance as the critical engine that translates governance structures into value creation. These results help clarify the inconsistent findings on the relationship between corporate governance and shareholder value in emerging markets. We provide regulators, boards, and management with a roadmap for strengthening internal audit capabilities and aligning audit and governance functions with corporate objectives to maximize shareholder value. Full article

(This article belongs to the Special Issue Research on Corporate Governance and Financial Reporting)

► Show Figures

Figure 1

24 pages, 685 KB

Open AccessArticle

Global Market Shocks and Tail Risk Spillovers: Evidence from a Copula-Based Contagion Framework

by Sundusit Saekow, Phisanu Chiawkhun, Woraphon Yamaka, Nawapon Nakharutai and Parkpoom Phetpradap

J. Risk Financial Manag. 2025, 18(9), 498; https://doi.org/10.3390/jrfm18090498 - 5 Sep 2025

Abstract

This study investigates the dynamics of financial contagion using a flexible mixture copula framework, specifically a combination of the Survival Clayton and Survival Gumbel copulas, to estimate the lower tail dependence coefficient, interpreted as a measure of extreme downside co-movement or contagion. The [...] Read more.

This study investigates the dynamics of financial contagion using a flexible mixture copula framework, specifically a combination of the Survival Clayton and Survival Gumbel copulas, to estimate the lower tail dependence coefficient, interpreted as a measure of extreme downside co-movement or contagion. The model captures nonlinear and asymmetric dependencies between the global stock market and nine national markets: Australia, China, Hungary, India, New Zealand, Spain, Thailand, the United Kingdom, and the United States. The analysis spans the period from 2018 to 2024 and focuses on three major global crises: the China–U.S. trade war, the COVID-19 pandemic, and the Russia–Ukraine conflict. The results reveal substantial heterogeneity in contagion intensity across countries and crises. The COVID-19 pandemic generated the highest and most synchronized levels of contagion, with tail dependence exceeding 0.8 in the United States and above 0.6 in several developed and emerging markets. The China–U.S. trade war resulted in moderate contagion, particularly in countries with close trade links to the U.S. and China. The Russia–Ukraine conflict produced elevated contagion in European and energy-sensitive markets such as the UK and Spain. Conversely, China and New Zealand exhibited relatively lower levels of contagion across all periods Full article

(This article belongs to the Special Issue Risk Management in Capital Markets)

► Show Figures

Figure 1

25 pages, 4388 KB

Open AccessArticle

Deep Hedging Under Market Frictions: A Comparison of DRL Models for Options Hedging with Impact and Transaction Costs

by Eric Huang and Yuri Lawryshyn

J. Risk Financial Manag. 2025, 18(9), 497; https://doi.org/10.3390/jrfm18090497 - 5 Sep 2025

Abstract

This paper investigates the use of reinforcement learning (RL) algorithms to learn adaptive hedging strategies for derivatives under realistic market conditions, incorporating permanent market impact, execution slippage, and transaction costs. Market frictions arising from trading have been explored in the optimal trade execution [...] Read more.

This paper investigates the use of reinforcement learning (RL) algorithms to learn adaptive hedging strategies for derivatives under realistic market conditions, incorporating permanent market impact, execution slippage, and transaction costs. Market frictions arising from trading have been explored in the optimal trade execution literature; however, their influence on derivative hedging strategies remains comparatively understudied within RL contexts. Traditional hedging methods have typically assumed frictionless markets with only transaction costs. We illustrate that the dynamic decision problem posed by hedging with frictions can be modelled effectively with RL, demonstrating efficacy across various market frictions to minimize hedging losses. The results include a comparative analysis of the performance of three RL models across simulated price paths, demonstrating their varying effectiveness and adaptability in these friction-intensive environments. We find that RL agents, specifically TD3 and SAC, can outperform traditional delta hedging strategies in both simplistic and complex, illiquid environments highlighted by 2/3rd reductions in expected hedging losses and over 50% reductions in 5th percentile conditional value at risk (CVaR). These findings demonstrate that DRL agents can serve as a valuable risk management tool for financial institutions, especially given their adaptability to different market conditions and securities. Full article

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

► Show Figures

Figure 1

26 pages, 1511 KB

Open AccessArticle

Accessing Alternative Finance in Europe: The Role of SMEs, Innovation, and Digital Platforms

by Javier Manso Laso, Ismael Moya-Clemente and Gabriela Ribes Giner

J. Risk Financial Manag. 2025, 18(9), 496; https://doi.org/10.3390/jrfm18090496 - 5 Sep 2025

Abstract

Access to business financing in Europe has historically been a challenge for small and medium-sized enterprises (SMEs), which represent a significant share of economic activity and employment in Europe. This issue has been significantly intensified since the global financial crisis, disproportionately affecting this [...] Read more.

Access to business financing in Europe has historically been a challenge for small and medium-sized enterprises (SMEs), which represent a significant share of economic activity and employment in Europe. This issue has been significantly intensified since the global financial crisis, disproportionately affecting this segment. This study analyzes firm-level determinants influencing access to alternative financing sources, including crowdfunding, venture capital, and other non-bank channels, using data from the 2023 SAFE covering 15,855 firms across Europe. Results indicate that firm size significantly affects access, with larger, established firms more likely to secure such funding. However, younger, innovation-driven firms demonstrate a higher propensity to pursue equity and crowdfunding options, driven by their need for flexible and early-stage capital. Sectoral patterns also emerge: industrial firms more often obtain public grants, while service-sector firms lead in adopting equity-based and crowdfunding models. The findings highlight the critical role of innovation capacity and international orientation in broadening financial access. Digital platforms are identified as key enablers in democratizing funding, particularly for SMEs. This research advances understanding of SME financing dynamics within evolving financial landscapes and provides actionable insights for policymakers and practitioners aiming to promote inclusive and sustainable access to finance. Full article

(This article belongs to the Special Issue Financial Technology (Fintech) and Sustainable Financing, 4th Edition)

► Show Figures

Figure 1

34 pages, 2601 KB

Open AccessArticle

Determinants of Financial Stability and Development in South Africa: Insights from a Quantile ARDL Model of the South African Financial Cycle

by Khwazi Magubane

J. Risk Financial Manag. 2025, 18(9), 495; https://doi.org/10.3390/jrfm18090495 - 4 Sep 2025

Abstract

This study investigates the short-run and long-run dynamics of the financial cycle in South Africa, focusing on its macroeconomic drivers and their asymmetric effects across different phases. It addresses the persistent challenge in emerging market economies of balancing financial development and stability amidst [...] Read more.

This study investigates the short-run and long-run dynamics of the financial cycle in South Africa, focusing on its macroeconomic drivers and their asymmetric effects across different phases. It addresses the persistent challenge in emerging market economies of balancing financial development and stability amidst volatile conditions. Using monthly data from 2000 to 2024, the research employs a quantile autoregressive distributed lag (QARDL) model to capture the heterogeneity and persistence of macro-financial linkages across the financial cycle’s distribution. The use of the QARDL model in this study allows for capturing asymmetric and quantile-specific relationships that traditional linear models might overlook. Findings reveal that monetary policy, and the housing sector are key drivers of long-term financial development in South Africa, showing positive effects. Conversely, exchange rate movements, inflation, money supply, and macroprudential policy dampen financial development. Short-term financial booms are associated with GDP growth, credit, share, and housing prices. Money supply and inflation are more closely linked to burst phases. These results underscore the importance of policy coordination, particularly between monetary and macroprudential authorities, to balance promoting financial development and ensuring stability in emerging markets. This study contributes to the empirical literature and offers practical insights for policymakers. Full article

(This article belongs to the Special Issue Advanced Studies in Empirical Macroeconomics and Finance)

► Show Figures

Figure 1

21 pages, 760 KB

Open AccessArticle

Forecasting Financial Volatility Under Structural Breaks: A Comparative Study of GARCH Models and Deep Learning Techniques

by Víctor Chung, Jenny Espinoza and Renán Quispe

J. Risk Financial Manag. 2025, 18(9), 494; https://doi.org/10.3390/jrfm18090494 - 4 Sep 2025

Abstract

The main objective of this study is to evaluate the predictive performance of traditional econometric models and deep learning techniques in forecasting financial volatility under structural breaks. Using daily data from four Latin American stock market indices between 2000 and 2024, we compare [...] Read more.

The main objective of this study is to evaluate the predictive performance of traditional econometric models and deep learning techniques in forecasting financial volatility under structural breaks. Using daily data from four Latin American stock market indices between 2000 and 2024, we compare GARCH models with neural networks such as LSTM and CNN. Structural breaks are identified through a modified ICSS algorithm and incorporated into the GARCH framework via regime segmentation. The results show that neglecting breaks overstates volatility persistence and weakens predictive accuracy, while accounting for them improves GARCH forecasts only in specific cases. By contrast, deep learning models consistently outperform GARCH alternatives at medium- and long-term horizons, capturing nonlinear and time-varying dynamics more effectively. This study contributes to the literature by bridging econometric and deep learning approaches and offers practical insights for policymakers and investors in emerging markets facing recurrent structural instability. Full article

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

► Show Figures

Figure 1

21 pages, 406 KB

Open AccessArticle

Investor Emotions and Cognitive Biases in a Bearish Market Simulation: A Qualitative Study

by Alain Finet, Kevin Kristoforidis and Julie Laznicka

J. Risk Financial Manag. 2025, 18(9), 493; https://doi.org/10.3390/jrfm18090493 - 4 Sep 2025

Abstract

Our paper investigates how emotions and cognitive biases shape small investors’ decisions in a bearish market or are perceived as such. Using semi-structured interviews and a focus group, we analyze the behavior of eight management science students engaged in a three-day trading simulation [...] Read more.

Our paper investigates how emotions and cognitive biases shape small investors’ decisions in a bearish market or are perceived as such. Using semi-structured interviews and a focus group, we analyze the behavior of eight management science students engaged in a three-day trading simulation with virtual portfolios. Our findings show that emotions are active forces influencing judgment. Fear, often escalating into anxiety, was pervasive in response to losses and uncertainty, while frustration and powerlessness frequently led to decision paralysis. Early successes sometimes generated happiness and pride but also resulted in overconfidence and excessive risk-taking. These emotional dynamics contributed to the emergence of cognitive biases such as loss aversion, anchoring, confirmation bias, overconfidence, familiarity bias and herd behavior. Emotions often acted as precursors to biases, which then translated into specific decisions—such as holding losing positions, impulsive “revenge” trades or persisting with unsuitable financial strategies. In some cases, strong emotions bypassed cognitive biases and directly drove behavior. Social comparison through portfolio rankings also moderated responses, offering both comfort and additional pressure. By applying a qualitative perspective—not commonly used in behavioral finance—our study highlights the dynamic chain of emotions → biases → decisions and the role of social context. While limited by sample size and the short simulation period, this research provides empirical insights into how psychological mechanisms shape investment behavior under stress, offering avenues for future quantitative studies. Full article

(This article belongs to the Special Issue Behaviour in Financial Decision-Making)

► Show Figures

Figure 1

25 pages, 1472 KB

Open AccessArticle

Self-Awareness in Business Acumen as a Cognitive Bridge Between Accounting Proficiency and Financial Performance in Thai Community Enterprises

by Kirana Yeesoonsam, Roengchai Tansuchat and Namchok Chimprang

J. Risk Financial Manag. 2025, 18(9), 492; https://doi.org/10.3390/jrfm18090492 - 4 Sep 2025

Abstract

This study investigates the mediating role of self-awareness within the broader framework of business acumen, emphasizing its connection to entrepreneurial accounting proficiency and financial performance in community enterprises across Thailand. The purpose is to advance theoretical understanding by integrating metacognition theory and the [...] Read more.

This study investigates the mediating role of self-awareness within the broader framework of business acumen, emphasizing its connection to entrepreneurial accounting proficiency and financial performance in community enterprises across Thailand. The purpose is to advance theoretical understanding by integrating metacognition theory and the resource-based view (RBV), and to provide practical insights for strengthening grassroots entrepreneurship. Using survey data from 210 enterprises, a hybrid Structural Equation Modeling–Artificial Neural Network (SEM–ANN) approach is applied to capture both linear and nonlinear relationships among cognitive, technical, and financial variables. The results confirm that accounting proficiency has a significant and positive effect on self-awareness with value of 0.125. However, self-awareness does not exert a direct influence on financial performance. These findings suggest that self-awareness may function as a cognitive enabler, facilitating the translation of entrepreneurial skills into effective decision-making, rather than serving as an independent predictor of financial outcomes. Empirical patterns further reveal that commercial enterprises report higher self-awareness than service firms, unregistered enterprises show greater awareness than registered ones, and financially stable firms display lower awareness, suggesting complacency or overconfidence. In contrast, regular participation in training significantly enhances awareness, underscoring the role of continuous learning. Full article

(This article belongs to the Section Business and Entrepreneurship)

► Show Figures

Figure 1

33 pages, 2389 KB

Open AccessSystematic Review

Integration of Blockchain in Accounting and ESG Reporting: A Systematic Review from an Oracle-Based Perspective

by Giulio Caldarelli

J. Risk Financial Manag. 2025, 18(9), 491; https://doi.org/10.3390/jrfm18090491 - 3 Sep 2025

Abstract

The Bitcoin network is a sophisticated accounting system that facilitates consensus and verification of transactions through cryptographic proof, eliminating the need for a central authority. Given its success, the underlying technology, generally referred to as blockchain, has been proposed as a means to [...] Read more.

The Bitcoin network is a sophisticated accounting system that facilitates consensus and verification of transactions through cryptographic proof, eliminating the need for a central authority. Given its success, the underlying technology, generally referred to as blockchain, has been proposed as a means to improve legacy accounting and reporting systems. However, integrating real-world data into a blockchain requires the use of oracles: third-party systems that, if poorly selected, may be less decentralized and transparent, potentially undermining the expected benefits. Through a systematic review of the existing literature, this study investigates whether research articles on the integration of blockchain technology in accounting and reporting have addressed the limitations posed by oracles, under the rationale that the omission of oracles constitutes a theoretical bias. Furthermore, this study examines oracle-based solutions proposed for reporting applications and classifies them based on their intended purpose. While the overall consideration of oracles remains limited, the findings indicate a steadily increasing interest in their role and implications within accounting, auditing, and ESG-related blockchain implementations. This growing attention is particularly evident in ESG reporting, where permissioned blockchains and attestation mechanisms are increasingly being examined as practical responses to data verification challenges. Full article

(This article belongs to the Special Issue Data and Technology: Shaping the Future of Finance, Accounting, and Business Systems Innovation)

► Show Figures

Figure 1

19 pages, 439 KB

Open AccessFeature PaperArticle

Expected Credit Spreads and Market Choice: Evidence from Japanese Bond Issuers

by Ikuko Shiiyama

J. Risk Financial Manag. 2025, 18(9), 490; https://doi.org/10.3390/jrfm18090490 - 3 Sep 2025

Abstract

This study explores the impact of credit spreads—defined as the difference between corporate bond yields and matched government bond yields—and macro-financial conditions on Japanese firms’ decision-making regarding whether to issue corporate bonds in domestic or international markets. Using firm-level panel data from 2010 [...] Read more.

This study explores the impact of credit spreads—defined as the difference between corporate bond yields and matched government bond yields—and macro-financial conditions on Japanese firms’ decision-making regarding whether to issue corporate bonds in domestic or international markets. Using firm-level panel data from 2010 to 2019, we employ fixed-effects regressions to identify the determinants of credit spreads and assess their influence on issuance location. The results suggest that firms strategically opt for foreign markets when anticipating narrower spreads, despite the typically higher borrowing costs associated with overseas issuance. Sensitivity to credit spreads systematically varies with issuer characteristics—such as leverage and credit ratings—and market elements—including the United States volatility and stock performance. Interaction models further demonstrate that market selection dynamically responds to pricing signals and uncertainty. By connecting credit spread formation to venue choice, this study provides a new perspective on cross-border financing in segmented capital markets. These findings offer theoretical insights and practical implications for understanding how firms adapt their debt strategies in response to global financial conditions. Full article

(This article belongs to the Section Financial Markets)

► Show Figures

Figure 1

23 pages, 3140 KB

Open AccessArticle

Explainable Machine Learning Models for Credit Rating in Colombian Solidarity Sector Entities

by María Andrea Arias-Serna, Jhon Jair Quiza-Montealegre, Luis Fernando Móntes-Gómez, Leandro Uribe Clavijo and Andrés Felipe Orozco-Duque

J. Risk Financial Manag. 2025, 18(9), 489; https://doi.org/10.3390/jrfm18090489 - 2 Sep 2025

Abstract

This paper proposes a methodology for implementing a custom-developed explainability model for credit rating using behavioral data registered during the lifecycle of the borrowing that can replicate the score given by the regulatory model for the solidarity economy in Colombia. The methodology integrates [...] Read more.

This paper proposes a methodology for implementing a custom-developed explainability model for credit rating using behavioral data registered during the lifecycle of the borrowing that can replicate the score given by the regulatory model for the solidarity economy in Colombia. The methodology integrates continuous behavioral and financial variables from over 17,000 real credit histories into predictive models based on ridge regression, decision trees, random forests, XGBoost, and LightGBM. The models were trained and evaluated using cross-validation and RMSE metrics. LightGBM emerged as the most accurate model, effectively capturing nonlinear credit behavior patterns. To ensure interpretability, SHAP was used to identify the contribution of each feature to the model predictions. The presented model using LightGBM predicted the credit risk assessment in accordance with the regulatory model used by the Colombian Superintendence of the Solidarity Economy, with a root-mean-square error of 0.272 and an R² score of 0.99. We propose an alternative framework using explainable machine learning models aligned with the internal ratings-based approach under Basel II. Our model integrates variables collected throughout the borrowing lifecycle, offering a more comprehensive perspective than the regulatory model. While the regulatory framework adjusts itself generically to national regulations, our approach explicitly accounts for borrower-specific dynamics. Full article

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

► Show Figures

Figure 1

34 pages, 2684 KB

Open AccessArticle

Risk Prediction of International Stock Markets with Complex Spatio-Temporal Correlations: A Spatio-Temporal Graph Convolutional Regression Model Integrating Uncertainty Quantification

by Guoli Mo, Wei Jia, Chunzhi Tan, Weiguo Zhang and Jinyu Rong

J. Risk Financial Manag. 2025, 18(9), 488; https://doi.org/10.3390/jrfm18090488 - 2 Sep 2025

Abstract

Against the backdrop of the “dual circulation” development pattern and the in-depth advancement of the Regional Comprehensive Economic Partnership (RCEP), the interconnection between China and global financial markets has significantly intensified. The spatio-temporal correlation risks faced in cross-border investment activities have become highly [...] Read more.

Against the backdrop of the “dual circulation” development pattern and the in-depth advancement of the Regional Comprehensive Economic Partnership (RCEP), the interconnection between China and global financial markets has significantly intensified. The spatio-temporal correlation risks faced in cross-border investment activities have become highly complex, posing a severe challenge to traditional investment risk prediction methods. Existing research has three limitations: first, traditional analytical tools struggle to capture the dynamic spatio-temporal correlations among financial markets; second, mainstream deep learning models lack the ability to directly output interpretable economic parameters; third, the uncertainty of model prediction results has not been systematically quantified for a long time, leading to a lack of credibility assessment in practical applications. To address these issues, this study constructs a spatio-temporal graph convolutional neural network panel regression model (STGCN-PDR) that incorporates uncertainty quantification. This model innovatively designs a hybrid architecture of “one layer of spatial graph convolution + two layers of temporal convolution”, modeling the spatial dependencies among global stock markets through graph networks and capturing the dynamic evolution patterns of market fluctuations with temporal convolutional networks. It particularly embeds an interpretable regression layer, enabling the model to directly output regression coefficients with economic significance, significantly enhancing the decision-making reference value of risk prediction. By designing multi-round random initialization perturbation experiments and introducing the coefficient of variation index to quantify the stability of model parameters, it achieves a systematic assessment of prediction uncertainty. Empirical results based on stock index data from 20 countries show that compared with the benchmark models, STGCN-PDR demonstrates significant advantages in both spatio-temporal feature extraction efficiency and risk prediction accuracy, providing a more interpretable and reliable quantitative analysis tool for cross-border investment decisions in complex market environments. Full article

(This article belongs to the Special Issue Financial Risk and Technological Innovation)

► Show Figures

Figure 1

18 pages, 465 KB

Open AccessArticle

Empirical Calibration of XGBoost Model Hyperparameters Using the Bayesian Optimisation Method: The Case of Bitcoin Volatility

by Saralees Nadarajah, Jules Clement Mba, Ndaohialy Manda Vy Ravonimanantsoa, Patrick Rakotomarolahy and Henri T. J. E. Ratolojanahary

J. Risk Financial Manag. 2025, 18(9), 487; https://doi.org/10.3390/jrfm18090487 - 2 Sep 2025

Abstract

Ensemble learning techniques continue to show greater interest in forecasting the volatility of cryptocurrency assets. In particular, XGBoost, an ensemble learning technique, has been shown in recent studies to provide the most accurate forecast of Bitcoin volatility. However, the performance of XGBoost largely [...] Read more.

Ensemble learning techniques continue to show greater interest in forecasting the volatility of cryptocurrency assets. In particular, XGBoost, an ensemble learning technique, has been shown in recent studies to provide the most accurate forecast of Bitcoin volatility. However, the performance of XGBoost largely depends on the tuning of its hyperparameters. In this study, we examine the effectiveness of the Bayesian optimization method for tuning the XGBoost hyperparameters for Bitcoin volatility forecasting. We chose to explore this method rather than the most commonly used manual, grid, and random hyperparameter choices due to its ability to predict the most promising areas of hyperparameter spaces through exploitation and exploration using acquisition functions, as well as its ability to minimize error with a reduced amount of time and resources required to find an optimal configuration. The obtained XGBoost configuration improves the forecast accuracy of Bitcoin volatility. Our empirical results, based on letting the data speak for itself, could be used for a comparative study on Bitcoin volatility forecasting. This would also be important for volatility trading, option pricing, and managing portfolios related to Bitcoin. Full article

(This article belongs to the Section Mathematics and Finance)

► Show Figures

Figure 1

Journal Description

Journal of Risk and Financial Management

Latest Articles

Journal Menu

Journal Browser

Highly Accessed Articles

Latest Books

E-Mail Alert

News

Topics

Conferences

Special Issues

Topical Collections

Further Information

Guidelines

MDPI Initiatives

Follow MDPI