Journal of Risk and Financial Management

28 pages, 3277 KB

Open AccessArticle

Non-Linear Impact of Environmental, Social, and Governance Scores on Deal Premiums

by Ralph Sonenshine and Yan Wang

J. Risk Financial Manag. 2025, 18(11), 599; https://doi.org/10.3390/jrfm18110599 (registering DOI) - 24 Oct 2025

Increasingly, scholars have been researching how ESG ratings appear to impact the returns of a merger as well as the expected synergies of the merger. This paper adds to the literature by using a non-linear method to test the impact that ESG ratings, [...] Read more.

Increasingly, scholars have been researching how ESG ratings appear to impact the returns of a merger as well as the expected synergies of the merger. This paper adds to the literature by using a non-linear method to test the impact that ESG ratings, differences in ESG ratings between the acquirer and the target, and ESG components have on the deal premium. We find overwhelming evidence, using multiple deal premium measurements, of an inverted U-shaped relationship between the target’s ESG scores at the time of the announcement and the deal premium. Moreover, we find some evidence that differences between the ESG scores of the acquirer and the target also impact the deal premium but in a U-shaped relationship. Finally, our results show that the social scores of both the acquirer and the target impact the deal premium, again in an inverted U-shaped manner, as does the governance rating of target, but only in relatively smaller deals. Full article

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

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22 pages, 964 KB

Open AccessSystematic Review

Using Data Analytics in Financial Statement Fraud Detection and Prevention: A Systematic Review of Methods, Challenges, and Future Directions

by Michail Gkegkas, Dimitrios Kydros and Michail Pazarskis

J. Risk Financial Manag. 2025, 18(11), 598; https://doi.org/10.3390/jrfm18110598 - 24 Oct 2025

Abstract

Reliable financial reporting is critical for maintaining market confidence and guiding stakeholders’ decision-making, yet traditional audit methods often fail to detect sophisticated fraud schemes that are hidden within large volumes of transactional data. This systematic literature review synthesizes 43 empirical and theoretical studies [...] Read more.

Reliable financial reporting is critical for maintaining market confidence and guiding stakeholders’ decision-making, yet traditional audit methods often fail to detect sophisticated fraud schemes that are hidden within large volumes of transactional data. This systematic literature review synthesizes 43 empirical and theoretical studies published between 2010 and 2024 that utilize data analytics techniques for the prevention and detection of fraud in financial statements. Following the PRISMA guidelines, we conducted a four-phase review—identification, screening, eligibility assessment, and inclusion—to ensure transparency and reproducibility. Our analysis categorizes techniques into supervised machine learning classifiers (e.g., decision trees and neural networks), statistical anomaly detection methods, network-based analyses, and real-time monitoring frameworks. We evaluate each approach’s comparative effectiveness, highlight persistent challenges such as data imbalance, model interpretability, and governance constraints, and also trace evolving methodological trends over time. The review reveals that integrating predictive analytics and continuous monitoring into accounting information systems can transform audits from reactive investigations into proactive fraud prevention mechanisms. We conclude by proposing a future research agenda focusing on developing explainable AI models for audit applications, establishing robust data governance frameworks to support automated monitoring, and conducting longitudinal field studies to assess the real-world impact of analytics-driven controls. Full article

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

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16 pages, 776 KB

Open AccessArticle

The Importance of Technological Progression in Impoverished Countries

by Mohammed T. Hussein, Munir Quddus and Lawrence J. Trautman

J. Risk Financial Manag. 2025, 18(11), 597; https://doi.org/10.3390/jrfm18110597 - 24 Oct 2025

Abstract

In mid-2023, United Nations Secretary-General António Guterres warned that almost 80 years following the end of World War Two, “the global financial architecture is outdated, dysfunctional, and unjust. It is no longer capable of meeting the needs of the 21st-century world: a multipolar [...] Read more.

In mid-2023, United Nations Secretary-General António Guterres warned that almost 80 years following the end of World War Two, “the global financial architecture is outdated, dysfunctional, and unjust. It is no longer capable of meeting the needs of the 21st-century world: a multipolar world characterized by deeply integrated economies and financial markets. But also marked by geopolitical tensions and growing systemic risks.” Further, the Secretary-General cautioned that “the current global financial system exacerbates inequalities, denying the poorest countries the credit and debt support they need and deserve”. We address the question: How does the transfer of modern technologies improve the economic development of impoverished nations? In this paper we demonstrate that rapid technological change is a double-edged sword—bringing significant productivity gains and economic progress while also causing profound societal disruptions and posing a threat of political instability in parts of the world. Nevertheless, we believe that a rapid and sustained transfer of these technologies holds great promise for the rapid development of today’s less developed nations. Full article

(This article belongs to the Section Economics and Finance)

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23 pages, 1870 KB

Open AccessArticle

Economic Policy Uncertainty, Geopolitical Risk, and the U.S.–China Relations: A Risk Transmission Perspective

by Jacky Yuk-Chow So and Un Loi Lao

J. Risk Financial Manag. 2025, 18(11), 596; https://doi.org/10.3390/jrfm18110596 - 24 Oct 2025

Abstract

This study examines risk transmission between the United States and China using integrated economic policy uncertainty (EPU) and geopolitical risk (GPR) indices. We employ a dual methodology that combines Vector Autoregressive (VAR) and Granger causality in quantiles tests to analyze interactions during systemic [...] Read more.

This study examines risk transmission between the United States and China using integrated economic policy uncertainty (EPU) and geopolitical risk (GPR) indices. We employ a dual methodology that combines Vector Autoregressive (VAR) and Granger causality in quantiles tests to analyze interactions during systemic leadership transitions, a dimension that is currently under-explored. Our dataset covers the period from June 2000 to June 2023. Results indicate that China is narrowing the economic influence gap and strengthening its role as a regional anchor. The U.S., however, maintains predominant global leadership. This dynamic reframes bilateral tensions as a “status dilemma” rather than a security conflict. Crucially, we identify asymmetric spillover effects: the U.S. uncertainty shocks spread globally, while China’s volatility remains regional. Our findings contribute to the understanding of financial stability by demonstrating that leadership asymmetries are critical determinants, providing valuable insights for designing systemic risk monitoring tools and contagion mitigation policies during periods of heightened uncertainty. Full article

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

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15 pages, 490 KB

Open AccessArticle

Determinant Factor of Individual Taxpayer Compliance in Indonesia: Integrates of TPB Theory and Social Identity Theory

by Nurhayati, Azhar Maksum, Narumondang B. Siregar and Fahmi Natigor Nasution

J. Risk Financial Manag. 2025, 18(11), 595; https://doi.org/10.3390/jrfm18110595 - 24 Oct 2025

Abstract

Studies on tax compliance have predominantly used the theory of planned behavior. This study combines the theory of planned behavior with social identity theory. This study examines tax fairness (subjective norms), tax complexity (perceived behavioral control), tax morality (attitudes toward behavior), and national [...] Read more.

Studies on tax compliance have predominantly used the theory of planned behavior. This study combines the theory of planned behavior with social identity theory. This study examines tax fairness (subjective norms), tax complexity (perceived behavioral control), tax morality (attitudes toward behavior), and national pride as social identity theory that explain their impact on tax compliance levels. Using non-probability random sampling, this study successfully collected 401 individual taxpayer respondents and analyzed them using PLS-SEM. The results of this study revealed that national pride is a crucial component in improving taxpayer compliance behavior. TPB theory still makes a significant contribution to tax compliance intentions through tax fairness and tax morality. This research suggests that tax authorities should manage tax funds by providing a sense of fairness and improving taxpayer morality. On the one hand, the government needs to promote national pride among taxpayers. This has the potential to remind taxpayers of the presence of taxes and encourage the mobilization of funds from the tax sector for national development. Full article

(This article belongs to the Section Business and Entrepreneurship)

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11 pages, 386 KB

Open AccessCommunication

GENIUS at Work in the US Financial System

by Jimmie H. Lenz and Reid Tymcio

J. Risk Financial Manag. 2025, 18(11), 594; https://doi.org/10.3390/jrfm18110594 - 23 Oct 2025

Abstract

This paper examines the profitability of Stablecoin Issuance as regulated by the GENIUS Act. We provide an analysis of the revenues and expenses that can be expected to be earned and incurred by any regulated Stablecoin Issuer, as well as the competitive dynamics [...] Read more.

This paper examines the profitability of Stablecoin Issuance as regulated by the GENIUS Act. We provide an analysis of the revenues and expenses that can be expected to be earned and incurred by any regulated Stablecoin Issuer, as well as the competitive dynamics at work in the industry. We argue that, since Issuers regularly pay a large portion of their reserve income to distributors, Issuer profitability is not a function of reserve yield but, rather, is determined by economies of scale and operational efficiencies. We also analyze the dynamics at work in the market for stablecoin distribution and conclude that US banks with brokerage subsidiaries are the firms most well-positioned to dominate both the issuance and distribution of stablecoins regulated under the GENIUS Act. Full article

(This article belongs to the Section Banking and Finance)

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28 pages, 2705 KB

Open AccessArticle

Systemic Risk Modeling with Expectile Regression Neural Network and Modified LASSO

by Wisnowan Hendy Saputra, Dedy Dwi Prastyo and Kartika Fithriasari

J. Risk Financial Manag. 2025, 18(11), 593; https://doi.org/10.3390/jrfm18110593 - 22 Oct 2025

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Abstract

Traditional risk models often fail to capture extreme losses in interconnected global stock markets. This study introduces a novel approach, Expectile Regression Neural Network with Modified LASSO regularization (ERNN-mLASSO), to model nonlinear systemic risk. By analyzing five major stock indices (JKSE, GSPC, GDAXI, [...] Read more.

Traditional risk models often fail to capture extreme losses in interconnected global stock markets. This study introduces a novel approach, Expectile Regression Neural Network with Modified LASSO regularization (ERNN-mLASSO), to model nonlinear systemic risk. By analyzing five major stock indices (JKSE, GSPC, GDAXI, FTSE, N225), we identify distinct market roles: developed markets, such as the GSPC, act as risk spreaders, while emerging markets, like the JKSE, act as risk takers. Our network systemic risk index, SNRI, accurately captures systemic shocks during the COVID-19 crisis. More importantly, the model projects increasing global financial fragility through 2025, providing an early warning signal for policymakers and risk managers of potential future instability. Full article

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

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J. Risk Financial Manag., Volume 18, Issue 11 (November 2025) – 7 articles

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