Journal of Risk and Financial Management

28 pages, 1066 KB

Open AccessArticle

Breaking Free from Managerial Myopia: Government and Corporate Governance as Catalysts for Firm Innovation

by Junchang Pan, Hamish Anderson, Junshi Chen and Jing Chi

J. Risk Financial Manag. 2026, 19(1), 94; https://doi.org/10.3390/jrfm19010094 - 22 Jan 2026

Viewed by 153

Employing textual analysis of the “short-term vision” vocabulary in annual reports, we investigate the impact of managerial myopia on firm innovation and performance. Our results indicate that managerial myopia hampers innovation, and this result remains robust across a battery of robustness checks. Managerial [...] Read more.

Employing textual analysis of the “short-term vision” vocabulary in annual reports, we investigate the impact of managerial myopia on firm innovation and performance. Our results indicate that managerial myopia hampers innovation, and this result remains robust across a battery of robustness checks. Managerial myopia also weakens the positive impact of innovation on firm growth, and value in the long run. We find that state ownership and good corporate governance mitigate the negative impact of managerial myopia. The evidence supports the upper echelon theory and time orientation theoretical framework. This paper enriches the research on the influencing factors of corporate innovation, by providing evidence that people’s perception of time affects decision making and provides support for government ownership and strong corporate governance practices in alleviating the negative consequences of managerial myopia. Full article

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

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15 pages, 323 KB

Open AccessArticle

Assessing the Link Between the Misery Index and Dollarization: Regional Evidence from Türkiye

by Gökhan Özkul and İbrahim Yaşar Gök

J. Risk Financial Manag. 2026, 19(1), 93; https://doi.org/10.3390/jrfm19010093 - 22 Jan 2026

Viewed by 92

Abstract

This study analyzes the relationship between macroeconomic distress and financial dollarization in Türkiye using annual regional panel data for 26 Nomenclature of Territorial Units for Statistics 2 regions over the period 2005–2021. Macroeconomic distress is captured using the misery index, computed as the [...] Read more.

This study analyzes the relationship between macroeconomic distress and financial dollarization in Türkiye using annual regional panel data for 26 Nomenclature of Territorial Units for Statistics 2 regions over the period 2005–2021. Macroeconomic distress is captured using the misery index, computed as the compound of inflation and unemployment rates, while the share of foreign-currency-denominated deposits in total deposits measures financial dollarization. Applying second-generation panel econometric models that account for regional heterogeneity, we investigate both long-run equilibrium relationships and short-run interactions. Panel cointegration tests show a long-run connection between macroeconomic distress and dollarization. Short-run effects estimated using a Panel Vector Error Correction Model and a Cross-Sectionally Augmented ARDL framework point to bidirectional causality. Long-run coefficient estimates obtained via Dynamic Ordinary Least Squares indicate an apparent asymmetry. Increases in dollarization exert a substantial and economically significant effect on macroeconomic distress, whereas the long-run impact of distress on dollarization is comparatively modest. The findings suggest that dollarization functions not only as a response to macroeconomic instability but also as a structural element that intensifies inflationary pressures and labor market distortions over time. Focusing on regional patterns rather than national aggregates, the paper provides new evidence on the spatial dimension of the dollarization–instability link. Full article

(This article belongs to the Section Financial Markets)

43 pages, 898 KB

Open AccessSystematic Review

Transforming Digital Accounting: Big Data, IoT, and Industry 4.0 Technologies—A Comprehensive Survey

by Georgios Thanasas, Georgios Kampiotis and Constantinos Halkiopoulos

J. Risk Financial Manag. 2026, 19(1), 92; https://doi.org/10.3390/jrfm19010092 - 22 Jan 2026

Viewed by 316

Abstract

(1) Background: The convergence of Big Data and the Internet of Things (IoT) is transforming digital accounting from retrospective documentation into real-time operational intelligence. This systematic review examines how Industry 4.0 technologies—artificial intelligence (AI), blockchain, edge computing, and digital twins—transform accounting practices through [...] Read more.

(1) Background: The convergence of Big Data and the Internet of Things (IoT) is transforming digital accounting from retrospective documentation into real-time operational intelligence. This systematic review examines how Industry 4.0 technologies—artificial intelligence (AI), blockchain, edge computing, and digital twins—transform accounting practices through intelligent automation, continuous compliance, and predictive decision support. (2) Methods: The study synthesizes 176 peer-reviewed sources (2015–2025) selected using explicit inclusion criteria emphasizing empirical evidence. Thematic analysis across seven domains—conceptual foundations, system evolution, financial reporting, fraud detection, audit transformation, implementation challenges, and emerging technologies—employs systematic bias-reduction mechanisms to develop evidence-based theoretical propositions. (3) Results: Key findings document fraud detection accuracy improvements from 65–75% (rule-based) to 85–92% (machine learning), audit cycle reductions of 40–60% with coverage expansion from 5–10% sampling to 100% population analysis, and reconciliation effort decreases of 70–80% through triple-entry blockchain systems. Edge computing reduces processing latency by 40–75%, enabling compliance response within hours versus 24–72 h. Four propositions are established with empirical support: IoT-enabled reporting superiority (15–25% error reduction), AI-blockchain fraud detection advantage (60–70% loss reduction), edge computing compliance responsiveness (55–75% improvement), and GDPR-blockchain adoption barriers (67% of European institutions affected). Persistent challenges include cybersecurity threats (300% incident increase, $5.9 million average breach cost), workforce deficits (70–80% insufficient training), and implementation costs ($100,000–$1,000,000). (4) Conclusions: The research contributes a four-layer technology architecture and challenge-mitigation framework bridging technical capabilities with regulatory requirements. Future research must address quantum computing applications (5–10 years), decentralized finance accounting standards (2–5 years), digital twins with 30–40% forecast improvement potential (3–7 years), and ESG analytics frameworks (1–3 years). The findings demonstrate accounting’s fundamental transformation from historical record-keeping to predictive decision support. Full article

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

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20 pages, 731 KB

Open AccessArticle

Option-Implied Zero-Coupon Yields: Unifying Bond and Equity Markets

by Ting-Jung Lee, W. Brent Lindquist, Svetlozar T. Rachev and Abootaleb Shirvani

J. Risk Financial Manag. 2026, 19(1), 91; https://doi.org/10.3390/jrfm19010091 - 22 Jan 2026

Viewed by 93

Abstract

This paper addresses a critical inconsistency in models of the term structure of interest rates (TSIR), where zero-coupon bonds are priced under risk-neutral measures distinct from those used in equity markets. We consider a unified TSIR framework that treats zero-coupon bonds as European [...] Read more.

This paper addresses a critical inconsistency in models of the term structure of interest rates (TSIR), where zero-coupon bonds are priced under risk-neutral measures distinct from those used in equity markets. We consider a unified TSIR framework that treats zero-coupon bonds as European options with deterministic payoffs, ensuring that they are priced under the same risk-neutral measure that governs equity derivatives. Using put–call parity, we extract zero-coupon bond implied yield curves from S&P 500 index options and compare them with the US daily treasury par yield curves. As the implied yield curves contain maturity time T and strike price K as independent variables, we investigate the

K

—dependence of the implied yield curve. Our findings, that at-the-money option-implied yield curves provide the closest match to treasury par yield curves, support the view that the equity options market contains information that is highly relevant for the TSIR. By insisting that the risk-neutral measure used for bond valuation is the same as that revealed by equity derivatives, we offer a new organizing principle for future TSIR research. Full article

(This article belongs to the Section Financial Markets)

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17 pages, 1448 KB

Open AccessArticle

The Impact of Artificial Intelligence on Accounting Information and Earnings Management: Bibliometric Analysis

by Dalenda Ben Ahmed

J. Risk Financial Manag. 2026, 19(1), 90; https://doi.org/10.3390/jrfm19010090 - 22 Jan 2026

Viewed by 373

Abstract

Artificial intelligence technology has increased in popularity in the domain of accounting. Previous studies have focused on analysing the impact of AI integration on accounting in general and on work performance, with few researchers analysing the impact of AI on accounting information. Our [...] Read more.

Artificial intelligence technology has increased in popularity in the domain of accounting. Previous studies have focused on analysing the impact of AI integration on accounting in general and on work performance, with few researchers analysing the impact of AI on accounting information. Our study aims to determine the impact of AI on accounting information, on the one hand, and earnings management, on the other, using a bibliometric analysis that examines trends in scientific output. Our analysis was based on the use of the Bibliometrix package of RStudio software. The information is obtained from the “Web of Science” database, which identified 98 articles published in 37 journals that are the subject of our bibliometric analysis for the period 2017–2025. Our study shows that integrating AI into accounting can resolve the problem of information asymmetry, increase the transparency of financial information, and both limit earnings management practices and promote more sophisticated forms of earnings management. The bibliometric results show an increase in scientific output on our topic from 2023 onwards, reaching its peak in 2025. Bibliometric analysis presents productivity over time, identifies the most developed topics and the most cited authors and articles, and reveals the most frequently used keywords. This study provides guidance for future research directions. Full article

(This article belongs to the Special Issue Financial Accounting)

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25 pages, 622 KB

Open AccessArticle

Bond vs. Equity Mutual Fund Performance Using False Discovery Rate (FDR)

by Lifa Huang, Wayne Y. Lee and Craig G. Rennie

J. Risk Financial Manag. 2026, 19(1), 89; https://doi.org/10.3390/jrfm19010089 - 21 Jan 2026

Viewed by 120

Abstract

This paper compares actively managed bond vs. equity mutual fund performance using modified False Discovery Rate (

q^{*}

) and percent simulated t(α) < Actual t(α). Bond funds are more likely to outperform than equity funds:

q^{*}

(%Sim < Act) shows [...] Read more.

This paper compares actively managed bond vs. equity mutual fund performance using modified False Discovery Rate (

q^{*}

) and percent simulated t(α) < Actual t(α). Bond funds are more likely to outperform than equity funds:

q^{*}

(%Sim < Act) shows 33.9% (30.0%) of bond funds generate positive t(α) on net excess returns vs. 1.8% (0.0%) for equity funds.

q^{*}

shows percent simulated t(α) < Actual t(α)results are sensitive to Type II error. Bond fund outperformance is associated with long-term holdings, and corporate bond fund excess returns tend to decline with fund size. Full article

(This article belongs to the Special Issue Investment Strategies and Market Dynamics)

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22 pages, 805 KB

Open AccessArticle

Morbidity-Based Pension Benefit Evaluation and Payment Option Comparison

by Dekun Zhai, Yvette Feng, Gao Niu, James Bishop and John T. Quinn

J. Risk Financial Manag. 2026, 19(1), 88; https://doi.org/10.3390/jrfm19010088 - 21 Jan 2026

Viewed by 101

Abstract

In this paper, the authors survey and summarize the widely researched morbidities and their life expectancy results. A constant impaired mortality adjustment for each morbidity is defined so that life expectancy is consistent with current medical research. Impaired mortality factors are derived and [...] Read more.

In this paper, the authors survey and summarize the widely researched morbidities and their life expectancy results. A constant impaired mortality adjustment for each morbidity is defined so that life expectancy is consistent with current medical research. Impaired mortality factors are derived and used to evaluate morbidity’s impact on retirement benefits. A morbidity-based pension benefit evaluation algorithm is proposed. Popular pension payment options, such as single life payment and joint life, are evaluated. The authors found that the optimal decision is highly sensitive to health status: lump sums are preferred when health is impaired, whereas annuities dominate for healthier individuals. Full article

(This article belongs to the Special Issue Pensions and Retirement Planning)

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18 pages, 312 KB

Open AccessArticle

ESG Performance and Corporate Value in an Emerging Market: The Moderating Role of Board Structures in Sustainability

by Nongnit Chancharat, Witchulada Vetchagool and Surachai Chancharat

J. Risk Financial Manag. 2026, 19(1), 87; https://doi.org/10.3390/jrfm19010087 - 21 Jan 2026

Viewed by 196

Abstract

This study examines the relationship between publicly traded Thai companies’ ESG performance and value as well as how board structures moderate this. In the Thai context, there is a limited number of empirical studies that employ the board of directors’ structure as a [...] Read more.

This study examines the relationship between publicly traded Thai companies’ ESG performance and value as well as how board structures moderate this. In the Thai context, there is a limited number of empirical studies that employ the board of directors’ structure as a moderating variable, despite the importance of the board’s role in corporate management. This study aims to address this research gap. A panel GMM regression model is employed to address endogeneity issues, and our sample consists of 94 Thai listed companies with available ESG data from 2019 to 2023, resulting in 470 firm-year observations. The results demonstrate positive direct impact of ESG score on corporate value. In addition, board independence is positively significant and relates to company value. However, this research found negative moderating effect of board independence on the relationship between ESG score and corporate value. Furthermore, the empirical results indicate that board size does not have a significant direct and moderate impact on corporate value. Moreover, firm size and leverage are not related to corporate value. The results confirm the agency theory and stakeholder theory. Based on the findings, company executives should integrate ESG practices into their strategic plans. Moreover, regulatory authorities should promote expertise diversity and independence within the board and promote ESG standards and disclosure, as well as offer tax incentives for companies with outstanding ESG. This would enable investors to consider ESG performance in their decision-making. This study represents a new contribution to literature, especially in the context of emerging markets. Full article

(This article belongs to the Special Issue Finance and Accounting in Times of Global Uncertainty: ESG, SDGs, and Digital Disruption)

18 pages, 1536 KB

Open AccessArticle

When Tracking Error Misleads: Risk Exposure Differences Between ETFs and Their Indices

by Naif Alfnaisan, Fatima Jebari and Mohammad Kabir Hassan

J. Risk Financial Manag. 2026, 19(1), 86; https://doi.org/10.3390/jrfm19010086 - 21 Jan 2026

Viewed by 225

Abstract

We investigate the underlying risk exposures of ETFs compared with their indices using a Principal Component Analysis approach. Then, we test whether ETFs’ tracking errors can capture the risk exposure difference between ETFs and their underlying benchmarks. We document a significant positive relation [...] Read more.

We investigate the underlying risk exposures of ETFs compared with their indices using a Principal Component Analysis approach. Then, we test whether ETFs’ tracking errors can capture the risk exposure difference between ETFs and their underlying benchmarks. We document a significant positive relation between tracking error and differences in risk exposure between ETFs and their corresponding indices. Even modest increases in tracking error are associated with economically meaningful divergences in risk exposure between an ETF and its benchmark. These findings suggest that comparisons of tracking error across index ETFs when making investment decisions may be misleading for investors seeking benchmark-consistent risk exposure. Full article

(This article belongs to the Section Financial Markets)

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16 pages, 758 KB

Open AccessArticle

Optimization of Working Capital for Financial Sustainability in Manufacturing Companies: A Statistical Model

by Karla Estefanía Morales, Edison Roberto Valencia-Nuñez, Josselyn Paredes-León and Freddy Armijos-Arcos

J. Risk Financial Manag. 2026, 19(1), 85; https://doi.org/10.3390/jrfm19010085 - 21 Jan 2026

Viewed by 153

Abstract

Background: Working capital management plays a critical role in ensuring business liquidity and financial sustainability. However, few studies in developing economies have employed multivariate statistical techniques to optimize working capital decisions. This study addresses this gap by applying discriminant analysis to classify Ecuadorian [...] Read more.

Background: Working capital management plays a critical role in ensuring business liquidity and financial sustainability. However, few studies in developing economies have employed multivariate statistical techniques to optimize working capital decisions. This study addresses this gap by applying discriminant analysis to classify Ecuadorian manufacturing firms according to their financial sustainability and business continuity. Methods: A quantitative approach was applied to a sample of 112 manufacturing companies located in Zone 3 of Ecuador, covering the 2017–2020 period. The model incorporated working capital indicators and the Z-Score index as independent variables, while company size served as the categorical dependent variable. Results: The discriminant function retained two significant predictors—Working Capital (2019) and Z-Score (2017)—with an eigenvalue of 0.191, a canonical correlation of 0.400, and an overall classification accuracy of 71.4%. Box’s M test (p = 0.000) indicated unequal covariance matrices, suggesting cautious interpretation but acceptable robustness of the model. Conclusions: This study concludes that working capital and Z-Score are effective indicators for assessing financial sustainability and predicting firm continuity. The findings provide practical insights for managers and policymakers to enhance financial efficiency and resource allocation. The originality of this work lies in the application of discriminant analysis to model financial sustainability in Ecuador’s manufacturing sector, offering a statistical foundation for future optimization models. Full article

(This article belongs to the Section Sustainability and Finance)

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11 pages, 317 KB

Open AccessArticle

Modeling the Private-to-Public Transition: IPOs, Direct Listings and De-SPAC Mergers

by Vasilios Margaris and Georgios Angelidis

J. Risk Financial Manag. 2026, 19(1), 84; https://doi.org/10.3390/jrfm19010084 - 21 Jan 2026

Viewed by 123

Abstract

We have developed a comprehensive mathematical framework that delineates the complete transition of a firm from private to public ownership. This framework explicitly formalizes the endogenous decision to list, pre-listing restructuring, regulatory feasibility constraints, information production, pricing and allocation mechanisms, and post-listing market [...] Read more.

We have developed a comprehensive mathematical framework that delineates the complete transition of a firm from private to public ownership. This framework explicitly formalizes the endogenous decision to list, pre-listing restructuring, regulatory feasibility constraints, information production, pricing and allocation mechanisms, and post-listing market dynamics. A unified structure is employed to represent traditional IPOs, direct listings, and de-SPAC mergers. The proposed framework integrates the concepts of information asymmetry, free-float constraints, and market impact with equilibrium offer prices, first-day returns, and post-listing volatility. This integration enables the formulation of testable predictions across a range of listing mechanisms. Full article

(This article belongs to the Section Economics and Finance)

13 pages, 2745 KB

Open AccessArticle

Stock Returns and Income Inequality

by Margaret Rutendo Magwedere and Godfrey Marozva

J. Risk Financial Manag. 2026, 19(1), 83; https://doi.org/10.3390/jrfm19010083 - 21 Jan 2026

Viewed by 139

Abstract

This study investigates the relationship between stock returns and income inequality in South Africa, a country marked by persistently high levels of income disparities and a sophisticated and structurally unique financial market. Despite the Johannesburg Stock Exchange (JSE) being one of the most [...] Read more.

This study investigates the relationship between stock returns and income inequality in South Africa, a country marked by persistently high levels of income disparities and a sophisticated and structurally unique financial market. Despite the Johannesburg Stock Exchange (JSE) being one of the most developed and liquid markets in Africa, stock ownership remains limited to a small segment of the population, often reinforcing pre-existing income inequalities. This study determines the relationship between stock returns and income distribution using the ARDL bound test methodology. Using time series data from 1975 to 2024, the study examines the extent to which stock market returns influence income distribution. The findings of the study suggest a positive relationship between stock returns and income distribution. This relationship suggests that higher stock market development disproportionately benefits capital holders. The long-term relationship seems to have limited feedback from inequality to stock returns. The findings aim to inform policies on inclusive financial participation and broad-based wealth generation to address South Africa’s structural inequalities. Full article

(This article belongs to the Section Financial Markets)

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39 pages, 6278 KB

Open AccessArticle

Towards Generative Interest-Rate Modeling: Neural Perturbations Within the Libor Market Model

by Anna Knezevic

J. Risk Financial Manag. 2026, 19(1), 82; https://doi.org/10.3390/jrfm19010082 - 21 Jan 2026

Viewed by 164

Abstract

This study proposes a neural-augmented Libor Market Model (LMM) for swaption surface calibration that enhances expressive power while maintaining the interpretability, arbitrage-free structure, and numerical stability of the classical framework. Classical LMM parametrizations, based on exponential decay volatility functions and static correlation kernels, [...] Read more.

This study proposes a neural-augmented Libor Market Model (LMM) for swaption surface calibration that enhances expressive power while maintaining the interpretability, arbitrage-free structure, and numerical stability of the classical framework. Classical LMM parametrizations, based on exponential decay volatility functions and static correlation kernels, are known to perform poorly in sparsely quoted and long-tenor regions of swaption volatility cubes. Machine learning–based diffusion models offer flexibility but often lack transparency, stability, and measure-consistent dynamics. To reconcile these requirements, the present approach embeds a compact neural network within the volatility and correlation layers of the LMM, constrained by structural diagnostics, low-rank correlation construction, and HJM-consistent drift. Empirical tests across major currencies (EUR, GBP, USD) and multiple quarterly datasets from 2024 to 2025 show that the neural-augmented LMM consistently outperforms the classical model. Improvements of approximately 7–10% in implied volatility RMSE and 10–15% in PV RMSE are observed across all datasets, with no deterioration in any region of the surface. These results reflect the model’s ability to represent cross-tenor dependencies and surface curvature beyond the reach of classical parametrizations, while remaining economically interpretable and numerically tractable. The findings support hybrid model designs in quantitative finance, where small neural components complement robust analytical structures. The approach aligns with ongoing industry efforts to integrate machine learning into regulatory-compliant pricing models and provides a pathway for future generative LMM variants that retain an arbitrage-free diffusion structure while learning data-driven volatility geometry. Full article

(This article belongs to the Special Issue Quantitative Finance in the Era of Big Data and AI)

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13 pages, 281 KB

Open AccessArticle

Is It a Case of Safe Haven? Analyzing Stablecoin Returns Considering Cryptocurrency Dynamics

by Vitor Fonseca Machado Beling Dias and Rodrigo Fernandes Malaquias

J. Risk Financial Manag. 2026, 19(1), 81; https://doi.org/10.3390/jrfm19010081 - 20 Jan 2026

Viewed by 245

Abstract

In this study, we evaluated the returns and return volatility of a Brazilian stablecoin linked to fertilizers during periods preceding its discontinuation. In light of the safe haven literature, we also tested the correlation between this stablecoin and a traditional cryptocurrency, Bitcoin, and [...] Read more.

In this study, we evaluated the returns and return volatility of a Brazilian stablecoin linked to fertilizers during periods preceding its discontinuation. In light of the safe haven literature, we also tested the correlation between this stablecoin and a traditional cryptocurrency, Bitcoin, and modeled its behavior during periods of Bitcoin’s extreme returns. In terms of methodology, we employ GARCH-family models (including DCC-GARCH) to analyze daily data from 1 December 2022 to 16 January 2025. We also employ an analysis using Large Language Models (LLMs), evaluating the stablecoin time series considering the period of its discontinuation. The results indicated that as the discontinuation date approached, the stablecoin exhibited statistically significant lower returns and higher volatility. While the DCC-GARCH indicated no correlation between the assets, we found that the stablecoin’s returns exhibited a negative relationship with Bitcoin’s extreme returns, challenging its potential efficacy as a safe haven. This article offers practical contributions for digital asset investors, indicating that even physically backed stablecoins, designed for stability, are subject to significant volatility, idiosyncratic risks, and potential discontinuation. Full article

(This article belongs to the Special Issue The Future of Money: Central Bank Digital Currencies, Cryptocurrencies and Stablecoins, 2nd Edition)

23 pages, 627 KB

Open AccessArticle

Harnessing Blockchain for Transparent and Sustainable Accounting in Creative MSMEs amid Digital Disruption: Evidence from Indonesia

by I Made Dwi Hita Darmawan, Ni Putu Noviyanti Kusuma, Nir Kshetri, Ketut Tri Budi Artani and Wina Pertiwi Putri Wardani

J. Risk Financial Manag. 2026, 19(1), 80; https://doi.org/10.3390/jrfm19010080 - 20 Jan 2026

Viewed by 282

Abstract

Blockchain is widely promoted as a tool for enhancing transparency, trust, and sustainability in business, yet little is known about how creative micro, small, and medium enterprises (MSMEs) in emerging economies can meaningfully adopt it for finance and accounting purposes in times of [...] Read more.

Blockchain is widely promoted as a tool for enhancing transparency, trust, and sustainability in business, yet little is known about how creative micro, small, and medium enterprises (MSMEs) in emerging economies can meaningfully adopt it for finance and accounting purposes in times of global uncertainty. This study explores how blockchain can be harnessed for transparent and sustainable accounting in Indonesian creative MSMEs amid rapid digital disruption. Using an exploratory qualitative design, we conducted semi-structured, in-depth interviews with 18 owners and key decision-makers across diverse creative subsectors and analysed the data thematically through an integrated Technology Acceptance Model (TAM) and Diffusion of Innovation (DOI) lens. The findings show that participants recognise blockchain’s potential benefits for transaction transparency, verifiable records, intellectual property protection, and secure payments, but adoption is constrained by technical complexity, financial constraints, limited digital and accounting capabilities, and perceived regulatory and reputational risks. Government initiatives are seen as important for legitimacy yet insufficient without concrete guidance, capacity-building, and financial support. The study extends TAM–DOI applications to blockchain-enabled accounting in creative MSMEs and highlights the need for sequenced, ecosystem-based interventions to translate blockchain’s technical promise into accessible, ESG- and SDG-oriented accounting solutions in the creative economy. Full article

(This article belongs to the Special Issue Finance and Accounting in Times of Global Uncertainty: ESG, SDGs, and Digital Disruption)

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22 pages, 405 KB

Open AccessArticle

A Cointegrated Ising Spin Model for Asynchronously Traded Futures Contracts: Spread Trading with Crude Oil Futures

by Kostas Giannopoulos

J. Risk Financial Manag. 2026, 19(1), 79; https://doi.org/10.3390/jrfm19010079 - 19 Jan 2026

Viewed by 209

Abstract

Pairs trading via futures calendar spreads offers a robust market-neutral approach to exploiting transient mispricings, yet real-time implementation is hindered by asynchronous trading. This paper introduces a Cointegrated Ising Spin Model, CISM, for real-time signal generation in high-frequency spread trading. The model [...] Read more.

Pairs trading via futures calendar spreads offers a robust market-neutral approach to exploiting transient mispricings, yet real-time implementation is hindered by asynchronous trading. This paper introduces a Cointegrated Ising Spin Model, CISM, for real-time signal generation in high-frequency spread trading. The model links the macro-level equilibrium of cointegration with micro-level agent interactions, representing prices as magnetizations in an agent-based system. A novel

Δ

-weighted arbitrage force dynamically adjusts agents’ corrective behavior to account for information staleness. Calibrated on tick-by-tick Brent crude oil futures, the model produces a time-varying probability of spread reversion, enabling probabilistic trading decisions. Backtesting demonstrates a 74.65% success rate, confirming the CISM’s ability to generate stable, data-driven arbitrage signals in asynchronous environments. The model bridges macro-level cointegration with micro-level agent interactions, representing prices as magnetizations within an agent-based Ising system. A novel feature is a $Δ$ -weighted arbitrage force, where the corrective pressure applied by agents in response to the standard Error Correction Term is dynamically amplified based on information staleness. The model is calibrated on historical tick data and designed to operate in real time, continuously updating its probability-based trading signals as new quotes arrive. The model is framed within the context of Discrete Choice Theory, treating agent transitions as utility-maximizing decisions within a Vector Logistic Autoregressive (VLAR) framework. Full article

(This article belongs to the Special Issue Financial Innovations and Derivatives)

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24 pages, 288 KB

Open AccessArticle

Regulations and the “Too-Big-to-Fail” Problem: Evidence from the Dodd–Frank Act

by Jenny Gu, Yingying Shao and Pu Liu

J. Risk Financial Manag. 2026, 19(1), 78; https://doi.org/10.3390/jrfm19010078 - 19 Jan 2026

Viewed by 286

Abstract

Before the enactment of the Dodd–Frank Act, firm size was taken into account by rating agencies in determining the credit ratings of banks. Therefore, the “too-big-to-fail” problem was, at least partially, reflected in big banks’ elevated ratings, which are more than justified by [...] Read more.

Before the enactment of the Dodd–Frank Act, firm size was taken into account by rating agencies in determining the credit ratings of banks. Therefore, the “too-big-to-fail” problem was, at least partially, reflected in big banks’ elevated ratings, which are more than justified by intrinsic creditworthiness. What is unclear is whether the bond market still gives an additional discount in yield to big banks over and above the lower yield spread that is already reflected in the elevated credit ratings due to their size. In this study, we examine this question and document a significant incremental yield discount for large banks even after controlling for credit ratings. Furthermore, we find that big banks with lower ratings pay lower borrowing costs than non-big banks with higher ratings. This additional discount, however, mostly disappeared after the Dodd–Frank Act. Full article

(This article belongs to the Special Issue Investment Strategies and Market Dynamics)

26 pages, 2118 KB

Open AccessArticle

A Hybrid HAR-LSTM-GARCH Model for Forecasting Volatility in Energy Markets

by Wiem Ben Romdhane and Heni Boubaker

J. Risk Financial Manag. 2026, 19(1), 77; https://doi.org/10.3390/jrfm19010077 - 17 Jan 2026

Viewed by 495

Abstract

Accurate volatility forecasting in energy markets is paramount for risk management, derivative pricing, and strategic policy planning. Traditional econometric models like the Heterogeneous Auto-regressive (HAR) model effectively capture the long-memory and multi-component nature of volatility but often fail to account for non-linearities and [...] Read more.

Accurate volatility forecasting in energy markets is paramount for risk management, derivative pricing, and strategic policy planning. Traditional econometric models like the Heterogeneous Auto-regressive (HAR) model effectively capture the long-memory and multi-component nature of volatility but often fail to account for non-linearities and complex, unseen dependencies. Deep learning models, particularly Long Short-Term Memory (LSTM) networks, excel at capturing these non-linear patterns but can be data-hungry and prone to overfitting, especially in noisy financial datasets. This paper proposes a novel hybrid model, HAR-LSTM-GARCH, which synergistically combines the strengths of the HAR model, an LSTM network, and a GARCH model to forecast the realized volatility of crude oil futures. The HAR component captures the persistent, multi-scale volatility dynamics, the LSTM network learns the non-linear residual patterns, and the GARCH component models the time-varying volatility of the residuals themselves. Using high-frequency data on Brent Crude futures, we compute daily Realized Volatility (RV). Our empirical results demonstrate that the proposed HAR-LSTM-GARCH model significantly outperforms the benchmark HAR, GARCH(1,1), and standalone LSTM models in both statistical accuracy and economic significance, offering a robust framework for volatility forecasting in the complex energy sector. Full article

(This article belongs to the Special Issue Mathematical Modelling in Economics and Finance)

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34 pages, 822 KB

Open AccessArticle

Climate Finance with Limited Commitment and Renegotiation: A Dynamic Contract Approach

by Byeong-Hak Choe

J. Risk Financial Manag. 2026, 19(1), 76; https://doi.org/10.3390/jrfm19010076 - 17 Jan 2026

Viewed by 166

Abstract

Taking climate funds (e.g., the Green Climate Fund) as the main financial mechanism for providing funding to developing countries, this paper examines a long-term climate funding relationship between two parties—the rich country and the poor country. Conflicts between the rich and poor countries [...] Read more.

Taking climate funds (e.g., the Green Climate Fund) as the main financial mechanism for providing funding to developing countries, this paper examines a long-term climate funding relationship between two parties—the rich country and the poor country. Conflicts between the rich and poor countries arise when determining (1) the size of climate funding that the rich country contributes to the poor country and (2) the funding allocation between climate adaptation and mitigation projects in the poor country. In addition, the rich country cannot be forced to commit contractual contributions to the poor country, and in each period, there is a probability that the countries can renegotiate the contract. This paper derives two main dynamic comparative–static results: (1) climate funds converge to the first-best in the long run, both in the size of climate funding in adaptation and mitigation projects, if and only if climate damage becomes sufficiently severe; (2) fewer renegotiations between the rich and poor countries make climate funding contracts more efficient, remedying inequality between the poor and rich countries. These results highlight how increasing climate damages and reducing the frequency of renegotiation can push climate funds closer to a first-best allocation, suggesting design principles for climate funding mechanisms like the Green Climate Fund. Full article

(This article belongs to the Special Issue Sustainable Production: Finance, Technology, and Institutional Quality, 2nd Edition)

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16 pages, 2847 KB

Open AccessArticle

Monetary Policy and Fiscal Conditions: Interest Rates, Nominal Growth Rates, Tax Revenues, and Government Expenditures

by Yutaka Harada and Makoto Suzuki

J. Risk Financial Manag. 2026, 19(1), 75; https://doi.org/10.3390/jrfm19010075 - 17 Jan 2026

Viewed by 179

Abstract

Two main perspectives exist regarding the interaction between fiscal deficits and expansionary monetary policy. The first perspective argues that fiscal deficits raise interest rates, thereby increasing interest payments and complicating monetary stabilization efforts. The second posits that expansionary monetary policy enhances nominal GDP [...] Read more.

Two main perspectives exist regarding the interaction between fiscal deficits and expansionary monetary policy. The first perspective argues that fiscal deficits raise interest rates, thereby increasing interest payments and complicating monetary stabilization efforts. The second posits that expansionary monetary policy enhances nominal GDP growth, which in turn reduces the government debt-to-GDP ratio and strengthens the fiscal position. Using panel data from the IMF World Economic Outlook covering advanced economies between 1980 and 2025, this study empirically evaluates which perspective is more consistent with observed data, while accounting for the dynamics of tax revenues, government expenditures, interest rates, and nominal GDP growth. Empirical evidence indicates that moderate monetary expansion—raising nominal GDP—tends to stabilize budget deficits, as government revenues generally outpace expenditures and interest rates do not increase proportionally with nominal growth. These results are further illustrated through case studies of Greece, Italy, Portugal, Spain, Japan, the United Kingdom, and the United States. Full article

(This article belongs to the Special Issue Monetary Policy and Debt)

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20 pages, 529 KB

Open AccessArticle

Fintech Firms’ Valuations: A Cross-Market Analysis in Asia

by Neha Parashar, Rahul Sharma, Pranav Saraswat, Apoorva Joshi and Sumit Banerjee

J. Risk Financial Manag. 2026, 19(1), 74; https://doi.org/10.3390/jrfm19010074 - 17 Jan 2026

Viewed by 216

Abstract

This study investigates the valuation dynamics of 30 publicly listed fintech firms across six Asian economies from January 2021 to December 2025. It examines how intrinsic firm-level scale (market capitalization) and extrinsic macroeconomic conditions (GDP growth) jointly influence fintech valuation ratios, as reflected [...] Read more.

This study investigates the valuation dynamics of 30 publicly listed fintech firms across six Asian economies from January 2021 to December 2025. It examines how intrinsic firm-level scale (market capitalization) and extrinsic macroeconomic conditions (GDP growth) jointly influence fintech valuation ratios, as reflected in price-to-earnings (P/E), price-to-book (P/B), and price-to-sales (P/S) measures. It also identifies significant structural heterogeneity and distributional asymmetries in valuation outcomes by implementing a multi-method empirical strategy that includes a Panel Autoregressive Distributed Lag (ARDL) framework, two-way fixed-effects models with interaction terms, and quantile regression. The findings reveal a robust, positive long-run relationship between market capitalization and valuation multiples across all ratios, confirming that firm-level scale as reflected in market capitalization is the primary driver of market value. Critically, the analysis identifies a dual-regime landscape in the Asian fintech sector: developed markets (South Korea, Japan, and Singapore) are fundamentally firm-scale driven, where intrinsic scale is the superior predictor of valuation. In contrast, developing markets (China, India, and Indonesia) are primarily macro-growth driven, exhibiting high sensitivity to GDP growth as a macroeconomic indicator of market expansion. The quantile regression results demonstrate a winner-takes-all effect, where the impact of scale on valuation is significantly more pronounced for highly valued firms in the 75th percentile. These results challenge the efficacy of universal valuation models and provide a context-dependent navigational framework for investors, analysts, and policymakers to distinguish between structural scale and cyclical growth in the rapidly evolving Asian fintech ecosystem. Full article

(This article belongs to the Special Issue The Role of Digitization in Corporate Finance)

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28 pages, 2086 KB

Open AccessArticle

Credit Risk Index as a Support Tool for the Financial Inclusion of Smallholder Coffee Producers

by María-Cristina Ordoñez, Ivan Dario López, Juan Fernando Casanova Olaya and Javier Mauricio Fernández

J. Risk Financial Manag. 2026, 19(1), 73; https://doi.org/10.3390/jrfm19010073 - 16 Jan 2026

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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. [...] Read more.

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. Full article

(This article belongs to the Special Issue Lending, Credit Risk and Financial Management)

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35 pages, 830 KB

Open AccessArticle

Predicting Financial Contagion: A Deep Learning-Enhanced Actuarial Model for Systemic Risk Assessment

by 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

Viewed by 408

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Financial Regulation and Risk Management amid Global Uncertainty)

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28 pages, 1100 KB

Open AccessArticle

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

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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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Financial Inclusion Strategies: Emerging Trends and Global Perspectives)

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21 pages, 2392 KB

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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)

by Gourav Salotra and Eugene Pinsky

J. Risk Financial Manag. 2026, 19(1), 70; https://doi.org/10.3390/jrfm19010070 - 15 Jan 2026

Viewed by 436

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Advances in Financial Modeling and Innovation)

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27 pages, 3314 KB

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Performance and Risk Analytics of Asian Exchange-Traded Funds

by 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

Viewed by 342

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 [...] Read more.

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. Full article

(This article belongs to the Collection Quantitative Advances and Risks in Asian Financial Markets)

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38 pages, 1895 KB

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ESG Risk Spillover Between Peers

by Lucas Walker and Shumi Akhtar

J. Risk Financial Manag. 2026, 19(1), 68; https://doi.org/10.3390/jrfm19010068 - 14 Jan 2026

Viewed by 315

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Corporate Social Responsibility and Governance)

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21 pages, 495 KB

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Does Earning Management Matter for the Tax Avoidance and Investment Efficiency Nexus? Evidence from an Emerging Market

by 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

Viewed by 277

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Tax Avoidance and Earnings Management)

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24 pages, 725 KB

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Strategic Risks and Financial Digitalization: Analyzing the Challenges and Opportunities for Fintech Firms and Neobanks

by 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

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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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Fintech, Digital Finance, and Socio-Cultural Factors)

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19 pages, 321 KB

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Corporate Reputation and Internal Control Quality: Evidence from Fortune 1000 Companies

by 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

Viewed by 328

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 [...] Read more.

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)

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