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Editor’s Choice Articles

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

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30 pages, 375 KB  
Article
Energy Market Uncertainty, ESG Performance, and Corporate Financial Stability
by Abdulazeez Y. H. Saif-Alyousfi, Abdullah Alsadan and Ahmed Alrashed
Int. J. Financ. Stud. 2026, 14(6), 163; https://doi.org/10.3390/ijfs14060163 - 12 Jun 2026
Viewed by 852
Abstract
This study examines how energy market uncertainty affects corporate financial stability and whether environmental, social, and governance (ESG) performance mitigates this relationship. Using a panel of 168 non-financial Australian firms from 2011 to 2023, we employ a two-step system generalized method of moments [...] Read more.
This study examines how energy market uncertainty affects corporate financial stability and whether environmental, social, and governance (ESG) performance mitigates this relationship. Using a panel of 168 non-financial Australian firms from 2011 to 2023, we employ a two-step system generalized method of moments (GMM) with extensive robustness checks. The results reveal three central findings. First, energy market uncertainty exerts a statistically significant and economically meaningful negative effect on corporate financial stability, indicating that heightened energy price volatility amplifies firms’ financial fragility. Second, ESG performance is positively associated with financial stability, suggesting that sustainability-oriented firms exhibit superior risk management and resilience. Third, ESG performance significantly attenuates the adverse impact of energy market uncertainty, providing strong evidence that ESG functions as an effective shock-absorbing mechanism. These findings are robust to alternative measures of financial stability and energy uncertainty, different lag structures, alternative estimation methods, and a wide range of subsample analyses. Further analyses show that the moderating role of ESG is not driven by a single pillar; rather, environmental, social, and governance dimensions jointly enhance firms’ capacity to withstand energy-related shocks. The buffering effect of ESG is stronger among high-ESG firms, in knowledge- and technology-intensive sectors, and during periods of heightened systemic stress such as the COVID-19 pandemic. Overall, the study provides novel firm-level evidence that ESG performance enhances corporate resilience to energy market uncertainty. The findings have important implications for policymakers, investors, and corporate managers seeking to strengthen financial stability in an era of elevated energy volatility and accelerating sustainability transitions. Full article
28 pages, 357 KB  
Article
Inflation Hedging Potential of Commodity Indices and Futures for U.S. Investors
by Ramesh Adhikari and YoungHa Ki
Int. J. Financ. Stud. 2026, 14(6), 162; https://doi.org/10.3390/ijfs14060162 - 11 Jun 2026
Viewed by 1009
Abstract
This study provides a comprehensive examination of the inflation-hedging potential of commodity indices and futures for U.S. investors using monthly data spanning July 1959 to December 2025 for 27 individual commodities, and January 1947 to November 2025 for 13 commodity indices. We employ [...] Read more.
This study provides a comprehensive examination of the inflation-hedging potential of commodity indices and futures for U.S. investors using monthly data spanning July 1959 to December 2025 for 27 individual commodities, and January 1947 to November 2025 for 13 commodity indices. We employ multiple complementary methodologies, including optimal hedge ratios with Newey–West standard errors, asymmetric hedging analysis, long-horizon regressions, rolling window stability tests, Granger causality analysis, out-of-sample validation, and Markov-switching vector error correction models (MS-VECM). Our results reveal substantial heterogeneity in hedging effectiveness across commodity sectors. Energy commodities, particularly gasoline and crude oil, demonstrate the strongest inflation-hedging properties with higher hedge ratios and hedging effectiveness. Industrial metals, represented by copper, also provide reliable hedging with stable performance across market conditions. In contrast, precious metals, including gold and silver, show weak contemporaneous hedging ability despite their traditional safe-haven reputation, though they may offer protection during specific market regimes. Agricultural commodities and livestock exhibit minimal or negative hedging effectiveness. The MS-VECM analysis confirms that hedging relationships are time-varying, with effectiveness differing significantly between stable and turbulent market regimes. These findings have important implications for portfolio construction and risk management strategies. Full article
28 pages, 1432 KB  
Article
The Winner’s Curse Reloaded: How Public Subscription Affects IPO First-Day Returns on Hong Kong’s Growth Enterprise Market
by Eddie Y. M. Lam, Joseph K. W. Fung and Calvin Y. C. Lee
Int. J. Financ. Stud. 2026, 14(6), 158; https://doi.org/10.3390/ijfs14060158 - 9 Jun 2026
Viewed by 1925
Abstract
This study revisits the winner’s curse hypothesis in Hong Kong’s Growth Enterprise Market, examining how public retail participation is associated with IPO first-day returns from 1999 to 2023. IPOs allocated through placement-only and placement plus sale methods deliver extraordinary first-day returns of 200.9% [...] Read more.
This study revisits the winner’s curse hypothesis in Hong Kong’s Growth Enterprise Market, examining how public retail participation is associated with IPO first-day returns from 1999 to 2023. IPOs allocated through placement-only and placement plus sale methods deliver extraordinary first-day returns of 200.9% and 231.0%, while those with public subscription dropped to 32.5% and 10.5%. Regression analysis further confirms the negative correlation between retail allocation and first-day returns. The study also underscores policy implications of the 2018 reforms mandating at least 10% public allocation, which coincide with, and may have contributed to, the sharp decline in the number of Hong Kong’s GEM IPOs. Full article
(This article belongs to the Special Issue Advances in Corporate Finance: Theory and Practice)
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23 pages, 709 KB  
Article
Firm-Level Determinants of the Cost of Debt: New Empirical Evidence from a Bank-Based Economy
by Zouhair Boumlik, Olivier Colot and Badia Oulhadj
Int. J. Financ. Stud. 2026, 14(6), 154; https://doi.org/10.3390/ijfs14060154 - 8 Jun 2026
Viewed by 960
Abstract
The purpose of this paper is to investigate the firm-level determinants of the cost of debt in a bank-based emerging economy, where debt serves as the primary external financing mechanism, enabling firms to maintain operations, pursue growth opportunities, and ensure long-term financial sustainability. [...] Read more.
The purpose of this paper is to investigate the firm-level determinants of the cost of debt in a bank-based emerging economy, where debt serves as the primary external financing mechanism, enabling firms to maintain operations, pursue growth opportunities, and ensure long-term financial sustainability. Using panel data from non-financial firms listed on the Casablanca Stock Exchange over the period 2018–2024, we document a robust nonlinear relationship between financial leverage and the cost of debt, whereby low and moderate debt levels reduce borrowing costs by signaling creditworthiness and financing capacity, while excessive indebtedness reverses this effect, with an optimal threshold estimated at approximately 34.8% of total assets. Firms with stronger growth prospects further benefit from more favorable financing conditions, as creditors interpret sustained asset expansion as a signal of financial strength and long-term viability. Financial performance is also found to reduce the cost of debt, although this effect is not fully robust to endogeneity controls. In contrast, asset tangibility, firm size, firm age, and liquidity do not emerge as significant determinants, suggesting that creditors in the Moroccan market adopt a financial health-oriented approach when assessing credit risk, placing greater emphasis on leverage and growth prospects than on collateral-based or reputational signals. Overall, the study highlights the coexistence of linear and nonlinear dynamics in debt pricing, thereby enriching the corporate finance literature and providing insights for managers and policymakers seeking to reduce borrowing costs, enhance access to debt financing, and support sustainable value creation. Full article
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21 pages, 414 KB  
Article
Climate Risk, CSR, and Financial Performance: An Interaction Perspective on Corporate Resilience in Europe
by Salma Zaiane, Fatma Ben Moussa, Souhir Masmoudi and Rahma Louati
Int. J. Financ. Stud. 2026, 14(6), 143; https://doi.org/10.3390/ijfs14060143 - 2 Jun 2026
Viewed by 772
Abstract
This paper explores the impact of climate risk on corporate social responsibility engagement and examines whether CSR moderates the relationship between climate risk and financial performance among European firms. The study is based on a panel of 3304 observations relating to companies in [...] Read more.
This paper explores the impact of climate risk on corporate social responsibility engagement and examines whether CSR moderates the relationship between climate risk and financial performance among European firms. The study is based on a panel of 3304 observations relating to companies in the STOXX Europe 600. We use two-stage least squares estimation with instrumental variables (2SLS-IV) to account for endogeneity issues. The results show that the companies most exposed to climate risk increase their CSR commitments, a relationship that remains particularly robust for environmentally sensitive firms. More importantly, the empirical results show a significant interaction between climate risk and CSR; while climate risk negatively affects financial performance, CSR engagement serves as a critical moderating mechanism that reduces this adverse effect. The strength of this moderating effect significantly increases following the Paris Agreement. Full article
(This article belongs to the Special Issue Corporate Financial Performance and Sustainability Practices)
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21 pages, 677 KB  
Article
Firm Performance and Corporate Social Responsibility: The Moderating Role of Board Skills
by Rihem Soussi Fathallah, Hamza Nizar, Houssam Bouzgarrou and Abdulrahman Alomair
Int. J. Financ. Stud. 2026, 14(6), 138; https://doi.org/10.3390/ijfs14060138 - 1 Jun 2026
Viewed by 784
Abstract
Purpose: This study examines the association between firm profitability and corporate social responsibility (CSR), with a particular focus on the moderating role of board skills, specifically those with financial and industry expertise. Design/methodology/approach: Based on a sample of 42,623 observations from 2002 to [...] Read more.
Purpose: This study examines the association between firm profitability and corporate social responsibility (CSR), with a particular focus on the moderating role of board skills, specifically those with financial and industry expertise. Design/methodology/approach: Based on a sample of 42,623 observations from 2002 to 2021, we use panel regression analysis with robust standard errors are clustered at the firm level. Findings: The results show that firm profitability is positively associated with CSR performance. However, the positive effect is less likely in the presence of board with financial and industry expertise. Indeed, boards dominated by financially and industry experienced directors tend to prioritize short-term financial returns over long-term CSR initiatives. Originality/value: This study offers a novel contribution to stakeholder and legitimacy theory perspectives by show that the association between financial performance and CSR depends significantly on the expertise embedded within the boardroom. While financial and industry expertise can bring valuable oversight, it may not adequately support CSR initiatives. In this regard, firms may need directors with additional skills and perspectives—such as sustainability or stakeholder management expertise—to better address CSR issues and balance financial objectives with long-term societal and legitimacy concerns. Practical implications: Policy and decision makers should carefully consider the composition of the board when seeking to align profitability with corporate social responsibility (CSR) outcomes. While financial and industry expertise are valuable for oversight, an overrepresentation of such skills on the board may inadvertently undermine long-term CSR commitments. Full article
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22 pages, 521 KB  
Article
AI for Financial Advice, Fraud Loss, and the Moderating Effect of Financial Knowledge Miscalibration
by Isha Chawla, Mindy Joseph, Kenneth White and Chasity Winder Scantling
Int. J. Financ. Stud. 2026, 14(6), 137; https://doi.org/10.3390/ijfs14060137 - 1 Jun 2026
Viewed by 860
Abstract
There is growing interest in using AI for financial advice, yet fraud and related financial losses remain widespread. While previous research has examined fraud victimization in general, there has been less focus on the losses resulting from fraud. Additionally, there is a limited [...] Read more.
There is growing interest in using AI for financial advice, yet fraud and related financial losses remain widespread. While previous research has examined fraud victimization in general, there has been less focus on the losses resulting from fraud. Additionally, there is a limited understanding of whether individuals’ willingness to use AI for financial advice is linked to these losses. This study utilizes data from the 2024 National Financial Capability Study (NFCS) and is grounded in Routine Activity Theory and Bounded Rationality. It examines the relationship between the willingness to use AI for financial advice and the likelihood of experiencing loss due to fraud. Furthermore, the study examines the moderating effect of financial knowledge miscalibration (overconfidence). Results from multivariate logistic regression models indicate a statistically significant interaction between the willingness to use AI and financial knowledge miscalibration. Specifically, overconfidence was positively associated with the likelihood of experiencing loss due to fraud among individuals who were willing to use AI for financial advice, whereas this association was not observed among those who were not willing to use AI. These findings have important implications for financial professionals and stakeholders involved in preventing fraud. Full article
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19 pages, 661 KB  
Article
Measuring Financial Repression in CFA Franc Zones: Index Construction and Implications for Investment Activity
by Amirreza Kazemikhasragh
Int. J. Financ. Stud. 2026, 14(6), 135; https://doi.org/10.3390/ijfs14060135 - 26 May 2026
Viewed by 769
Abstract
This study develops a composite index of financial repression to overcome persistent gaps and inconsistencies in financial data across the CFA franc zones. The index aggregates proxies such as interest rate spreads, real interest rates, domestic credit to the private sector as a [...] Read more.
This study develops a composite index of financial repression to overcome persistent gaps and inconsistencies in financial data across the CFA franc zones. The index aggregates proxies such as interest rate spreads, real interest rates, domestic credit to the private sector as a percentage of GDP, broad money supply as a percentage of GDP, and bank liquid-reserves-to-assets ratio, with inversion applied to align higher values with greater repression. Fixed-effects panel regressions reveal a significant negative impact of repression on gross capital formation, indicating a 2.8 percentage point reduction per unit increase, robust to controls including GDP per capita growth, trade openness, population growth, public debt, and inflation. Findings underscore repression’s role in impeding investment activity in CFA franc zones, where centralized controls crowd out private allocation amid fiscal dependencies. Policy implications advocate for gradual liberalization to enhance intermediation, while future research could extend to dynamic interdependencies via vector autoregression. This contribution advances repression measurement in African contexts, bridging theoretical distortions with empirical evidence for sustainable growth. Full article
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25 pages, 1769 KB  
Article
A Design Science Approach to Predicting ESG Performance Using Ensemble Machine Learning
by Yara Ibrahim, Khaled Hussainey and Taghred Mokhtar Sayed Moawad
Int. J. Financ. Stud. 2026, 14(5), 133; https://doi.org/10.3390/ijfs14050133 - 19 May 2026
Cited by 1 | Viewed by 1494
Abstract
Environmental, Social, and Governance (ESG) metrics have become a cornerstone to sustainable finance, yet their measurement and predictability remain constrained by data heterogeneity, methodological divergence, and disclosure bias. This study develops a comprehensive ESG prediction framework grounded in the Design Science Research paradigm, [...] Read more.
Environmental, Social, and Governance (ESG) metrics have become a cornerstone to sustainable finance, yet their measurement and predictability remain constrained by data heterogeneity, methodological divergence, and disclosure bias. This study develops a comprehensive ESG prediction framework grounded in the Design Science Research paradigm, integrating advanced machine learning techniques with rigorous data preprocessing, feature selection, and temporal validation. Using firm-level data from Refinitiv and Bloomberg, the analysis distinguishes between ESG composite performance and disclosure-based robustness, addressing a critical gap in the literature. Ensemble learning models, including Random Forest and XGBoost, are evaluated alongside deep learning architectures using multiple sampling strategies and rolling-window validation. The results demonstrate that ESG performance is moderately forecastable, with ensemble methods consistently outperforming neural networks in structured datasets. In contrast, disclosure robustness exhibits lower predictability, reflecting its dependence on discretionary strategic reporting and institutional factors. The findings highlight the importance of data quality, model selection, and validation design in ESG analytics, while emphasizing the limitations of deep learning in tabular financial contexts. The integration of explainable artificial intelligence further enhances interpretability by identifying key predictors of ESG outcomes. Overall, the study contributes to the literature by providing a robust, interpretable, and methodologically rigorous framework for ESG prediction, with implications for investors, regulators, and corporate decision-making. Full article
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15 pages, 1453 KB  
Article
Biodiversity Mutual Funds and ETFs: Characteristics, Performance, Risk, and Fees
by Fei Fang and Di Luo
Int. J. Financ. Stud. 2026, 14(5), 117; https://doi.org/10.3390/ijfs14050117 - 5 May 2026
Cited by 1 | Viewed by 1091
Abstract
This paper provides an exploratory analysis of biodiversity-themed funds and offers early evidence on their characteristics, performance, risk, fees, and sustainability metrics. Using a sample of 24 open-end biodiversity funds (18 mutual funds and 6 ETFs), we find that these funds are predominantly [...] Read more.
This paper provides an exploratory analysis of biodiversity-themed funds and offers early evidence on their characteristics, performance, risk, fees, and sustainability metrics. Using a sample of 24 open-end biodiversity funds (18 mutual funds and 6 ETFs), we find that these funds are predominantly European-domiciled equity funds, recently launched, small in size, and generally receive high sustainability ratings. However, both active and passive funds underperform their benchmarks over their short track records and charge higher fees than comparable funds, consistent with the early-stage development of this segment. We also examine fund manager characteristics and find no consistent relationship with performance. Our results highlight the need for greater fee transparency, and clearer communication of sustainability–return trade-offs. Full article
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27 pages, 5577 KB  
Article
The Risk Premia from the European Equity Market: An Application of the Three-Pass Estimation Methodology
by Elisa Ossola and Irina Trifan
Int. J. Financ. Stud. 2026, 14(4), 96; https://doi.org/10.3390/ijfs14040096 - 8 Apr 2026
Viewed by 1191
Abstract
We develop an empirical application on a large dataset of European stock returns in order to estimate the risk premia. While traditional factor models often struggle with high levels of pricing errors and noisy proxies in fragmented markets, we show that the Three-Pass [...] Read more.
We develop an empirical application on a large dataset of European stock returns in order to estimate the risk premia. While traditional factor models often struggle with high levels of pricing errors and noisy proxies in fragmented markets, we show that the Three-Pass Estimation Method (3PEM) serves as both a robust estimator and a diagnostic tool for factor purification. By assuming the Fama–French five-factor model as the baseline model, we first show that the 3PEM yields risk premium estimates for the European market that are more economically plausible and statistically robust than those obtained using the traditional two-pass estimation method (2PEM). Moreover, our results show that the 3PEM is able to detect noise in tradable factors. Furthermore, the 3PEM is used to denoise the observed factors, providing purified versions that better capture the systematic components of risk. We also identify both noisy factors and denoised factor series that improve the estimation of stock-level exposures and expected returns. Full article
(This article belongs to the Special Issue Advances in Financial Econometrics)
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18 pages, 676 KB  
Article
The Integration-Contagion Paradox: Global Linkages and Crisis Transmission in South Asian Stock Markets
by Dinesh Gajurel and Bharat Singh Thapa
Int. J. Financ. Stud. 2026, 14(4), 86; https://doi.org/10.3390/ijfs14040086 - 2 Apr 2026
Cited by 1 | Viewed by 1426
Abstract
This study examines financial integration and contagion across South Asia’s emerging and frontier markets during the 2001–2013 period, encompassing both the global financial and Eurozone crises. Employing a multi-factor asset pricing model within an EGARCH framework, we disentangle systematic global exposures from idiosyncratic [...] Read more.
This study examines financial integration and contagion across South Asia’s emerging and frontier markets during the 2001–2013 period, encompassing both the global financial and Eurozone crises. Employing a multi-factor asset pricing model within an EGARCH framework, we disentangle systematic global exposures from idiosyncratic shocks originating in the U.S. and Eurozone. By formally testing for structural changes in both mean returns and conditional variance, we uncover a striking “integration-contagion paradox.” While frontier markets (Bangladesh, Nepal) appear segmented from global pricing signals in tranquil times, they remain acutely susceptible to second-moment volatility contagion during stress periods. In contrast, India exhibits strong systematic return integration yet remains relatively insulated from volatility cascades. These results challenge the conventional view that financial segmentation offers a robust shield against systemic risk, revealing that a lack of global integration does not immunize markets against the transmission of global uncertainty. Full article
(This article belongs to the Special Issue Stock Market Developments and Investment Implications)
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29 pages, 1416 KB  
Article
Geopolitical Risks and Global Stock Market Dynamics: A Quantile-Based Approach
by Adrian-Gabriel Enescu and Monica Răileanu Szeles
Int. J. Financ. Stud. 2026, 14(4), 85; https://doi.org/10.3390/ijfs14040085 - 2 Apr 2026
Cited by 1 | Viewed by 9826
Abstract
This study investigates the impact of geopolitical risk measures (aggregate geopolitical risk, geopolitical acts, and geopolitical threats) on 40 global stock market indexes from developed and emerging markets for a sample of 20 years. By employing simultaneous quantile regression and a Two-Stage Quantile-on-Quantile [...] Read more.
This study investigates the impact of geopolitical risk measures (aggregate geopolitical risk, geopolitical acts, and geopolitical threats) on 40 global stock market indexes from developed and emerging markets for a sample of 20 years. By employing simultaneous quantile regression and a Two-Stage Quantile-on-Quantile Regression (QQR) framework, we analyze the risk transmission mechanisms across the conditional distribution of stock returns. The empirical results reveal a notable regime-dependent reversal: a negative influence is exerted by geopolitical risk during a bullish market regime, while a counterintuitive positive association is present for the bearish market conditions. This effect is more pronounced for emerging and commodity-rich markets, which may provide a potential hedge during supply-side shocks. Moreover, the QQR analysis focused on the United States of America stock market provides an examination of the different potential transmission mechanisms of geopolitical variants. The results suggest that geopolitical threats (GPRT) represent a persistent factor that negatively affects the market for normal and bullish market regimes, while geopolitical acts (GPRA) represent a tail-risk catalyst that exacerbates losses during severe market crashes. The results remain robust to an alternative specification of returns and indicate the necessity of distinguishing between geopolitical acts and threats from a risk management standpoint, as well as correctly identifying the market regime. Full article
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29 pages, 1501 KB  
Review
Sustainability Reporting Between Financial Market Forces and Regulatory Mandates: A Global Bibliometric Analysis
by Anissa Naouar, Hajer Zarrouk and Teheni El Ghak
Int. J. Financ. Stud. 2026, 14(4), 82; https://doi.org/10.3390/ijfs14040082 - 1 Apr 2026
Cited by 1 | Viewed by 2059
Abstract
This study examines the evolution of sustainability reporting research by integrating financial market dynamics, regulatory frameworks, and digital transformation into a unified analytical lens. It explores how these forces shape the credibility, comparability, and strategic relevance of sustainability disclosure. A bibliometric analysis of [...] Read more.
This study examines the evolution of sustainability reporting research by integrating financial market dynamics, regulatory frameworks, and digital transformation into a unified analytical lens. It explores how these forces shape the credibility, comparability, and strategic relevance of sustainability disclosure. A bibliometric analysis of 683 publications indexed in the Web of Science (2006–2025) was conducted. Performance indicators and science-mapping techniques were applied to identify the intellectual structure of the field. Four major thematic clusters were detected: (i) corporate social responsibility and disclosure performance, (ii) governance and accountability, (iii) regulatory and institutional frameworks, and (iv) financial market and digital innovation drivers. Findings reveal that Disclosure, corporate social responsibility, and performance remain the field’s core anchors, while governance, accountability, innovation, and strategy increasingly shape reporting credibility. Sustainability reporting reduces information asymmetry, lowers financing costs, and builds stakeholder trust; however, persistent fragmentation, greenwashing, and weak assurance highlight the need for global harmonization. Regulatory initiatives and market instruments are converging to institutionalize sustainability disclosure. The study advances a policy and managerial agenda advocating stronger governance oversight, harmonized disclosure frameworks, and technology-enabled assurance mechanisms to enhance transparency, accountability, and investor confidence. Full article
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13 pages, 264 KB  
Article
What Explains Bitcoin Volatility? Evidence from an Extended HAR Framework
by Zhaoying Lu and Yuanju Fang
Int. J. Financ. Stud. 2026, 14(4), 81; https://doi.org/10.3390/ijfs14040081 - 1 Apr 2026
Viewed by 2124
Abstract
This study investigates the dynamics of Bitcoin’s realized volatility by extending the Heterogeneous Autoregressive (HAR) framework to incorporate external shocks from major financial and commodity markets, namely the NASDAQ-100, Brent crude oil, and gold. To capture potential asymmetries, external market returns are decomposed [...] Read more.
This study investigates the dynamics of Bitcoin’s realized volatility by extending the Heterogeneous Autoregressive (HAR) framework to incorporate external shocks from major financial and commodity markets, namely the NASDAQ-100, Brent crude oil, and gold. To capture potential asymmetries, external market returns are decomposed into positive and negative components. In addition, structural changes in volatility dynamics are examined using structural break tests. The empirical results reveal strong volatility persistence at the daily and weekly horizons, consistent with the HAR structure. Shocks associated with the NASDAQ and gold markets are significantly related to Bitcoin’s realized volatility, whereas the association with crude oil prices is limited. Moreover, both negative and positive gold-market shocks display stronger linkages in the post-2022 period, suggesting time variation in the volatility relationship between Bitcoin and gold. Full article
(This article belongs to the Special Issue Cryptocurrency and Financial Market)
23 pages, 776 KB  
Article
Central Bank Digital Currencies: Digital Euro and Its Implications for Uncovered and Covered Deposits
by Mattia Calosci, Antonino Crisafulli, Mattia Giantomassi and Saverio Giorgio
Int. J. Financ. Stud. 2026, 14(4), 80; https://doi.org/10.3390/ijfs14040080 - 1 Apr 2026
Viewed by 3460
Abstract
The introduction of central bank digital currencies (“CBDCs”)—notably the digital euro—stands to reshape the financial system’s structure. This study initially conducts a comparative analysis of household deposit outflow across the Eurozone, the United Kingdom, Canada, and China, before focusing specifically on the potential [...] Read more.
The introduction of central bank digital currencies (“CBDCs”)—notably the digital euro—stands to reshape the financial system’s structure. This study initially conducts a comparative analysis of household deposit outflow across the Eurozone, the United Kingdom, Canada, and China, before focusing specifically on the potential outflow from covered deposits protected by Deposit Guarantee Schemes (“DGSs”) in the first jurisdiction. The originality of our contribution lies in proposing a formula that calculates household deposit outflow while incorporating two weighting coefficients—both consistent with the literature: one to estimate the propensity to adopt digital instruments based on age clusters (which decreases with advancing age), and another to reflect the extent of digital currency adoption (which likewise decreases with age). The findings suggest that both the calibration of the holding limit and the demographic composition of the population exert a substantial influence on the potential outflow of household deposits and covered deposits, with implications for DGSs. Overall, the digital euro can enhance banking system efficiency and competitiveness, but requires a design balancing innovation, deposit stability, and depositor protection for banks of all sizes. Full article
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20 pages, 345 KB  
Article
Institutional Investors, Dividend Policy, and Idiosyncratic Volatility: Evidence from European Equity Markets
by Adrian-Gabriel Enescu and Monica Răileanu Szeles
Int. J. Financ. Stud. 2026, 14(2), 50; https://doi.org/10.3390/ijfs14020050 - 21 Feb 2026
Viewed by 2418
Abstract
This paper investigates the relationship between institutional ownership and firm-level idiosyncratic volatility across European equity markets, with a particular focus on the moderating role of dividend policy. Using a sample of STOXX Europe 600 constituents from 2005 to 2025, we estimate idiosyncratic volatility [...] Read more.
This paper investigates the relationship between institutional ownership and firm-level idiosyncratic volatility across European equity markets, with a particular focus on the moderating role of dividend policy. Using a sample of STOXX Europe 600 constituents from 2005 to 2025, we estimate idiosyncratic volatility via the Fama-French three-factor model and employ fixed-effects regressions with clustered standard errors. Our empirical results reveal a positive and statistically significant association between institutional ownership and idiosyncratic volatility, suggesting a destabilizing rather than stabilizing role in European markets. This volatility-enhancing effect is significantly more pronounced among dividend-paying firms and is primarily driven by transient institutional investors with high portfolio turnover. Furthermore, we find that: (1) larger firm size (market capitalization) and higher leverage (debt-to-capital ratio) are positively associated with heightened volatility; (2) growth-oriented firms (high market-to-book ratios) exhibit increased volatility, particularly among non-dividend payers; and (3) higher profitability (ROE) and favorable analyst coverage (buy recommendations) act as stabilizers, reducing idiosyncratic risk. These findings persist in both contemporaneous and lagged specifications. This study contributes to the literature by identifying dividend policy as a key channel through which institutional trading behavior amplifies firm-specific risk, providing novel evidence on the asset class effect within major European benchmark indices. Full article
21 pages, 533 KB  
Article
Enhancing Intraday Momentum Prediction: The Role of Volume-Based Information Uncertainty in the Chinese Stock Market
by Decheng Yang and Qiang He
Int. J. Financ. Stud. 2026, 14(2), 47; https://doi.org/10.3390/ijfs14020047 - 14 Feb 2026
Viewed by 5555
Abstract
This study introduces a novel intraday volume-based uncertainty (IVU) proxy—the ratio of opening-half-hour volume to total volume of the preceding seven intervals—to predict final half-hour return direction in the Chinese stock market. Using threshold regression, we identify a statistically significant IVU critical value [...] Read more.
This study introduces a novel intraday volume-based uncertainty (IVU) proxy—the ratio of opening-half-hour volume to total volume of the preceding seven intervals—to predict final half-hour return direction in the Chinese stock market. Using threshold regression, we identify a statistically significant IVU critical value of 0.476225 (p < 0.001), which splits the sample into distinct uncertainty regimes. Logistic regression incorporating this threshold reveals that the joint condition of high opening volume and low IVU (high uncertainty) significantly amplifies the predictive power of initial returns, achieving 63.04% accuracy in the high-uncertainty, high-volume regime. XGBoost further captures complex non-linear interactions, with IVU-related features ranking among the most important predictors and achieving 71.43% out-of-sample accuracy under high-volume, high-uncertainty conditions. A machine learning trading strategy leveraging these predictions yields a total return of 117.99% with a Sharpe ratio of 3.02 over seven years, significantly outperforming benchmarks. Our findings highlight information uncertainty as a critical moderator of intraday momentum and a valuable source of actionable alpha. Full article
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17 pages, 379 KB  
Article
Macro-Financial Blind Spots in Emerging Markets: Non-Bank Intermediation, Funding Liquidity, and the Persistence of Global Shock Transmission
by Gustavo Henrique Rodrigues Pessoa and Ricardo Ratner Rochman
Int. J. Financ. Stud. 2026, 14(2), 40; https://doi.org/10.3390/ijfs14020040 - 5 Feb 2026
Cited by 1 | Viewed by 1774
Abstract
Despite significant advances in bank regulation and the widespread adoption of macroprudential frameworks, emerging market economies remain persistently vulnerable to global financial shocks. Episodes such as the Global Financial Crisis, the COVID-19 market turmoil, and recent monetary tightening cycles reveal that financial stress [...] Read more.
Despite significant advances in bank regulation and the widespread adoption of macroprudential frameworks, emerging market economies remain persistently vulnerable to global financial shocks. Episodes such as the Global Financial Crisis, the COVID-19 market turmoil, and recent monetary tightening cycles reveal that financial stress originating in core markets continues to transmit rapidly and forcefully to emerging economies. This paper argues that such vulnerability reflects structural features of contemporary financial systems rather than deficiencies in domestic banking regulation alone. Adopting a conceptual and analytical approach, the article develops an integrated framework of macro-financial blind spots that links global financial cycles, non-bank financial intermediation, and regulatory fragmentation. The analysis highlights how funding liquidity, collateral valuation, margin dynamics, and market-based leverage amplify global shocks through channels that lie largely outside traditional, bank-centric macroprudential frameworks. As market-based finance expands, systemic risk increasingly originates in activities rather than institutions, limiting the effectiveness of entity-based regulation and reinforcing emerging markets’ role as price-takers in global portfolios. The paper contributes to the literature by synthesizing insights from macroprudential policy, market liquidity, and non-bank finance to explain the persistence of emerging market vulnerability in an era of globalized funding. It further derives policy implications for macro-financial governance, emphasizing the need for system-wide, activity-based approaches, improved data and transparency, and stronger domestic and international regulatory coordination. These findings are relevant for policymakers seeking to reconcile financial integration with systemic resilience in emerging markets. Full article
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28 pages, 764 KB  
Article
How Does Artificial Intelligence Reshape Bank Profitability in China?—Evidence from a Multi-Period Difference-in-Differences Model
by Xiaoli Li, Dongsheng Zhang, Na Zeng and Defeng Meng
Int. J. Financ. Stud. 2026, 14(2), 39; https://doi.org/10.3390/ijfs14020039 - 4 Feb 2026
Cited by 2 | Viewed by 3235
Abstract
Artificial intelligence (AI) has become an integral driver of digital transformation in the banking sector, fundamentally influencing operational efficiency, resource allocation, and profitability. This study investigates how AI adoption affects the profitability of Chinese commercial banks and through which mechanisms these effects occur, [...] Read more.
Artificial intelligence (AI) has become an integral driver of digital transformation in the banking sector, fundamentally influencing operational efficiency, resource allocation, and profitability. This study investigates how AI adoption affects the profitability of Chinese commercial banks and through which mechanisms these effects occur, within the context of the country’s broader financial digitalization process. Using panel data for 17 A-share listed banks in China from 2009 to 2022, we employ a multi-period difference-in-differences (DID) framework—whose validity rests on the parallel trend assumption, empirically verified through an event-study specification—and combine it with propensity score matching (PSM) and placebo simulations to ensure credible causal identification. The results indicate that AI adoption significantly improves bank profitability. Mechanism analyses suggest that AI enhances profitability through two overarching channels—operational efficiency and resource allocation—manifested in (i) higher cost elasticity of income, (ii) improved deposit–loan turnover adaptability via more efficient liquidity and funding-cycle management, and (iii) optimized cross-business capital allocation efficiency through better risk–return matching in diversified operations. The effects are stronger for banks with higher digital investment intensity and tighter customer stickiness–liability cost coupling, and vary systematically across ownership types, bank sizes, and policy cycles. Overall, the findings provide policy-relevant evidence on how AI-driven digital transformation can enhance bank performance and risk management in modern financial systems. This study contributes by constructing a disclosure-based AI adoption measure from bank annual reports and exploiting staggered adoption with a multi-period DID design to provide causal evidence from China’s listed banking sector. Full article
(This article belongs to the Special Issue Artificial Intelligence in Banking and Insurance)
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44 pages, 2282 KB  
Article
Particle Swarm Optimization with Stretching and Clustering for Asset Allocation
by Julien Chevallier
Int. J. Financ. Stud. 2026, 14(2), 38; https://doi.org/10.3390/ijfs14020038 - 4 Feb 2026
Viewed by 1335
Abstract
This paper develops a novel hybrid framework that integrates clustering-enhanced Particle Swarm Optimization (PSO) with stretching techniques to solve Markowitz’s quadratic portfolio optimization problem. The proposed approach avoids local optima traps that plague traditional optimization methods, while the stretching function modifications enhance the [...] Read more.
This paper develops a novel hybrid framework that integrates clustering-enhanced Particle Swarm Optimization (PSO) with stretching techniques to solve Markowitz’s quadratic portfolio optimization problem. The proposed approach avoids local optima traps that plague traditional optimization methods, while the stretching function modifications enhance the algorithm’s global search capabilities. The framework comprises four distinct algorithmic variants: a baseline SWARM PSO with stretching algorithm, and three clustering-enhanced extensions incorporating Hierarchical, K-means, and DBSCAN techniques. These clustering enhancements strategically group assets based on risk–return characteristics to improve portfolio diversification and risk management. Implementation in R enables comprehensive analysis of portfolio weight allocation patterns and diversification metrics across varying market structures. Empirical validation using daily price data from six major international stock market indices spanning January 2020 to December 2025 demonstrates the framework’s generalization capability in constructing buy-and-hold investment portfolios. The results reveal significant market-specific algorithmic effectiveness, with K-means variants achieving competitive efficacy in Eurostoxx and Belgian markets, DBSCAN demonstrating strong effectiveness in Chinese equity markets, Hierarchical clustering showing robust results in Indian market conditions, and the baseline SWARM algorithm exhibiting relative efficiency in French and Danish indices. Performance evaluation encompasses comprehensive risk-adjusted metrics, including Portfolio Return, Volatility, Sharpe Ratio, Calmar Ratio, and Value at Risk, providing portfolio managers with an adaptive, market-responsive optimization toolkit. Full article
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21 pages, 309 KB  
Article
Online Search Activity and Market Reaction to Earnings Announcements
by Saurabh Ahluwalia
Int. J. Financ. Stud. 2026, 14(2), 33; https://doi.org/10.3390/ijfs14020033 - 3 Feb 2026
Viewed by 2814
Abstract
This paper leverages Google Trends search volume data from 2004 to 2008 as a proxy for investor information demand. The analysis documents that greater search activity prior to earnings announcements is positively associated with future market reaction to earnings announcements, pre-earnings announcement drift, [...] Read more.
This paper leverages Google Trends search volume data from 2004 to 2008 as a proxy for investor information demand. The analysis documents that greater search activity prior to earnings announcements is positively associated with future market reaction to earnings announcements, pre-earnings announcement drift, and buying pressure. The results are consistent with investors gathering value-relevant information through online research, which is subsequently incorporated into prices through trading around earnings announcements. Notably, search volume is positively associated with market reaction to earnings announcements and pre-announcement drifts for more obscure firms where data is scarce. Overall, this paper provides large-sample evidence validating theoretical models where dispersed private information is incorporated into stock prices. The findings suggest that broader data access may facilitate pricing efficiency by promoting more informed market participation. Full article
(This article belongs to the Special Issue Stock Market Developments and Investment Implications)
23 pages, 2448 KB  
Article
Taxes, Growth, and Equity: The Illusions of Fiscal Policy
by Anil Hira, Tim Swartz and Jiguo Cao
Int. J. Financ. Stud. 2026, 14(2), 30; https://doi.org/10.3390/ijfs14020030 - 2 Feb 2026
Viewed by 2658
Abstract
For over a century now, one of the central debates of economic policy has been around fiscal policy. Taxation and government spending have been a feature of most political campaigns, with one (more vocal) side claiming that taxation chokes economic growth and benefits [...] Read more.
For over a century now, one of the central debates of economic policy has been around fiscal policy. Taxation and government spending have been a feature of most political campaigns, with one (more vocal) side claiming that taxation chokes economic growth and benefits special interests, while leaving a legacy of debt. Another side sees taxation as a necessary tool for creating equal opportunity and ensuring adequate investment in collective public goods, including human capital. Using newly constructed datasets that we will make available, we take a fresh look at fiscal policy on the global level and across U.S. states, measuring its effects on growth and equity. We utilize a new technique, functional data analysis (FDA). We find limited evidence for both the conservative and progressive arguments around fiscal policy in the short term. Rather, the data suggest persistent fiscal patterns across space and time that reflect long-term social value choices around the tradeoffs of growth vs. public investment and equity. Full article
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24 pages, 479 KB  
Article
How Environmental Uncertainty Drives Asymmetric Mispricing in China: Dual Channels and Heterogeneous Media Effect
by Shuya Hu and Shengnian Wang
Int. J. Financ. Stud. 2026, 14(1), 23; https://doi.org/10.3390/ijfs14010023 - 14 Jan 2026
Viewed by 732
Abstract
The essay delves into the impact of environmental uncertainty on asymmetric mispricing, utilizing the data from listed firms in China spanning from 2007 to 2023. Our analysis reveals that environmental uncertainty amplifies stock mispricing within capital markets, whether upward or downward. Diverging from [...] Read more.
The essay delves into the impact of environmental uncertainty on asymmetric mispricing, utilizing the data from listed firms in China spanning from 2007 to 2023. Our analysis reveals that environmental uncertainty amplifies stock mispricing within capital markets, whether upward or downward. Diverging from prior research, we distinguish between upward and downward mispricing and reveal the black box of environmental uncertainty affecting stock mispricing from dual channels. Specifically, environmental uncertainty intensifies upward mispricing through heightened earnings management and exacerbates downward mispricing by boosting investor irrationality. Furthermore, we explore the heterogeneous impact of different media coverage. In the downward mispricing sample, negative media exacerbated the relationship between the two, while positive coverage played a mitigating role. In the upward mispricing sample, only negative reports have a significant impact and mitigate the impact of uncertainty on mispricing. Our research on media heterogeneity once again proves that it is a double-edged sword. Our research indicates that improving the capacity to recognize different mispricing mechanisms in various market directions can greatly boost decision-making efficiency. Meanwhile, it is vital to strengthen professional ethics in media organizations and encourage more objective reporting. These efforts can jointly contribute to improving the efficiency of emerging capital markets. Full article
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18 pages, 594 KB  
Article
Quantum-Based Method to Estimate Future Tax Compositions: Application to the Case of Foreign Trade in Mexico
by Sergio Lagunas-Puls and Oliver Cruz-Milán
Int. J. Financ. Stud. 2026, 14(1), 15; https://doi.org/10.3390/ijfs14010015 - 7 Jan 2026
Viewed by 1148
Abstract
Using a method inspired by quantum principles, this study estimates the composition of various types of tax contributions expected from foreign trade operations. The estimation approach is proposed considering the superposition of expectations and disturbances—fundamental elements of quantum methods—that add complexity to the [...] Read more.
Using a method inspired by quantum principles, this study estimates the composition of various types of tax contributions expected from foreign trade operations. The estimation approach is proposed considering the superposition of expectations and disturbances—fundamental elements of quantum methods—that add complexity to the forecasts of tax collections. For instance, the contributions of international trade-related taxes may be determined not only by the country’s degree of regional integration but also by the composition of tax revenue that depends on the kind and use of merchandise. Using the case of Mexico’s imports, the methodology illustrates how the expectations of collecting certain taxes—like the General Import Tariff (GIT) and the Value Added Tax (VAT)—would be impacted by fluctuations in others—such as the Special Tax on Production and Services (STPS). The hypothesis of this study is that, through the proposed quantum-inspired methodology, it is possible to establish future scenarios of tax revenue compositions while maintaining fiscal consistency by anticipating potential outcomes in the adjustments of contributions if the recently proposed fiscal reform is approved by the Mexican Government. This work contributes to the academic literature on public finance management by advancing a methodology that can support the strategic formulation of fiscal expectations and policy. Full article
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19 pages, 2986 KB  
Article
The Financial Lobster Bias
by Óscar De los Reyes Marín, Iria Paz Gil, Jose Torres-Pruñonosa and Raúl Gómez-Martínez
Int. J. Financ. Stud. 2026, 14(1), 17; https://doi.org/10.3390/ijfs14010017 - 7 Jan 2026
Viewed by 2861
Abstract
The Financial Lobster Bias describes how SMEs, driven by distorted liquidity perceptions, engage in aggressive expansion until financial breakdown occurs. Using data from 10,412 Spanish SMEs (2000–2024), this study shows that liquidity misperception—measured through two versions of the Liquidity Misperception Index (PEL), one [...] Read more.
The Financial Lobster Bias describes how SMEs, driven by distorted liquidity perceptions, engage in aggressive expansion until financial breakdown occurs. Using data from 10,412 Spanish SMEs (2000–2024), this study shows that liquidity misperception—measured through two versions of the Liquidity Misperception Index (PEL), one based on financial structure and another on payment–collection timing (PMP–PMC)—is a significant driver of expansion–collapse cycles. The financial PEL displays a strong temporal trend (R2 = 0.736), while the PMP–PMC-based PEL also increases over time (R2 = 0.411), evidencing a persistent widening between perceived and real liquidity. The Illusory Confidence in Liquidity Index (ICEL) reveals that confidence peaks coincide with periods of systemic fragility. The Unsustainable Expansion Index (IEI) identifies pre-crisis overexpansion (IEI = 2.34 in 2005; 2.87 in 2006; 1.72 in 2007), preceding the 2008 failure surge. Together, these indicators provide early-warning mechanisms that uncover hidden fragility and help anticipate liquidity-driven collapse. Full article
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18 pages, 296 KB  
Article
Lender of Last Resort and Financial Systemic Risks in Times of Economic Stability: Evidence from 55 Countries
by Wenlong Miao, Yuxian Ma and Yuanyuan Huo
Int. J. Financ. Stud. 2026, 14(1), 9; https://doi.org/10.3390/ijfs14010009 - 6 Jan 2026
Cited by 1 | Viewed by 1896
Abstract
As a cornerstone of the modern financial safety net, the Lender of Last Resort (LOLR) is essential in mitigating liquidity crises and containing financial contagion. However, during periods of economic stability, risk-taking incentives in the banking sector may undermine its effectiveness. Using quarterly [...] Read more.
As a cornerstone of the modern financial safety net, the Lender of Last Resort (LOLR) is essential in mitigating liquidity crises and containing financial contagion. However, during periods of economic stability, risk-taking incentives in the banking sector may undermine its effectiveness. Using quarterly panel data from 55 countries over the period 2010–2023, this study employs a two-way fixed effects model to assess the impact of LOLR support on systemic financial risk and its transmission mechanisms. We find that LOLR support significantly increases systemic risk during stable economic periods. Mechanism analysis indicates that this effect is channeled through the erosion of bank asset liquidity, expansion of financial leverage, and deterioration in asset quality. Moreover, the adverse impact is more pronounced in emerging economies, bank-dominated financial systems, countries with low capital adequacy ratios, underdeveloped regulatory frameworks, and lower levels of digital technology adoption. This study provides cross-country evidence on the potential negative consequences of central bank rescue functions during calm periods and offers important policy insights for optimizing the LOLR framework and building a more resilient financial safety net. Full article
12 pages, 666 KB  
Article
Has IPO Market Structure Fundamentally Changed? Evidence from Negative Binomial Regression with Structural Breaks
by Michael D. Herley
Int. J. Financ. Stud. 2026, 14(1), 6; https://doi.org/10.3390/ijfs14010006 - 5 Jan 2026
Viewed by 1953
Abstract
This paper introduces Bai-Perron structural break detection combined with negative binomial regression to model overdispersed U.S. IPO count data. Using monthly data from 1995 to 2024, we identify five breaks that partition IPO activity into six distinct regimes, each with fundamentally different variance [...] Read more.
This paper introduces Bai-Perron structural break detection combined with negative binomial regression to model overdispersed U.S. IPO count data. Using monthly data from 1995 to 2024, we identify five breaks that partition IPO activity into six distinct regimes, each with fundamentally different variance characteristics. We then employ negative binomial regression that incorporates these breaks. IPO data show substantial overdispersion (variance-to-mean ratios: 2.77 to 33.74). The negative binomial model reveals that market uncertainty (as measured by the VIX) and financing costs (as indicated by 10-year Treasury rates) reduce IPO activity, while lagged IPO volume drives activity in the current period. Regime-specific likelihood ratio tests reveal that statistically significant overdispersion first emerges during the 2008 financial crisis, subsides during the post-recession period, and returns with unprecedented intensity after May 2020. An OLS model without the identified structural breaks incorrectly suggests positive interest rate effects. Full article
(This article belongs to the Special Issue Advances in Financial Econometrics)
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34 pages, 1141 KB  
Article
A Momentum-Based Normalization Framework for Generating Profitable Analyst Sentiment Signals
by Shawn McCarthy and Gita Alaghband
Int. J. Financ. Stud. 2026, 14(1), 4; https://doi.org/10.3390/ijfs14010004 - 1 Jan 2026
Viewed by 2727
Abstract
The diverse rating scales used by brokerage firms pose significant challenges for aggregating analyst recommendations in financial research. We develop a momentum-based normalization framework that transforms heterogeneous rating changes into standardized sentiment signals using firm-relative, past-only empirical distribution functions with event-based lookback and [...] Read more.
The diverse rating scales used by brokerage firms pose significant challenges for aggregating analyst recommendations in financial research. We develop a momentum-based normalization framework that transforms heterogeneous rating changes into standardized sentiment signals using firm-relative, past-only empirical distribution functions with event-based lookback and expanding global quantile classification. Using 68,660 rating events from 270 brokerage firms covering 106 large-cap U.S. stocks (2019–2025), our approach generates statistically significant Buy–Sell spreads at all horizons: 1-month (0.96%, t = 3.07, p = 0.002), 2-month (1.36%, t = 3.07, p = 0.002), and 3-month (1.94%, t = 3.66, p < 0.001). Fama–French six-factor regressions confirm 13.6% annualized alpha for Buy signals (t = 3.81) after controlling for market, size, value, profitability, investment, and momentum factors. True out-of-sample validation on May–September 2025 data achieves 107% retention of in-sample 1-month performance (four of five months positive), indicating robust signal generalization. The framework provides a theoretically grounded and empirically validated methodology for standardizing analyst sentiment suitable for quantitative investment strategies and academic research. Full article
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17 pages, 308 KB  
Article
Assessing the Impact of Digital Transformation on Manufacturing Enterprises’ Performances: An Efficiency Perspective
by Chenxi Wang, Jing Yang, Yan Lin and Biao Xue
Int. J. Financ. Stud. 2025, 13(4), 241; https://doi.org/10.3390/ijfs13040241 - 16 Dec 2025
Viewed by 3425
Abstract
In recent years, the impacts of the new scientific and technological revolution on the industrial system and production modes have begun to emerge. Digital transformation is gradually being integrated into the production behaviors of manufacturing enterprises and has become a component of the [...] Read more.
In recent years, the impacts of the new scientific and technological revolution on the industrial system and production modes have begun to emerge. Digital transformation is gradually being integrated into the production behaviors of manufacturing enterprises and has become a component of the micro-economy. We aim to find better methods for measuring digital transformation and to analyze its impact on both market performance and innovation performance within manufacturing enterprises. To achieve our goals, we employ an empirical study to examine the influence of digital transformation on market and innovation performance using panel data for Chinese listed manufacturing enterprises from 2012 to 2021. We emphasize digital transformation efficiency and affirm its role in relieving financing constraints. Our study shows that digital transformation effectively improves both the market and innovation performance of manufacturing enterprises. Moreover, it mitigates the financing constraint dilemma, resulting in performance enhancement. Heterogeneity analysis indicates that digital transformation has a more significant promotional effect on the market and innovation performance of large-scale and mature enterprises. Our research offers fresh perspectives for better understanding digital transformation, enriching the body of work on the impact of digital transformation in manufacturing enterprises and its underlying mechanisms. Full article
(This article belongs to the Special Issue Financial System in the Digital Age: Opportunities, Challenges and Future Directions)
19 pages, 1105 KB  
Article
Financial Traits and Convertible Bond Motives: China’s Evidence
by Jiaqi Chen, Xiuwen Lu and Xiongzhi Wang
Int. J. Financ. Stud. 2025, 13(4), 240; https://doi.org/10.3390/ijfs13040240 - 16 Dec 2025
Viewed by 3444
Abstract
Convertible bond financing has gained significant traction in China’s capital market, yet it poses financial risks, particularly for highly leveraged firms. This study investigates how corporate financial traits influence the decision to issue convertible bonds, challenging the direct applicability of Western theoretical frameworks [...] Read more.
Convertible bond financing has gained significant traction in China’s capital market, yet it poses financial risks, particularly for highly leveraged firms. This study investigates how corporate financial traits influence the decision to issue convertible bonds, challenging the direct applicability of Western theoretical frameworks in China’s unique institutional context. We employ a natural experiment design, constructing a binary logistic regression model to analyze data from Chinese A-share listed companies that issued convertible bonds, corporate bonds, seasoned equity offerings, or rights offerings between 2022 and 2023. Our results reveal a paradox: contrary to risk-transfer theory, firms with lower leverage exhibit a stronger propensity to issue convertible bonds. Instead, motives are driven by high profitability, operational inefficiencies, and robust operating cash flow generation—traits that align with signaling and backdoor equity theories. The study identifies China’s convertible bond market as a dual-track system where regulatory screening distorts classical motives while market frictions amplify the role of convertible bonds in resolving information asymmetry. We conclude with targeted policy implications for regulators and corporate treasurers to enhance market efficiency and governance. Full article
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22 pages, 631 KB  
Article
Executive Pay-Rank Inversion and M&A Decisions: Evidence from Chinese State-Owned Enterprises
by Shaoni Zhou, Qiyue Du and Zhitian Zhou
Int. J. Financ. Stud. 2025, 13(4), 239; https://doi.org/10.3390/ijfs13040239 - 15 Dec 2025
Viewed by 1532
Abstract
In typical executive compensation structures, higher corporate ranks are associated with greater pay. However, the reform of state-owned enterprises (SOEs) in China introduced strict salary caps for top executives, while lower-tier managers continued to receive market-based compensation, resulting in a phenomenon of pay-rank [...] Read more.
In typical executive compensation structures, higher corporate ranks are associated with greater pay. However, the reform of state-owned enterprises (SOEs) in China introduced strict salary caps for top executives, while lower-tier managers continued to receive market-based compensation, resulting in a phenomenon of pay-rank inversion—where subordinates earn more than their superiors. Leveraging this anomaly as a quasi-natural experiment, this study investigates the specific impact and underlying mechanism of pay-rank inversion on mergers and acquisitions (M&A) decisions and subsequent value realization within Chinese SOEs, thereby addressing the broad academic discourse on optimal executive compensation design. Employing a difference-in-differences (DID) approach with panel data spanning from 2007 to 2022, our analysis reveals that pay-rank inversion significantly reduces firms’ M&A intentions. Mechanistic analysis suggests that this negative effect arises primarily from diminished executive risk-taking. Furthermore, we find that the adverse impact is attenuated when CEOs possess longer tenures or receive equity-based incentives, but it ultimately undermines the realization of value post-M&A. These findings highlight the unintended consequences of high-level compensation reforms and emphasize the critical role of a well-structured pay hierarchy in sustaining executive incentives for strategic decision-making. Despite providing robust evidence, this study is subject to limitations, including its focus on measuring inversion only between the first and second management tiers. Future research should extend the analysis to the pay inversion between the listed firm and its controlling SOE group and explore alternative causal pathways beyond risk-taking, such as CEO work motivation, to deepen the understanding of high-level executive behavior. Full article
(This article belongs to the Special Issue Emerging Trends in Corporate Finance: ESG, Climate Risk, and Other Contemporary Issues)
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19 pages, 484 KB  
Article
The Impact of Social Trust on the Development of Digital Finance
by Fan Zeng and Benyong Hu
Int. J. Financ. Stud. 2025, 13(4), 232; https://doi.org/10.3390/ijfs13040232 - 4 Dec 2025
Cited by 5 | Viewed by 2209
Abstract
Social trust is a fundamental element in the evolution of digital finance, significantly influencing its development. This study presents an innovative exploration of the role and internal mechanisms of social trust in digital finance, utilizing using provincial panel data from 27 provinces in [...] Read more.
Social trust is a fundamental element in the evolution of digital finance, significantly influencing its development. This study presents an innovative exploration of the role and internal mechanisms of social trust in digital finance, utilizing using provincial panel data from 27 provinces in China spanning the period from 2012 to 2021. By focusing on trust as a core element, the study enriches the research framework on digital finance development, revealing that beyond traditional factors such as technology and the economy, social and psychological factors also affect digital finance growth. These findings provide new perspectives on understanding digital finance development. The study elucidates the complex substitution and interdependence between formal and informal institutions, offering valuable insights for optimizing institutional frameworks to promote digital finance. It also uncovers significant regional heterogeneity in the influence of social trust on digital finance, and social trust primarily enhances the depth and digitization of digital finance, while its effect on the breadth of digital finance is statistically insignificant. These insights serve as a valuable reference for policymakers aiming to ensure the sustainable expansion of the digital finance sector. Full article
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33 pages, 731 KB  
Article
Does the Stock Market Encourage Sustainability? Evidence from UK Investment Announcements
by Kuburat Olayinka Lawal, Edward Jones and Lucy (Jia) Lu
Int. J. Financ. Stud. 2025, 13(4), 215; https://doi.org/10.3390/ijfs13040215 - 12 Nov 2025
Cited by 1 | Viewed by 2810
Abstract
This paper examines the stock market reaction to company investment decisions with and without a sustainability objective. Abnormal returns are estimated using a standard event study methodology for a sample of 517 investment announcements for listed UK firms for the period 2013 to [...] Read more.
This paper examines the stock market reaction to company investment decisions with and without a sustainability objective. Abnormal returns are estimated using a standard event study methodology for a sample of 517 investment announcements for listed UK firms for the period 2013 to 2021. Using a sample of 90 sustainable investments and 427 non-sustainable investments, we test whether 90 announcements with a sustainability agenda are more positively viewed by market participants than 427 announcements without a sustainability agenda. This study documents significant positive stock market reactions to both sets of investments, but abnormal returns are higher for investments without a sustainability agenda. The difference in abnormal returns between both sets of investments is not statistically significant. The findings reported in this study suggest that classifying corporate investment decisions according to information content indicative of a sustainability agenda contains price-sensitive information. This has implications for information made available to the market and will therefore promote price discovery, reducing the information asymmetry between informed and uninformed investors and allowing improved market efficiency in categorizing investment decisions according to investment objectives. In a market-based system, the positive valuation of investments associated with sustainability undertakings has implications for allocative efficiency, because firms become more attractive regarding the future allocation of funds to investment projects that address sustainability concerns, indicating that new sustainable investments should be encouraged. Full article
23 pages, 1356 KB  
Article
Digital Transformation in Accounting: An Assessment of Automation and AI Integration
by Carlos Sampaio and Rui Silva
Int. J. Financ. Stud. 2025, 13(4), 206; https://doi.org/10.3390/ijfs13040206 - 5 Nov 2025
Cited by 22 | Viewed by 17198
Abstract
This study conducts a bibliometric analysis of the scientific literature on digital, automated, and AI-assisted accounting systems. The data include documents listed in the Web of Science and Scopus databases. The analysis identifies the main authors, countries/territories, sources, and thematic trends. The results [...] Read more.
This study conducts a bibliometric analysis of the scientific literature on digital, automated, and AI-assisted accounting systems. The data include documents listed in the Web of Science and Scopus databases. The analysis identifies the main authors, countries/territories, sources, and thematic trends. The results reveal that the scientific output within this research field has increased since 2018, emphasising the integration of artificial intelligence (AI), robotic process automation, and blockchain technologies in accounting. The findings also suggest that automation enhances efficiency, accuracy, and reliability while also raising concerns about ethics, cybersecurity, and job displacement. This study evaluates the accounting research from early discussions on information systems and automation to current topics such as digital transformation, sustainability, and intelligent decision-making. Furthermore, it contributes to the understanding of the scientific development of digital accounting and addresses future research directions involving AI and machine learning for predictive analytics and fraud detection, blockchain for secure and transparent accounting systems, sustainability through the integration of ESG reporting, and interdisciplinary collaboration between accounting, computer science, and business management to develop intelligent financial systems. The findings provide insights for academics and practitioners aiming to understand the ongoing digital transformation of accounting systems. Full article
(This article belongs to the Special Issue Technologies and Financial Innovation)
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21 pages, 512 KB  
Article
Determinants of M&A Acquisition Premiums on the European Market in the Period of 2009 to 2022
by Marc Brixius, Jens Kai Perret, Jörg Schröder and Kamilė Taujanskaitė
Int. J. Financ. Stud. 2025, 13(4), 204; https://doi.org/10.3390/ijfs13040204 - 3 Nov 2025
Viewed by 5039
Abstract
This study analyzes the development and determinants of control premiums in mergers and acquisitions in the European market from 2009 to 2022 (i.e., stock volatility, liquidity via money supply, sectoral growth, transaction volume, market capitalization, free cash flows, presence of a toehold, public [...] Read more.
This study analyzes the development and determinants of control premiums in mergers and acquisitions in the European market from 2009 to 2022 (i.e., stock volatility, liquidity via money supply, sectoral growth, transaction volume, market capitalization, free cash flows, presence of a toehold, public listing, cross-border transactions, payment types, and sectoral relatedness), whereby control premiums represent the premium that buyers pay above the current market value of a company to gain control. The empirical analysis implements linear as well as quantile regression analyses. Results reveal that the average and median premiums fluctuated notably between 2009 and 2022, with the lowest premiums paid in 2009 and the highest in 2022. Factors such as the volatility of the stock market, capital liquidity, and deal activity within certain sectors have a consistently significant influence on the level of premiums if a longer period of analysis is selected. Cross-border status, payment structure, stock market listing of the acquiring company, and the build-up of a toehold influence the premiums paid in shorter- and longer-term analyses. In contrast, neither the market capitalization nor the free cash flow of the target company has a significant influence on the premiums paid. Full article
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23 pages, 930 KB  
Article
Stock Price Prediction Using a Stacked Heterogeneous Ensemble
by Michael Parker, Mani Ghahremani and Stavros Shiaeles
Int. J. Financ. Stud. 2025, 13(4), 201; https://doi.org/10.3390/ijfs13040201 - 28 Oct 2025
Cited by 2 | Viewed by 8746
Abstract
Forecasting stock price ranges remains a significant challenge because of the non-linear nature of financial data. This study proposes and evaluates a stacking ensemble model for range-based volatility forecasting, using open, high, low, and close (OHLC) prices. The model integrates a diverse, heterogeneous [...] Read more.
Forecasting stock price ranges remains a significant challenge because of the non-linear nature of financial data. This study proposes and evaluates a stacking ensemble model for range-based volatility forecasting, using open, high, low, and close (OHLC) prices. The model integrates a diverse, heterogeneous set of base learners, such as statistical (ARIMA), machine learning (Random Forest), and deep learning (LSTM, GRU, Transformer) models, with an XGBoost meta-learner. Applied to several major financial indices and a single stock, the proposed framework demonstrates high predictive accuracy, achieving R2 scores between 0.9735 and 0.9905. These results highlight the efficacy of a multi-faceted stacking approach in navigating the complexities of financial forecasting. Full article
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30 pages, 659 KB  
Article
Hedge Fund Activism, Voice and Value Creation
by Christos Bouras and Efstathios Karpouzis
Int. J. Financ. Stud. 2025, 13(4), 200; https://doi.org/10.3390/ijfs13040200 - 24 Oct 2025
Viewed by 2084
Abstract
We construct a novel hand-collected large dataset of 205 U.S. hedge funds and 1025 activist events over the period 2005–2013, which records both the Schedule 13D filing date and the voice date, and explore the role of voice in value creation. We employ [...] Read more.
We construct a novel hand-collected large dataset of 205 U.S. hedge funds and 1025 activist events over the period 2005–2013, which records both the Schedule 13D filing date and the voice date, and explore the role of voice in value creation. We employ alternative inferential statistical approaches, including parametric, non-parametric, and heteroscedasticity-robust tests. We reveal that the voice date is important in creating short-term firm value and provide strong evidence that voice is associated with positive abnormal returns. These findings suggest that voice leads to information revelation, with implications for U.S. stock market arbitrage. Full article
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17 pages, 758 KB  
Article
Impact of ESG Preferences on Investors in China’s A-Share Market
by Yihan Sun, Diyang Jiao, Yiqu Yang, Yumeng Peng and Sang Hu
Int. J. Financ. Stud. 2025, 13(4), 191; https://doi.org/10.3390/ijfs13040191 - 15 Oct 2025
Cited by 1 | Viewed by 3571
Abstract
This study explores the time-varying influence of Environmental, Social, and Governance (ESG) factors on asset pricing in China’s A-share market from 2017 to 2023, integrating investor heterogeneity categorized as ESG-unaware (Type-U), ESG-aware (Type-A), and ESG-motivated (Type-M). taxonomy. It adopts a linear regression model [...] Read more.
This study explores the time-varying influence of Environmental, Social, and Governance (ESG) factors on asset pricing in China’s A-share market from 2017 to 2023, integrating investor heterogeneity categorized as ESG-unaware (Type-U), ESG-aware (Type-A), and ESG-motivated (Type-M). taxonomy. It adopts a linear regression model with seven control variables (including firm systematic risk, asset turnover ratio, and ownership concentration) to quantify ESG’s marginal effect on stock returns. Annual regressions (2017–2022) reveal distinct ESG coefficient shifts: insignificant negative coefficients in 2017–2018, significantly positive coefficients in 2019–2020, and significantly negative coefficients in 2021–2022. Heterogeneity analysis across five non-financial industries (Utilities, Properties, Conglomerates, Industrials, Commerce) shows industry-specific ESG effects. Portfolio performance tests using 2023 data (stocks divided into eight ESG groups) indicate that portfolios with medium ESG scores outperform high/low ESG portfolios and the traditional mean-variance model in risk-adjusted returns (Sharpe ratio) and volatility control, avoiding poor governance risks (low ESG) and excessive ESG resource allocation issues (high ESG). Overall, policy shocks and institutional maturation transformed the market from ESG indifference to ESG-motivated pricing within a decade, offering insights for stakeholders in emerging ESG markets. Full article
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21 pages, 498 KB  
Article
Employee Tenure, Earnings Management, and the Moderating Role of Foreign Investors: Evidence from South Korea
by Dongkuk Lim and Dong Hyun Son
Int. J. Financ. Stud. 2025, 13(4), 190; https://doi.org/10.3390/ijfs13040190 - 14 Oct 2025
Viewed by 2361
Abstract
This study examines the influence of employee tenure on earnings management and the moderating role of foreign investors in Korean listed firms. Drawing on agency theory and entrenchment perspectives, we argue that longer employee tenure, while fostering stability and firm-specific expertise, can entrench [...] Read more.
This study examines the influence of employee tenure on earnings management and the moderating role of foreign investors in Korean listed firms. Drawing on agency theory and entrenchment perspectives, we argue that longer employee tenure, while fostering stability and firm-specific expertise, can entrench practices that enable opportunistic reporting. In contrast, consistent with resource dependence theory, foreign investors act as effective external monitors who can mitigate such behavior, particularly in emerging markets with weaker governance institutions. Using 11,381 firm-year observations from 2011 to 2019, we estimate discretionary accruals with the modified Jones model and performance-matched model. The results indicate that employee tenure is positively associated with accrual-based earnings management, but this effect is significantly reduced in firms with higher foreign investor ownership. Robustness tests, including instrumental variable estimation, confirm the validity of these findings. This study’s main contributions are introducing employee tenure as an underexplored governance factor, integrating internal and external monitoring perspectives, and showing that foreign investors moderate workforce-related risks. Practically, it highlights that investors can use tenure as a reporting risk signal, managers should complement workforce stability with strong governance, and policymakers should promote tenure disclosure and foreign participation to enhance transparency. Full article
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22 pages, 3211 KB  
Article
The Measurement and Characteristic Analysis of the Chinese Financial Cycle
by Siyuan Qiu
Int. J. Financ. Stud. 2025, 13(4), 187; https://doi.org/10.3390/ijfs13040187 - 3 Oct 2025
Viewed by 1683
Abstract
In this paper, based on Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model, five financial serials are dynamically weighted, and then China’s Financial Conditions Index is synthesized to measure China’s financial cycle. After that, using the monthly data of 2000–2023 as sample space, this paper [...] Read more.
In this paper, based on Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model, five financial serials are dynamically weighted, and then China’s Financial Conditions Index is synthesized to measure China’s financial cycle. After that, using the monthly data of 2000–2023 as sample space, this paper utilizes the Markov Switching (MS) model to analyze the characteristics of China’s financial cycle and to investigate the four-zone system. Then, the Vector Autoregression (VAR) model focuses on investigating the macroeconomic effects of China’s financial cycle. The findings are as follows: Firstly, the dynamic weighting approach based on GARCH model is more suitable for valuating China’s financial cycle. Secondly, China’s financial cycle has a strong inertia at the state of transition and the imbalance of China’s overall financial situation is very common. Additionally, China’s financial cycle is distinctly characterized by the double asymmetry of fewer contractions and more expansions, shorter expansions, and longer expansions. Thirdly, China’s financial expansion offers a nine-month short-term stimulus to output and exerts lasting upward pressure on prices. Full article
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36 pages, 1441 KB  
Article
When Financial Awareness Meets Reality: Financial Literacy and Gen Z’s Entrepreneurship Interest
by Eva Kicova, Jakub Michulek, Olga Ponisciakova and Juraj Fabus
Int. J. Financ. Stud. 2025, 13(3), 171; https://doi.org/10.3390/ijfs13030171 - 11 Sep 2025
Cited by 2 | Viewed by 10224
Abstract
Financial literacy is a key competence for responsible decision-making and entrepreneurial readiness. This study looks at how Generation Z’s entrepreneurial participation is impacted by objective, subjective, and calibrated FL. The alignment of perceived and actual knowledge or calibration is highlighted as an understudied [...] Read more.
Financial literacy is a key competence for responsible decision-making and entrepreneurial readiness. This study looks at how Generation Z’s entrepreneurial participation is impacted by objective, subjective, and calibrated FL. The alignment of perceived and actual knowledge or calibration is highlighted as an understudied factor that influences entrepreneurial behaviour. A mixed-methods approach was applied, combining a survey of 403 Slovak students with structured interviews with secondary school and university teachers. Quantitative analysis used Chi-square tests, Cramer’s V, sign schemes, and MLR. Qualitative interviews provided contextual insights into educational gaps and perceived barriers to entrepreneurship. The findings confirm that a higher financial literacy is positively related to entrepreneurial interest. Objective literacy has a slightly greater predictive value than self-assessed literacy, while calibration emerged as the strongest predictor: realistically, financially literate individuals displayed the highest entrepreneurial engagement, whereas both over- and underestimation of financial knowledge reduced it. Interviews highlighted insufficient financial education, limited practical experience, and fear of risk as major obstacles. By combining three aspects of financial literacy with business goals and offering fresh data from Slovakia, this study makes a contribution to the literature. In similar situations, it makes suggestions for enhancing financial education to support Generation Z’s entrepreneurial potential. Full article
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24 pages, 1322 KB  
Article
Predictive Power of ESG Factors for DAX ESG 50 Index Forecasting Using Multivariate LSTM
by Manuel Rosinus and Jan Lansky
Int. J. Financ. Stud. 2025, 13(3), 167; https://doi.org/10.3390/ijfs13030167 - 4 Sep 2025
Cited by 4 | Viewed by 4201
Abstract
As investors increasingly use Environmental, Social, and Governance (ESG) criteria, a key challenge remains: ESG data is typically reported annually, while financial markets move much faster. This study investigates whether incorporating annual ESG scores can improve monthly stock return forecasts for German DAX-listed [...] Read more.
As investors increasingly use Environmental, Social, and Governance (ESG) criteria, a key challenge remains: ESG data is typically reported annually, while financial markets move much faster. This study investigates whether incorporating annual ESG scores can improve monthly stock return forecasts for German DAX-listed firms. We employ a multivariate long short-term memory (LSTM) network, a machine learning model ideal for time series data, to test this hypothesis over two periods: an 8-year analysis with a full set of ESG scores and a 16-year analysis with a single disclosure score. The evaluation of model performance utilizes standard error metrics and directional accuracy, while statistical significance is assessed through paired statistical tests and the Diebold–Mariano test. Furthermore, we employ SHapley Additive exPlanations (SHAP) to ensure model explainability. We observe no statistically significant indication that incorporating annual ESG data enhances forecast accuracy. The 8-year study indicates that using a comprehensive ESG feature set results in a statistically significant increase in forecast error (RMSE and MAE) compared to a baseline model that utilizes solely historical returns. The ESG-enhanced model demonstrates no significant performance disparity compared to the baseline across the 16-year investigation. Our findings indicate that within the one-month-ahead projection horizon, the informative value of low-frequency ESG data is either fully incorporated into the market or is concealed by the significant forecasting capability of the historical return series. This study’s primary contribution is to demonstrate, through out-of-sample testing, that standard annual ESG information holds little practical value for generating predictive alpha, urging investors to seek more timely, alternative data sources. Full article
(This article belongs to the Special Issue Editorial Board Members’ Collection Series: ESG Ratings and Disclosures)
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29 pages, 13156 KB  
Article
Exchange Rate Forecasting: A Deep Learning Framework Combining Adaptive Signal Decomposition and Dynamic Weight Optimization
by Xi Tang and Yumei Xie
Int. J. Financ. Stud. 2025, 13(3), 151; https://doi.org/10.3390/ijfs13030151 - 22 Aug 2025
Cited by 6 | Viewed by 6710
Abstract
Accurate exchange rate forecasting is crucial for investment decisions, multinational corporations, and national policies. The nonlinear nature and volatility of the foreign exchange market hinder traditional forecasting methods in capturing exchange rate fluctuations. Despite advancements in machine learning and signal decomposition, challenges remain [...] Read more.
Accurate exchange rate forecasting is crucial for investment decisions, multinational corporations, and national policies. The nonlinear nature and volatility of the foreign exchange market hinder traditional forecasting methods in capturing exchange rate fluctuations. Despite advancements in machine learning and signal decomposition, challenges remain in high-dimensional data handling and parameter optimization. This study mitigates these constraints by introducing an innovative enhanced prediction framework that integrates the optimal complete ensemble empirical mode decomposition with adaptive noise (OCEEMDAN) method and a strategically optimized combination weight prediction model. The grey wolf optimizer (GWO) is employed to autonomously modify the noise parameters of OCEEMDAN, while the zebra optimization algorithm (ZOA) dynamically fine-tunes the weights of predictive models—Bi-LSTM, GRU, and FNN. The proposed methodology exhibits enhanced prediction accuracy and robustness through simulation experiments on exchange rate data (EUR/USD, GBP/USD, and USD/JPY). This research improves the precision of exchange rate forecasts and introduces an innovative approach to enhancing model efficacy in volatile financial markets. Full article
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24 pages, 566 KB  
Article
Liquidity Drivers in Illiquid Markets: Evidence from Simulation Environments with Heterogeneous Agents
by Lars Fluri, Ahmet Ege Yilmaz, Denis Bieri, Thomas Ankenbrand and Aurelio Perucca
Int. J. Financ. Stud. 2025, 13(3), 145; https://doi.org/10.3390/ijfs13030145 - 18 Aug 2025
Viewed by 2704
Abstract
This study investigates the liquidity dynamics in non-traditional financial markets by simulating trading environments for fractional ownership of illiquid alternative investments, grounded in empirical tick data from a Swiss FinTech platform covering December 2022 to June 2024. The research translates an operational digital [...] Read more.
This study investigates the liquidity dynamics in non-traditional financial markets by simulating trading environments for fractional ownership of illiquid alternative investments, grounded in empirical tick data from a Swiss FinTech platform covering December 2022 to June 2024. The research translates an operational digital secondary market into a heterogeneous agent-based simulation model within the theoretical framework of market microstructure and complex systems theory. The main objective is to assess whether a simple agent-based model (ABM) can replicate empirical liquidity patterns and to evaluate how market rules and parameter changes influence simulated liquidity distributions. The findings show that (i) the simulated liquidity closely matches empirical distributions not only in mean and variance but also in higher-order moments; (ii) the ABM reproduces key stylized facts observed in the data; and (iii) seemingly simple interventions in market rules can have unintended consequences on liquidity due to the complex interplay between agent behavior and trading mechanics. These insights have practical implications for digital platform designers, investors, and regulators, highlighting the importance of accounting for agent heterogeneity and endogenous market dynamics when shaping secondary market structures. Full article
(This article belongs to the Special Issue Market Microstructure and Liquidity)
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26 pages, 333 KB  
Article
Financial Discrimination: Consumer Perceptions and Reactions
by Miranda Reiter, Di Qing, Kenneth White and Morgen Nations
Int. J. Financ. Stud. 2025, 13(3), 136; https://doi.org/10.3390/ijfs13030136 - 24 Jul 2025
Viewed by 2712
Abstract
Access to traditional financial institutions plays a key role in enhancing positive financial outcomes. However, some consumers within the United States experience discrimination from these same institutions. In particular, discrimination based on race and gender has historically been tied to outcomes such as [...] Read more.
Access to traditional financial institutions plays a key role in enhancing positive financial outcomes. However, some consumers within the United States experience discrimination from these same institutions. In particular, discrimination based on race and gender has historically been tied to outcomes such as lower service quality and a lack of access to credit. While the previous literature has discussed some of the discriminatory practices that these groups have faced, there is a lack of research on how these groups respond to discrimination from financial institutions. Through a series of logistic regressions, the authors analyzed how race, ethnicity, and gender are related to reporting experiences of discrimination. The authors then explored how consumers react to discrimination by looking at five reported reactions. Primary results show that Black consumers were more likely than most other racial groups to experience financial discrimination. Additionally, women were less likely than men to report financial discrimination. Race was shown to be a significant factor in four of the five reactions to discrimination, while gender was a factor in two of the reactions. The findings further show that after experiencing financial discrimination, most individuals turned to non-traditional financial services as a direct result of the bias or racism. Full article
16 pages, 350 KB  
Article
Bitcoin Return Dynamics Volatility and Time Series Forecasting
by Punit Anand and Anand Mohan Sharan
Int. J. Financ. Stud. 2025, 13(2), 108; https://doi.org/10.3390/ijfs13020108 - 9 Jun 2025
Cited by 4 | Viewed by 11350
Abstract
Bitcoin and other cryptocurrency returns show higher volatility than equity, bond, and other asset classes. Increasingly, researchers rely on machine learning techniques to forecast returns, where different machine learning algorithms reduce the forecasting errors in a high-volatility regime. We show that conventional time [...] Read more.
Bitcoin and other cryptocurrency returns show higher volatility than equity, bond, and other asset classes. Increasingly, researchers rely on machine learning techniques to forecast returns, where different machine learning algorithms reduce the forecasting errors in a high-volatility regime. We show that conventional time series modeling using ARMA and ARMA GARCH run on a rolling basis produces better or comparable forecasting errors than those that machine learning techniques produce. The key to achieving a good forecast is to fit the correct AR and MA orders for each window. When we optimize the correct AR and MA orders for each window using ARMA, we achieve an MAE of 0.024 and an RMSE of 0.037. The RMSE is approximately 11.27% better, and the MAE is 10.7% better compared to those in the literature and is similar to or better than those of the machine learning techniques. The ARMA-GARCH model also has an MAE and an RMSE which are similar to those of ARMA. Full article
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20 pages, 343 KB  
Article
Is the ESG Score Part of the Set of Information Available to Investors? A Conditional Version of the Green Capital Asset Pricing Model
by Lucía Galicia-Sanguino and Rubén Lago-Balsalobre
Int. J. Financ. Stud. 2025, 13(2), 88; https://doi.org/10.3390/ijfs13020088 - 21 May 2025
Cited by 2 | Viewed by 1876
Abstract
In this paper, we propose a linear factor model that incorporates investor preferences toward sustainability to analyze indirect effects that climate concerns may have on asset prices. Our approach is based on the relationship between environmental, social, and governance (ESG) investing and climate [...] Read more.
In this paper, we propose a linear factor model that incorporates investor preferences toward sustainability to analyze indirect effects that climate concerns may have on asset prices. Our approach is based on the relationship between environmental, social, and governance (ESG) investing and climate change considerations by investors. We use ESG scores as a part of the information set used by investors to determine the unconditional version of the conditional capital asset pricing model (CAPM). Our results show that the ESG score allows the linearized version of the conditional CAPM to greatly outperform the classic CAPM and the Fama–French three-factor model for different sorts of stock portfolios, contributing significantly to reducing pricing errors. Furthermore, we find a negative price of risk for stocks that covary positively with ESG growth, which suggests that green assets may perform better than brown ones if ESG concerns suddenly become more pressing over time. Thus, our paper constitutes a step forward in the attempt to shed light on how climate change is priced regardless of the climate risk measure used. Full article
30 pages, 635 KB  
Article
Tax Compliance Determinants in a Challenging Fiscal Environment: Evidence from a Greek Experiment
by Skoura V. Angeliki and Dasaklis K. Thomas
Int. J. Financ. Stud. 2025, 13(2), 83; https://doi.org/10.3390/ijfs13020083 - 10 May 2025
Cited by 6 | Viewed by 5753
Abstract
This study investigates the factors influencing tax compliance among Greek entrepreneurs functioning within a difficult fiscal landscape. Through a randomized field experiment, we analyze the effects of differing tax rates, audit likelihoods, and legal frameworks on compliance behavior. Utilizing regression analysis alongside robustness [...] Read more.
This study investigates the factors influencing tax compliance among Greek entrepreneurs functioning within a difficult fiscal landscape. Through a randomized field experiment, we analyze the effects of differing tax rates, audit likelihoods, and legal frameworks on compliance behavior. Utilizing regression analysis alongside robustness checks, our results indicate that greater transparency in audits and customized penalty systems markedly improve compliance rates. These findings highlight the critical role of cultural and regulatory elements in determining taxpayer conduct and provide valuable insights for policymakers in both national and international tax systems. This study contributes to the ongoing discourse surrounding tax evasion and compliance, positioning Greece as a potential reference point for comparable economies in the European Union. Full article
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27 pages, 1202 KB  
Article
Financial Sentiment Analysis and Classification: A Comparative Study of Fine-Tuned Deep Learning Models
by Dimitrios K. Nasiopoulos, Konstantinos I. Roumeliotis, Damianos P. Sakas, Kanellos Toudas and Panagiotis Reklitis
Int. J. Financ. Stud. 2025, 13(2), 75; https://doi.org/10.3390/ijfs13020075 - 2 May 2025
Cited by 18 | Viewed by 15977
Abstract
Financial sentiment analysis is crucial for making informed decisions in the financial markets, as it helps predict trends, guide investments, and assess economic conditions. Traditional methods for financial sentiment classification, such as Support Vector Machines (SVM), Random Forests, and Logistic Regression, served as [...] Read more.
Financial sentiment analysis is crucial for making informed decisions in the financial markets, as it helps predict trends, guide investments, and assess economic conditions. Traditional methods for financial sentiment classification, such as Support Vector Machines (SVM), Random Forests, and Logistic Regression, served as our baseline models. While somewhat effective, these conventional approaches often struggled to capture the complexity and nuance of financial language. Recent advancements in deep learning, particularly transformer-based models like GPT and BERT, have significantly enhanced sentiment analysis by capturing intricate linguistic patterns. In this study, we explore the application of deep learning for financial sentiment analysis, focusing on fine-tuning GPT-4o, GPT-4o-mini, BERT, and FinBERT, alongside comparisons with traditional models. To ensure optimal configurations, we performed hyperparameter tuning using Bayesian optimization across 100 trials. Using a combined dataset of FiQA and Financial PhraseBank, we first apply zero-shot classification and then fine tune each model to improve performance. The results demonstrate substantial improvements in sentiment prediction accuracy post-fine-tuning, with GPT-4o-mini showing strong efficiency and performance. Our findings highlight the potential of deep learning models, particularly GPT models, in advancing financial sentiment classification, offering valuable insights for investors and financial analysts seeking to understand market sentiment and make data-driven decisions. Full article
(This article belongs to the Special Issue Modern Financial Econometrics)
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