International Journal of Financial Studies doi: 10.3390/ijfs14060165

Authors: Hanzhong Zheng Xuemeng Guo Lingpeng Kong

Using panel data for 135 Chinese prefecture-level cities from 2007 to 2023, this study investigates the impact of new digital infrastructure on green finance development. The new digital infrastructure indicator is constructed based on the proportion of relevant keywords appearing in government work reports, while the green finance index is reconstructed using the entropy-weighting method across seven dimensions. The estimation results indicate that new digital infrastructure exerts a significant positive effect on green finance development. This conclusion remains robust after a series of robustness checks, including alternative variable measurements, winsorization treatment, and instrumental-variable estimation. Mechanism analysis reveals that industrial structure upgrading, particularly the advancement of industrial structure, serves as an important transmission channel. Further heterogeneity analysis shows that the promoting effect is more pronounced in cities with larger economic scale, those located outside major urban agglomerations, and cities with higher levels of financial resource aggregation. These findings provide empirical evidence for the role of digital infrastructure in fostering green finance and facilitating sustainable regional development.

International Journal of Financial Studies doi: 10.3390/ijfs14060164

Authors: Vytautas Snieska Daiva Burksaitiene Valentinas Navickas

This paper examines the drivers of house price cycles across EU countries between 2005 and 2024 and measures their synchronicity. We used panel data methods—fixed effects, dynamic panel models (Arellano–Bond GMM), and a pooled VAR framework—to capture static and dynamic relationships between house price growth and key macroeconomic variables. The results show that the dynamics of house prices are highly persistent. GDP growth has a clear positive effect, while higher unemployment and interest rates push prices down. Migration flows, however, are not statistically significant at the EU aggregate level. Property taxation shows a positive coefficient, which probably reflects structural and institutional differences rather than a direct dampening effect on prices. Dynamic analysis suggests that macroeconomic shocks have persistent and economically meaningful impacts on house price growth. Hierarchical cluster analysis revealed three distinct groups of countries, meaning that house price cycles are only partially synchronized across the EU. Unlike previous studies that typically examine individual determinants or synchronization separately, this study integrates panel econometric methods, dynamic VAR analysis, and hierarchical clustering within a unified framework to jointly assess macroeconomic drivers, dynamic interactions, and structural heterogeneity of house price cycles across EU countries. In general, common macroeconomic drivers and structural heterogeneity coexist—this is important for the stability of the housing market and sustainable development.

International Journal of Financial Studies doi: 10.3390/ijfs14060163

Authors: Abdulazeez Y. H. Saif-Alyousfi Abdullah Alsadan Ahmed Alrashed

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.

International Journal of Financial Studies doi: 10.3390/ijfs14060162

Authors: Ramesh Adhikari YoungHa Ki

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.

International Journal of Financial Studies doi: 10.3390/ijfs14060161

Authors: Fadi Al-Asfour

This paper provides a theory-informed synthesis of earnings management research through a review of the 100 most cited studies in the accounting literature. Rather than functioning as a purely bibliometric review, the study integrates theoretical, empirical, methodological, and survey-based contributions to examine how influential research has conceptualized, measured, and interpreted earnings management. Citation data were collected from Web of Science and Google Scholar as of 5 January 2025 using predefined search criteria, filtering procedures, and classification protocols. While citation counts are used to identify influential studies, they are not treated as direct indicators of research quality due to concerns regarding citation bias, publication visibility, and proxy limitations. The review organizes the literature around major themes, including corporate governance, audit quality, managerial incentives, institutional environments, market reactions, and regulatory change. The analysis highlights enduring debates concerning proxy validity, endogeneity and identification challenges, the distinction between statistical detection and economic significance, and the trade-off between accrual-based and real earnings management. The synthesis also incorporates emerging research streams involving family firms, gender diversity, ESG reporting, textual analysis, and AI-assisted analytics within broader agency and institutional theory perspectives. A central contribution of the paper is the development of an integrative analytical framework linking proxy validity, strategic substitution between reporting mechanisms, and institutional constraints within a unified interpretation of earnings management behavior. The review shows that advances in empirical design, textual analysis, machine learning, and predictive analytics extend rather than replace foundational insights, while persistent limitations in causal inference and measurement remain unresolved. Overall, the findings suggest that earnings management is best understood as a strategic response to incentives, monitoring, and institutional constraints rather than as a uniform indicator of opportunistic behavior. The paper concludes by outlining future research directions focused on theory-driven empirical design, methodological triangulation, AI-assisted detection approaches, and improved measurement frameworks across diverse reporting environments.

International Journal of Financial Studies doi: 10.3390/ijfs14060160

Authors: Viktor Koziuk Jurij Klapkiv

This paper examines whether legal origins influence the anti-inflationary effectiveness of central banks. While prior literature emphasizes the role of institutional frameworks in shaping financial systems, less attention has been paid to how legal traditions affect the relationship between central bank independence and inflation stability. Using a distance-to-frontier approach, we construct a gap measure between central bank independence and inflation performance. The results indicate that countries with a common law origin exhibit a significantly larger negative gap, suggesting higher anti-inflationary effectiveness despite lower formal central bank independence. In contrast, civil law countries tend to rely more heavily on formal institutional strengthening to achieve comparable inflation outcomes. Regression analysis confirms that the common law proxy remains statistically significant across most model specifications and demonstrates stronger explanatory power than traditional governance indicators such as the rule of law.

International Journal of Financial Studies doi: 10.3390/ijfs14060159

Authors: Titi Khoiriah Rofikoh Rokhim Buddi Wibowo

The 2008 financial crisis pushed policymakers around the world to rethink how banks could manage risk, leading to the implementation of stricter regulations, including capital buffers and bail-in mechanisms, aimed at making the financial system more resilient. This study examines how three key regulations under Basel III, namely, the Countercyclical Capital Buffer (CCyB), the Capital Conservation Buffer (CCB), and the Capital Surcharge (CS), shape lending patterns in Indonesian banks. The effectiveness of the bail-in policy in helping banks strengthen their capital base is also examined. This study uses difference-in-differences analysis on panel data from 97 banks between 2010 and 2024 to examine the impact of stricter capital regulations on banks’ ability to channel credit to the public and business sectors. Basel III aims to strengthen the resilience of banks; however, this policy could impact credit access and banking stability in Indonesia. This study found a positive impact on LDR of large banks after the treatment, which indicates the banks’ efforts to use the funds collected through credit distribution. This study empirically examines the impact of capital buffer regulation and the bail-in instrument in Indonesia as an emerging-market country with a segmented banking sector and banks’ classification by ownership and core capital value.

International Journal of Financial Studies doi: 10.3390/ijfs14060156

Authors: Muhammad Haroon Rasheed Rabia Farooq Abdulrahman Alomair Mohammed Alomair

The global financial system is constantly evolving through technological integration. This has led to the inception and rise in the cryptocurrency market, opening new avenues of comparative studies on market behavior. Therefore, the current study aimed to identify nuances in stock and cryptocurrency behavior. Based on the socionomic theory of finance, the study is a pioneer in considering the interplay of economic, market, and social media sentiments while providing a comparative view of cryptocurrencies and stocks. The study utilizes data of economic news sentiments, cryptocurrency fear and greed index, CNN fear and greed index, and Twitter sentiments against the movement of S&P Cryptocurrencies and S&P 500 stock index return spanning from 2018 to 2023. The study applied a vector autoregressive-based spillover model to assess the theorized linkage and applied robustness measures, including linear regression and the Granger causality test, for validation. The findings unveil distinct weak and moderate associations of sentiments across cryptocurrencies and stocks, respectively. The former is primarily driven by market sentiments while shaping economic news and social media sentiments. Meanwhile, the findings for stock return movements are found to be significantly associated with economic and market sentiments. This led to the inference that the cryptocurrency environment is an isolated system driven by internal sentiments, while stock markets are more economically integrated, and in both cases, social media sentiments are found to be the receiver of market spillover, weakly influencing economic news. The study is pioneering in its exploration of the interlinkage between selected macroeconomic sentiments; additionally, the comparative findings further add to the existing debate on influence of sentiment across financial markets. The varying realities identified in the findings hold significant practical implications for portfolio optimization, risk assessment and policy making.

International Journal of Financial Studies doi: 10.3390/ijfs14060158

Authors: Eddie Y. M. Lam Joseph K. W. Fung Calvin Y. C. Lee

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.

International Journal of Financial Studies doi: 10.3390/ijfs14060157

Authors: Birhanu Daba Chali Vilmos Lakatos

This bibliometric review examines the relationship between corporate governance and financial performance by synthesising evidence from a broad range of empirical studies. It also identifies key patterns in publication output, citation trends, and scholarly impact within the field. Following the PRISMA guidelines, a bibliometric review was conducted using articles indexed in the Scopus database. A total of 2095 articles published between 2020 and September 2025 were initially retrieved synthesising via a keyword search with the string “Corporate Governance” AND “Financial Performance.” After applying the inclusion criteria (full-text availability, English language, and relevance to the topic), 887 articles were retained for analysis. The findings indicate that most studies report a positive association between corporate governance practices and financial performance. The literature is primarily concentrated around themes such as corporate governance, financial performance, ESG practices, and board characteristics, with the connection between governance and a firm’s financial performance appearing generally positive, albeit context dependent. The results also reveal a growing research emphasis on sustainability-oriented governance, particularly ESG-related factors, reflecting a broader shift in the field towards long-term value creation. This review underscores the importance of nuanced corporate governance frameworks for stakeholders seeking to enhance the sustainability of financial performance, while also deepening understanding of the impact of governance on firm financial performance among both academics and practitioners. In addition, the review offers a broader perspective on the existing literature and identifies several gaps that warrant further investigation.

International Journal of Financial Studies doi: 10.3390/ijfs14060155

Authors: Yifei Chen Shuo Wang

As a major policy initiative, China’s Industry–Finance Cooperation (IFC) Pilot Program aims to address the enduring difficulties enterprises face in securing affordable financing. Despite its intent, the policy’s actual efficacy in alleviating corporate financing constraints remains ambiguous. Based on panel data of Chinese A-share listed firms (2011–2023, 19,742 observations), this paper adopts a difference-in-differences (DID) estimator to investigate the effect of China’s IFC Pilot Policy on corporate financing constraints. The results demonstrate that the IFC Pilot Policy significantly alleviates such constraints. Further mechanism and heterogeneity analyses reveal that it operates primarily by reducing earning management, lowering financing costs, and mitigating business risks. This study contributes to the field by establishing the finance-easing effect and risk management mechanism of industry–finance cooperation, offering valuable guidance for policymakers seeking to refine and optimize similar supply-side financial reform measures.

International Journal of Financial Studies doi: 10.3390/ijfs14060154

Authors: Zouhair Boumlik Olivier Colot Badia Oulhadj

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.

International Journal of Financial Studies doi: 10.3390/ijfs14060153

Authors: Abdulaziz S. Al Naim Abdulrahman Alomair Alan Farley Helen Yang

This study examines the moderating effect of firm attributes on the value relevance of accounting information in Saudi Arabia. Using a sample of 630 firm-year observations from 126 Saudi listed firms over 2018–2022, the research evaluates whether audit quality, size, leverage, growth potential, board diversity, and profitability complement the valuation role of earnings per share (EPS) and book value per share (BVPS) and if so then which direction of the attribute gave greater value relevance. Results reveal that all the firm attributes tested have a significant moderating effect on value relevance. Lower leverage, higher growth potential, greater board diversity, and profitability all lead to higher predicted market value for given EPS and BVPS. Big 4 audit quality and larger firm size are found to moderate the value relevance of accounting information rather than to influence share price directly. Both attributes strengthen the value relevance of earnings per share (EPS)—the EPS coefficient is significantly higher for firms audited by a Big 4 firm and for larger firms—while weakening the value relevance of book value per share (BVPS), with the BVPS coefficient being significantly lower in both cases. The combined effect is that earnings carry greater pricing weight, and book values carry lesser pricing weight, when audit quality is high and when firms are larger. Results also reveal that cohorts with Big 4 auditor, larger size, lower leverage, higher growth potential, more diverse boards, and profitability all have greater value relevance (higher R2) than cohorts with the alternative for each attribute. Hence, tests provide evidence that these attributes strengthen the association between selective accounting figures (EPS and BVPS) and share prices. The findings contribute to agency, information asymmetry, and value-relevance theory by showing that firm attributes condition the EPS and BVPS pricing weights rather than affecting price directly. The results have implications for regulators and firms seeking to improve financial reporting credibility and usefulness amid concentrated ownership. This study contributes timely empirical evidence on the multifaceted drivers of value relevance in an under-researched Middle Eastern emerging market.

International Journal of Financial Studies doi: 10.3390/ijfs14060152

Authors: Ramalingam Shanmugam Michael Mileski Bradley Beauvais Zo Ramamonjiarivelo Jose Betancourt Gerald Pacheco Rohit Pradhan

This study introduces a multivariate distance-based framework for analyzing hospital liquidity stress using three financial indicators: cash reserves, days with negative cash, and accounts receivable. Using Definitive Healthcare data from 2020–2025, the study applies principal component analysis (PCA), Mahalanobis distance, Aitchison distance, and ternary plots to characterize structural relationships among these liquidity variables. The results show that the first two principal components explain more than 94% of the variation in the transformed variables, indicating that the joint financial structure can be represented in a lower-dimensional space. Beginning in 2023, accounts receivable became more geometrically separated from the cash-based variables, suggesting that revenue-cycle dynamics may have become a more independent dimension of hospital liquidity stress. Importantly, this manuscript does not directly predict hospital closure or bankruptcy because verified event/non-event outcome data are not available in the analytic file. Instead, its contribution is methodological and exploratory: it demonstrates how distance-based and compositional methods can identify structural liquidity instability and potential early warning signals that warrant further validation with longitudinal closure, bankruptcy, or severe-distress outcomes.

International Journal of Financial Studies doi: 10.3390/ijfs14060151

Authors: Prabin Bajgai Zhaoxian Zhou

Next-day S&P 500 direction forecasting matters for allocation, hedging, and risk management because broad-index movements transmit quickly across portfolios. Does structured news sentiment help predict next-day S&P 500 direction? We test four feature sets over 2008–2023 in an ablation sequence: technical indicators only (Set A), with FinBERT headline sentiment (Set B), with BERTopic topic-linked sentiment (Set C), and with realized-volatility weighting (Set D). This design makes two contributions: it separates the incremental value of increasingly structured sentiment features, and it tests whether sentiment value is state-dependent across volatility regimes. CatBoost, XGBoost, LightGBM, LSTM, and GRU are evaluated under walk-forward cross-validation, nested cross-validation, and formal statistical tests. On the full sample, sentiment does not deliver a measurable forecasting edge. Walk-forward AUCs sit near 0.50 for every feature set, and pairwise tests find no significant differences. However, this average masks a consistent pattern. Sentiment becomes more informative during high-volatility periods, suggesting that its value is state-dependent rather than uniform. Rolling AUC swings from 0.28 to 0.71 depending on the market period. When we split by VIX regime, Set D reaches 0.5684 AUC during high-volatility episodes (n=50, permutation p=0.213) while adding almost nothing in calm markets. Set D also has the lowest fold-to-fold variance and the shallowest drawdown in trading simulations. These results imply that the relevant question is not whether sentiment works in general, but when it does. Sentiment does not help on average; whether it helps during stress is suggestive but unconfirmed and needs more crisis-period data to settle.

International Journal of Financial Studies doi: 10.3390/ijfs14060150

Authors: Angelo Leogrande Fabio Anobile Alberto Costantiello Carlo Drago Massimo Arnone

This paper studies the topic that has been rather less explored until now—the internal diversification of trading. Unlike looking at aggregate measures of financial development such as market capitalization and liquidity, the study focuses on trading diversification, defined as the portion of trading volume attributed to firms other than the ten most actively traded (VTX). The empirical analysis is based on the World Bank’s Global Financial Development database. It covers an unbalanced cross-country dataset of 2004–2021. Due to limited data availability, the resulting database became smaller and has an unbalanced panel structure. Four main independent variables in the core regression specification are related to financial structure (bank deposits) and financial integration (remittances, international public debt), as well as external measures of financial development (market capitalization, excluding firms within VTX). A broad range of control variables are introduced into the model to account for macroeconomic conditions, financial development, market size, liquidity, and participation. Lagged regressors are introduced to address persistence, delays, and potential endogeneity issues. The methodology relies on panel data econometrics, hierarchical clustering, and machine learning. The findings show that market structure and remittances positively affect trading diversification, whereas banks’ dominance and international public debt contribute to its concentration. The results persist across alternative specifications and robustness tests. The country-level analysis shows a core–periphery pattern, while machine learning demonstrates the critical importance of market structure.

International Journal of Financial Studies doi: 10.3390/ijfs14060149

Authors: Insaf Arfa Wided Khiari Houssein Ballouk Foued Ben Said

The article provides a systematic and bibliometric review of the literature on the relationship between countries’ ESG scores and their sovereign ratings. The study, which follows the PRISMA 2020 guidelines, employs a bibliometric methodology using a sample of 168 peer-reviewed articles sourced from Scopus and WoS (2006–2024) to analyze the progression of research in this growing area. The analysis shows that academic output is concentrated in Europe (35% of publications) and North America (12% of publications), and that there is increasing interest in incorporating ESG factors into sovereign risk assessment, especially since 2019. The bibliometric mapping shows that themes concerning ESG integration into sovereign risk assessment, rating methodologies, and sustainability-driven financial risk pricing are predominant. Four main research streams are revealed through co-occurrence and clustering analyses, which also highlight an expanding yet disjointed body of knowledge. The results also suggest large differences in how ESG ratings are calculated, which brings into question how comparable and reliable they are for use in empirical studies. This study helps structure the field and proposes a more coherent research agenda by identifying four key research puzzles. Future research should concentrate on enhancing ESG measurement, breaking down its components, and crafting tailored approaches for emerging markets.

International Journal of Financial Studies doi: 10.3390/ijfs14060148

Authors: Abraham Kisembe Wawire Christine Nanjala Simiyu Munene Laiboni Rogers Ochenge

In frontier markets, financial volatility exhibits long-memory properties and regime-dependent asymmetries that standard linear models do not capture. This leads to inaccuracies in forecasting risk when a single model is applied across regimes. This study investigates the horizon- and regime-dependent performance of volatility models within a horizon- and regime-sensitive evaluation framework that applies single-regime Generalized Autoregressive Conditional Heteroscedasticity (GARCH) variants alongside a Hidden Markov Model (HMM). We evaluate the predictive accuracy of GARCH, Exponential GARCH (EGARCH), Glosten-Jagannathan-Runkle GARCH (GJR-GARCH), Asymmetric Power ARCH (APARCH), Fractionally Integrated GARCH (FIGARCH), and an HMM. Diebold–Mariano test statistics reveal that predictive superiority is sensitive to the chosen benchmark. When EGARCH is the benchmark, results highlight the importance of leverage effects, whereas a FIGARCH benchmark demonstrates that short-memory models are rejected as horizons increase. While short-memory models capture immediate clustering, FIGARCH maintains stable performance via hyperbolic decay. HMM provides a superior in-sample fit by capturing transitions between calm and turbulent regimes. Economic validation through Value-at-Risk (VaR) and Expected Shortfall (ES) backtesting indicates that FIGARCH and APARCH offer more reliable coverage for early warning systems during market stress. The findings emphasize that forecasting in a frontier market requires asset-specific approaches where benchmark selection dictates the interpretation of model superiority.

International Journal of Financial Studies doi: 10.3390/ijfs14060147

Authors: Clement Olalekan Olaniyi

This study enhances the knowledge base by providing an empirical inquiry into the asymmetric sensitivity of economic complexity (ECI) to changes in financial development (FD), using data from 30 African countries for the period of 1995–2023. To deliver robust estimates in the face of econometric pitfalls, this study employs estimators such as Hatemi-J data decomposition procedures, robust standard-error regression of Driscoll and Kraay, Feasible Generalised Least Squares, Lewbel’s IV-Two-Stage Least Squares, and Quantile regression via moments. The findings from the linear model indicate that FD enhances ECI upgrades in Africa. The findings provide robust evidence of asymmetric structures in ECI’s sensitivity to changes in FD. It highlights that both positive and negative change components (financial sector expansionary and contractionary policies, respectively) in the FD significantly contribute to ECI upgrades. These findings reveal the obscure aspects of how FD change components contribute differently to ECI upgrades in African countries. These findings highlight that expansionary financial sector policies aid the development of knowledge-based productivity, technology diffusion, and manufacturing capabilities, enabling the production of a chain of high-tech, high-quality, and globally competitive products for export. On the other hand, contractionary financial sector policies in African countries spur cumulative reductions in the channelling of financial resources and other technical support to ECI-impeding initiatives, thereby making more resources available to fund ECI-enhancing initiatives that aid the manufacturing of quality, competitive products for exports. This study draws and outlines relevant policy implications of the findings.

International Journal of Financial Studies doi: 10.3390/ijfs14060146

Authors: Hui Xia Yilong Huang Shanshan Fang Qin Wang Jinyu Shen

Financial information plays a critical role in decision-making for stakeholders, including investors, regulators, and corporate managers. However, financial data is susceptible to deliberate manipulation, where some firms may distort disclosures to mislead stakeholders and potentially engage in fraudulent activities. With the rapid expansion of capital markets and advancements in information technology, financial fraud has grown increasingly sophisticated and concealed. As a result, conventional detection methods often struggle to identify emerging fraud patterns, rendering fraud prevention increasingly complex and less effective. In this paper, we propose a novel multi-layer architecture model that integrates business, internal control, and strategic features. Our framework leverages multi-layer neural networks for effective feature extraction and concatenates their outputs for classification. Furthermore, we develop this framework by incorporating explainable artificial intelligence (XAI) techniques to enhance interpretability. Empirical results show that the proposed framework provides competitive discriminatory ability and produces conservative, low-false-alarm fraud warnings under the full multi-layer feature setting while also offering interpretable insights for the diverse needs of stakeholders. This study contributes to the development of fraud detection tools that are both operationally useful and interpretable.

International Journal of Financial Studies doi: 10.3390/ijfs14060144

Authors: Majdi Anwar Quttainah Imen Ayadi

This study examines the relationship between environmental performance, digital integration, information asymmetry, and default risk among European firms. It seeks to understand how sustainability and digitalization jointly enhance corporate financial stability. The sample comprises 1303 non-financial firms from 20 European countries over the period 2016–2023. This study uses a Thomson Reuters sample composed of European publicly listed companies with ESG (environmental, social, and governance) ratings. Europe represents an ideal setting for this analysis due to its dual green and digital transition, supported by some of the most advanced regulatory policies in the world. Methodologically, the analysis employs a dynamic panel model estimated using the two-step system GMM approach, complemented by a robustness check based on 2SLS-IV estimation to address potential endogeneity concerns. The empirical findings reveal that both environmental performance and digital integration significantly reduce default risk whereas information asymmetry increases it. Moreover, sustainability and digital transformation attenuate the adverse effect of information asymmetry on financial stability, confirming their complementary role as resilience-enhancing mechanisms. These results underscore the critical importance of transparency, innovation, and organizational capabilities in mitigating financial risk. Overall, the study makes an original contribution to the literature on sustainable governance by demonstrating that environmental performance and digital integration are not merely regulatory requirements but constitute strategic intangible assets that strengthen financial soundness and reduce default risk within the European context.

International Journal of Financial Studies doi: 10.3390/ijfs14060145

Authors: Sarthak Pattnaik Chhayank Jain Eugene Pinsky

This paper presents a comprehensive spatiotemporal decomposition of equity returns for nine top-weighted constituents of the Dow Jones Industrial Average (DJIA) over a twenty-year period spanning January 2004 through December 2023, encompassing 5033 trading days and multiple market regimes, including the Global Financial Crisis (2008–2009), the COVID-19 crash and recovery (2020), and the Federal Reserve tightening cycle (2022–2023). Daily price movements are systematically partitioned into two orthogonal sessions: the open-to-close (OTC, or daytime) session, capturing within-session price discovery, and the close-to-open (CTO, or overnight) session, capturing the accumulated information arrival and liquidity dynamics between market closes and subsequent opens. Within this bipartite return framework, we construct and rigorously evaluate 24 distinct trading strategies, spanning directional (long/short), neutral (cash), momentum (inertia), and contrarian (reversal) approaches, applied independently to each session or in combinatorial cross-session configurations. Each strategy is evaluated under three transaction cost regimes (0, 1, and 2 basis points per trade) using an initial investment of $100, and assessed using annualized return, annualised volatility, Sharpe ratio, Sortino ratio, and maximum drawdown. The study universe—comprising UnitedHealth Group (UNH), Goldman Sachs (GS), Microsoft (MSFT), Home Depot (HD), Caterpillar (CAT), Amgen (AMGN), McDonald’s (MCD), Salesforce (CRM), and Honeywell (HON)—captures cross-sector heterogeneity across Healthcare, Financials, Technology, Consumer Discretionary, Industrials, Biotech, and Consumer Staples. The universe is selected from the top-weighted DJIA constituents as of early 2026; the paper is, therefore, best read as a focused, in-depth case study of index-representative large-cap names rather than a general cross-sectional statement about all U.S. equities. The principal findings are threefold. First, the overnight session consistently delivers superior risk-adjusted performance: seven of nine stocks record higher Sharpe ratios during the overnight period versus the daytime period, with the mean overnight Sharpe ratio (0.662) substantially exceeding the mean daytime Sharpe ratio (0.357), a statistically and economically significant overnight premium. Second, the hybrid Strategy #18—Long Overnight coupled with Daytime Reversal—emerges as the dominant cross-asset configuration, generating portfolio values as high as $8464 from a $100 initial investment (AMGN; Sharpe: 0.991) over the 20-year horizon. Third, Trajectory Change Analysis reveals (i) Lévy-stable tails with a mean stability index α¯=1.667 across all constituents, substantially below the Gaussian benchmark of α=2.0; (ii) Hurst exponents clustering below 0.5 (H¯=0.417), confirming dominant mean-reverting dynamics; and (iii) positive rolling CAPM alpha in 51–79% of rolling windows, indicating persistent risk-adjusted outperformance above the S&P 500 benchmark. These findings provide a rigorous empirical foundation for session-aware algorithmic trading system design and challenge the prevailing assumption of temporal homogeneity in equity return processes.

International Journal of Financial Studies doi: 10.3390/ijfs14060143

Authors: Salma Zaiane Fatma Ben Moussa Souhir Masmoudi Rahma Louati

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.

International Journal of Financial Studies doi: 10.3390/ijfs14060142

Authors: Attila Bányai Judit Bárczi Gergő Thalmeiner

This paper introduces the Normative Lean Performance Score (NLPS) model designed to evaluate lean operational performance using publicly available financial and accounting metrics, without requiring advanced analytics for practical implementation. The study applies an empirical research design based on a longitudinal dataset, where firms are first classified into lean-oriented groups, followed by logistic regression to identify significant indicators and Random Forest models to estimate their relative importance. The resulting index provides an objective, interpretable, and easily implementable performance measure suitable for cross-firm benchmarking and managerial decision support. Empirical testing using automotive manufacturers demonstrates strong alignment with lean classification and efficiency outcomes, providing evidence for the model’s relevance as an accounting-based benchmarking tool. In addition to its practical applicability, the framework contributes to lean performance measurement by translating machine learning insights into a reproducible index that can be applied in data-constrained environments. This approach ensures that the resulting index remains both empirically grounded and practically interpretable, while avoiding reliance on arbitrary or expert-assigned weighting schemes and qualitative assessment-based approaches. The model therefore offers a scalable and transparent alternative for practitioners, analysts, and researchers seeking robust lean performance evaluation when advanced modelling resources are unavailable. The study contributes a transparent, accounting-based normative index that reframes lean performance as a financial configuration rather than an operational maturity construct. The empirical analysis uses quarterly financial data from 17 publicly listed automotive manufacturers over the period 1994Q1–2024Q4.

International Journal of Financial Studies doi: 10.3390/ijfs14060141

Authors: Adil Boutfssi Youssef Zizi Mehdi Bensouda

This paper examines the short-run dynamics of monetary policy transmission in a bank-dominated emerging economy, with a focus on the relative timing of adjustments across financial valuations, balance-sheet aggregates, and inflation. Using monthly data over the period 2018–2024, the analysis relies on a reduced-form VAR framework. The results indicate that monetary policy innovations are more rapidly reflected in bank equity valuations proxied by the MASI banking index at short horizons, while balance-sheet variables exhibit more limited and less persistent adjustments. Inflation dynamics remain difficult to identify clearly within the short-run horizon, consistent with the gradual nature of price adjustments. These findings suggest that financial variables react more quickly to monetary policy innovations, while credit and macroeconomic variables adjust more gradually due to institutional constraints, risk considerations, and nominal rigidities. This pattern reflects heterogeneous adjustment speeds across variables rather than a structurally identified transmission mechanism. This paper provides evidence on the timing of short-run adjustments across financial and macroeconomic variables, highlighting the importance of temporal dynamics in the analysis of monetary transmission.

International Journal of Financial Studies doi: 10.3390/ijfs14060140

Authors: Félix Casares-Conforme Ángel Maridueña-Larrea Rocío González-Reyes Javier Patricio Cadena-Silva Patricio Álvarez-Muñoz

In dollarised economies, the absence of autonomous monetary policy shifts the burden of macroeconomic adjustment onto the banking system, where deposits and credit constitute the principal channel through which liquidity is conveyed to commercial activity. The literature has documented this relationship using aggregate national data, yet its behaviour at the monthly provincial scale remains underexplored for Latin America, particularly in fully dollarised economies and over recent periods marked by severe shocks. This article addresses that gap for Ecuador using a monthly panel of its 24 provinces over 2019–2025, estimated as a Panel VAR by two-step GMM, with monthly sales declared to the Internal Revenue Service used as a high-frequency indicator of provincial economic activity. The pandemic is incorporated as an exogenous control. The theoretical framework combines the supply-leading hypothesis, the credit-channel literature on transmission lags arising from financial frictions, and financial intermediation theory on liquidity and asset transformation. The system exhibits a predominantly supply-leading dynamic: deposits and credit retain predictive capacity over provincial sales, with no robust evidence of reverse feedback. Transmission speed is heterogeneous across channels. Deposits affect sales with a one-period lag, whereas credit requires an additional period—a pattern consistent with the differential role of each channel in banks’ asset-transformation function. The provincial-scale evidence for a dollarised economy shows that the macroeconomic relevance of financial intermediation depends on the heterogeneous transmission speeds of its components, with implications for territorial policy.

International Journal of Financial Studies doi: 10.3390/ijfs14060139

Authors: Jargalmaa Amarsanaa Honoka Nabeshima Yoshihiko Kadoya

Rapid digitalization has reshaped financial decision-making, and anxiety about later life is an important concern among middle-aged and older investors. Yet it remains unclear whether the traditional Big Three financial knowledge component captures the aspects of financial capability most closely associated with lower anxiety in digital financial environments. This study examines the association between old-age anxiety and digital financial literacy (DFL) components among digitally active Japanese retail investors aged 40–64. Using data from a large-scale survey of 94,695 investors, we estimate ordered probit models to examine overall DFL and its eight subdimensions. While overall DFL is negatively associated with anxiety about life after age 65, decomposing the index reveals substantial heterogeneity across components. The traditional Big Three financial knowledge component does not show a robust independent negative association with old-age anxiety once actionable and protective digital competencies are accounted for. In contrast, practical know-how, positive financial attitude, and self-protection are more consistently associated with lower anxiety. Supplementary heterogeneity analyses suggest that the positive conditional association between financial knowledge and anxiety is most visible among men aged 50–59, although these subgroup patterns should be interpreted cautiously. These findings do not imply that financial knowledge is unimportant. Rather, they suggest that Big Three financial knowledge alone may be an insufficient proxy for the dimensions of financial capability associated with lower self-reported old-age anxiety in digital financial environments. Given the cross-sectional design, the findings are interpreted as conditional associations rather than causal effects.

International Journal of Financial Studies doi: 10.3390/ijfs14060138

Authors: Rihem Soussi Fathallah Hamza Nizar Houssam Bouzgarrou Abdulrahman Alomair

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.

International Journal of Financial Studies doi: 10.3390/ijfs14060137

Authors: Isha Chawla Mindy Joseph Kenneth White Chasity Winder Scantling

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.

International Journal of Financial Studies doi: 10.3390/ijfs14060136

Authors: Mpho Sephetho Fabian Moodley

Periods of geopolitical uncertainty have increasingly shaped the performance of global financial markets, yet the extent to which these risks influence the co-movement of asset markets in South Africa remains unclear. Although co-movement has emerged as a crucial factor for investors seeking portfolio diversification, existing studies present mixed findings, with some suggesting that geopolitical risk strengthens financial integration, defined as the extent to which markets move together in response to global shocks, while others find that it weakens these linkages by triggering market segmentation. Against this backdrop, this study examines the impact of geopolitical risk’s influence on the co-movement of South African asset markets, focusing on how shifts in global uncertainty interact with local market dynamics. Using time-series monthly data from December 2004 to January 2025, the study applies a dual-method approach. The multivariate generalised autoregressive conditional heteroskedasticity asymmetric dynamic conditional correlation (MGARCH-ADCC) model is first employed to estimate time-varying correlations across the equity, bond, and property markets. Thereafter, the autoregressive distributed lag (ARDL) model is used to assess both the short- and long-run effects of geopolitical risk on these co-movement patterns. The results indicate that geopolitical risk significantly increases co-movement between South African asset markets in both the short and long run, thereby diminishing the traditional benefits of diversification. These findings reinforce the view that market participants respond collectively to uncertainty rather than fundamentals. Overall, the study contributes to the empirical understanding of market integration under geopolitical stress and highlights the need for investors and policymakers to incorporate geopolitical risk indicators into investment and policy frameworks to strengthen market resilience.

International Journal of Financial Studies doi: 10.3390/ijfs14060135

Authors: Amirreza Kazemikhasragh

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.

International Journal of Financial Studies doi: 10.3390/ijfs14050134

Authors: Yoshitaka Kubota Fumiko Takeda

This study investigates the effects of International Financial Reporting Standards (IFRS) adoption on foreign investment in the Japanese equity market. Previous research suggests that a positive relationship between IFRS adoption and foreign investment typically emerges when a country meets specific conditions, such as a strong regulatory environment or credible improvements in reporting uniformity. We contribute to this literature by re-examining the effects of voluntary IFRS adoption on the foreign shareholding ratio of Japanese companies from 2010 to 2023. Our analysis explicitly controls for the impact of reduced cross-shareholdings and increased share buybacks—structural factors likely to affect the capacity for foreign ownership. Using a difference-in-differences approach combined with propensity score matching to mitigate endogeneity, we compare 168 voluntary IFRS adopters against a control group of non-adopters. Unlike previous studies that reported no significant relationship in Japan—largely attributable to different denominator constructions for foreign shareholding ratios or shorter observation periods—our approach demonstrates that IFRS adoption significantly increases foreign investment over an extended horizon.

International Journal of Financial Studies doi: 10.3390/ijfs14050133

Authors: Yara Ibrahim Khaled Hussainey Taghred Mokhtar Sayed Moawad

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.

International Journal of Financial Studies doi: 10.3390/ijfs14050132

Authors: Ben Le Nischala Reddy Phong Nguyen Paula Hearn Moore

We study the effect of financial liberalization, political connections, audit quality and the interaction of these factors on the cost of debt using a dataset for Vietnam for the period 2007–2024. Our findings show that firms were able to borrow at a lower cost after financial liberalization due to better access to capital and diversification opportunities. We test how financial liberalization moderates the relationship of auditor quality on the cost of debt and of political connections on the cost of debt. Following financial liberalization, the benefit of engaging a Big 4 auditor in reducing firms’ cost of debt diminishes. Greater transparency and reduced information asymmetry after financial liberalization help offset the need for the Big 4 auditor’s financial report quality certification. Hence, we find that financial liberalization moderates the effect of a Big 4 auditor on the cost of debt. We find that firms with political connections have a lower cost of debt, and this relationship is impacted by financial liberalization. Specifically, as liberalization deepens, the cost of debt declines more for firms with higher levels of political connections. Lastly, politically connected firms do not need to rely on high-quality auditor certification to secure lower borrowing costs due to their easier access to debt from state-owned commercial banks. The political connections moderate the relationship between auditor quality and the cost of debt.

International Journal of Financial Studies doi: 10.3390/ijfs14050131

Authors: Huthaifa Alqaralleh

This paper examines how market-based and geopolitical uncertainty relate to volatility dynamics in innovation-focused equity portfolios across investment horizons. Using daily data from mid-2015 to early-2025 for the VIX, a geopolitical risk index (GPR), and four innovation benchmarks (MSCI Digital Economy, S&P Kensho Moonshots, QQQ, and ARKK), we implement a GARCH–wavelet–VAR connectedness framework, complemented by wavelet coherence evidence. The results show that uncertainty–market dependence is regime dependent and becomes more pronounced at medium and long horizons. Market-based uncertainty (VIX) remains the central contributor to system-wide variance sharing across horizons, while geopolitical risk is comparatively muted in the short run but becomes more relevant over longer horizons. Innovation portfolios are highly exposed to these uncertainty dynamics, with the strongest vulnerability concentrated in the innovation-heavy indices, particularly at longer horizons. Dynamic connectedness further indicates substantial time variation, with persistent connectedness strengthening during major stress episodes. Overall, the findings support frequency-aware risk management and macro-financial monitoring of innovation allocations.

International Journal of Financial Studies doi: 10.3390/ijfs14050130

Authors: Ricardo Francisco Reier Forradellas Gregorio Acedo Benítez

The real estate market is a complex and dynamic sector that plays a key role in economic stability and wealth generation. In many regions, real estate assets represent around 80% of household wealth, while rising housing prices have turned access to housing into a major social and economic challenge. In this context, the availability of accurate and accessible information is essential for decision-making by buyers, investors, and public administrations. This study proposes the development of an advanced technological tool based on Artificial Intelligence and Machine Learning techniques to predict and analyze real estate market dynamics within a specific geographic area. Using the city of Madrid as a case study, the research presents a digital application capable of estimating the market value of a property by analyzing comparable recently sold properties and incorporating key housing characteristics. By entering an address and a set of property features, the system generates a precise and data-driven valuation. The results demonstrate that AI-based approaches can significantly improve the accuracy and accessibility of real estate valuation processes. The proposed methodology enables real-time price estimation, graphical comparisons, and dynamic market analysis. Furthermore, the framework is scalable and can be extended to other geographic areas where relevant data are available, providing valuable insights for both academic research and practical decision-making in the real estate sector.

International Journal of Financial Studies doi: 10.3390/ijfs14050129

Authors: Salem Hadi Al Mustanyir

This study provides the first empirical analysis of how energy-sector corporate filing events transmit to financial markets, bridging a critical gap between corporate financial distress literature and commodity market dynamics. The analysis employs an event study methodology with Wilcoxon signed-rank tests and panel regression models to examine 51 U.S. energy firms that experienced financial distress (2015–2021) across the NYSE and NASDAQ. Post-announcement cumulative abnormal returns (CARs) show positive median values (WSR: 40.5 for NYSE in 10-day window, p < 0.10; 97.8 for NASDAQ in 10-day window, p < 0.05; 36.24 for NASDAQ in 5-day window, p < 0.10). Panel regression results show significant differences in post-announcement CARs relative to the event day for both indices (NYSE: 10-day window coefficient = 117.1, p < 0.05; NASDAQ: 10-day = 199.6, p < 0.01; 5-day = 150.8, p < 0.05), as well as in pre-announcement windows for NYSE (5-day coefficient = 93.5, p < 0.10; 10-day = 86.6, p < 0.10). The findings suggest that markets respond to energy-sector corporate distress events without broad-based disruption, likely due to early signals of financial distress, clarified expectations regarding recovery paths under Chapter 11 restructuring, and reduced information asymmetry through disclosures. Policymakers can leverage these insights to refine corporate filing frameworks for commodity-dependent sectors.

International Journal of Financial Studies doi: 10.3390/ijfs14050128

Authors: Caner Özdurak

This paper investigates the dynamic linkages and volatility transmission among global food prices, biofuel commodity prices, and crude oil prices, with a focus on the profound disruptions caused by the COVID-19 pandemic. While interdependencies between energy and agricultural markets are well-studied, the specific role of biofuels as a transmission channel and the exacerbating effects of the crisis remain underexplored, especially through a robust multivariate volatility framework. Utilizing A VECM-CCC-GARCH models, this study captures both mean and conditional variance dynamics, allowing for the examination of asymmetric news impacts and volatility spillovers. The analysis employs a comprehensive dataset including the FAO Food Price Index, key biofuel, ethanol, biodiesel, and crude oil prices (Brent and WTI), alongside proxies for the pandemic’s severity. The research hypothesizes that the COVID-19 pandemic significantly amplified the volatility and strengthened the price transmission channels. We expect to find increased co-movement and volatility spillovers, reflecting reduced demand for transport fuels, agricultural supply chain disruptions, and shifting biofuel production incentives. The TARCH component will discern if negative news (e.g., sharp drops in oil demand) had a disproportionately larger impact on volatility than positive news. By providing a nuanced understanding of these complex interdependencies, this study offers valuable insights for policymakers addressing food security, energy transition strategies, and macroeconomic stability in the post-pandemic world, particularly concerning the strategic role of biofuels.

International Journal of Financial Studies doi: 10.3390/ijfs14050127

Authors: Sarah Yasser Abdel-Fattah Tânia Menezes Montenegro

This study examines the association between Integrated Reporting (IR) quality and tax avoidance among South African listed firms from 2012 to 2021, and whether this relationship differs across the highest and lowest levels of IR quality. The extent to which the adoption of a Combined Assurance (CA) model strengthens the IR monitoring role in reducing tax avoidance, as well as the IR quality link with ESG-related implications of tax avoidance, are also explored. IR quality is directly derived from the EY Excellence in Integrated Reporting Awards ranking. This ranking evaluates firms’ adherence to the IR framework and is thus employed as a comprehensive proxy for IR quality. Tax avoidance is captured through multiple proxies. The main findings reveal no significant overall association between IR quality and tax avoidance, suggesting a decoupling between IR and tax behavior. However, when examining firms at the highest and lowest levels of IR quality, a significant negative relationship emerges only for the top performers (highest IR quality), indicating that IR constrains tax avoidance only when supported by a strong ethical corporate culture. Firms adopting CA exhibit higher tax avoidance, suggesting that IR and CA may be constrained by underlying corporate culture and used symbolically. Higher IR quality is also associated with lower tax avoidance relative to GDP and reduced potential revenue losses relative to government expenditures on education, health, and environmental protection. These findings contribute to the literature on IR, corporate governance, and tax avoidance, while also informing policymakers and regulators on the need to strengthen IR and CA frameworks through enhanced tax transparency requirements, thereby supporting equitable resource mobilization, institutional trust, and long-term sustainable development.

International Journal of Financial Studies doi: 10.3390/ijfs14050126

Authors: Rabindra Kumar Jena

The rapid emergence of virtual assets, blockchain-based transactions, and immersive digital economies presents major challenges to financial reporting processes (recognition, measurement, disclosure, and assurance). This study aims to investigate stakeholder readiness for digital financial reporting in the context of virtual assets, focusing on the human capital dimension, which was often overlooked in prior research. A mixed-methods design was employed to obtain comprehensive insights from both experts and students. Qualitative interviews with 16 academics and practitioners were conducted to capture expert perspectives on the inclusion of metaverse-related courses in accounting curricula. Furthermore, a survey of 438 accounting students was analyzed to examine the determinants of Digital Financial Reporting Readiness (DFRR) using the Stimulus–Organism–Response (S-O-R) framework. Experts highlighted opportunities for enhanced professional judgment but raised concerns about automation risks and institutional capacity. Quantitative results indicated that perceived importance and usefulness significantly increased student interest, which strongly predicted DFRR, while perceived difficulty reduced student interest. By interpreting the findings through the lens of Experiential Learning Theory (ELT), this study provides a process-oriented explanation of how cognitive evaluations translate into professional preparedness. This study contributes by conceptualizing DFRR as a human capital construct and offering a multi-stakeholder perspective by integrating student readiness with expert insights to inform the adoption of the metaverse in accounting education.

International Journal of Financial Studies doi: 10.3390/ijfs14050125

Authors: Ebrahim Merza Mohammad Alawin

This paper explores how financial development and income inequality interact across different country income groups and what this means for business environments and market participation in both emerging and advanced economies. Using an Unobserved Components Model (UCM) with time-series data covering 1990–2023, the analysis shows that the link between finance and inequality varies markedly with the level of economic development. An inverted U-shaped relationship appears only in high-income and upper-middle-income countries, suggesting that once financial systems reach a certain level of maturity, further deepening tends to support more inclusive outcomes. By contrast, in lower-middle-income countries, financial development is associated with a positive and monotonic increase in inequality, while in low-income countries, the relationship remains weak, unstable, and statistically insignificant. A closer breakdown indicates that financial markets, rather than financial institutions, play a stronger role in influencing inequality in higher-income economies. Overall, the findings highlight that the distributional impact of financial development—and its implications for business conditions, market access, and investment incentives—is strongly income-dependent, reinforcing the need for financial frameworks that align with countries’ stages of development.

International Journal of Financial Studies doi: 10.3390/ijfs14050124

Authors: Tian Meng Gaojin Yu Minfeng Lu

The rapid expansion of global stablecoins is generating new challenges for monetary sovereignty, financial stability, and cross-border regulatory governance. This paper develops an integrated analytical framework of “risk transmission–institutional constraints–compliance response–dynamic monitoring” to examine how stablecoin-related risks may be transmitted into China’s financial system. Drawing on financial risk theory, institutional analysis, and comparative regulatory perspectives, the study identifies three major channels of risk transmission: monetary sovereignty erosion, financial stability shocks, and regulatory arbitrage accompanied by legal and data-governance challenges. It argues that the actual impact of these risks is shaped by China’s specific institutional and technological conditions, including cross-border jurisdictional frictions, technical standard barriers, coordination difficulties under “one country, two systems”, and limitations in regulatory technology capacity. On this basis, the paper proposes a multi-layered compliance response system centered on risk-based penetrative supervision, strict corporate compliance boundaries, and the digital renminbi (e-CNY) as core infrastructure, while emphasizing the need for stronger international regulatory coordination. It further introduces a dynamic monitoring perspective to evaluate regulatory effectiveness, risk suppression, and the substitution effect of the e-CNY ecosystem. The paper contributes a structured and policy-oriented framework for understanding and containing external stablecoin risks in China’s institutional context.

International Journal of Financial Studies doi: 10.3390/ijfs14050123

Authors: Abdullah Almutairi

This study was conducted to investigate the impact of climate change disclosure on the cost of debt and gain deep insight into the usefulness of political connections and ESG disclosure for reducing the cost of debt. A sample of 83 listed firms in the Egyptian context, spanning 498 observations over 6 years from 2018 to 2023, was used. A quantitative approach was adopted to examine the key hypotheses. This research reveals that climate change disclosure decreases the cost of debt. Furthermore, political connections and ESG disclosure moderate the main nexus. Multiple robustness checks were conducted to confirm these findings. Crucial policy implications for regulators, investors, and sustainability experts were developed by highlighting the latest practices of corporations aligned with achieving Sustainable Development Goals. The significance of this study lies in filling several gaps in the literature regarding climate change disclosure, political connections, and ESG disclosure and how a company’s strategic approach can impact the cost of capital.

International Journal of Financial Studies doi: 10.3390/ijfs14050122

Authors: Jwa Emma Said Jan Lánský

This paper proposes a CBDC design trilemma, the claim that central banks cannot simultaneously maximize privacy, financial stability, and regulatory compliance when designing retail central bank digital currencies and finds the existing literature consistent with this proposition. Through a systematic review of 140 peer-reviewed articles (Web of Science SCIE/SSCI indexes, 2014–2026, supplemented by Scopus and SSRN), evidence is synthesized across four thematic dimensions: design frameworks and architecture, financial stability and banking risk, privacy and security trade-offs, and user adoption and institutional quality. Cross-tabulation of coded data supports all three pairwise tensions: privacy-enhancing designs weaken AML/CFT enforcement, anonymous holdings amplify bank-run risk, and stringent prudential safeguards constrain transaction monitoring. The literature converges on two-tier, hybrid architectures with tiered privacy as the dominant compromise a “zone of feasible design”, that sacrifices full optimality on each vertex. Nine research gaps are identified, most critically the scarcity of empirical evidence from live deployments, the neglect of wholesale CBDC, and insufficient analysis of cross-border interoperability. The framework offers policymakers a structured lens for evaluating retail CBDC design trade-offs and researchers a testable proposition for future empirical work.

International Journal of Financial Studies doi: 10.3390/ijfs14050121

Authors: Fukai Wang Wei Zhou Zhen Zhang

The persistence of greenwashing as a strategic corporate behavior reflects a financial tradeoff between risk and return. Current literature lacks an integrative framework explaining how these risks and institutional arrangements vary across distinct contexts. This study maps the intellectual structure and contextual heterogeneity of corporate greenwashing research through a bibliometric analysis of 818 publications indexed in the Web of Science Core Collection from 2000 to 2025. The results indicate an evolutionary shift in research focus from early ethical and reputational debates toward empirical investigations of capital market consequences, ESG controversies, and the dark side of corporate sustainability. This transition is accompanied by thematic movement from voluntary disclosure and legitimacy concerns toward mandatory compliance, sustainable finance, green bond pricing, and digital detection using artificial intelligence and natural language processing. The analysis reveals substantial structural heterogeneity. Heavy-asset industries are closely associated with technological decoupling under physical and compliance constraints, whereas financial and service sectors rely heavily on information asymmetry, green label arbitrage, and greenhushing. These sectoral patterns intersect with regional governance trajectories shaped by market-driven, regulation-oriented, and state-led contexts, generating distinct incentive structures and risk conditions, while firm-level governance further moderates these behaviors. The findings position greenwashing as a context-dependent corporate strategy and provide a structured synthesis for future research and differentiated regulatory responses.

International Journal of Financial Studies doi: 10.3390/ijfs14050120

Authors: Irina Ivashkovskaya Sergei Grishunin Elena Makeeva Egor Pashkov

Inclusive growth has increasingly emerged as a central framework for understanding how firms can align economic performance with social inclusion and environmental responsibility, particularly in emerging markets characterized by institutional volatility. In the context of geopolitical shocks and economic sanctions, such as those faced by Russia during 2022–2023, the normative meaning of inclusive growth is redefined toward prioritizing employment stability, industrial continuity, and strategic resilience at the firm level. This study aims to develop a systematic and transparent firm-level measure of inclusive growth that integrates strategic resilience with long-term business model potential. It further seeks to empirically assess cross-firm heterogeneity in inclusive growth performance within the Russian metallurgical and mining sector under geopolitical disruption conditions. This study constructs a composite Inclusive Growth Index using publicly available financial and non-financial disclosures, combining indicator normalization, variance-based weighting, and geometric aggregation. The index is applied to a panel of major Russian metallurgical and mining companies for the period 2021–2024 to evaluate their strategic resilience, business model potential, and industry-level dynamics under sanctions. The results reveal substantial heterogeneity in inclusive growth performance across firms, with higher index values being associated with stronger strategic resilience and more stable operational outcomes. The analysis further identifies a divergence between improving resilience and declining business model potential during 2022–2024, indicating a trade-off between short-term stabilization and long-term inclusive growth capabilities under the geopolitical stress. The findings suggest that inclusive growth at the firm level in a sanctioned emerging market context follows a distinct sovereignty-oriented logic in which employment stability and operational continuity take precedence over long-term innovation and governance enhancement. Overall, the proposed Inclusive Growth Index provides a robust analytical framework for assessing corporate adaptation to structural shocks and informing managerial and policy decisions in emerging market economies.

International Journal of Financial Studies doi: 10.3390/ijfs14050119

Authors: Chaoyang Chen Ziting Wang

This study investigates how information transparency affects organizational value in the Chinese institutional setting, where firms operate under a heavily regulated disclosure regime while increasingly referencing Decentralized Autonomous Organization (DAO) or blockchain-based decentralized governance concepts. Using a panel of 10,029 firm-year observations from 1368 Shenzhen A-share listed firms over the period 2012–2022, we employ two-way fixed effects regressions and robustness tests, with information transparency proxied by Shenzhen Stock Exchange disclosure ratings. We find that higher transparency is positively and significantly associated with organizational value (measured by Tobin’s Q). Heterogeneity analyses show that this positive relationship is stronger among state-owned enterprises, firms with lower digital maturity, and firms led by innovation-oriented executives. Comparative tests further reveal that the transparency–value link holds primarily among DAO-referencing firms, whereas it turns negative (though marginally significant) for non-referencing firms. These results suggest that signaling interest in decentralized governance mechanisms can enhance the value relevance of disclosure in regulated emerging markets. Practical implications for managers and policymakers are discussed, along with limitations and directions for future research.

International Journal of Financial Studies doi: 10.3390/ijfs14050118

Authors: Ahmad Farhan Alshira’h

This study aims to examine the influence of geographical distance, social distance, auditor independence, technical quality, and process quality on the quality of financial reporting, providing a comprehensive understanding of the aspects that determine audit effectiveness. The research employs a quantitative technique, using Partial Least Squares Structural Equation Modeling (PLS-SEM) with bootstrapping (5000 resamples) to assess hypotheses grounded on agency and signaling theories. Data was collected via a survey administered to managers and proprietors of small and medium-sized firms (SMEs). A total of 186 valid answers were gathered and examined to investigate the relationships among the variables. All five recommended parameters had a favorable and substantial influence on the quality of financial reporting. Spatial distance was recognized as the primary predictor, followed by process quality, social distance, technical quality, and auditor independence. The model explained 26% of the variance in financial reporting quality. This research is one of the first investigations to systematically analyze how certain variables of audit quality—specifically physical distance, social distance, auditor independence, technical competence, and process quality—affect financial reporting results. The main contribution is in the use of original survey data, which provides novel information about how organizations evaluate and choose audit service providers and how these decisions eventually affect reporting quality. The focus on small and medium-sized organizations enhances the importance, since these companies often encounter limitations that impede their capacity to uphold elevated reporting requirements. This research enhances the current literature by consolidating many dimensions of audit quality into a unified analytical framework, so providing a more thorough knowledge of the interrelated variables influencing financial reporting procedures, especially in emerging nations. The results provide pragmatic insights for regulators and practitioners aiming to bolster audit efficacy and improve the dependability of financial reporting in these circumstances.

International Journal of Financial Studies doi: 10.3390/ijfs14050117

Authors: Fei Fang Di Luo

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.

International Journal of Financial Studies doi: 10.3390/ijfs14050116

Authors: Mubariz Mammadli Natavan Namazova Zivar Zeynalova

This study examines how external regulatory conditions and internal organizational capabilities shape corporate tax compliance among foreign investment enterprises (FIEs) in Azerbaijan. It develops an integrated framework that brings together enforcement-based factors, tax planning capacity, and institutional and governance quality. Using survey data from 266 foreign-owned firms, the study applies structural equation modeling (SEM) to analyse direct, mediating, and moderating relationships. The results show that stronger enforcement is associated with higher levels of compliance and encourages firms to develop tax planning capabilities. In turn, these capabilities contribute positively to compliance behaviour. The findings also indicate that tax planning capacity partially mediates the relationship between enforcement and compliance. In addition, institutional and governance quality moderates the link between enforcement and tax planning capacity, with the effect varying across institutional environments. Overall, the results suggest that corporate tax compliance is influenced not only by external regulatory pressure but also by firms’ internal capabilities and the broader institutional context. The study provides useful insights for policymakers seeking to improve compliance through coordinated regulatory and institutional reforms.

International Journal of Financial Studies doi: 10.3390/ijfs14050115

Authors: Marius Sorin Dincă Frank Akomeah

This study investigates the key determinants of firm profitability in the global automotive sector, examining whether superior returns on assets (ROA) stem from operational efficiency, strategic leverage, or innovation intensity, and highlighting the potential trade-off between efficiency and investment in capital-intensive industries. Analysing a global panel dataset of 192 automotive firms from 38 countries/regions over 2010–2024, a fixed effects regression model with Driscoll–Kraay standard errors was applied to control for unobserved heterogeneity, heteroskedasticity, and cross-sectional dependence across 11 financial and strategic variables. The findings reveal that firm size and inventory turnover are significant positive drivers of profitability, while research and development (R&D) intensity exerts a strong negative impact. The positive association with the effective tax rate reflects reverse causality, where more profitable firms incur higher tax burdens, rather than a causal effect of taxation on performance. Notably, working capital management, leverage, sales growth, and capital expenditure showed no statistically significant effects after controlling for firm and time effects. Temporal fluctuations, including a marked profitability decline in 2024, underscore the sector’s sensitivity to macroeconomic shocks. This study contributes robust, large-scale empirical evidence on the short-term profitability trade-off associated with R&D intensity in a globally integrated industry, addressing cross-sectional dependence through its methodological approach.

International Journal of Financial Studies doi: 10.3390/ijfs14050114

Authors: Stoyan Zlatev Milena Petkova Mariyan Milev Nadya Velinova-Sokolova

In 1952, Harry Markowitz established the foundations of Modern Portfolio Theory by introducing a mean–variance framework for constructing investment portfolios that optimize the trade-off between risk and expected return. Expanding upon this classical framework, this paper develops an analytical algorithm to determine optimal asset weights for long-only portfolios that minimize total risk. We derive the necessary and sufficient conditions through rigorous determinant-based identities, providing a closed-form solution for achieving the global minimum variance. The theoretical findings are demonstrated through three numerical examples: the first examines a standard six-asset portfolio using an admissible covariance matrix; the second serves as an algebraic cautionary case by identifying negative variance when the input matrix fails to satisfy the criteria for positive definiteness; and the third example validates the practical application of the proposed identities using empirical market data.

International Journal of Financial Studies doi: 10.3390/ijfs14050113

Authors: Mohamed Ilyes Gritli Teheni El Ghak Fatma Marrakchi Charfi

Nowadays, Global Value Chains (GVCs) play a vital role in job creation, income generation, knowledge diffusion, and productivity growth. However, significant disparities exist across countries in terms of their integration into GVCs, and Tunisia is no exception to this pattern. In this regard, the question about factors that influence GVCs’ participation is yet to be discussed, to formulate and implement appropriate strategies and reforms. Thus, using firm-level data from the 2025 World Bank Enterprise Survey, this paper examines the role of female leadership and innovation in determining Tunisian firms’ participation in GVCs. Participation in GVCs is captured by a dummy variable indicating the firm’s export and import status. Estimation results from the logit model show that female representation in decision-making positions significantly increases the likelihood of firms’ participation in GVCs. The results also highlight the importance of process innovation in GVC participation, while product innovation appears to have no significant effect. Notably, when firms combine both types of innovation, their likelihood of joining GVCs increases further. Regarding control variables, firm size appears to be an important determinant, as larger firms display a greater tendency to participate in GVCs. The findings further indicate that firm certification and foreign equity participation significantly promote integration into GVCs, while corruption constitutes a major constraint on the integration of Tunisian firms. From a policy perspective, these findings highlight the need to rethink industrial policies, with a stronger focus on process innovation as a key lever of productive sector modernization. Achieving this transformation also requires the development of an inclusive policy ecosystem that supports meaningful and sustainable progress in female’s leadership representation.

International Journal of Financial Studies doi: 10.3390/ijfs14050112

Authors: Prapassara Singsiri Jiraphat Yokrattanasak

Market indices serve as a benchmark for performance comparison, guide asset allocation decisions, and reflect overall market sentiment and economic conditions, thereby influencing investment strategies by representing a segment of the market. Unquestionably, investor sentiment impacts price movement. In this paper, the objectives were to study the effectiveness of the Money Flow Index (MFI) in enhancing the performance of predictive analysis by capturing market psychology, developing an investment strategy, and analyzing the performance of the method mentioned. This study applies machine learning algorithms with technical indicators and optimizes portfolio allocation based on three notable market indices in Southeast Asia (SEA): SET50 in Thailand, STI in Singapore, and VN30 in Vietnam. Firstly, we combined technical indicators with machine learning—Support Vector Classifier (SVC), Random Forest (RF), and Extreme Gradient Boosting (XGBoost)—by comparing datasets with and without MFI over the period from 2013 to 2023. The results showed that XGBoost with MFI delivered the best predictive performance across three indices. These findings indicate that MFI significantly enhances prediction accuracy, even during volatile market conditions (COVID-19). Additionally, the predictions were integrated into the Markowitz Mean-Variance (MV) model to construct an optimal portfolio, which was then benchmarked against an equal-weight portfolio (1/N). Ultimately, the findings demonstrate that incorporating the machine learning predictions into the MV framework efficiently generates wealth.

International Journal of Financial Studies doi: 10.3390/ijfs14050111

Authors: Ashok Kumar Panigrahi Anita Sharma Varun Sarda

Understanding how quickly trading liquidity recovers after volatility shocks is central to evaluating market resilience and trading costs in financial markets. The purpose of this study is to examine how quickly trading liquidity recovers after volatility-based stress shocks in an emerging equity market and to evaluate whether recovery horizons vary systematically across shock severity, market fear, downside-risk conditions, and sectors. Using a balanced panel of NIFTY-50 firms over 2018–2024, comprising 91,350 firm-day observations, the analysis employs a non-parametric event-time framework, combined with bootstrap inference and episode-level regression diagnostics, to trace the adjustment in market liquidity following episodes of elevated volatility. Liquidity conditions are measured using the Amihud illiquidity indicator, while stress episodes are identified through firm-specific volatility shocks derived from a standardised realised-volatility measure. The framework introduces duration-based recovery metrics—liquidity half-life and time-to-normalisation—to quantify the persistence of post-shock trading frictions relative to firm-specific pre-stress baselines. Across 602 declustered stress episodes, liquidity deteriorates sharply on the stress day and recovers only gradually thereafter. The estimated mean recovery half-life is slightly above five trading days, while nearly one-third of episodes do not fully normalise within twenty trading days, indicating economically meaningful persistence in post-shock illiquidity. Recovery dynamics also vary systematically across stress severity, market-wide fear conditions (India VIX), downside-risk regimes, and sectors, highlighting that market resilience is state-dependent rather than uniform. The findings provide new evidence on the temporal structure of liquidity adjustment in emerging equity markets and introduce operational recovery-horizon metrics that can inform liquidity risk management, trading execution strategies, and market surveillance during periods of elevated volatility. These recovery-horizon measures have direct practical relevance for portfolio managers and institutional traders because they provide an operational basis for planning execution strategies when market liquidity remains impaired after volatility shocks. They are also useful for exchanges and regulators seeking to complement volatility monitoring with post-shock liquidity surveillance, thereby improving the assessment of market functioning during periods of elevated stress.

International Journal of Financial Studies doi: 10.3390/ijfs14050110

Authors: Wladimir Chuquimia-Rivero Elizabeth Emperatriz García-Salirrosas Dany Yudet Millones-Liza Miluska Villar-Guevara

Entrepreneurs represent a key piece in the generation of jobs and contribution to the economy through the performance of their businesses. Taking into account that literacy and financial inclusion constitute a business facilitator for the development of businesses, this study was based on analyzing the three variables, aiming to identify whether inclusion and financial literacy influence business performance. Through a non-experimental, quantitative study based on structural equations, a sample of 469 entrepreneurs from Peru, Bolivia, and Colombia was studied. The hypotheses were supported by observing the positive effect of one component of financial literacy (Cash Forecasting) and three components of financial inclusion (Access, Barriers, and Use) on Business Performance. However, the proposed model shows that the direct effect of two components (Bookkeeping and Financial Education) of financial literacy is not statistically significant. Therefore, these factors are vital tools that can help Latin American entrepreneurs make informed financial decisions, manage resources effectively, and build solid and sustainable businesses.

International Journal of Financial Studies doi: 10.3390/ijfs14050108

Authors: Satria Amiputra Amimakmur Sutrisno T Aulia Fuad Rahman Sari Atmini

This study examines how operational efficiency, credit risk, and third-party funds affect the stock prices of banks listed on the Indonesia Stock Exchange, with financial performance acting as a mediating variable. Focusing on banks included on the main board during 2020–2024, the study uses panel data collected from annual reports and financial statements published on the official Indonesia Stock Exchange website. The sample consists of 29 commercial banks selected through purposive sampling, yielding 145 observations. Operational efficiency is measured using Stochastic Frontier Analysis (SFA), while the relationships among variables are tested through Structural Equation Modeling with the Partial Least Squares approach. The results show that third-party funds and operational efficiency contribute positively to stock prices, whereas credit risk does not have a direct effect. At the same time, all three independent variables exert positive indirect effects through financial performance. These findings indicate that financial performance serves as an important mechanism linking banks’ internal conditions to market valuation. The study underscores the relevance of managerial efficiency and strong funding capacity in enhancing investor confidence and offers novelty through the application of SFA and a simultaneous mediation model in the context of Indonesia’s post-pandemic banking sector.

International Journal of Financial Studies doi: 10.3390/ijfs14050109

Authors: Prawat Benyasrisawat Pakawat Kuboonya-arags

This study empirically examines the joint effect of financial anomaly risk and ESG performance on accounting conservatism using accrual models, market models, and earnings time-series models. Financial anomaly scores are obtained using unsupervised machine learning to identify reporting anomalies for firms. Our findings suggest that higher financial anomaly risk is negatively related to accounting conservatism through delayed or reduced loss recognition. ESG engagement serves as a moderating variable to mitigate conditional conservatism losses partially for both accrual- and earnings-based models, conditional on financial anomaly risk; otherwise, ESG engagement has a weak or insignificant effect on market-based models. ESG practice is therefore a state-dependent conditional governor to complement traditional governance structures, depending on both levels of anomaly risk as well as accounting models used to derive conservatism measures. Our findings have practical implications for investors and government regulators, as well as managers, which emphasize that ESG practice is not universally beneficial to conservatism but can further improve reporting quality, conditional on certain risk levels.

International Journal of Financial Studies doi: 10.3390/ijfs14050107

Authors: Khurelbaatar Ganbat Tsolmon Sodnomdavaa Asralt Buyantsogt Ganbat Dangaa

This study examines the relationship between firms’ financial adaptability and performance during periods of macroeconomic stress. Using panel data on companies listed on the Mongolian Stock Exchange from 2015 to 2024, the analysis measures financial adaptability through a Firm Adaptability Index (FAI) constructed from observable indicators of liquidity, coverage capacity, and asset-use efficiency. The index is constructed using principal component analysis (PCA) to avoid arbitrary equal-weighting assumptions, and the debt ratio is deliberately excluded to prevent multicollinearity with the leverage control variable used in the regression models. The empirical framework primarily relies on panel regression models with interaction terms, supplemented by a DID-style comparison and an event-study-based diagnostic. The validity of the quasi-experimental design is confirmed by a formal parallel-trend test and placebo checks using artificial shock dates. The findings do not support the view that financial adaptability exerts a uniformly strong and stable direct effect on firm performance across all conditions. Instead, its empirical relevance becomes more visible when macroeconomic conditions worsen. In particular, the interaction result related to interest rates suggests that firms with higher levels of financial adaptability tend to exhibit less pronounced profitability sensitivity to financing cost pressure. Additional analyses point to short-term liquidity buffers as a plausible channel and show that the strength of this relationship varies by firm size and sectoral characteristics. This study contributes to the literature by bringing together the related concepts of financial flexibility, organizational resilience, dynamic capabilities, and strategic adaptability within a firm-level empirical setting. It also proposes a practical way to measure financial adaptability not through a single proxy, but through a composite index that integrates several observable financial dimensions. Overall, the evidence suggests that financial adaptability is better understood not as a constant determinant of profitability, but as an internal capability whose relevance becomes more apparent under conditions of heightened uncertainty.

International Journal of Financial Studies doi: 10.3390/ijfs14050106

Authors: Ngoc Giau Nguyen Ngoc Tien Nguyen

As emerging economies expedite their integration into global capital markets, comprehending the implications of voluntary International Financial Reporting Standards (IFRS) adoption has become increasingly critical for regulators, investors, and corporations. This study examines the influence of voluntary IFRS adoption on the quality of financial reporting and the value of firms in Vietnam, a transitional economy characterized by a unique code-law legal tradition, a substantial disparity between domestic accounting standards and IFRS, and a government-mandated adoption roadmap that establishes a distinctive quasi-voluntary adoption phase. The study utilizes a panel dataset of 562 firms listed on the Ho Chi Minh Stock Exchange (HOSE) and the Hanoi Stock Exchange (HNX) from 2019 to 2022, employing a fixed-effects regression model with robust standard errors to account for unobservable firm heterogeneity. Utilizing agency theory and signaling theory, the research anticipates and validates that voluntary IFRS adoption correlates positively with diminished discretionary accruals (serving as an indicator of financial reporting quality) and elevated Tobin’s Q (acting as a measure of firm value). The estimated effect corresponds to a 10.7% reduction in discretionary accruals and a 13.1% increase in Tobin’s Q relative to sample means—magnitudes that are both statistically and economically significant. Unlike prior studies that rely exclusively on archival data, this study employs a survey-based measure of voluntary IFRS adoption activity to capture preparatory behaviors that are not yet observable in public financial disclosures, representing a methodological contribution to the literature. The results have useful implications for policymakers in Vietnam and other developing countries that are considering adopting IFRS on either a voluntary or mandatory basis. They show that taking the initiative to follow international reporting standards makes reports more trustworthy and the market more valuable.

International Journal of Financial Studies doi: 10.3390/ijfs14050105

Authors: Sana’ Atari Ruaa Bin Saddig Bahaa Subhi Awwad

This paper examines whether the interaction between credit risk and liquidity conditions helps explain bank stability in a fragile and institutionally constrained banking environment. Using an annual panel of 13 Palestinian banks over 2011–2024 and measuring stability by the (log) Z-score, we estimate static panel models (pooled OLS, fixed effects, and random effects), a simultaneous two-stage least squares (2SLS) system to probe the direction of causality between credit risk and liquidity, and a dynamic panel GMM specification to address persistence and endogeneity. The static models show that credit risk is negatively associated with stability and that the interaction term is economically meaningful but not robust across static specifications. In the dynamic GMM model, credit risk remains significantly destabilizing, liquidity holdings are stabilizing, and the interaction term is positive and significant—consistent with liquidity buffers mitigating the adverse stability implications of higher credit risk. The 2SLS system suggests no strong contemporaneous reciprocal causality between credit risk and liquidity once controls are included, while regulatory and conflict-period dummies are associated with shifts in the risk profiles. The results highlight the importance of integrated risk management and liquidity buffers for banking stability in high-uncertainty contexts.

International Journal of Financial Studies doi: 10.3390/ijfs14050104

Authors: Muhammad Asif Muhammad Akram Naseem Rana Tanveer Hussain Faisal Qadeer Muhammad Ishfaq Ahmad

The latest research on tax avoidance indicates that the number of female directors on a board increases the accounting accuracy and company performance by decreasing tax avoidance. The empirical research illustrates that women’s higher risk aversion and more conservative characteristics are key for company decision-making, especially when considering a tax strategy. We posit that the risk avoidance of women and other board attributes that enhance diversity influence the company’s sustainability through their effects on the company’s taxpaying activities. To verify this relationship, an empirical analysis was conducted using data for the period from 2009 to 2025 for the non-financial enterprises listed on the Pakistan Stock Exchange. The results showed that enhancing diversity on the board by attributes such as gender inclusion paves the way for firms to achieve firm performance. The results showed that tax avoidance partially mediates the relationship between corporate board attributes and firm performance. Effective board diversity encourages firms to engage in more tax-paying activities, which leads to positive firm performance. The research outcomes strengthen the existing proof of the link between board diversity and company financial performance, with tax avoidance behavior serving as an intervening factor. This also provides insights for policy-making authorities, encouraging them to make tax-related regulations that better promote long-term growth and prosperity. This study fills a gap in the research by highlighting the influence of board diversity on tax avoidance behavior and corporate financial performance.

International Journal of Financial Studies doi: 10.3390/ijfs14050103

Authors: Petar Zhivkov Venelin Todorov Slavi Georgiev

We apply rolling-window econometric methods, including GARCH(1,1) estimation, Bai–Perron structural break detection, CUSUM stability testing, and Granger causality analysis in bivariate VAR frameworks, to analyze the temporal dynamics of market integration in cryptocurrency perpetual futures, tracking funding rate correlations, arbitrage prevalence, and volatility persistence across 26 exchanges and 812 symbols over two months (November 2025 through January 2026). Using 53 overlapping seven-day rolling windows on 9.1 million hourly observations, we find that the two-tiered market structure previously documented in a static snapshot (centralized exchanges tightly integrated, decentralized exchanges fragmented) persists qualitatively but varies substantially in magnitude, with the integration gap ranging from −0.041 to 0.222. Structural break tests detect no discrete regime shifts; the market evolves through gradual drift. GARCH(1,1) analysis reveals that near-integrated (IGARCH) volatility behavior, previously reported as a general property, appears in only 24.5% of windows, concentrated in specific time periods. Granger causality tests show that mid-tier exchanges lead the largest venue (Binance) more frequently than the reverse, challenging a simple size-based price discovery hierarchy. Intraday spread patterns are statistically significant and linked to funding rate settlement mechanics, with spreads peaking approximately two hours after standard settlement times. These findings have implications for systemic risk assessment: market surveillance frameworks that focus on the largest venue may miss price discovery signals originating from mid-tier exchanges.

International Journal of Financial Studies doi: 10.3390/ijfs14050102

Authors: Nour Alouane Samira Haddou

This study investigates the impact of revenue diversification on the performance and stability of listed Tunisian banks over the period 2008–2023, with the objective of assessing whether diversification strategies enhance bank performance and promote financial stability in an emerging-market context. The analysis relies on a panel dataset of Tunisian listed banks and employs a two-stage least squares (2SLS) estimation approach to address potential endogeneity issues, using ownership structure as an instrumental variable. Bank performance is measured by Return on Assets (ROA) and Net Interest Margin (NIM), while financial stability is captured by the Z-score. The empirical results show that revenue diversification has a positive and significant effect on bank performance, as measured by ROA, and on financial stability. However, it exerts a negative and significant impact on NIM, indicating that although diversification improves overall performance and strengthens stability, it may weaken traditional intermediation income. This study contributes to the limited literature on banking in emerging markets by jointly examining performance and stability effects while addressing endogeneity concerns through robust econometric techniques, and by providing new evidence from the Tunisian banking sector, which has experienced significant political and economic disruptions during the study period.

International Journal of Financial Studies doi: 10.3390/ijfs14050101

Authors: Daba Geremew Seid Muhammed Prihoda Emese

This study investigates the relationship between banking sector stability and economic growth in Ethiopia, employing a dynamic panel data approach with the Two-Step System Generalized Method of Moments (GMM). The analysis uses a balanced dataset from 13 Ethiopian commercial banks covering 2014 to 2023, gathered from the World Bank database, the National Bank of Ethiopia, and audited financial statements. Banking sector stability is assessed using indicators such as Z-score, non-performing loan (NPL) ratio, capital adequacy ratio (CAR), liquidity ratio (LR), return on assets (ROA), and loan-to-deposit ratio (LDR), along with key macroeconomic and institutional factors. The results show that banking stability, as indicated by Z-score, liquidity ratios, and profitability, has a positive and significant effect on economic growth, confirming the sector’s role in promoting development. Surprisingly, a positive correlation between NPLs and economic growth suggests unique structural features in the Ethiopian banking system that warrant further investigation. Other variables, such as inflation rates, government expenditure, and gross domestic savings, positively influence economic growth, whereas foreign direct investment is negatively associated with it. The study highlights the importance of enhancing the stability of the banking sector by implementing robust regulatory frameworks, prudent risk management practices, and improved profitability to support sustainable economic development in Ethiopia, while calling for additional research into the unexpected effects of NPLs and FDI amid ongoing financial reforms.

International Journal of Financial Studies doi: 10.3390/ijfs14040100

Authors: Kazeem Abimbola Sanusi Zandri Dickason-Koekemoer

This paper examines whether government debt financing crowds out private consumption in South Africa or whether household behaviour is consistent with Ricardian equivalence. Using quarterly data from 1960Q1 to 2025Q1, the study employs a Bayesian time-varying parameter framework that accommodates non-stationarity, structural change, and evolving fiscal transmission mechanisms, and is complemented by a Markov-switching Bayesian VAR as a robustness check. All variables are expressed relative to GDP to avoid scale effects, and inference is based on posterior distributions. The results reveal pronounced state dependence in the debt–consumption relationship. In earlier decades, increases in the debt-to-GDP ratio are associated with statistically meaningful declines in the private consumption share, consistent with crowding-out or precautionary behaviour under weaker fiscal credibility. Over time, however, this negative association weakens and converges toward neutrality, with post-2010 estimates indicating no significant effect of debt on consumption. Conditioning on fiscal stance and financial conditions shows that debt does not exert an independent influence on consumption once government expenditure, tax revenue, and interest rates are taken into account. A constant-parameter Bayesian benchmark masks these dynamics, producing an average effect close to zero. Evidence from a Markov-switching Bayesian VAR similarly finds no persistent regime-specific crowding-out effects. Overall, the findings suggest that observed debt–consumption linkages in South Africa operate primarily through broader fiscal and macroeconomic conditions rather than debt accumulation itself, highlighting the importance of fiscal credibility and policy composition.

International Journal of Financial Studies doi: 10.3390/ijfs14040099

Authors: Abdul Aziz Abdul Rahman Uswa Imran Farah Naz Ayesha Irfan

This study investigates the evolving relationship between supply chain integration (SCI) and firm performance through a comprehensive bibliometric analysis of 148 publications retrieved from the Scopus database. Using VOSviewer 1.6.20 software, the research maps the intellectual structure of the field, highlighting influential authors, journals, and thematic developments. Findings reveal that SCI conceptualized across internal, supplier, and customer integration has consistently been linked to improved operational efficiency, responsiveness, and competitive advantage. However, empirical evidence also indicates mixed outcomes, particularly under conditions of environmental uncertainty and excessive dependence on partners. Recent scholarship demonstrates a notable shift toward sustainability-oriented integration and the adoption of digital technologies such as blockchain, big data analytics, and artificial intelligence, which collectively enhance resilience and adaptability. The analysis underscores gaps in research across developing economies and service industries, suggesting opportunities for future inquiry. Overall, the study deepens understanding of SCI’s role in shaping resilient, sustainable, and technologically enabled supply chains.

International Journal of Financial Studies doi: 10.3390/ijfs14040098

Authors: Ndonwabile Zimasa Mabandla

This research enhances the literature on bank capital structure by combining financial intermediation theory with technological innovation to analyse the impact of FinTech adoption and liquidity management on leverage choices in South African banks. Utilising panel data spanning 2015 to 2024 and applying the Generalised Method of Moments (GMM) to tackle endogeneity and dynamic persistence, the research presents new findings from an overlooked emerging market setting. The results show a diverse effect of technology on leverage. Conventional banking systems, represented by automated teller machines (ATMs), show a positive relationship with the total debt ratio (TDR), suggesting a capital-intensive nature of tangible assets. Conversely, digital technologies such as mobile banking and a composite FinTech Index display a notable negative correlation with leverage, indicating that digital transformation improves efficiency, strengthens internal funding capacity, and reduces dependence on external debt. Moreover, increased liquidity levels are negatively correlated with leverage, suggesting that well-capitalised banks with robust liquidity rely less on debt funding. By examining FinTech and liquidity dynamics, the research contributes to both theory and practice, emphasising digital innovation as an alternative to external funding and stressing the importance of sound liquidity management amid evolving regulatory environments such as Basel III.

International Journal of Financial Studies doi: 10.3390/ijfs14040097

Authors: Nontuthuko Khanyile Masibulele Phesa

This study evaluates the extent and quality of tax transparency reporting among the Top 40 firms listed on the Johannesburg Stock Exchange (JSE), distinguishing between mandatory tax disclosures and voluntary transparency practices. A qualitative, disclosure-based research design was employed, involving content analysis of publicly available annual reports, integrated reports, and sustainability reports. A structured tax transparency framework grounded in stakeholder theory and legitimacy theory, and adapted from prior empirical studies was applied to systematically assess tax-related disclosures. Findings indicate high compliance with mandatory tax disclosure requirements, reflecting strong adherence to accounting standards and regulatory obligations. In contrast, voluntary tax transparency shows considerable variation: firms predominantly provide narrative, policy-oriented, and governance-related information, while detailed, forward-looking, and jurisdiction-specific disclosures remain limited. The discussion highlights that voluntary transparency is shaped by stakeholder expectations, legitimacy concerns, and perceived reputational and commercial risks, leading to selective disclosure. Regulatory compliance emerges as the primary driver of tax reporting, whereas voluntary practices are influenced by firm-specific and contextual factors. The results hold relevance for investors, regulators, and policymakers seeking greater corporate accountability, and for standard-setters aiming to enhance the consistency and depth of tax transparency reporting. Overall, the study enriches the limited literature on corporate tax transparency in emerging markets by offering contemporary empirical evidence from South Africa and identifying key areas requiring improvement in voluntary tax disclosures.

International Journal of Financial Studies doi: 10.3390/ijfs14040096

Authors: Elisa Ossola Irina Trifan

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.

International Journal of Financial Studies doi: 10.3390/ijfs14040095

Authors: Ferdinantos Kottas

This study develops and evaluates an equity selection pipeline that converts quarterly fundamentals into a monthly frequency, constructs profitability, leverage, liquidity, and growth characteristics, and learns a linear ranking model via a genetic algorithm (GA). The GA is trained to maximize either (i) mean monthly NDCG@30 using 12-tile relevance labels or (ii) mean monthly Spearman information coefficient (IC). The learned ranker is tested out-of-sample using monthly forward returns, benchmarked against the S&P 500, with different types of allocation weights, and further evaluated under sector concentration limits. In the last layer, the monthly-selected stock universe is used in a daily dynamic allocation which is solved by the penalized Max-Sharpe or Min-Variance optimization problems under only long positions and transaction fees. Performance is examined across Pre-COVID, COVID, Post-COVID (Train), and Final Test regimes, demonstrating how ranking objectives and diversification constraints impact performance and stability. Results show that TTM-based accounting signals, when optimized through genetic learning and disciplined allocation, yield economically meaningful stock selection and robust portfolio performance across market regimes.

International Journal of Financial Studies doi: 10.3390/ijfs14040094

Authors: Puxuan Wang Shuangjin Wang Maggie Foley Jingjing Li

Green innovation is central to industrial ecological transition, yet heavily polluting firms often exhibit low-quality green innovation investment. Grounded in the government–enterprise–research–intermediary Quadruple Helix innovation ecosystem framework, this study integrates Necessary Condition Analysis (NCA) and fuzzy set qualitative comparative analysis (fsQCA) to examine 66 publicly listed heavily polluting manufacturing firms in China. The results show that fiscal subsidies and environmental taxes are necessary but not sufficient conditions for achieving high-quality green innovation investment. Moreover, high-quality outcomes arise through three equifinal pathways: the Government–Intermediary Dual-Drive Model, the Government–Enterprise–Intermediary Co-Directional Model, and the Government–Enterprise Symbiotic Model. Six configurations lead to non-high-quality green innovation investment, which cluster into Resource-Scarcity and Regulatory-Constrained models. A favorable macro environment further strengthens high-quality outcomes. These findings clarify how policy instruments and multi-actor collaboration jointly shape green innovation investment quality and provide actionable implications for heavily polluting firms and policymakers seeking sustainable development.

International Journal of Financial Studies doi: 10.3390/ijfs14040093

Authors: Laura Aibolovna Kuanova Aizhan Nartaiqyzy Otegen

Artificial intelligence (AI) is increasingly embedded in banking risk management, yet academic research on this topic remains conceptually fragmented and dispersed across multiple disciplines. This study examines global publication trends and thematic structures related to AI applications in banking risk management through a bibliometric analysis of 83 peer-reviewed articles indexed in the Web of Science Core Collection for the period 2020–2024. The analysis was conducted using Bibliometrix (R-package, version 4.1), its web interface Biblioshiny (2024 release), to evaluate publication dynamics, citation performance, authorship patterns, and thematic clusters. Results show a substantial rise in scientific interest, with annual publication growth of 41.4% and international co-authorship reaching 30%. Five major thematic clusters were identified, including AI-enabled credit risk assessment, fraud detection, operational and cyber-risk mitigation, FinTech adoption, and regulatory compliance. Approximately 30% of the articles appeared in the top ten journals publishing on the topic, and the dataset recorded more than 3800 cited references. The findings indicate that AI contributes to enhanced predictive accuracy, real-time anomaly detection, and supervisory efficiency in banking risk management, while persistent challenges relate to model transparency, data quality, and regulatory adaptation. This study offers a systematic, data-driven understanding of the intellectual landscape and research evolution of AI-driven banking risk management from 2020 to 2024.

International Journal of Financial Studies doi: 10.3390/ijfs14040092

Authors: Nouredine Belhadj Sami Ben Mim

This paper examines the impact of real effective exchange rate (REER) misalignment on banking stability, while emphasizing the moderating effect of institutional quality. We also aim to investigate the non-linearity of this relationship. Based on a panel of 40 emerging countries covering the period from 2000 to 2020, and using the system generalized method of moments (SGMM) estimator, we show that REER misalignment positively impacts banking stability. A second set of estimations provides a more nuanced view. The results reveal that overvaluation contributes to enhance banking stability, while undervaluation proves to be a source of instability. The results also suggest that institutional development boosts both the positive and negative effects. Further investigations show that the considered relationship is conditional on the magnitude of the exchange rate misalignment and on the level of banking stability. The empirical results reveal the existence of an inverted U-shaped relationship between REER misalignment and banking stability: low levels of exchange rate misalignment contribute to boost stability, while high levels of misalignment exacerbate instability. In addition, REER misalignment promotes stability during calm periods, while it contributes to fuel instability during financial turmoil. Misalignment thus proves to be a double-edged weapon, which should be used with great caution to avoid systemic crisis.

International Journal of Financial Studies doi: 10.3390/ijfs14040091

Authors: Nadia Mansour

Through assessing the roles of artificial intelligence (AI), Internet of Things (IoT), and blockchain in augmented finance, a critical synthesis of the literature for addressing the complex financial challenges that accompany climate change is provided. This systematic review synthesizes the existing literature to identify how these technologies may help in the context of sustainable finance. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we reviewed and analyzed 42 peer-reviewed studies published between 2018 and 2025. Our results are applicable in three general areas: (1) increased measurement, reporting, and verification (MRV) of environmental impacts through employing IoT and blockchain to ensure transparency and traceability; (2) better physical and transition risk control using predictive AI modeling; and (3) better environmental, social, and governance (ESG) analysis and detection of greenwashing and risk reduction via alternative data. We highlight the power of these technologies to address problems such as information asymmetry and transparency gaps in impact chains. However, significant challenges such as algorithmic bias, difficulties associated with data governance, and regulatory delays persist. This study addresses this critical gap by synthesizing the evidence into a cohesive overview of the augmented finance landscape, identifying key challenges and priorities for future research. It also proposes a future research agenda with emphasis on impact assessment, algorithmic transparency, and impact on financial stability.

International Journal of Financial Studies doi: 10.3390/ijfs14040090

Authors: Abdulrahman Alsamaani Huda Aldhahi

This study provides a comprehensive evaluation of six volatility forecasting models applied to twelve dominant and less dominant cryptocurrencies across multiple time horizons using high-frequency intraday data. The exponential generalized autoregressive conditional heteroskedastic (EGARCH), integrated GARCH (IGARCH), standard GARCH, GJR-GARCH, lagged realized volatility (LRE), and heterogeneous autoregressive (HAR) models are systematically compared using 5 min computed return data from September 2018 to September 2020. Our analysis encompasses three forecast horizons (1-day, 7-day, and 30-day) to assess model performance under varying temporal constraints. Through univariate Mincer–Zarnowitz regressions, encompassing tests, and out-of-sample evaluation using root mean squared error (RMSE) and quasi-likelihood loss (QLIKE) functions, we identify significant performance heterogeneity across models and cryptocurrencies. The HAR model exhibits stronger predictive accuracy at short horizons, while EGARCH exhibits relatively stronger performance at longer horizons, although overall explanatory power declines as forecast horizon increases. Importantly, no single model consistently provides optimal forecasts across all cryptocurrencies. Consistent with prior evidence suggesting model performance varies across assets. Encompassing regressions reveal that combining HAR with EGARCH specifications significantly enhances explanatory power across all temporal frames. Out-of-sample Diebold–Mariano tests indicate that HAR generates the lowest forecast errors for most cryptocurrencies, though EGARCH performs exceptionally well for high-market-capitalization assets. These findings provide regime-conditional insights into horizon- and asset-specific volatility dynamics during the pre-institutionalization phase of cryptocurrency markets. The study contributes to emerging literature by incorporating less-dominant cryptocurrencies and offering robust empirical evidence on the asymmetric and persistent volatility characteristics unique to digital asset markets. These findings should be interpreted within the context of the 2018–2020 sample period, representing a pre-institutionalized phase of cryptocurrency markets, and may not fully generalize to structurally different market regimes characterized by increased institutional participation and regulatory development.

International Journal of Financial Studies doi: 10.3390/ijfs14040089

Authors: Mohammad Alawin

This study examines why currency substitution proves so difficult to reverse, even after countries succeed in stabilizing inflation. Focusing on Bolivia, Brazil, Mexico, and Turkey—economies that endured severe inflationary episodes before implementing stabilization programs—the paper asks a simple but important question: why does reliance on foreign currency persist long after inflation has been brought down? To answer this, the analysis adopts a structural time-series state-space framework that allows behavioral parameters to evolve gradually over time. Rather than assuming persistence, the model lets it emerge from the data and, crucially, compares alternative ways in which agents might form expectations about exchange rate movements. The evidence reveals a consistent pattern. By the end of the sample period, currency substitution remains statistically and economically significant in all four countries. The dominant expectation mechanism is extrapolative: agents tend to look at recent depreciation and assume it will continue. This tendency creates a reinforcing loop—when currencies depreciate, expectations of further depreciation strengthen, and the incentive to hold foreign currency intensifies. What makes these findings particularly striking is that this dynamic does not vanish once inflation is stabilized. Even in periods of relative macroeconomic calm, substitution persists. Past instability leaves a lasting imprint on expectations, and concerns about the durability of policy reforms continue to shape monetary behavior. In several cases, ongoing depreciation against the U.S. dollar further validates these cautious beliefs. As a result, the findings suggest that currency substitution is not merely a mechanical residue of past inflation. It is sustained by the way people form and update expectations in environments marked by credibility challenges. Stabilizing inflation is therefore a necessary step, but it is not enough on its own. Durable confidence in the domestic currency requires rebuilding credibility in a way that gradually reshapes expectations and restores trust over time.

International Journal of Financial Studies doi: 10.3390/ijfs14040088

Authors: Khaled Muhammad Hosni Sobehy Lassaad Ben Mahjoub Ahmed Gomaa Ahmed Radwan

Current accounting standards do not allow recognition of intangible assets for indigenously created properties, resulting in a discrepancy between the book value and market value of firms operating within digital economies, where investments like cybersecurity and data governance are grossed up immediately on the statement of financial position as they are considered to be expensed under IFRS. This paper investigates whether voluntary Digital Reputation Risk Disclosure (DRRD) rectifies this valuation gap for the non-financial firms listed on the Saudi Exchange. Based on an automated bilingual dictionary-based textual analysis of 891 corporate documents and a two-step System GMM estimator run on an unbalanced panel of 619 firm-year observations from a sample of 132 firms for the period 2020–2024, we show that DRRD is statistically significantly negatively related to firm value at conventional levels, implying that investors perceive such disclosures as indications of higher risk exposure rather than stronger governance capabilities. While statistically insignificant, the moderating effect of firm size shows that negative valuation effects are concentrated on large firms according to sub-sample analysis. These findings are confirmed across several alternative specifications in the robustness checks. The findings demonstrate that voluntary digital risk disclosure, in the absence of standards-based frameworks, is not effective at bridging this valuation gap, and may instead activate functional fixation among investors. These findings highlight the importance of IASB’s standardization agenda regarding intangible assets and present relevant empirical data for developing capital markets.

International Journal of Financial Studies doi: 10.3390/ijfs14040087

Authors: Ayşegül Ciğer Filiz Yetiz Bülent Kınay

This study examines the relationship between banks’ ESG performance and core risk and balance sheet indicators in the Turkish banking sector. Using an unbalanced panel of eight banks listed on Borsa Istanbul over the period 2008–2023, we estimate bank fixed-effects models with one-year-lagged ESG measures and controls and report Driscoll–Kraay standard errors. Two complementary specifications are employed: one based on the composite ESG score and another based on its environmental (E), social (S), and governance (G) pillars. The findings suggest that the composite ESG score is positively associated with non-performing loans and capital adequacy, while its relationship with liquidity risk and net balance sheet position/equity is less stable across specifications. When the ESG pillars are examined separately, substantial heterogeneity emerges across the E, S, and G dimensions. In particular, the environmental score is negatively associated with capital adequacy, whereas the social score is negatively associated with net balance sheet position/equity. Governance-related results appear weaker and more sensitive to specification choice. Overall, the findings indicate that ESG does not operate through a uniform risk channel in banking and should be interpreted as associational rather than causal. The study contributes evidence from an emerging-market banking system and highlights the importance of disaggregated ESG analysis.

International Journal of Financial Studies doi: 10.3390/ijfs14040086

Authors: Dinesh Gajurel Bharat Singh Thapa

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.

International Journal of Financial Studies doi: 10.3390/ijfs14040085

Authors: Adrian-Gabriel Enescu Monica Răileanu Szeles

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.

International Journal of Financial Studies doi: 10.3390/ijfs14040084

Authors: Maha Shehadeh Omar Arabiat Hashem Alshurafat Khaled Hussainey Abdalmuttaleb M. A. Musleh Al-Sartawi

Purpose: Rapid bank digitisation has heightened cybersecurity risks and increased stakeholder expectations for transparent cyber risk governance and disclosure. However, research on whether women’s board involvement enhances financial success varies and depends on the context, particularly within different institutional settings. Therefore, this study investigates the impact of Women on Boards (WIB) on Earnings per Share (EPS) of Jordanian banks during 2010 to 2022 and further examines the moderating effect of Cyber Security Disclosure (CSD) on the relationship between WIB and EPS. Design: Combining manual content analysis of each Jordanian bank’s annual report with regression analysis to assess the correlation between EPS, WIB, and CSD. The study also controls for audit quality estimates, financial leverage, bank age, and size. Findings: Our results reveal a negative correlation between EPS and the increasing number of women on boards; thus, simply having more women on boards does not necessarily lead to higher EPS. Additionally, there is a positive interaction between WIB and CSD on EPS, indicating that strong cybersecurity practices can mitigate the negative effects of gender diversity on the board. The ongoing negative association between board diversity and EPS underscores the complexity of gender relations in corporate governance issues. Originality: This research is the first to examine both gender diversity and cybersecurity practices within the same context, as they jointly influence corporate governance and financial performance in new ways. It emphasises the importance of viewing cybersecurity disclosures as a strategic component that can positively impact the financial outcomes of board diversity.

International Journal of Financial Studies doi: 10.3390/ijfs14040083

Authors: Dmitrii Gimmelberg Iveta Ludviga

Large language models (LLMs) are increasingly used by retail traders to interpret information and design complex strategies, yet existing adoption constructs do not capture the decision-time experience of being cognitively scaffolded by an LLM. We define Perceived Cognitive Assistance (PCA) as the trader’s felt expansion of cognitive capability at the moment of a trading decision when an LLM is available, and we report initial content validation of a PCA item pool. Study 1 specified the PCA content domain using a two-tier qualitative corpus (eight interviews and 44 YouTube narratives on LLM-assisted trading, plus 24 qualitative and mixed-method studies on robo-advice and social trading). Reflexive thematic analysis yielded five facilitative assistance facets and one adjacent risk facet (over-reliance), and these were translated into a 16-item PCA pool. Study 2 used a naïve-judge sort-and-rate task with 48 retail traders to test whether items show definitional correspondence to PCA and definitional distinctiveness from similar constructs: perceived usefulness, perceived ease of use, trust in the LLM, and trading self-efficacy. The resulting nine-item set is ready for subsequent factor-analytic and predictive validation. This study advances our understanding of how large language models shape retail trading behaviour by identifying and empirically grounding Perceived Cognitive Assistance as the decision-time psychological experience through which LLMs cognitively scaffold traders, clarifying how LLM use differs from generic technology adoption, trust, or self-efficacy effects.

International Journal of Financial Studies doi: 10.3390/ijfs14040082

Authors: Anissa Naouar Hajer Zarrouk Teheni El Ghak

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.

International Journal of Financial Studies doi: 10.3390/ijfs14040081

Authors: Zhaoying Lu Yuanju Fang

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.

International Journal of Financial Studies doi: 10.3390/ijfs14040079

Authors: Antonija Mandić Katerina Fotova Čiković Tanja Jakšić

Despite extensive research on financial literacy and financial decision-making, the scholarly literature remains conceptually fragmented, particularly regarding how behavioral biases mediate or moderate the relationship between knowledge and financial behavior. The existing literature often focuses on financial literacy or behavioral biases in isolation, limiting a systematic understanding of their interaction. This study addresses this gap by conducting a bibliometric analysis of research at the intersection of financial literacy, behavioral finance, and decision-making. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we analyzed 267 peer-reviewed publications indexed in Web of Science and Scopus over the period 2010–2025 using the Bibliometrix 5.2.1 R package and VOSviewer 1.6.20 for co-occurrence, thematic clustering, and trend analysis. The results identify three interconnected research clusters: (i) socio-demographic and educational determinants of financial literacy, (ii) cognitive and behavioral biases influencing financial decision processes, and (iii) applied investment decision contexts. Overconfidence and herding dominate the literature, whereas biases such as framing, mental accounting, and intertemporal inconsistency remain comparatively underexplored. The analysis further reveals a post-2022 surge in publications, increasing internationalization, and emerging integration of digital finance and artificial intelligence themes. By systematically mapping the intellectual structure of this research domain, this study clarifies theoretical fragmentation, identifies under-researched behavioral mechanisms, and provides an evidence-based framework to guide future interdisciplinary and policy-relevant research on how financial literacy translates into financial behavior.

International Journal of Financial Studies doi: 10.3390/ijfs14040080

Authors: Mattia Calosci Antonino Crisafulli Mattia Giantomassi Saverio Giorgio

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.

International Journal of Financial Studies doi: 10.3390/ijfs14040078

Authors: Hyunah Lee Sungju Chun

Prior empirical studies suggest that technical indicators may contain information useful for predicting the equity market risk premium and may complement forecasting models based on macroeconomic variables. This paper examines the predictive power of technical indicators in conjunction with macroeconomic variables in the Korean market, focusing on whether technical indicators enhance the predictability of the equity market risk premium. Using monthly data from October 2000 to December 2023, this study evaluates the performance of individual variables and groups of macroeconomic variables and/or technical indicators by extracting principal components and estimating predictive regressions. Both in-sample and out-of-sample tests are conducted to assess the economic implications of the principal component predictive regressions. Contrary to findings from the U.S. and China, the results show that technical indicators in Korea exhibit weak predictive power at a monthly frequency when considered in isolation. However, combining technical indicators with macroeconomic variables substantially improves predictability. In-sample regressions based on principal components extracted from the combined information set yield higher explanatory power than models based solely on macroeconomic variables or technical indicators. Out-of-sample results further confirm that incorporating technical indicators into macroeconomic information leads to meaningful gains in forecasting accuracy for the Korean equity market risk premium.

International Journal of Financial Studies doi: 10.3390/ijfs14040077

Authors: Michael C. S. Wong

The convergence of environmental responsibility and the finance sector is transforming worldwide investment markets and corporate operations in the current century [...]

International Journal of Financial Studies doi: 10.3390/ijfs14030076

Authors: Eveling Sussety Balcazar-Paiva Alexander Fernando Haro-Sarango Juan Amilcar Villanueva-Calderón

This article develops a rigorous and reproducible systematic review of the integration of artificial intelligence (AI) in financial education during the period 2020–2025, structured in accordance with -5.3-PRISMA and explicitly oriented toward detecting narrative and perception. The search was conducted in three complementary databases (Scopus, ScienceDirect, and Taylor & Francis), using search strings equivalent to those of the platform and a selection workflow that begins with 388 records and culminates in 50 included studies, prompting a narrative synthesis given the methodological heterogeneity. From a methodological contribution perspective, the study combines bibliometric mapping with text mining and an NLP process that triangulates sentiment using lexicon-based approaches (VADER, TextBlob) and a multilingual transformer model (XLM-RoBERTa), producing continuous indicators (sentiment index) and reproducible research artifacts. The results position AI as an integrative nexus linking financial literacy, decision-making, sustainability, and language technologies (including ChatGPT-5.3.), highlighting its potential for personalization, virtual tutoring, and immediate gains in comprehension and motivation; however, evidence of sustained behavioral change remains nascent. Critical gaps remain, such as a shortage of longitudinal/controlled studies, a lack of standardized metrics, limited transparency and validation of models, and constraints in terms of geographic and cultural diversity, while privacy, fairness, and algorithmic bias emerge as structural conditions for responsible adoption.

International Journal of Financial Studies doi: 10.3390/ijfs14030075

Authors: Jiayang Yu Kuo-Chu Chang

This study proposes a hierarchical signal-to-policy learning framework for risk-aware portfolio optimization that integrates model-based return forecasting, explainable machine learning, and deep reinforcement learning (DRL) within a unified architecture. In the first stage, next-period returns are estimated using gradient-boosted tree models, and SHAP-based feature attributions are extracted to provide transparent, factor-level explanations of the predictive signals. In the second stage, a Proximal Policy Optimization (PPO) agent incorporates both predictive forecasts and explanatory signals into its state representation and learns dynamic allocation policies under a mean–CVaR reward function that explicitly penalizes tail risk while controlling trading frictions. By separating signal extraction from policy learning, the proposed architecture allows the use of economically interpretable predictive signals to incorporate into the policy’s state representation while preserving the flexibility and adaptability of reinforcement learning. Empirical evaluations on U.S. sector ETFs and Dow Jones Industrial Average constituents show that the hierarchical framework delivers higher and stable out-of-sample risk-adjusted returns relative to both a single-layer DRL agent trained solely on technical indicators, a mean–CVaR optimized portfolio using the same parameters used in the proposed hierarchical model and standard equal weight as well as index-based benchmarks. These results demonstrate that integrating explainable predictive signals with risk-sensitive reinforcement learning improves the robustness and stability of data-driven portfolio strategies.

International Journal of Financial Studies doi: 10.3390/ijfs14030074

Authors: Alina Georgiana Manta Claudia Gherțescu Roxana Maria Bădîrcea Nicoleta Mihaela Doran

This study delves into how the literature conceptualizes banking efficiency as a capability shaping sustainability-oriented pathways under conditions of systemic uncertainty, including recurrent economic–financial disruptions and geopolitical shocks. Using records indexed in the Web of Science Core Collection, the study combines bibliometric mapping with conceptual structuring to examine publication dynamics, collaboration networks, and the thematic evolution of research linking bank efficiency, green finance intermediation, sustainable digital innovation, and risk governance. The study reveals a multidimensional knowledge base organized around two converging streams: (i) research on efficiency, stability, and crisis transmission emphasizing intermediation quality, performance under stress, and prudential responses; and (ii) sustainability and innovation scholarship focusing on how financial systems enable eco-innovation diffusion and low-carbon transition through capital allocation, governance mechanisms, and digitally enabled transformation. Across these streams, banking efficiency is increasingly discussed not merely as a performance ratio, but as a strategic capability that becomes particularly salient in crisis environments: it can reduce intermediation frictions when funding conditions tighten, strengthen screening and monitoring of green projects amid elevated uncertainty, and support the continuity and scaling of eco-innovations by improving decision speed and resource allocation through digital tools. Collaboration patterns indicate growing interdisciplinary engagement—especially among European and Asian institutions—where crisis, sustainability, and innovation perspectives are integrated into systems-based approaches to green finance. Building on these insights, the article outlines a research agenda oriented toward innovation outcomes in turbulent contexts, emphasizing (a) measurement strategies that connect efficiency to eco-innovation diffusion and adoption rates during stress periods; (b) comparative analyses of how policy incentives and green market signals interact with bank efficiency across crisis episodes; and (c) hybrid methodological designs combining econometric identification, network analytics, scenario-based stress framing, and AI-enabled analytical tools to capture nonlinear dynamics in efficiency–innovation linkages. Overall, the study clarifies how banking efficiency may condition the capacity of financial institutions to sustain green investment intermediation and advance eco-innovation pathways when uncertainty is systemic rather than episodic.

International Journal of Financial Studies doi: 10.3390/ijfs14030073

Authors: Eddy Suprihadi Nevi Danila Zaiton Ali Gede Pramudya Ananta

Forecasting returns in Real Estate Investment Trust (REIT) markets remains challenging because REIT performance is shaped by nonlinear and time-varying interactions with macro-financial conditions. This study evaluates the forecasting performance of Long Short-Term Memory (LSTM) neural networks relative to a TVP–VAR proxy implemented as an expanding window VAR for weekly U.S. U.S. REIT returns. All models are assessed within a harmonized experimental framework that applies consistent data preprocessing, feature construction, and strictly time-ordered out-of-sample evaluation. The results indicate that the baseline LSTM model delivers modest but more stable error-based performance than the TVP–VAR proxy, with improvements concentrated in RMSE and MAE, while evidence for directional predictability is weak and not consistently distinguishable from benchmark performance. To enhance transparency, SHapley Additive exPlanations (SHAPs) are used to interpret the LSTM forecasts. The attribution analysis highlights recent REIT returns, global equity indicators—particularly the Hang Seng Index—and crude oil prices as influential predictors, and shows that their contributions vary across volatility regimes, consistent with time-varying spillovers and changing risk transmission. Overall, the study positions LSTM forecasting combined with SHAP-based interpretation as a transparent and reproducible framework for comparative evaluation and driver analysis in weekly REIT returns, rather than as a strong directional timing tool.

International Journal of Financial Studies doi: 10.3390/ijfs14030072

Authors: Mostafa Saidur Rahim Khan Yoshihiko Kadoya

Investment in risky financial assets plays a crucial role in individual wealth accumulation and broader financial market development. However, existing research has primarily emphasized financial literacy while giving limited attention to behavioral mechanisms that may weaken its influence on investment behavior. In particular, hyperbolic discounting, reflecting time-inconsistent preferences that favor immediate rewards over long-term gains, may constrain the effective translation of financial knowledge into forward-looking financial decisions. Against this background, this study examines whether hyperbolic discounting mediates the association between financial literacy and investment in risky assets using large-scale survey data from Japan’s Money and Life survey. Employing regression-based mediation analysis within a cross-sectional framework, the results indicate that financial literacy is strongly and positively associated with risky asset investment, while hyperbolic discounting exerts a statistically significant but economically small mediating effect that slightly attenuates this relationship. The findings suggest that cognitive financial capability remains the dominant driver of participation in risky financial markets, whereas present-biased preferences play a secondary behavioral role. These results provide important implications for investors, educators, and policymakers by highlighting that policies aimed at improving financial literacy are likely to yield substantial investment benefits, while complementary interventions addressing behavioral biases may offer additional, though more modest, gains in promoting long-term, forward-looking financial decision-making.

International Journal of Financial Studies doi: 10.3390/ijfs14030071

Authors: Morgak Kassem Golpet Patricia Lindelwa Makoni Godfrey Marozva

This study investigates the cointegrating relationships among financial inclusion, institutional quality, and economic growth in 20 Sub-Saharan African nations from 2008 to 2024. Employing the Pooled Mean Group (PMG) estimator in an Autoregressive Distributed Lag (ARDL) panel, the analysis showed a significant and favourable long-term association between economic growth, financial inclusion and institutional quality. In particular, regardless of the proxy for economic growth, the long-term association between financial inclusion and economic growth is positive and statistically significant. Similarly, institutional quality demonstrates a favourable and significant long-run linkage to economic growth, suggesting that improvements in institutional frameworks are related to sustained economic expansion. In contrast, short-run dynamics differs. There is a short-term correlation between institutional quality and economic growth but not between financial inclusion and economic growth. These findings show the importance of institutional quality as a catalyst for economic growth in the region. Consequently, the study recommends that governments in Sub-Saharan Africa should prioritise setting up strong institutions and policies to foster financial inclusion, which has a correlation with sustainable economic growth. This is crucial for both overall economic development and the creation of job opportunities.

International Journal of Financial Studies doi: 10.3390/ijfs14030070

Authors: Seydou Nourou Ndiaye Zakari-yaou Doulla Harouna Adama Sow Badji Babacar Sène

The quality of governance is a key driver of resource mobilisation in a context marked by successive shocks that exacerbate fiscal imbalances. This study aims to analyse the role of institutional quality in the relationship between public expenditure and tax revenue in a panel of 162 countries, broken down into developed and emerging economies between 2000 and 2023. Using causality tests and the cross-sectional autoregressive model with staggered lags (CS-ARDL) to control for cross-sectional heterogeneity and cross-dependence, the results reveal a bidirectional causality linking expenditure and revenue for the entire panel; emerging countries are more sensitive to fiscal policies; public expenditure significantly stimulates tax revenue in the short and long term, with an effect amplified by institutional quality; long-term sustainability depends crucially on the institutional framework. This study highlights the need for targeted institutional reforms and fiscal rules differentiated according to countries’ level of economic development.

International Journal of Financial Studies doi: 10.3390/ijfs14030069

Authors: José Matheus Ferreira Gomes dos Passos Marcelo Nunes Fonseca Rodrigo Martins Baptista Wilson Toshiro Nakamura Jonas Poutilho de Morais Pereira

This paper presents a bibliometric analysis and literature review of methodologies for optimal capital structure decision making, focusing on research published between 2000 and 2024. This study reviews current research, identifies gaps, and outlines a plan to support with financial decisions. A mixed-methods approach was employed, combining data from the Web of Science and Scopus databases using the search string “capital structure” AND (“decision making” OR “optimal structure”). The study used Bibliometrix(R), VOSviewer, and NVivo tools, and followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flowchart for choosing studies. The findings show that this field is well-developed but still changing. The intellectual structure is organized around two main clusters: one focused on testing classical theories and another oriented toward optimization and managerial applications, revealing a clear theory–practice divide. The mapping also highlights the dominance of Chinese and U.S. scholarship and the central role of practitioner-oriented journals such as Managerial Finance, indicating both a shift toward emerging markets and a strong demand for applicable research. The study provides three key contributions. First, it identifies important countries, authors, outlets, and themes. Second, it uses a method that combines bibliometric and text-mining tools. Third, it introduces a new decision-support framework that is thorough, context-sensitive, and flexible. There are some limitations. These include relying on Scopus and Web of Science, language limits, and the fact that bibliometrics cannot judge the quality of methods. Future research should empirically validate the proposed framework in different contexts, expand studies in emerging markets, test emerging theories such as Brusov–Filatova–Orekhova (BFO) theory, and develop more dynamic and stochastic models to better capture financial uncertainty.

International Journal of Financial Studies doi: 10.3390/ijfs14030068

Authors: Jaco Moolman

The importance of financial literacy for professional athletes is undeniable. This study aimed to build on previous research by identifying the financial literacy content areas that require the highest level of competence for professional athletes competing in team sports. To address this, 12 structured one-on-one interviews were conducted with a purposively selected sample of participants drawn from a network of potential actors capable of influencing the financial decisions of professional athletes, as informed by Actor-Network Theory. The research findings show that skills to avoid unethical behaviour, the acumen to navigate the transition to a post-sports career, savings and financial control are the content areas that require a higher level of competence. This study innovatively visualizes research findings through a heatmap to identify and prioritize focus areas. This study offers insights that may assist professional athletes to reduce their exposure to financial risks. The study may also engage sport’s governing bodies, professional clubs, players’ associations, researchers, and financial advisors aiming to deepen their knowledge of the financial literacy competencies required by professional athletes.

International Journal of Financial Studies doi: 10.3390/ijfs14030067

Authors: Yu-Heng Hsieh Chiung-Han Lai Shyan-Ming Yuan

This study develops a novel AI-based trading framework designed to consistently generate profits across cyclical bullish and bearish futures markets. Unlike conventional strategies that rely on static rules or a single predictive model, the proposed framework introduces a dual-agent deep reinforcement learning (DRL) architecture, where one agent specializes in bullish conditions and the other in bearish conditions, while a trading decision selector dynamically predicts market regimes and allocates execution accordingly. This design enables the system to adapt to regime shifts and mitigate risks arising from market volatility and extreme events. Using Mini Taiwan Stock Exchange Index Futures (MTX) as a case study, a four-year historical backtest is conducted covering multiple disruptive periods, including the tax adjustment and the Russia–Ukraine conflict. The empirical results show that, under a monthly capital reset and loss-compensation rule with a fixed investment of TWD 500,000 per month, the proposed framework achieves an average cumulative return of 2240%, an annualized return of 109%, and a Sharpe ratio of 0.31, with the cumulative ROI exceeding twice the MTX index growth over the same period. Although the Sharpe ratio remains moderate, this outcome reflects the framework’s emphasis on directional trading and absolute return maximization, where profitable trades outweigh intermittent losses despite higher short-term volatility. These findings suggest that adaptive, regime-aware DRL architectures are particularly effective for futures trading in markets characterized by frequent trend reversals, offering both methodological innovation and practical applicability under realistic market conditions, with strong returns achieved at a moderate risk-adjusted level.

International Journal of Financial Studies doi: 10.3390/ijfs14030066

Authors: Maryanti Mediaty Andi Harmoko Arifin Anis Anshari Mas’ud

This study investigates the determinants of financial performance among Micro, Small, and Medium Enterprises (MSMEs) in Indonesia, addressing the critical issues of low accountability and limited access to capital. Grounded in the Resource-Based View and Dynamic Capabilities Theory, the research examines the impact of accounting information systems, management knowledge capability, and digital platform capability on financial performance, mediated by digital innovation. A quantitative approach was employed, utilizing a cluster random sampling survey of 403 MSME owners across Indonesia’s major islands. Data were analyzed using Structural Equation Modeling (SEM) with AMOS software. The results reveal that accounting information systems, management knowledge capability, and digital platforms significantly enhance financial performance. Notably, digital platform capability emerged as the most potent driver. Furthermore, digital innovation proved to be a vital mediator, transforming management knowledge and platform capabilities into tangible financial outcomes. The study concludes that while digital tools provide essential infrastructure, innovation serves as the critical mechanism for unlocking value. These findings suggest that MSMEs must transition from passive technology adoption to active digital innovation to achieve sustainable financial success in the digital economy.