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.

International Journal of Financial Studies doi: 10.3390/ijfs14030065

Authors: Óscar de los Reyes Marín Iria Paz Gil Jose Torres-Pruñonosa Raul Gómez-Martínez

This study examines the False Reality Bias in treasury management, a cognitive distortion through which small and medium-sized enterprises (SMEs) infer financial stability from salient bank balances while overlooking pending obligations and cash-flow timing. Using a firm-level dataset of 50 Spanish meat-processing SMEs, the analysis develops two behavioral-finance indicators: the Liquidity Misperception Index (PEL), capturing the divergence between salient liquidity cues and effective short-term obligations, and the Liquidity Misconfidence Index (ICEL), measuring managerial overconfidence in liquidity assessments. Results show that 41% of firms overestimate liquidity (average PEL = 1.21), while 40% exhibit excessive confidence (ICEL > 1.3), both significantly associated with liquidity distress. Econometric estimates indicate that firms with PEL values above 1.2 are 4.48 times more likely to experience liquidity crises, even after controlling for bank balance levels. Predictive models are used in an exploratory capacity, achieving classification accuracies above 80% and supporting the robustness of the behavioral signals identified. In addition, AI-assisted cash-flow simulations reduce liquidity misperception by 34.7% (p < 0.01). Overall, the findings provide micro-level evidence that cognitive biases systematically distort SME treasury decisions but can be partially corrected through targeted decision-support tools, offering practical insights for managers, advisors, and policymakers.

International Journal of Financial Studies doi: 10.3390/ijfs14030064

Authors: Beáta Reider-Pesti Alex Suta Árpád Tóth

Economic crises significantly restrict corporate access to external financing, and regional differences in recovery capacity deserve close attention. This study examines the financial structure and debt of large enterprises in the Visegrád Four (V4) countries (Hungary, Czechia, Poland, Slovakia), focusing on firms with annual revenues above €10 million. Using data from 2021 to 2023, the analysis explores the relationship between corporate debt—including total debt and loan volumes—and regional economic characteristics at the NUTS 3 level. Financial indicators are assessed in comparison with regional productivity data and a sector-specific specialization index sourced from Eurostat. The analysis targets the post-COVID-19 recovery period, which significantly influenced corporate financial behavior. The results indicate that corporate debt increased sharply at the onset of the COVID-19 pandemic and subsequently declined, while remaining strongly concentrated in capital regions. Higher firm concentration and employment scale are associated with greater regional indebtedness, whereas stronger productive capacity is linked to lower reliance on external debt outside metropolitan cores. Overall, the findings highlight pronounced structural and regional heterogeneity, illustrating how spatial concentration and underlying regional characteristics shape corporate debt dynamics during periods of economic stress.

International Journal of Financial Studies doi: 10.3390/ijfs14030063

Authors: Arash Sioofy Khoojine Lin Xiao Hao Chen Congyin Wang

Financial markets often display nonlinear and turbulent dynamics during periods of stress, and crude-oil and global equity systems frequently demonstrate closely connected forms of instability. Earlier studies report multifractality, chaotic features and regime-dependent spillovers across commodities and equities, yet existing approaches rarely succeed in capturing both the intrinsic complexity of oil-market behavior and the changing structure of cross-asset dependence. This limitation reduces the ability to distinguish calm from turbulent regimes and weakens short-horizon risk assessment. The present study introduces a unified framework that quantifies and predicts systemic instability within the coupled oil–equity system. The analysis constructs a crude-oil complexity index based on multifractal fluctuation analysis, permutation and approximate entropy, and Lyapunov-based indicators of chaotic dynamics. At the same time, it develops an information-theoretic network of global equity and energy-sector returns and summarizes its instability through measures of edge turnover, spectral radius, degree entropy and strength dispersion. These components are combined to form the Coupled Multiscale Chaos Index (CMCI), a scalar state variable that distinguishes calm, transitional and chaotic market regimes. Empirical results indicate that Brent and WTI exhibit pronounced multifractality, elevated entropy and positive Lyapunov exponents, while the dependence network becomes more centralized, more clustered and more capable of shock amplification during high-CMCI states. The CMCI moves closely with realized volatility and provides significant predictive content for five-day variance across major global equity benchmarks, with performance superior to models that rely only on macro-financial controls. Out-of-sample evaluation shows that forecasts incorporating measures of complexity record substantially lower MSE and QLIKE losses. The findings indicate that systemic instability reflects the interaction between local chaotic dynamics in crude-oil markets and turbulence in the global dependence network. The CMCI offers a practical early-warning indicator that supports risk management, forecasting and macroprudential supervision.

International Journal of Financial Studies doi: 10.3390/ijfs14030061

Authors: Alexandros Garefalakis Filia Stratidaki Erasmia Angelaki Danai Antonaki Christos Papademetriou

This study investigates the financial and strategic implications of ESG implementation in the hotel sector, focusing on cost structures, stakeholder engagement, and risk-related outcomes. Empirical evidence remains limited in tourism-intensive economies, particularly regarding operational practices. Using a mixed-methods approach, we combine survey data from hotel managers in Crete, Greece, with a case study of a leading hotel enterprise. The findings reveal that environmental initiatives require substantial investment but can lead to operational efficiencies and regulatory alignment. Social and governance practices, while less capital intensive, play a key role in internal stakeholder trust. The study concludes that the integration of ESG into measurable key performance indicators, when strategically aligned with corporate objectives, contributes to long-term financial viability. These insights reinforce ESG’s growing role in enhancing resilience and governance effectiveness within the hospitality sector.

International Journal of Financial Studies doi: 10.3390/ijfs14030062

Authors: Kgotso Rudolf Moremoholo Sandile Charles Shongwe Frans Frederick Koning

To mitigate credit risk, banks are required to set aside a specific amount as a safety net to absorb the expected loss on a banks’ loan portfolio called loan loss provisions (LLPs) or provisions for bad debts. All banks worldwide had to adopt International Financial Reporting Standard 9 (IFRS 9) as the financial reporting standard. Unlike its predecessor (i.e., International Accounting Standard 39, IAS 39), IFRS 9 accelerates the recognition of losses by requiring provisions to cover both already-incurred losses and some losses expected in the future by calculating the expected credit loss (ECL). To evaluate if the obligor’s credit quality has deteriorated, the IFRS 9 standard requires banks to compare the obligor’s probability of default (PD) at the inception phase of the loan and at the reporting date. Thus, three methodologies are explored in this study (i.e., Cox proportional hazard (PH), Extended Cox PH, and Random Boosting Forest (RBF)) for computation of the PD term structures using Kaplan–Meier as the benchmark model under IFRS 9. The purpose of this research is to illustrate the application of three methodologies on the publicly available mortgage loan portfolio from Freddie Mac using different measures of goodness-of-fit and the predictive accuracy measure, i.e., the Concordance index (C-index). The comparison analysis reveals that the extended Cox PH and RBF models provide better predictive accuracy (higher C-index) but at the cost of increased complexity and potential overfitting (higher information criteria). However, Cox PH has shown the most efficient fit, and offers a stable and understandable hazard trajectory. Finally, for reproducibility, the SAS and R codes are included to illustrate how each of the results (in form of a table or figure) were obtained.

International Journal of Financial Studies doi: 10.3390/ijfs14030060

Authors: Islahuddin Islahuddin Yossi Diantimala Zera Ayudiastika Muhammad Putra Aprullah

This study investigates how greenwashing affects firm value and whether corporate governance can mitigate its negative impact. The analysis is based on 760 companies in the energy, basic materials, and industrial sectors in Indonesia during 2020–2024. Moderated regression analyses using a random effect model were conducted to test the hypotheses. The results show that greenwashing has a significant negative relationship with firm value. As hypothesized, corporate governance weakens this negative effect, indicating it reduces greenwashing’s impact. This study offers novelty by combining the presence of CSR committees and internationally experienced directors as measures of corporate governance to examine their moderating role in the relationship between greenwashing and firm value.

International Journal of Financial Studies doi: 10.3390/ijfs14030059

Authors: Willem Thorbecke

The correlation between sovereign bond prices and stock prices was positive from the 1970s to 2000 and then turned negative. Researchers have investigated this phenomenon using data from the 1970s to the present. This paper uses data beginning in the 1960s, when there was a negative correlation between bond and stock prices, to investigate how positive bond–stock price comovements arose. Evidence from identified vector autoregressions indicates that monetary policy shocks beginning in the late 1960s moved bonds and stocks in the same direction, causing bond and stock prices to covary positively. Evidence from estimating a multi-factor model indicates that news of both monetary policy and inflation drove bonds and stocks in the same direction, contributing to positive bond–stock comovements. These findings imply that rising inflation that elicits contractionary monetary policy could alter bonds’ risk characteristics, causing them to covary positively with stocks. Policymakers today should be vigilant that large budget deficits and other factors do not stoke inflation.

International Journal of Financial Studies doi: 10.3390/ijfs14030058

Authors: Selin Kamiloğlu Elif Güneren Genç

This study examines whether targets’ environmental, social, and governance (ESG) performance is associated with acquirers’ post-merger ESG outcomes and market valuation over the merger year and the subsequent two years. We treat controversies-adjusted ESG scores (ESGC) as outcome-based indicators. Using a global panel of 4572 acquirer-year observations from 47 countries between 2002 and 2023, we analyze the association between targets’ ESGC and acquirers’ post-merger ESG trajectories and market value. Tobit estimations trace combined and pillar-level ESG dynamics over the merger year and the first two post-merger years. The results indicate that target ESG performance is associated with persistent improvements in acquirer sustainability, with the strongest effects in social and environmental dimensions and more gradual adjustments in governance, reflecting institutional and organizational integration complexity. Heterogeneity analyses reveal that cross-border within-industry acquisitions generate the largest ESG gains, whereas domestic within-industry transactions are associated with ESG deterioration. Regarding market valuation, acquirers’ own ESG performance is reflected in Tobin’s Q, while target ESG becomes value-relevant with a one-year lag, highlighting a two-stage valuation mechanism linked to post-merger absorption and institutionalization. Adopting a multi-period perspective, the study shows that ESGC track post-merger sustainability outcomes in ways consistent with learning, institutionalization, and legitimacy-based interpretations.

International Journal of Financial Studies doi: 10.3390/ijfs14030057

Authors: Musaab Alnaim Abdelmoneim Bahyeldin Mohamed Metwally

The current research examines the impact of climate change disclosure (CCD) on firm value (FV) of Egyptian listed non-financial companies. The current research also investigates the moderating role of board gender diversity (BGD). The study sample incorporates Egyptian non-financial companies indexed in EGX 100 whose reports were available from 2018 to 2023. The final sample comprises 82 companies with 492 observations. Statistical analysis was conducted using a POLS and Fixed Effects Model, GMM, and the 2SLS method to address potential endogeneity and dynamic panel concerns. The results revealed a positive and significant impact of CCD on FV. Furthermore, BGD had a positive and significant moderating impact as BGD enhanced the relationship between CCD and FV. Moreover, the critical mass (CM) analysis of female representation revealed that the number of females on the board significantly moderates the CCD-FV relationship; as CM increases, the effect on the CCD-FV relationship becomes stronger. Although advanced panel techniques and instrumental variable approaches are used to mitigate identification concerns, the results should be interpreted in light of the observational nature of the data and the reliance on disclosure-based proxies. These findings are significant for governments, regulators, investors, and company leaders because the moderating role of BGD demonstrates how board governance affects firm value, particularly in emerging markets. This research adds to the academic discussion by emphasizing the beneficial effects of both BGD and CCD on FV, with a particular focus on developing economies.

International Journal of Financial Studies doi: 10.3390/ijfs14030056

Authors: Yuanyuan Huo Shengnan Wang Wenlong Miao

Digital transformation in commercial banks is a critical enabler of modern financial development. While technological advancement and resource allocation are key drivers, managerial attributes also play a decisive role in shaping transformation trajectories. Managerial myopia—often arising from short-term performance pressures, evolving regulatory expectations, and cyclical macroeconomic conditions—warrants particular attention. This study examines how managerial myopia constrains banks’ digital transformation by analyzing its direct impact, underlying behavioral mechanisms, and contingent boundary conditions. Using panel data from 55 Chinese listed commercial banks from 2010 to 2021, we construct a text-based measure of managerial myopia through linguistic analysis of annual reports and employ fixed-effects models for estimation. The results show that a short-term managerial orientation significantly impedes digital transformation, primarily by reducing banks’ propensity for proactive risk-taking. However, this inhibitory effect weakens when managers anticipate longer tenures, management teams exhibit greater diversity in overseas experience and functional expertise, or the average educational level is higher. Moreover, the adverse effects are less pronounced in larger banks and those with stronger corporate governance. Increased external scrutiny and intensified market competition further mitigate this negative influence. These findings offer actionable insights for banking stakeholders aiming to strengthen governance, extend managerial time horizons, and foster an innovation-oriented culture conducive to sustained digital advancement.

International Journal of Financial Studies doi: 10.3390/ijfs14030055

Authors: Ayuba Zakka Dangs Stephen Aanu Ojeka Ahmad Bukola Uthman Lucky O. Onmonya

This study investigates how liquidity and leverage shape financial performance in sub-Saharan Africa’s listed healthcare firms and concludes that internal liquidity capacity is a more reliable driver of performance than debt. Using an ex post facto design, the study examines 60 firm year observations for 12 listed healthcare and pharmaceutical firms across six countries over 2020–2024. It measures performance with return on assets (ROA), return on equity (ROE), and Tobin’s Q, while liquidity and leverage are proxied by current and debt–equity ratios. Fixed-effects panel regression with robust standard errors is employed after confirming heteroskedasticity and overall model significance through Wald tests. Liquidity exerts a positive and statistically significant impact on ROA (β = 0.003706, p < 0.01) and reinforces ROE in complementary estimations, demonstrating that stronger liquidity positions consistently enhance accounting-based financial performance in this capital-constrained sector. By contrast, leverage shows negative and statistically insignificant effects on ROA (β = 0.113666, p = 0.257), ROE (β = −1.42683, p = 0.109), and Tobin’s Q (β = −0.64563, p = 0.612), providing no evidence that higher debt improves either accounting returns or market valuation. Collectively, these results strongly support the primacy of internal financial flexibility over external borrowing for sustaining performance in sub-Saharan African healthcare firms and offer robust, region-wide empirical grounding for refining resource-based, pecking order, and trade-off arguments in healthcare capital structure debates.

International Journal of Financial Studies doi: 10.3390/ijfs14030054

Authors: Afef Slama Imen Khelil

Financial volatility increasingly challenges firms to maintain operational sustainability; yet the mechanisms through which cash flow uncertainty (CFU) shapes environmental practices remain unclear. Based on an international unbalanced panel of 14,798 firm-year observations (2010–2021), this study analyzes how CFU affects waste generation and recycling. Panel regression models are employed, complemented by robustness checks using generalized method of moments (GMM) estimations to mitigate endogeneity concerns. The findings suggest that higher CFU is associated with lower waste generation at the source due to more disciplined resource allocation, alongside higher recycling levels, reflecting a strategic response to operational risk and stakeholder expectations. Moreover, these effects are amplified in contexts characterized by stricter environmental policy stringency, the existence of corporate social responsibility committees, and sustainable supply chain management, underscoring the importance of institutional and organizational settings in shaping environmental operational outcomes. Overall, the results indicate that financial uncertainty can act both as a constraint and a catalyst, encouraging more efficient and circular practices. This study offers novel empirical evidence on the operational implications of CFU, providing valuable insights for managers and policymakers aiming to align financial management with sustainable and resilient production strategies.

International Journal of Financial Studies doi: 10.3390/ijfs14030053

Authors: Tsolmon Sodnomdavaa

Cryptocurrency markets are characterized by extreme volatility, fat-tailed return distributions, and frequent regime shifts, challenging traditional mean–variance portfolio optimization. In such environments, downside risk management becomes central, and tail-sensitive measures such as Conditional Value-at-Risk (CVaR) are increasingly adopted. However, empirical evidence remains mixed regarding whether CVaR-based strategies provide consistent protection across market regimes and tail depths. This study conducts a comprehensive empirical evaluation of tail-risk-based portfolio strategies using cryptocurrency data from 2018 to 2025. A rolling-window back-testing framework with weekly rebalancing is employed. We compare traditional benchmarks, moment-based and robust CVaR strategies, regime-dependent CVaR optimization, regression-enhanced ES–CVaR hybrids, and reinforcement learning-based CVaR policies. Performance is evaluated using mean return, volatility, CVaR at multiple confidence levels (90%, 95%, and 99%), and maximum drawdown. Market regimes are identified through volatility-based rules, and robustness is assessed via sensitivity analysis and block-bootstrap confidence intervals. The results show that no single strategy dominates across all conditions. Hybrid ES–Reg–CVaR strategies provide stable protection under moderate tail risk, reinforcement learning-based CVaR strategies adapt better to extreme tails, and regime-based CVaR optimization consistently limits drawdowns during stress periods. These findings demonstrate that effective CVaR-based portfolio management in cryptocurrency markets requires a regime- and tail-depth-dependent approach rather than a universal optimization rule.

International Journal of Financial Studies doi: 10.3390/ijfs14030052

Authors: Phuc Tran Nguyen

Vietnam’s sustained poverty reduction has coincided with rising foreign direct investment (FDI) and a major shift from income-based to multidimensional poverty measurement, raising challenges for interpreting poverty dynamics and the role of FDI across regimes. This study examines the relationship between FDI and poverty reduction in Vietnam by accounting for poverty persistence, regional heterogeneity, and changes in poverty measurement. Using provincial panel data for 2002–2022 and a System GMM framework, three main findings emerge. First, poverty dynamics differ across measurement regimes: during the income-poverty period (2002–2016), poverty dynamics exhibited lower persistence and faster convergence, whereas under the multidimensional framework (2016–2022), poverty became more persistent and convergence slowed, reflecting the increasingly structural nature of remaining deprivation. Second, FDI is negatively associated with poverty under both measures, but its effects are conditional and uneven. Interaction effects indicate that the poverty-reducing impact of FDI depends on provincial income levels and initial deprivation, with weaker effects in provinces facing deeper multidimensional poverty. Third, higher FDI exposure is associated with greater poverty persistence, reflecting the spatial concentration of FDI in better-off regions rather than a poverty-increasing effect. The analysis is subject to limitations related to measurement regimes, and results are interpreted as conditional associations. Policy implications highlight that the poverty-reducing effects of FDI depend critically on investment quality, the strength of local production linkages, and complementary public spending, particularly in provinces facing persistent deprivation.

International Journal of Financial Studies doi: 10.3390/ijfs14030051

Authors: Sana Bhiri Houda BenMabrouk

This paper examines the influence of Monetary Policy Transparency on Foreign Equity Portfolio Allocation by addressing the informational frictions that shape cross-border investment in Financial Markets. Building on recent developments in central bank communication, we construct a multidimensional measure of Monetary Policy Transparency that extends traditional frameworks by incorporating Accounting Information Transparency and Financial Stability Transparency. This enhanced index provides a more comprehensive representation of the informational environment faced by foreign investors. Using a panel of developed and emerging economies over a twenty-year period, the empirical analysis combines OLS and system GMM estimations to account for endogeneity, dynamic effects, and unobserved heterogeneity. The results indicate that higher levels of Monetary Policy Transparency significantly increase the attractiveness of domestic equity markets to foreign investors, with heterogeneous effects across country groups linked to differences in institutional credibility and financial integration. Overall, the findings highlight multidimensional transparency as a key determinant of Foreign Equity Portfolio Allocation, underscoring the strategic importance of Accounting Information Transparency and Financial Stability Transparency in shaping foreign equity portfolio allocation.

International Journal of Financial Studies doi: 10.3390/ijfs14020050

Authors: Adrian-Gabriel Enescu Monica Răileanu Szeles

This paper investigates the relationship between institutional ownership and firm-level idiosyncratic volatility across European equity markets, with a particular focus on the moderating role of dividend policy. Using a sample of STOXX Europe 600 constituents from 2005 to 2025, we estimate idiosyncratic volatility via the Fama-French three-factor model and employ fixed-effects regressions with clustered standard errors. Our empirical results reveal a positive and statistically significant association between institutional ownership and idiosyncratic volatility, suggesting a destabilizing rather than stabilizing role in European markets. This volatility-enhancing effect is significantly more pronounced among dividend-paying firms and is primarily driven by transient institutional investors with high portfolio turnover. Furthermore, we find that: (1) larger firm size (market capitalization) and higher leverage (debt-to-capital ratio) are positively associated with heightened volatility; (2) growth-oriented firms (high market-to-book ratios) exhibit increased volatility, particularly among non-dividend payers; and (3) higher profitability (ROE) and favorable analyst coverage (buy recommendations) act as stabilizers, reducing idiosyncratic risk. These findings persist in both contemporaneous and lagged specifications. This study contributes to the literature by identifying dividend policy as a key channel through which institutional trading behavior amplifies firm-specific risk, providing novel evidence on the asset class effect within major European benchmark indices.

International Journal of Financial Studies doi: 10.3390/ijfs14020049

Authors: Xin Huang Yang Gao

In this study, we employ the panel smooth transition (PSTR) model to establish a nonlinear framework for examining the relationship between international oil prices and stock returns in China. We specifically investigate how economic policy uncertainty (EPU) acts as a threshold-driven moderator in this relationship. We analyze data from August 2007 to November 2023 and select the EPU index from seven representative countries to examine its cross-border effects on China’s oil–stock relationship. Furthermore, we incorporate an analysis of industry heterogeneity to gain a deeper understanding of how international crude oil prices impact stocks in both upstream and downstream industries. Our findings reveal the following: (1) Under the influence of EPU, there is a significant nonlinear regime-switching effect between international oil prices and Chinese stock returns. (2) Sensitivity to U.S. EPU is the highest, but its effect on risk magnification is the weakest. In contrast, European EPU shows lower sensitivity but a more pronounced risk magnification effect. (3) Chinese EPU significantly amplifies the risk for midstream manufacturing stocks more than for downstream consumer service stocks. This variation reflects the differing abilities of industries to transfer costs along the supply chain; however, there are no substantial differences in sensitivity.

International Journal of Financial Studies doi: 10.3390/ijfs14020048

Authors: Peter G. Kreysa

This study examines campus affinity card agreements under the Credit Card Accountability, Responsibility, and Disclosure (CARD) Act, focusing on how portfolio size, new account openings, and institutional governance affect issuer payments. Using cross sectional data from 6145 issuer institution agreements reported to the Consumer Financial Protection Bureau (CFPB) in 2022, the analysis employs descriptive statistics, correlation tests, and multivariate regression models to identify predictors of payment volume. Results show that total open accounts strongly predict issuer payments, while new account openings exhibit a weak positive bivariate correlation but exert a modest negative effect in multivariate models once portfolio scale and institution type are controlled for. Foundations received the highest average issuer payments, followed by hybrid organizations and universities. This pattern reflects differences in governance structure, administrative capacity, and bargaining leverage. Transparency requirements under the CARD Act reveal broad patterns but omit incentive timing and interchange revenue, limiting full accountability. This is among the first large-scale empirical analyses of CFPB’s affinity card dataset, advancing understanding of equity in campus credit markets and offering policy relevant insights for regulators and administrators.

International Journal of Financial Studies doi: 10.3390/ijfs14020047

Authors: Decheng Yang Qiang He

This study introduces a novel intraday volume-based uncertainty (IVU) proxy—the ratio of opening-half-hour volume to total volume of the preceding seven intervals—to predict final half-hour return direction in the Chinese stock market. Using threshold regression, we identify a statistically significant IVU critical value of 0.476225 (p < 0.001), which splits the sample into distinct uncertainty regimes. Logistic regression incorporating this threshold reveals that the joint condition of high opening volume and low IVU (high uncertainty) significantly amplifies the predictive power of initial returns, achieving 63.04% accuracy in the high-uncertainty, high-volume regime. XGBoost further captures complex non-linear interactions, with IVU-related features ranking among the most important predictors and achieving 71.43% out-of-sample accuracy under high-volume, high-uncertainty conditions. A machine learning trading strategy leveraging these predictions yields a total return of 117.99% with a Sharpe ratio of 3.02 over seven years, significantly outperforming benchmarks. Our findings highlight information uncertainty as a critical moderator of intraday momentum and a valuable source of actionable alpha.

International Journal of Financial Studies doi: 10.3390/ijfs14020046

Authors: Abdelhamid Ben Jbara Marjène Rabah Gana Mejda Dakhlaoui

This study revisits the Efficient Markets Hypothesis by employing a GRU-D neural network to predict stock return distributions across global equity markets, accounting for missing and irregular data. It examines whether stock returns exhibit statistically significant departures from purely random behavior. By combining price, technical and fundamental inputs, it tests both weak and semi-strong market efficiency. We implement the GRU-D model on a global dataset of stock returns, where daily returns are classified into quartiles. Model performance is assessed using Micro-Average Area Under the Curve (AUC) and Relative Classifier Information (RCI). Robustness checks include sub-sample tests across countries and sectors, an examination of the COVID-19 sub-period, and a price-memory persistence analysis. The results reveal that the GRU-D model achieves a ranking accuracy of approximately 75% when classifying returns, with statistical significance at the 99.99% confidence level, and exhibits modest but robust deviations from strict market efficiency. These deviations persist for up to 200 trading days. Notably, the findings indicate that the GRU-D model is more robust during the COVID-19 period. These findings are consistent with the Adaptive Markets Hypothesis and underscore the relevance of machine-learning frameworks, particularly those designed for imperfect data environments, for identifying time-varying departures from strict market efficiency in global equity markets.

International Journal of Financial Studies doi: 10.3390/ijfs14020045

Authors: Huiling Tang Shili Tang Jiyuan Li

Independent directors play a critical role in overseeing company management, safeguarding the interests of both the company and its shareholders, and ensuring that decisions made by the board are scientific, rational, and fair. Directors with industry expertise bring greater experience and knowledge to their roles, enabling them to prevent short-sighted decision-making while preserving their professional reputations. This research empirically examines whether the industry expertise trait of independent directors can inhibit the irregularities of the companies they serve, using a fixed-effects model that controls for industry, company, and year, with Chinese A-share-listed companies from 2003 to 2023 as the observational sample. Endogeneity issues are addressed by using the Heckman two-stage model and the propensity score matching (PSM) model. The findings reveal that (1) independent directors with industry expertise significantly mitigate corporate violations; and (2) their influence primarily stems from improvements in the quality of information disclosure, enhancements to internal control systems, and the resolution of principal–agent conflicts. Further analysis indicates that the restraining effect of independent directors with industry expertise is particularly pronounced in environments characterized by low institutional ownership and fewer analysts, highlighting their stronger supervisory role in such contexts.

International Journal of Financial Studies doi: 10.3390/ijfs14020044

Authors: Blerina Dervishaj Melaize Gropa

We examine whether gender influences formal tax compliance among self-employed taxpayers in Southern Albania—focusing on two observable behaviors: paying taxes on time and the amount of unpaid tax debt (arrears). The study does not examine tax evasion or tax avoidance, as these behaviors cannot be directly observed in the available data. Using administrative data on 500 taxpayers in Fier, Vlorë, Berat, Gjirokastër, and Sarandë (January 2022–March 2025), we estimate the likelihood of timely payment with logistic and probit models and study unpaid liabilities using linear regression. Female-led businesses are more likely to meet deadlines and hold lower unpaid debts than male-led firms. These differences persist across sectors after controlling for firm size, region, income, and time. A negative and significant Gender × Sector term indicates that sectoral composition does not offset women’s compliance advantage in these formal outcomes. The effect size is relatively large for an environment with imperfect monitoring, suggesting that moral norms, reputational concerns, and perceived control weigh more heavily where deterrence is limited. From a policy perspective, adding gender to compliance-risk models and tailoring taxpayer services may indirectly improve voluntary payments and reduce arrears by refining compliance-risk assessment and targeting. To our knowledge, this is the first study in Albania using official administrative microdata to analyze gendered formal tax behavior, addressing a clear empirical gap in Southeastern Europe and providing evidence relevant for discussions of fair and inclusive fiscal policy in an EU-harmonization context. While the findings are derived from Southern Albania, they offer indicative insights for comparable transition economies in Southeastern Europe, rather than direct generalization.

International Journal of Financial Studies doi: 10.3390/ijfs14020043

Authors: Swati Kumaria Puri Jiali Fang Udomsak Wongchoti Wei Hao

With the enactment of the Insolvency and Bankruptcy Code (IBC) in 2016, India reformed and unified its fragmented insolvency frameworks into a single comprehensive law designed to prioritize asset maximization and ensure time-bound resolutions. Through a quasi-experimental setting, we examine the impact of the IBC reform on the corporate borrowings of publicly listed Indian firms against comparable companies in the neighboring South Asia countries (Pakistan, Sri Lanka and Bangladesh). With a panel dataset of Indian and non-Indian firms from 2011–2020, we observe an increase in firms’ overall borrowing in India, along with lowered borrowing costs. The results from our difference-in-differences cross-country setting were also shown to be robust through a placebo test. We report that it was not only financially distressed firms that particularly benefited from the reform. In fact, Indian companies with relatively low leverage and high growth harnessed access to more credit at lower costs even more than their counterparts. Our results highlight the boost of the overall credit supply at the country level as a result of improved bankruptcy laws.

International Journal of Financial Studies doi: 10.3390/ijfs14020042

Authors: Marcelo Fukui Leonardo Fernando Cruz Basso

Risk management has become increasingly important in the financial world. Considering its importance, it is necessary to measure these risks. The financial market uses two risk measures: Value at Risk (VaR) and Expected Shortfall (ES). After the subprime crisis, the market began to emphasize ES instead of VaR. The hypothesis of this paper to be tested is that longer periods provide better information than shorter, more recent periods for measuring ES volatility to hedge trades. The ES can be adopted using parametric, semi-parametric, and non-parametric methods, and the analyses of the log return indicators started on 3 January 2000 and ended on 5 May 2023. The analyses carried out to evaluate these log return indicators covered the period from 6 May 2023 to 1 August 2025, where it was found that the exchange rate volatility of the Brazilian Real exceeded the VaR limits and even reached the Expected Shortfall risk zone. Then, a different analysis was performed, starting on 11 March 2020 and ending on 5 May 2023. This second analysis, as the first analysis, was carried out to evaluate these log return indicators that covered the period from 6 May 2023 to 1 August 2025. In this latest period analysis, the exchange rate volatility of the Brazilian Real reached the Exchange Shortfall risk zone in a different way compared to the first way. All three types of methods—parametric, non-parametric, and semi-parametric—show distinct behaviors depending on the period evaluated. The hypothesis was rejected, but the hedging strategies should account for asset volatility. The software used to calculate the estimators was Microsoft Excel 365 and Stata 14.2.

International Journal of Financial Studies doi: 10.3390/ijfs14020041

Authors: Affaf Asghar Butt Aamer Shahzad Sadia Anis Luís Miguel Marques Flávio Morais

The effect of corporate social responsibility (CSR) disclosure on zero-leverage policies is examined for listed firms at the Pakistan Stock Exchange (PSX) from 2010 to 2021. Binary logistic regression models show a statistically significant positive relationship between CSR disclosure and zero leverage. Increased CSR disclosure raises the propensity of firms to have zero leverage. Moreover, the negative effect of CSR disclosure on debt ratios further confirms these findings. Results show that highly disclosed CSR firms face less information asymmetry and prefer equity financing over bank debt. Regulators should develop incentive programs to increase their CSR disclosure and strengthen stakeholders’ relationships.

International Journal of Financial Studies doi: 10.3390/ijfs14020040

Authors: Gustavo Henrique Rodrigues Pessoa Ricardo Ratner Rochman

Despite significant advances in bank regulation and the widespread adoption of macroprudential frameworks, emerging market economies remain persistently vulnerable to global financial shocks. Episodes such as the Global Financial Crisis, the COVID-19 market turmoil, and recent monetary tightening cycles reveal that financial stress originating in core markets continues to transmit rapidly and forcefully to emerging economies. This paper argues that such vulnerability reflects structural features of contemporary financial systems rather than deficiencies in domestic banking regulation alone. Adopting a conceptual and analytical approach, the article develops an integrated framework of macro-financial blind spots that links global financial cycles, non-bank financial intermediation, and regulatory fragmentation. The analysis highlights how funding liquidity, collateral valuation, margin dynamics, and market-based leverage amplify global shocks through channels that lie largely outside traditional, bank-centric macroprudential frameworks. As market-based finance expands, systemic risk increasingly originates in activities rather than institutions, limiting the effectiveness of entity-based regulation and reinforcing emerging markets’ role as price-takers in global portfolios. The paper contributes to the literature by synthesizing insights from macroprudential policy, market liquidity, and non-bank finance to explain the persistence of emerging market vulnerability in an era of globalized funding. It further derives policy implications for macro-financial governance, emphasizing the need for system-wide, activity-based approaches, improved data and transparency, and stronger domestic and international regulatory coordination. These findings are relevant for policymakers seeking to reconcile financial integration with systemic resilience in emerging markets.

International Journal of Financial Studies doi: 10.3390/ijfs14020039

Authors: Xiaoli Li Dongsheng Zhang Na Zeng Defeng Meng

Artificial intelligence (AI) has become an integral driver of digital transformation in the banking sector, fundamentally influencing operational efficiency, resource allocation, and profitability. This study investigates how AI adoption affects the profitability of Chinese commercial banks and through which mechanisms these effects occur, within the context of the country’s broader financial digitalization process. Using panel data for 17 A-share listed banks in China from 2009 to 2022, we employ a multi-period difference-in-differences (DID) framework—whose validity rests on the parallel trend assumption, empirically verified through an event-study specification—and combine it with propensity score matching (PSM) and placebo simulations to ensure credible causal identification. The results indicate that AI adoption significantly improves bank profitability. Mechanism analyses suggest that AI enhances profitability through two overarching channels—operational efficiency and resource allocation—manifested in (i) higher cost elasticity of income, (ii) improved deposit–loan turnover adaptability via more efficient liquidity and funding-cycle management, and (iii) optimized cross-business capital allocation efficiency through better risk–return matching in diversified operations. The effects are stronger for banks with higher digital investment intensity and tighter customer stickiness–liability cost coupling, and vary systematically across ownership types, bank sizes, and policy cycles. Overall, the findings provide policy-relevant evidence on how AI-driven digital transformation can enhance bank performance and risk management in modern financial systems. This study contributes by constructing a disclosure-based AI adoption measure from bank annual reports and exploiting staggered adoption with a multi-period DID design to provide causal evidence from China’s listed banking sector.

International Journal of Financial Studies doi: 10.3390/ijfs14020038

Authors: Julien Chevallier

This paper develops a novel hybrid framework that integrates clustering-enhanced Particle Swarm Optimization (PSO) with stretching techniques to solve Markowitz’s quadratic portfolio optimization problem. The proposed approach avoids local optima traps that plague traditional optimization methods, while the stretching function modifications enhance the algorithm’s global search capabilities. The framework comprises four distinct algorithmic variants: a baseline SWARM PSO with stretching algorithm, and three clustering-enhanced extensions incorporating Hierarchical, K-means, and DBSCAN techniques. These clustering enhancements strategically group assets based on risk–return characteristics to improve portfolio diversification and risk management. Implementation in R enables comprehensive analysis of portfolio weight allocation patterns and diversification metrics across varying market structures. Empirical validation using daily price data from six major international stock market indices spanning January 2020 to December 2025 demonstrates the framework’s generalization capability in constructing buy-and-hold investment portfolios. The results reveal significant market-specific algorithmic effectiveness, with K-means variants achieving competitive efficacy in Eurostoxx and Belgian markets, DBSCAN demonstrating strong effectiveness in Chinese equity markets, Hierarchical clustering showing robust results in Indian market conditions, and the baseline SWARM algorithm exhibiting relative efficiency in French and Danish indices. Performance evaluation encompasses comprehensive risk-adjusted metrics, including Portfolio Return, Volatility, Sharpe Ratio, Calmar Ratio, and Value at Risk, providing portfolio managers with an adaptive, market-responsive optimization toolkit.

International Journal of Financial Studies doi: 10.3390/ijfs14020036

Authors: Quan Zhang Yichuan Wang Le Zhu Suying Song

Using the panel data of Chinese A-share firms from 2012 to 2023, we find that executives’ focus on digitalization is significantly and positively associated with corporate sustainability performance. The finding holds firm after a suite of endogeneity and robustness tests. Heterogeneity tests indicate that such a favorable impact is more salient for large enterprises, industry players with superior competitiveness, and entities situated in eastern China. The mechanism tests reveal that executives’ digital attention enhances corporate sustainable development by improving resource structuring capability, resource bundling capability, and resource leveraging capability. Additionally, financing constraints weaken, while media attention will enhance this promoting effect. Additional dimension-focused analyses demonstrate that the direct promotional impact of executives’ digital attention on corporate financial performance remains statistically insignificant, whereas it exerts a markedly positive catalytic effect on corporate environmental performance. This research offers novel theoretical interpretations and practical implications regarding the role of executive cognitive orientation in advancing corporate sustainable development against the backdrop of digital transformation.

International Journal of Financial Studies doi: 10.3390/ijfs14020037

Authors: Davinder K. Malhotra Shaurya Batra Rahul Singh

This study examines the extent to which Indian technology equities generate sufficient returns relative to their inherent volatility and assesses whether intra-sector diversification can improve outcomes in this dynamic, high-risk sector. Drawing on data from January 2020 to April 2025, ten leading firms are analyzed using an integrated approach that incorporates traditional risk-adjusted indicators, downside-sensitive metrics, and a six-factor model featuring momentum. The results show clear heterogeneity in performance. Mid-cap innovators such as Persistent Systems and Coforge deliver positive and, in some cases, statistically significant alphas, while large-cap stocks including Infosys, Tata Consultancy Services (TCS), and Wipro provide stability but limited excess returns. At the portfolio level, an equally weighted allocation improves downside protection. However, factor-model analysis finds no statistically significant portfolio alpha once systematic exposures are accounted for. These findings highlight the importance of active firm-level selection within the Indian technology sector, while also underscoring the role of intra-sector diversification in mitigating extreme losses.

International Journal of Financial Studies doi: 10.3390/ijfs14020035

Authors: Dani Aoun Rita Rahal Layal Sfeir Nada Jabbour Al Maalouf

This study investigates how financial literacy, FinTech adoption, and financial attitudes shape economic decision-making among millennials in Lebanon, a crisis-affected emerging economy. The study examines whether enhancing financial literacy can strengthen economic resilience through improved financial behavior, with financial attitudes acting as a mediator. Guided by Behavioral Finance Theory, the study employs a quantitative approach using data from 390 Lebanese millennials collected via a structured questionnaire. Structural equation modeling was applied to test direct and mediating effects. Both financial literacy and FinTech adoption were found to significantly influence millennials’ financial behavior, with financial literacy emerging as the stronger predictor. The findings also revealed that financial attitude significantly mediates the link between literacy and behavior, suggesting that financial knowledge alone is insufficient without attitudinal reinforcement. This study fills a critical empirical gap in the MENA region by offering evidence from a highly under-researched, crisis-affected emerging market. It introduces an integrated model combining technological, cognitive, and attitudinal dimensions of financial behavior. The study offers practical implications for policymakers, financial institutions, and international development actors seeking to strengthen financial inclusion and household stability in similar turbulent contexts.

International Journal of Financial Studies doi: 10.3390/ijfs14020034

Authors: Daniela Mogîldea Mihai Carp

The informational efficiency of stock prices is conditioned by the level of quality of financial reports, contributing to an accurate assessment of the company’s future performance. By approaching informational quality from two perspectives, we conducted an analysis of the impact of faithful representation and readability of annual reports on the reaction of the Romanian capital market, measured by annual stock returns (SR) and cumulative abnormal returns (CAR). The findings revealed an accentuated concern of investors regarding the faithful representation of the firm’s financial results (both at the time of financial statements’ publication and at the year-end) and a diminished significance of the comprehensibility level of financial information in the investment decision-making process. The annual reports of a sample of firms listed on the BSE between 2017 and 2023 have an increased level of linguistic complexity, which entails processing costs, and are intended for sophisticated users with financial expertise. Along with the specialized language, the extensive length of reports delays the incorporation of all information into the stock price, decreasing the informational efficiency of the market. This empirical study applies several indices to assess the readability and conciseness of financial information (FOG index, Flesch–Kincaid index, Flesch Reading Ease Score, and report length) and contributes to the expanding literature by providing a useful basis for future analysis of the influence of financial report quality on investors’ perceptions.

International Journal of Financial Studies doi: 10.3390/ijfs14020033

Authors: Saurabh Ahluwalia

This paper leverages Google Trends search volume data from 2004 to 2008 as a proxy for investor information demand. The analysis documents that greater search activity prior to earnings announcements is positively associated with future market reaction to earnings announcements, pre-earnings announcement drift, and buying pressure. The results are consistent with investors gathering value-relevant information through online research, which is subsequently incorporated into prices through trading around earnings announcements. Notably, search volume is positively associated with market reaction to earnings announcements and pre-announcement drifts for more obscure firms where data is scarce. Overall, this paper provides large-sample evidence validating theoretical models where dispersed private information is incorporated into stock prices. The findings suggest that broader data access may facilitate pricing efficiency by promoting more informed market participation.