International Journal of Financial Studies

19 pages, 706 KB

Open AccessArticle

Financing Constraints and High-Quality Development of Chinese Listed Firms: Mechanisms of Investment Efficiency and Contingent Factors

by Jun Yan, Zexia Zhao and Yan Liu

Int. J. Financial Stud. 2025, 13(3), 179; https://doi.org/10.3390/ijfs13030179 - 18 Sep 2025

Viewed by 299

Abstract

Against the backdrop of tightened credit conditions, external financing constraints have increasingly become an important factor affecting enterprises’ high-quality development. This study focuses on the impact of financing constraints on the high-quality development of Chinese listed firms and constructs an analytical framework involving [...] Read more.

Against the backdrop of tightened credit conditions, external financing constraints have increasingly become an important factor affecting enterprises’ high-quality development. This study focuses on the impact of financing constraints on the high-quality development of Chinese listed firms and constructs an analytical framework involving investment efficiency as a mediator and contextual factors such as managerial effectiveness and internal control quality as moderators. Using a longitudinal dataset of China’s A-share listed companies from 2007 to 2021, multivariate regression and mediation effect tests are conducted. The observational findings reveal a statistically meaningful U-shaped association between financial constraints and the high-quality development of enterprises. Further analysis confirms that investment efficiency partially mediates the relationship between financing constraints and high-quality development, while managerial effectiveness and internal control quality play significant moderating roles in this relationship. Additionally, the study reveals heterogeneous impacts of financing constraints on high-quality development across different regions. These findings provide insights into how enterprises can mitigate the adverse effects of financing constraints and promote high-quality development. Full article

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25 pages, 2507 KB

Open AccessArticle

The Road to Tax Collection Digitalization: An Assessment of the Effectiveness of Digital Payment Systems in Nigeria and the Role of Macroeconomic Factors

by Cordelia Onyinyechi Omodero and Gbenga Ekundayo

Int. J. Financial Stud. 2025, 13(3), 178; https://doi.org/10.3390/ijfs13030178 - 17 Sep 2025

Viewed by 539

Abstract

The global movement towards a cashless society has prompted the payment of tax obligations through digital platforms and sources. In this international race to ensure that transaction payments are not hindered by the lack of physical cash, Nigeria is also making progress. Therefore, [...] Read more.

The global movement towards a cashless society has prompted the payment of tax obligations through digital platforms and sources. In this international race to ensure that transaction payments are not hindered by the lack of physical cash, Nigeria is also making progress. Therefore, the focus of this study is to assess the implications of digital payment systems in enhancing the effectiveness of tax revenue collection in Nigeria. The analysis spans from the first quarter of 2009 to the fourth quarter of 2023, utilizing the Autoregressive Distributed Lag and Error Correction Model. The research uses the most active digital payment systems that have been in operation during the study period. These electronic payment types include digital cheques (CHQs), Automated Teller Machines (ATMs), Point-of-Sales (POSs), Mobile payment (MPY), and Web-based payment (WPY). These are the predictor variables, while the tax revenue collection (TXC) during this period is the dependent variable. The control variables include information and telecommunication technology penetration rate (ICTPR), inflation, and gross domestic product. The outcomes of this study reveal that, over the long term, a percentage change in CHQs, ATMs, MPY, and ICTPR is linked to a decline of 8.1%, 12.5%, 6.7%, and 22.4% in TXC, respectively. In contrast, WPY indicates a 7.2% positive increase in TXC while inflation exerts a positive increase of 46.7%. The Error Correction Model (ECM) suggests that the deviations from the long-term equilibrium in earlier years are being corrected at a rate of 3.9% in the current year. In the short term, it is noted that digital payment systems do not influence TXC. On the other hand, GDP maintains a significant negative influence on TXC, in both the long- and short-term. Given these results, the study recommends the establishment of a robust information and communication technology (ICT) infrastructure to enhance effective tax collection, even from rural areas and the informal sector. It is also important for the government to develop strategies that will bring the informal sector into the tax net. Full article

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26 pages, 1224 KB

Open AccessArticle

Modeling Market Expectations of Profitability Mean Reversion: A Comparative Analysis of Adjustment Models

by Miroslava Vlčková and Tomáš Buus

Int. J. Financial Stud. 2025, 13(3), 177; https://doi.org/10.3390/ijfs13030177 - 17 Sep 2025

Viewed by 375

Abstract

This paper investigates how market expectations regarding profitability mean reversion are reflected in stock prices. We propose a model that infers implicit expectations of future earnings using publicly available share prices based on the assumption that markets efficiently incorporate forward-looking information. The study [...] Read more.

This paper investigates how market expectations regarding profitability mean reversion are reflected in stock prices. We propose a model that infers implicit expectations of future earnings using publicly available share prices based on the assumption that markets efficiently incorporate forward-looking information. The study compares several adjustment models, including the classical partial adjustment framework and a mean reversion model, to identify the most suitable mechanism to capture the dynamics of expected earnings. Special attention is paid to the statistical characteristics of accounting data and ratio-based measures, which influence model performance. Using a dataset covering a twenty-year period, we find that the mean reversion model consistently outperforms partial adjustment models in explaining the behavior of cyclical and random components converging toward a long-term trend. The findings suggest that market prices embed rational expectations of profitability reversion, especially in periods of above average performance. These results align with previous research and provide a robust framework for understanding how earnings expectations are formed and adjusted in financial markets. Full article

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27 pages, 380 KB

Open AccessArticle

Generational Insights into Herding Behavior: The Moderating Role of Investment Experience in Shaping Decisions Among Generations X, Y, and Z

by Abdul Syukur, Amron Amron, Fery Riyanto, Febrianur Ibnu Fitroh Sukono Putra and Rifal Richard Pangemanan

Int. J. Financial Stud. 2025, 13(3), 176; https://doi.org/10.3390/ijfs13030176 - 16 Sep 2025

Viewed by 739

Abstract

Understanding generational differences in herding behavior is crucial for policymakers, financial educators, and market regulators, particularly in emerging markets where retail investor participation is rapidly growing. This study investigates the influence of herding behavior on investment decision-making among Generations X, Y, and Z [...] Read more.

Understanding generational differences in herding behavior is crucial for policymakers, financial educators, and market regulators, particularly in emerging markets where retail investor participation is rapidly growing. This study investigates the influence of herding behavior on investment decision-making among Generations X, Y, and Z in Indonesia, as well as the moderating role of investment experience. Using a multi-group structural equation modeling (SEM) approach with data from 1293 retail investors, the research compares behavioral tendencies across cohorts. Results reveal that herding behavior has a positive and significant impact on investment decision-making in all generations, with the strongest effect observed in Generation X, followed by Generation Z and Generation Y. Investment experience significantly weakens herding behavior’s influence for Generation X but shows no significant moderating effect for Generations Y and Z, suggesting that psychological and social influences, particularly from digital platforms, may outweigh experiential learning in younger cohorts. These findings align with behavioral finance theory, which explains herding as a cognitive and emotional bias heightened by market uncertainty. The results provide practical implications for designing targeted financial education programs and regulatory measures to promote independent decision-making and reduce susceptibility to biased market information, especially among younger generations in digitally driven investment environments. Full article

20 pages, 1155 KB

Open AccessArticle

The Role of Fear of Missing out (FOMO), Loss Aversion, and Herd Behavior in Gold Investment Decisions: A Study in the Vietnamese Market

by Xuan Hung Nguyen, Dieu Anh Bui, Nam Anh Le and Quynh Trang Nguyen

Int. J. Financial Stud. 2025, 13(3), 175; https://doi.org/10.3390/ijfs13030175 - 15 Sep 2025

Viewed by 830

Abstract

This study investigates the influence of FOMO, loss aversion, and herd behavior on gold investment decisions in the Vietnamese market. Employing data collected from 727 investors and the Partial Least Squares Structural Equation Modeling (PLS-SEM) method, the analysis results confirm the pivotal role [...] Read more.

This study investigates the influence of FOMO, loss aversion, and herd behavior on gold investment decisions in the Vietnamese market. Employing data collected from 727 investors and the Partial Least Squares Structural Equation Modeling (PLS-SEM) method, the analysis results confirm the pivotal role of FOMO, with both direct and indirect impacts on gold investment decisions. Notably, both loss aversion and herd behavior positively influence FOMO, thereby indirectly encouraging relatively hasty and inadequately considered investment decisions. The study also finds that FOMO has a negative relationship with anticipated regret but is positively correlated with subjective expected pleasure. Furthermore, as determined through Multi-Group Analysis (MGA), psychological messages featuring “self-decision” or “risk warning” demonstrate a significant moderating role, potentially reducing or enhancing the influence of FOMO on investment decisions. These findings contribute to enriching behavioral finance theory and provide an empirical basis for developing effective risk management policies and gold market regulation aimed at mitigating the negative impacts of FOMO. Full article

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15 pages, 295 KB

Open AccessArticle

Bank Risk-Taking During COVID-19: The Role of Private and Public Ownership in GCC

by Abdullah Aldousari, Ahmed Mohammed and Sarah Lindop

Int. J. Financial Stud. 2025, 13(3), 174; https://doi.org/10.3390/ijfs13030174 - 12 Sep 2025

Viewed by 393

Abstract

This study explores the ownership–risk relationship in the GCC emerging economies during the COVID-19 pandemic, examining 44 commercial banks classified as private and publicly owned banks. The two-stage least squares (2SLS) method is employed to identify endogeneity issues, with robustness checks using panel [...] Read more.

This study explores the ownership–risk relationship in the GCC emerging economies during the COVID-19 pandemic, examining 44 commercial banks classified as private and publicly owned banks. The two-stage least squares (2SLS) method is employed to identify endogeneity issues, with robustness checks using panel data techniques. We analyzed the ownership–risk relationship, including non-linear and interaction effects. The results reveal that public ownership exhibits an inverted U-shaped relationship with NPLs, where moderate public concentration increases credit risk, while high public control marginally reduces it. Private ownership is linked to higher risk once bank-specific characteristics are controlled, reflecting riskier lending driven by profitability motives. We show that public banks demonstrate resilience due to stable deposits and implicit backing, whereas private banks are more vulnerable to systemic shocks. The impact of ownership structure on credit risk is context-dependent, reflecting heterogeneous ownership objectives in the GCC. Full article

26 pages, 737 KB

Open AccessArticle

Capital Structure Theories in US Corporate Divestitures: A Study on Spin-Off Firms

by Xian Chen, Sanjib Guha and Tahsina Haque Simu

Int. J. Financial Stud. 2025, 13(3), 173; https://doi.org/10.3390/ijfs13030173 - 12 Sep 2025

Viewed by 459

Abstract

Some giant US conglomerates are now undergoing corporate spin-offs or are considering such spin-offs in the near future. Corporate spin-offs offer a unique opportunity to assess corporate capital structure decisions. The leverage ratio of the spin-off firms represents their initial capital structure. We [...] Read more.

Some giant US conglomerates are now undergoing corporate spin-offs or are considering such spin-offs in the near future. Corporate spin-offs offer a unique opportunity to assess corporate capital structure decisions. The leverage ratio of the spin-off firms represents their initial capital structure. We investigate the capital structure of corporate spin-offs and find evidence that they adhere to the trade-off theory. This study provides evidence that the subsidiary firms tend to aim for a target capital ratio during the sample period. The results indicate that the partial adjustment model with firm fixed effects is a good fit for the data sample. The parent companies in corporate spin-offs exhibit a similar pattern but with a slower adjustment speed. The tendency to target capital ratios is observable in both market value and book value leverage measures for the parent and subsidiary firms. Indicators of the pecking order assumption do not possess statistically significant coefficients. Changes in share price affect market debt ratios in the short term. With alternative definitions of leverage, the estimated adjustment speeds vary. In the case of longer horizons, the results align with a continuous rate of adjustment. Full article

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21 pages, 2257 KB

Open AccessReview

The Philosophy of Financial Performance: A Bibliometric and Conceptual Review

by Ionela Munteanu, Liliana Ionescu-Feleagă, Bogdan Ștefan Ionescu, Alexandra-Maria Spânu and Mircea Iosif Rus

Int. J. Financial Stud. 2025, 13(3), 172; https://doi.org/10.3390/ijfs13030172 - 11 Sep 2025

Viewed by 437

Abstract

Financial performance research has increasingly intersected with philosophical debates on ethics, sustainability, and stakeholder value, yet a clear framework linking these perspectives to actionable financial metrics remains underdeveloped. This study aims to explore how philosophical perspectives (normative, epistemological, and behavioral) inform the evolving [...] Read more.

Financial performance research has increasingly intersected with philosophical debates on ethics, sustainability, and stakeholder value, yet a clear framework linking these perspectives to actionable financial metrics remains underdeveloped. This study aims to explore how philosophical perspectives (normative, epistemological, and behavioral) inform the evolving concept of financial performance, using bibliometric and science mapping techniques to analyze key research trends from 2006 to 2023. The analysis identifies four dominant thematic areas: corporate social responsibility (CSR), organizational performance, ethical governance, and circular economy innovation. We synthesize these into a practical framework that connects each theme to measurable financial indicators, enabling managers to refine capital allocation, investors to incorporate non-financial drivers into valuation models, and policymakers to design sustainability reporting standards that integrate both economic and ethical considerations. By bridging philosophical insights and financial decision-making tools, this study contributes to both the theoretical development and applied practice of performance assessment in finance. Full article

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36 pages, 1441 KB

Open AccessArticle

When Financial Awareness Meets Reality: Financial Literacy and Gen Z’s Entrepreneurship Interest

by Eva Kicova, Jakub Michulek, Olga Ponisciakova and Juraj Fabus

Int. J. Financial Stud. 2025, 13(3), 171; https://doi.org/10.3390/ijfs13030171 - 11 Sep 2025

Viewed by 735

Abstract

Financial literacy is a key competence for responsible decision-making and entrepreneurial readiness. This study looks at how Generation Z’s entrepreneurial participation is impacted by objective, subjective, and calibrated FL. The alignment of perceived and actual knowledge or calibration is highlighted as an understudied [...] Read more.

Financial literacy is a key competence for responsible decision-making and entrepreneurial readiness. This study looks at how Generation Z’s entrepreneurial participation is impacted by objective, subjective, and calibrated FL. The alignment of perceived and actual knowledge or calibration is highlighted as an understudied factor that influences entrepreneurial behaviour. A mixed-methods approach was applied, combining a survey of 403 Slovak students with structured interviews with secondary school and university teachers. Quantitative analysis used Chi-square tests, Cramer’s V, sign schemes, and MLR. Qualitative interviews provided contextual insights into educational gaps and perceived barriers to entrepreneurship. The findings confirm that a higher financial literacy is positively related to entrepreneurial interest. Objective literacy has a slightly greater predictive value than self-assessed literacy, while calibration emerged as the strongest predictor: realistically, financially literate individuals displayed the highest entrepreneurial engagement, whereas both over- and underestimation of financial knowledge reduced it. Interviews highlighted insufficient financial education, limited practical experience, and fear of risk as major obstacles. By combining three aspects of financial literacy with business goals and offering fresh data from Slovakia, this study makes a contribution to the literature. In similar situations, it makes suggestions for enhancing financial education to support Generation Z’s entrepreneurial potential. Full article

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30 pages, 5994 KB

Open AccessArticle

Predicting the Canadian Yield Curve Using Machine Learning Techniques

by Ali Rayeni and Hosein Naderi

Int. J. Financial Stud. 2025, 13(3), 170; https://doi.org/10.3390/ijfs13030170 - 9 Sep 2025

Viewed by 679

Abstract

This study applies machine learning methods to predict the Canadian yield curve using a comprehensive set of macroeconomic variables. Lagged values of the yield curve and a wide array of Canadian and international macroeconomic variables are utilized across various machine learning models. Hyperparameters [...] Read more.

This study applies machine learning methods to predict the Canadian yield curve using a comprehensive set of macroeconomic variables. Lagged values of the yield curve and a wide array of Canadian and international macroeconomic variables are utilized across various machine learning models. Hyperparameters are estimated to minimize mispricing across government bonds with different maturities. The Group Lasso algorithm outperforms the other models studied, followed by Lasso. In addition, the majority of the models outperform the Random Walk benchmark. The feature importance analysis reveals that oil prices, bond-related factors, labor market conditions, banks’ balance sheets, and manufacturing-related factors significantly drive yield curve predictions. This study is one of the few that uses such a broad array of macroeconomic variables to examine Canadian macro-level outcomes. It provides valuable insights for policymakers and market participants, with its feature importance analysis highlighting key drivers of the yield curve. Full article

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23 pages, 4767 KB

Open AccessFeature PaperArticle

Dynamics of Cryptocurrencies, DeFi Tokens, and Tech Stocks: Lessons from the FTX Collapse

by Nader Naifar and Mohammed S. Makni

Int. J. Financial Stud. 2025, 13(3), 169; https://doi.org/10.3390/ijfs13030169 - 9 Sep 2025

Viewed by 846

Abstract

The FTX collapse marked a significant shock to global crypto markets, prompting concerns about systemic contagion. This paper investigates the dynamic connectedness between cryptocurrencies, DeFi tokens, and tech stocks, focusing on the systemic impact of the FTX collapse. We decompose total, internal, and [...] Read more.

The FTX collapse marked a significant shock to global crypto markets, prompting concerns about systemic contagion. This paper investigates the dynamic connectedness between cryptocurrencies, DeFi tokens, and tech stocks, focusing on the systemic impact of the FTX collapse. We decompose total, internal, and external connectedness across asset groups using a time-varying parameter VAR model. The results show that post-FTX, Bitcoin and Ethereum intensified their roles as core shock transmitters, while Tether consistently acted as a volatility absorber. DeFi tokens exhibited heightened intra-group spillovers and occasional external influence, reflecting structural fragility. Tech stocks remained largely insulated, with reduced cross-market linkages. Network visualizations confirm a post-crisis fragmentation, characterized by denser internal crypto-DeFi ties and weaker inter-group contagion. These findings have important policy implications for regulators, investors, and system designers, indicating the need for targeted risk monitoring and governance within decentralized finance. Full article

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32 pages, 1030 KB

Open AccessArticle

Effects of Liquidity on TE and Performance of Japanese ETFs

by Atsuyuki Naka, Jiayuan Tian and Seungho Shin

Int. J. Financial Stud. 2025, 13(3), 168; https://doi.org/10.3390/ijfs13030168 - 9 Sep 2025

Viewed by 585

Abstract

This study identifies a nonlinear relationship among liquidity, tracking error, and risk-adjusted performance in JETFs. Collecting daily data for 1077 JETFs from January 2008 to April 2022, we find a concave association, whereby both highly liquid and highly illiquid JETFs exhibit lower risk-adjusted [...] Read more.

This study identifies a nonlinear relationship among liquidity, tracking error, and risk-adjusted performance in JETFs. Collecting daily data for 1077 JETFs from January 2008 to April 2022, we find a concave association, whereby both highly liquid and highly illiquid JETFs exhibit lower risk-adjusted returns and higher tracking errors. Employing quantile regression, we further show that smaller, less liquid JETFs tend to deliver superior risk-adjusted performance. When comparing across listing venues—Japan, the U.S., Ireland, and Luxembourg—we find that the impact of liquidity on performance is most pronounced in the Japanese market, which also shows the highest average tracking error. In contrast, U.S.-listed JETFs offer the lowest tracking error. These results suggest that investors may benefit from choosing smaller JETFs listed in Japan. Full article

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24 pages, 1322 KB

Open AccessArticle

Predictive Power of ESG Factors for DAX ESG 50 Index Forecasting Using Multivariate LSTM

by Manuel Rosinus and Jan Lansky

Int. J. Financial Stud. 2025, 13(3), 167; https://doi.org/10.3390/ijfs13030167 - 4 Sep 2025

Viewed by 597

Abstract

As investors increasingly use Environmental, Social, and Governance (ESG) criteria, a key challenge remains: ESG data is typically reported annually, while financial markets move much faster. This study investigates whether incorporating annual ESG scores can improve monthly stock return forecasts for German DAX-listed [...] Read more.

As investors increasingly use Environmental, Social, and Governance (ESG) criteria, a key challenge remains: ESG data is typically reported annually, while financial markets move much faster. This study investigates whether incorporating annual ESG scores can improve monthly stock return forecasts for German DAX-listed firms. We employ a multivariate long short-term memory (LSTM) network, a machine learning model ideal for time series data, to test this hypothesis over two periods: an 8-year analysis with a full set of ESG scores and a 16-year analysis with a single disclosure score. The evaluation of model performance utilizes standard error metrics and directional accuracy, while statistical significance is assessed through paired statistical tests and the Diebold–Mariano test. Furthermore, we employ SHapley Additive exPlanations (SHAP) to ensure model explainability. We observe no statistically significant indication that incorporating annual ESG data enhances forecast accuracy. The 8-year study indicates that using a comprehensive ESG feature set results in a statistically significant increase in forecast error (RMSE and MAE) compared to a baseline model that utilizes solely historical returns. The ESG-enhanced model demonstrates no significant performance disparity compared to the baseline across the 16-year investigation. Our findings indicate that within the one-month-ahead projection horizon, the informative value of low-frequency ESG data is either fully incorporated into the market or is concealed by the significant forecasting capability of the historical return series. This study’s primary contribution is to demonstrate, through out-of-sample testing, that standard annual ESG information holds little practical value for generating predictive alpha, urging investors to seek more timely, alternative data sources. Full article

(This article belongs to the Special Issue Editorial Board Members’ Collection Series: ESG Ratings and Disclosures)

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20 pages, 2303 KB

Open AccessArticle

Estimating the Impact of ESG on Financial Forecast Predictability Using Machine Learning Models

by Marius Sorin Dincă, Vlad Ciotlăuși and Frank Akomeah

Int. J. Financial Stud. 2025, 13(3), 166; https://doi.org/10.3390/ijfs13030166 - 4 Sep 2025

Viewed by 828

Abstract

This study examines whether the integration of Environmental, Social, and Governance (ESG) factors enhances the accuracy of financial forecasts. Using a dataset of 2548 publicly listed companies from 98 countries, we evaluate a range of machine learning models—from ARIMA to XGBoost—by comparing the [...] Read more.

This study examines whether the integration of Environmental, Social, and Governance (ESG) factors enhances the accuracy of financial forecasts. Using a dataset of 2548 publicly listed companies from 98 countries, we evaluate a range of machine learning models—from ARIMA to XGBoost—by comparing the forecast performance of firms with high and low ESG scores (based on the sample median). Model accuracy is assessed through MAE, RMSE, MSE, MAPE, and R², complemented by statistical significance tests. Results show no consistent improvement in predictive performance for high-ESG firms, with only the Business Services sector displaying a marginal effect. These findings challenge the assumption that ESG integration inherently reduces forecast uncertainty, suggesting instead that ESG scores contribute little to predictive accuracy under long-term investment conditions. The study highlights the importance of model choice, careful control of exogenous variables, and rigorous testing, while underscoring the broader need for standardized ESG metrics in financial research. Full article

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23 pages, 377 KB

Open AccessArticle

The Impact of Non-Performing Loans on Bank Growth: The Moderating Roles of Bank Size and Capital Adequacy Ratio—Evidence from U.S. Banks

by Richard Arhinful, Leviticus Mensah, Bright Akwasi Gyamfi and Hayford Asare Obeng

Int. J. Financial Stud. 2025, 13(3), 165; https://doi.org/10.3390/ijfs13030165 - 4 Sep 2025

Viewed by 1283

Abstract

Banks in the United States face persistent challenges from non-performing loans (NPLs), despite conducting thorough client evaluations before issuing loans. To mitigate the impact of NPLs and support both local and global growth, banks must adopt effective risk management strategies. This study investigates [...] Read more.

Banks in the United States face persistent challenges from non-performing loans (NPLs), despite conducting thorough client evaluations before issuing loans. To mitigate the impact of NPLs and support both local and global growth, banks must adopt effective risk management strategies. This study investigates the effect of NPLs on bank growth and the moderating of bank size and Capital Adequacy Ratio (CAR) through the lens of the Resource-Based View (RBV) theory. A sample of 253 banks listed on the New York Stock Exchange from 2006 to 2023 was selected using specific inclusion criteria from the Thomson Reuters Eikon DataStream. To address cross-sectional dependence and endogeneity, advanced estimation techniques—Feasible Generalized Least Squares (FGLS), Driscoll and Kraay standard errors, and the Generalized Method of Moments (GMM)—were employed. The results show that NPLs have a significant negative impact on banks’ asset and income growth. Furthermore, bank size and capital adequacy ratio (CAR) negatively and significantly moderate this relationship. These findings underscore the need for banks to enhance credit risk management by strengthening loan approval processes and leveraging advanced analytics to assess borrower risk more accurately. Full article

(This article belongs to the Special Issue Risks and Uncertainties in Financial Markets)

24 pages, 329 KB

Open AccessArticle

Does Financial Development Shape the Energy–FDI–Growth Nexus? New Evidence from BRICS+ Countries Using Dynamic Panel Estimation

by Geoffrey Gatharia Gachino

Int. J. Financial Stud. 2025, 13(3), 163; https://doi.org/10.3390/ijfs13030163 - 4 Sep 2025

Viewed by 558

Abstract

This study investigates how energy consumption and foreign direct investment (FDI) influenced economic growth in BRICS+ countries from 1990 to 2021, using a two-step System GMM estimator to address endogeneity and dynamic effects. While the results show that both energy and FDI positively [...] Read more.

This study investigates how energy consumption and foreign direct investment (FDI) influenced economic growth in BRICS+ countries from 1990 to 2021, using a two-step System GMM estimator to address endogeneity and dynamic effects. While the results show that both energy and FDI positively affected growth, disaggregated analysis revealed that renewable energy promoted growth, whereas non-renewables hindered it. Similarly, FDI directed toward gross fixed capital formation (FDI_GFCF) consistently boosted growth, unlike aggregate FDI. Financial development moderated these effects, amplifying the benefits of energy use but dampening FDI’s growth impact in more developed financial systems. The effects of energy and FDI remained stable before and after the Paris Agreement, supporting the robustness of the findings. These results underscore the importance of tailored energy and FDI strategies, financial sector reforms, and supportive policy environments to advance sustainable growth in BRICS+ economies. Full article

26 pages, 418 KB

Open AccessArticle

Financial Leverage and Firm Performance in Moroccan Agricultural SMEs: Evidence of Nonlinear Dynamics

by Imad Nassim, Salma Nassim and Abdelkarim Moussa

Int. J. Financial Stud. 2025, 13(3), 164; https://doi.org/10.3390/ijfs13030164 - 3 Sep 2025

Viewed by 564

Abstract

This study investigates the nexus between leverage and financial performance in a sample of 54 Moroccan agricultural small- and medium-sized enterprises (SMEs) over the period of 2017–2022. Drawing on trade-off, pecking order, and agency theories, this analysis examines whether different levels of indebtedness [...] Read more.

This study investigates the nexus between leverage and financial performance in a sample of 54 Moroccan agricultural small- and medium-sized enterprises (SMEs) over the period of 2017–2022. Drawing on trade-off, pecking order, and agency theories, this analysis examines whether different levels of indebtedness influence performance, as measured by return on assets (ROA). Using panel data regression models, both linear and nonlinear specifications were tested to explore the potential curvature of the leverage–performance relationship. The empirical results reveal a significant and negative linear relationship between both short-term and long-term leverage and ROA, suggesting that increased indebtedness impairs financial performance. A quadratic specification reveals a persistently negative effect of short-term leverage and a U-shaped relationship between long-term leverage and ROA, indicating that performance may improve beyond certain debt thresholds. To address endogeneity concerns and validate the findings, dynamic panel estimation using the generalized method of moments (GMM) was employed, confirming the leverage’s adverse effects on performance. Thus, this study provides policy-relevant insights into optimal capital structure decisions for small agribusinesses and underscores the need for tailored financial strategies to support their sustainable development. Full article

19 pages, 300 KB

Open AccessArticle

Monetary Governance and Currencies Resilience in Times of Crisis

by Ayyoub Ben El Rhadbane and Abdeslam El Moudden

Int. J. Financial Stud. 2025, 13(3), 162; https://doi.org/10.3390/ijfs13030162 - 2 Sep 2025

Viewed by 448

Abstract

This paper explores the central role of monetary governance, i.e., high politics and low politics, in protecting a currency’s exchange rate and reducing its volatility during periods of global crisis. Using annual panel data from 15 developed and emerging economies between 2001 and [...] Read more.

This paper explores the central role of monetary governance, i.e., high politics and low politics, in protecting a currency’s exchange rate and reducing its volatility during periods of global crisis. Using annual panel data from 15 developed and emerging economies between 2001 and 2023, and applying a panel ARDL approach, the study assesses the effectiveness of high politics—captured through governance indicators—and low politics—captured through economic indicators—as a shield against external shocks, such as the 2008 financial crisis, the COVID-19 pandemic, and the Russo–Ukrainian conflict. The findings demonstrate that strong monetary governance significantly strengthens the Real Effective Exchange Rate (REER) and dampens its volatility in the long-term. In contrast, macroeconomic variables such as inflation, public spending, and trade openness exert destabilizing effects. The results highlight the strategic importance of governance as a long-term anchor of exchange rate resilience, suggesting that countries with robust institutional frameworks are better equipped to withstand global disruptions. These insights offer crucial policy implications for reinforcing monetary governance, especially in emerging economies vulnerable to financial and geopolitical turbulence. Full article

14 pages, 1268 KB

Open AccessArticle

Debt, Equity, and the Pecking Order: Evidence from Financing Decisions of Dividend-Paying Firms

by Konstantinos Kakouris and Dimitrios Psychoyios

Int. J. Financial Stud. 2025, 13(3), 161; https://doi.org/10.3390/ijfs13030161 - 1 Sep 2025

Viewed by 623

Abstract

This study investigates whether, and to what extent, dividend-paying firms follow pecking order behavior when altering their capital structure. Using a panel of 3173 U.S. firms from 1960 to 2020 (49,424 firm-year observations), we examine four financing activities: equity and debt issuance under [...] Read more.

This study investigates whether, and to what extent, dividend-paying firms follow pecking order behavior when altering their capital structure. Using a panel of 3173 U.S. firms from 1960 to 2020 (49,424 firm-year observations), we examine four financing activities: equity and debt issuance under a financing deficit, and equity repurchases and debt redemptions under a financing surplus. We find that firms generally follow the pecking order when issuing or redeeming debt but deviate from it when issuing or repurchasing equity. Adherence to the pecking order also varies with issuance and repurchase size. Very large debt issues and redemptions are associated with lower pecking order coefficients, while large equity issues and repurchases are associated with higher pecking order coefficients, although equity coefficients remain below 0.7. Our findings provide novel evidence of how financing choices, along with issuance and repurchase magnitudes, shape pecking order behavior among dividend-paying firms, offering new insights into capital structure literature. Full article

(This article belongs to the Special Issue Emerging Trends in Corporate Finance: ESG, Climate Risk, and Other Contemporary Issues)

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25 pages, 509 KB

Open AccessArticle

A Study of the Impact of Corporate Financialisation on Capital-Deepening Enterprises’ Output—Empirical Evidence from China’s A-Share Market

by Yunsong Wang

Int. J. Financial Stud. 2025, 13(3), 160; https://doi.org/10.3390/ijfs13030160 - 30 Aug 2025

Viewed by 647

Abstract

With the continuous deepening of the financialisation level of Chinese enterprises, the output of capital-deepening enterprises is inevitably affected. Taking A-share listed companies on the Shanghai and Shenzhen Stock Exchanges in China from 2007 to 2021 as the research sample, this paper explores [...] Read more.

With the continuous deepening of the financialisation level of Chinese enterprises, the output of capital-deepening enterprises is inevitably affected. Taking A-share listed companies on the Shanghai and Shenzhen Stock Exchanges in China from 2007 to 2021 as the research sample, this paper explores the impact of enterprise financialisation on the output of capital-deepening enterprises and its underlying mechanism. The research findings indicate that enterprise financialisation negatively influences the output of capital-deepening enterprises. Through the analysis of the theoretical model in this paper, it is found that the mechanism leading to this economic effect is that enterprise financialisation significantly inhibits capital deepening. The heterogeneity analysis reveals no significant differences in the negative impact of enterprise financialisation on capital output, deepening enterprises across different aspects such as ownership, region and industry. This paper provides theoretical support for curbing the excessive financialisation of capital-deepening enterprises. It is conducive to the long-term and sustainable development of capital-deepening enterprises and offers a new perspective for researching the economic effects and internal mechanisms of enterprise financialisation. Full article

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20 pages, 405 KB

Open AccessArticle

More Money, More Ethical Commitment? How Corporate Financial Performance Influences Environmental Social and Governance Practices

by Ertz Myriam, Gautier George Yao Quenum, Mouhamadou Moustapha Gueye, Chourouk Ouerghemmi and Moussa Sacko

Int. J. Financial Stud. 2025, 13(3), 159; https://doi.org/10.3390/ijfs13030159 - 30 Aug 2025

Viewed by 627

Abstract

This article explores the relationship between corporate financial performance (CFP) and commitment to ESG (environmental, social and governance) practices, using a sample of companies listed on the S&P 500 and TSX 60 indices. By employing a linear regression model, the study examines how [...] Read more.

This article explores the relationship between corporate financial performance (CFP) and commitment to ESG (environmental, social and governance) practices, using a sample of companies listed on the S&P 500 and TSX 60 indices. By employing a linear regression model, the study examines how financial indicators such as Earnings Before Interest, Taxes, Depreciation and Amortization (EBITDA), return on assets (ROA), Assets and Debt influence ESG scores. The results show that financial indicators such as EBITDA, ROA and Assets are positively associated with increased ability to commit resources to ESG practices, except in some cases like when costs associated with ESG initiatives can reduce the competitiveness and profitability of companies in the short term, where ROA is negatively correlated with the adoption of ESG criteria. Also, with regard to the size of companies, thanks to their greater resources, larger companies are more inclined to adopt ESG criteria. These findings enhance the understanding of financial conditions that enable or constrain ESG adoption and provide managerial insights for strategic resource allocation in the pursuit of sustainability goals. Full article

27 pages, 416 KB

Open AccessArticle

What’s Trending? Stock-Level Investor Sentiment and Returns

by Karolina Krystyniak, Hongqi Liu and Huajing Hu

Int. J. Financial Stud. 2025, 13(3), 158; https://doi.org/10.3390/ijfs13030158 - 28 Aug 2025

Viewed by 959

Abstract

We study a direct, firm-level measure of investor sentiment derived from social media (BTSS sentiment). While related to firm fundamentals, BTSS sentiment contains a substantial non-fundamental component. We decompose sentiment into fundamental and pure sentiment and show that return predictability and reversal are [...] Read more.

We study a direct, firm-level measure of investor sentiment derived from social media (BTSS sentiment). While related to firm fundamentals, BTSS sentiment contains a substantial non-fundamental component. We decompose sentiment into fundamental and pure sentiment and show that return predictability and reversal are primarily driven by the latter. Sentiment is persistent and systematic in the short term. High sentiment predicts elevated concurrent returns and subsequent reversal within a year. The effect is strongest in hard-to-value stocks, such as small and young firms, where limits to arbitrage are more binding. Full article

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24 pages, 2859 KB

Open AccessArticle

Time-Varying Efficiency and Economic Shocks: A Rolling DFA Test in Western European Stock Markets

by Christophe Musitelli Boya

Int. J. Financial Stud. 2025, 13(3), 157; https://doi.org/10.3390/ijfs13030157 - 26 Aug 2025

Viewed by 523

Abstract

This paper investigates the time-varying efficiency of Western European stock markets and examines how macroeconomic events defined as endogenous and exogenous shocks influence the degree of efficiency by either long-range dependence or mean reverting. We apply a rolling-window detrended fluctuation analysis (DFA) with [...] Read more.

This paper investigates the time-varying efficiency of Western European stock markets and examines how macroeconomic events defined as endogenous and exogenous shocks influence the degree of efficiency by either long-range dependence or mean reverting. We apply a rolling-window detrended fluctuation analysis (DFA) with two window sizes, complemented by the Efficiency Index to synthetize multiple measures of market efficiency. The results confirm that efficiency evolves dynamically in response to macroeconomic disruptions. Specifically, endogenous shocks tend to generate anti-persistent behavior, while exogenous shocks are associated with long-memory effect. These shifts in efficiency are also reflected in rolling Kurtosis estimates, suggesting that only the most severe shocks produce spikes in Kurtosis, fat-tailed returns distributions, and structural inefficiencies. This dual approach allows us to classify shocks as major or minor based on their joint impact on both market efficiency and tail behavior. Overall, our findings support the adaptive market hypothesis and extend its implications through the fractal market hypothesis by underlining the role of heterogenous investment horizons during periods of turmoil. The combined use of dynamic DFA and Kurtosis offer a framework to assess how financial markets adapt to different types of macroeconomic shocks. Full article

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31 pages, 13101 KB

Open AccessArticle

Strategic Risk Spillovers from Rare Earth Markets to Critical Industrial Sectors

by Oana Panazan and Catalin Gheorghe

Int. J. Financial Stud. 2025, 13(3), 156; https://doi.org/10.3390/ijfs13030156 - 25 Aug 2025

Viewed by 698

Abstract

This study investigates the nonlinear, regime-dependent, and frequency-specific interdependencies between rare earth element (REE) markets and key global critical sectors, including artificial intelligence, semiconductors, clean energy, defense, and advanced manufacturing, under varying levels of geopolitical and financial uncertainty. The main objective is to [...] Read more.

This study investigates the nonlinear, regime-dependent, and frequency-specific interdependencies between rare earth element (REE) markets and key global critical sectors, including artificial intelligence, semiconductors, clean energy, defense, and advanced manufacturing, under varying levels of geopolitical and financial uncertainty. The main objective is to assess how REE markets transmit and absorb systemic risks across these critical domains. Using a mixed-methods approach combining Quantile-on-Quantile Regression (QQR), Continuous Wavelet Transform (CWT), and Wavelet Transform Coherence (WTC), we examine the dynamic connections between two REE proxies, SOLLIT (Solactive Rare Earth Elements Total Return) and MVREMXTR (MVIS Global Rare Earth Metals Total Return), and major sectoral indices based on a dataset of daily observations from 2018 to 2025. Our results reveal strong evidence of asymmetric, regime-specific risk transmission, with REE markets acting as systemic amplifiers during periods of extreme uncertainty and as sensitive receptors under moderate or localized geopolitical stress. High co-volatility and persistent low-frequency coherence with critical sectors, especially defense, technology, and clean energy, indicate deeply embedded structural linkages and a heightened potential for cross-sectoral contagion. These findings confirm the systemic relevance of REEs and underscore the importance of integrating critical resource exposure into global supply chain risk strategies, sector-specific stress testing, and national security frameworks. This study offers relevant insights for policymakers, risk managers, and institutional investors aiming to anticipate disruptions and strengthen resilience in critical industries. Full article

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30 pages, 651 KB

Open AccessArticle

A Fusion of Statistical and Machine Learning Methods: GARCH-XGBoost for Improved Volatility Modelling of the JSE Top40 Index

by Israel Maingo, Thakhani Ravele and Caston Sigauke

Int. J. Financial Stud. 2025, 13(3), 155; https://doi.org/10.3390/ijfs13030155 - 25 Aug 2025

Viewed by 704

Abstract

Volatility modelling is a key feature of financial risk management, portfolio optimisation, and forecasting, particularly for market indices such as the JSE Top40 Index, which serves as a benchmark for the South African stock market. This study investigates volatility modelling of the JSE [...] Read more.

Volatility modelling is a key feature of financial risk management, portfolio optimisation, and forecasting, particularly for market indices such as the JSE Top40 Index, which serves as a benchmark for the South African stock market. This study investigates volatility modelling of the JSE Top40 Index log-returns from 2011 to 2025 using a hybrid approach that integrates statistical and machine learning techniques through a two-step approach. The ARMA(3,2) model was chosen as the optimal mean model, using the auto.arima() function from the forecast package in R (version 4.4.0). Several alternative variants of GARCH models, including sGARCH(1,1), GJR-GARCH(1,1), and EGARCH(1,1), were fitted under various conditional error distributions (i.e., STD, SSTD, GED, SGED, and GHD). The choice of the model was based on AIC, BIC, HQIC, and LL evaluation criteria, and ARMA(3,2)-EGARCH(1,1) was the best model according to the lowest evaluation criteria. Residual diagnostic results indicated that the model adequately captured autocorrelation, conditional heteroskedasticity, and asymmetry in JSE Top40 log-returns. Volatility persistence was also detected, confirming the persistence attributes of financial volatility. Thereafter, the ARMA(3,2)-EGARCH(1,1) model was coupled with XGBoost using standardised residuals extracted from ARMA(3,2)-EGARCH(1,1) as lagged features. The data was split into training (60%), testing (20%), and calibration (20%) sets. Based on the lowest values of forecast accuracy measures (i.e., MASE, RMSE, MAE, MAPE, and sMAPE), along with prediction intervals and their evaluation metrics (i.e., PICP, PINAW, PICAW, and PINAD), the hybrid model captured residual nonlinearities left by the standalone ARMA(3,2)-EGARCH(1,1) and demonstrated improved forecasting accuracy. The hybrid ARMA(3,2)-EGARCH(1,1)-XGBoost model outperforms the standalone ARMA(3,2)-EGARCH(1,1) model across all forecast accuracy measures. This highlights the robustness and suitability of the hybrid ARMA(3,2)-EGARCH(1,1)-XGBoost model for financial risk management in emerging markets and signifies the strengths of integrating statistical and machine learning methods in financial time series modelling. Full article

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16 pages, 656 KB

Open AccessArticle

Do Climate Stock and Low-Carbon Stock Respond to Oil Prices and Energy Stocks During an Oil Crisis? Implications for Sustainable Development

by Minh Thi Hong Dinh

Int. J. Financial Stud. 2025, 13(3), 154; https://doi.org/10.3390/ijfs13030154 - 24 Aug 2025

Viewed by 504

Abstract

This research investigates the responsiveness of climate and low-carbon (green) stock returns to oil prices and conventional energy stock returns, focusing on both contemporaneous and causal relationships, during an oil crisis. Two methodologies are used: vector auto-regressive (VAR) for testing the causal relationship, [...] Read more.

This research investigates the responsiveness of climate and low-carbon (green) stock returns to oil prices and conventional energy stock returns, focusing on both contemporaneous and causal relationships, during an oil crisis. Two methodologies are used: vector auto-regressive (VAR) for testing the causal relationship, and ordinary least squares (OLS) for investigating the contemporaneous relationship. The main empirical results suggest that green stocks have a bidirectional positive contemporaneous relationship with oil prices and energy stock returns but no significant bidirectional causal relationship. The results reveal that oil prices and energy stock returns play a larger role in contemporaneous than causal relationships with green stock returns. In addition, green stock returns seem to have a stronger positive relationship with energy stock return than oil prices. Full article

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25 pages, 1142 KB

Open AccessArticle

Has US (Un)Conventional Monetary Policy Affected South African Financial Markets in the Aftermath of COVID-19? A Quantile–Frequency Connectedness Approach

by Mashilana Ngondo and Andrew Phiri

Int. J. Financial Stud. 2025, 13(3), 153; https://doi.org/10.3390/ijfs13030153 - 23 Aug 2025

Viewed by 530

Abstract

The US has undertaken both unconventional and conventional monetary policy stances in response to the COVID-19 pandemic and the Ukraine–Russia conflict, and there has been much debate on the effects of these various monetary policies on global financial markets. Our study considers the [...] Read more.

The US has undertaken both unconventional and conventional monetary policy stances in response to the COVID-19 pandemic and the Ukraine–Russia conflict, and there has been much debate on the effects of these various monetary policies on global financial markets. Our study considers the debate in the context of South Africa and uses the quantile–frequency connectedness approach to examine static and dynamic systemic spillover between the US shadow short rate (SSR) and South African equity, bond and currency markets between 1 December 2019 and 2 March 2023. The findings from the static analysis reveal that systemic connectedness is concentrated at their tail-end quantile distributions and US monetary policy plays a dominant role in transmitting these systemic shocks, albeit these shocks are mainly high frequency with very short cycles. However, the dynamic estimates further reveal that US monetary policy exerts longer-lasting spillover shocks to South African financial markets during periods corresponding to FOMC announcements of quantitative ‘easing’ or ‘tapering’ policies. Overall, these findings are useful for evaluating the effectiveness of the Reserve Bank’s macroprudential policies in ensuring market efficiency, as well as for enhancing investor decisions, portfolio allocation and risk management. Full article

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24 pages, 748 KB

Open AccessArticle

When Models Fail: Credit Scoring, Bank Management, and NPL Growth in the Greek Recession

by Vasileios Giannopoulos and Spyridon Kariofyllas

Int. J. Financial Stud. 2025, 13(3), 152; https://doi.org/10.3390/ijfs13030152 - 22 Aug 2025

Viewed by 533

Abstract

The significant increase in non-performing loans (NPLs) during the escalating recession of the Greek economy motivates us to study the predictive power of credit rating models in periods of economic shocks. In parallel, we examined the responsibilities of bank management in the expansion [...] Read more.

The significant increase in non-performing loans (NPLs) during the escalating recession of the Greek economy motivates us to study the predictive power of credit rating models in periods of economic shocks. In parallel, we examined the responsibilities of bank management in the expansion of NPLs in this adverse environment. Certain studies connect bad loans with turbulent conditions. Our paper weighs the relative significance of both economic shock and management effectiveness using data at an individual level, which provides the originality of our study. We use a unique dataset of small business loans that were granted during 2005 (expansion period) by a large commercial Greek bank, and we explore their performance between 2010 and 2012 (early recession period). In the context of a stepwise methodology, we compare the Bank’s credit scoring model with three other prediction models (binomial logistic regression, decision tree, and multilayer perceptron neural network) to check both the predictive ability of credit scoring models during recession and the effectiveness of bank management. The comparative analysis confirms the management’s responsibilities in granting NPLs, since the Bank’s model exhibited the worst predictive performance. Additionally, we find that adverse external conditions lead to an increase in NPLs and decrease the predictive performance of all credit scoring models. The study offers a reliable methodological tool for lending management in economic downturns. Full article

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29 pages, 13156 KB

Open AccessArticle

Exchange Rate Forecasting: A Deep Learning Framework Combining Adaptive Signal Decomposition and Dynamic Weight Optimization

by Xi Tang and Yumei Xie

Int. J. Financial Stud. 2025, 13(3), 151; https://doi.org/10.3390/ijfs13030151 - 22 Aug 2025

Viewed by 684

Abstract

Accurate exchange rate forecasting is crucial for investment decisions, multinational corporations, and national policies. The nonlinear nature and volatility of the foreign exchange market hinder traditional forecasting methods in capturing exchange rate fluctuations. Despite advancements in machine learning and signal decomposition, challenges remain [...] Read more.

Accurate exchange rate forecasting is crucial for investment decisions, multinational corporations, and national policies. The nonlinear nature and volatility of the foreign exchange market hinder traditional forecasting methods in capturing exchange rate fluctuations. Despite advancements in machine learning and signal decomposition, challenges remain in high-dimensional data handling and parameter optimization. This study mitigates these constraints by introducing an innovative enhanced prediction framework that integrates the optimal complete ensemble empirical mode decomposition with adaptive noise (OCEEMDAN) method and a strategically optimized combination weight prediction model. The grey wolf optimizer (GWO) is employed to autonomously modify the noise parameters of OCEEMDAN, while the zebra optimization algorithm (ZOA) dynamically fine-tunes the weights of predictive models—Bi-LSTM, GRU, and FNN. The proposed methodology exhibits enhanced prediction accuracy and robustness through simulation experiments on exchange rate data (EUR/USD, GBP/USD, and USD/JPY). This research improves the precision of exchange rate forecasts and introduces an innovative approach to enhancing model efficacy in volatile financial markets. Full article

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24 pages, 2329 KB

Open AccessArticle

Monetary Policy Tightening and Financial Market Reactions: A Comparative Analysis of Soft and Hard Landings

by Gimede Gigante, Fernando Piccolantonio and Francesca Scarlini

Int. J. Financial Stud. 2025, 13(3), 150; https://doi.org/10.3390/ijfs13030150 - 22 Aug 2025

Viewed by 614

Abstract

This paper investigates the macro-financial consequences of recent monetary policy tightening cycles, focusing on the distinction between soft and hard landings. Using an OLS regression framework applied to U.S. and Euro Area data from 1994 to 2023, we analyze the response of equity [...] Read more.

This paper investigates the macro-financial consequences of recent monetary policy tightening cycles, focusing on the distinction between soft and hard landings. Using an OLS regression framework applied to U.S. and Euro Area data from 1994 to 2023, we analyze the response of equity and bond markets, inflation, and GDP growth to central bank interest rate hikes. The findings suggest that, in most past tightening episodes, central banks succeeded in engineering soft landings without severe disruptions to market conditions or economic growth. However, the current post-pandemic context may lead to a two-stage adjustment, as inflation persistence and geopolitical shocks alter standard transmission dynamics. The study contributes to the ongoing policy debate on the timing and intensity of rate hikes, offering historical insights and empirical evidence from capital market signals. Full article

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Int. J. Financial Stud., Volume 13, Issue 3 (September 2025) – 66 articles

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