Journal Description
International Journal of Financial Studies
International Journal of Financial Studies
is an international, peer-reviewed, scholarly open access journal on financial market, instruments, policy, and management research published monthly online by MDPI.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, ESCI (Web of Science), EconLit, EconBiz, RePEc, and other databases.
- Journal Rank: JCR - Q2 (Business, Finance) / CiteScore - Q2 (Finance)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 19.7 days after submission; acceptance to publication is undertaken in 5.9 days (median values for papers published in this journal in the second half of 2025).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
Impact Factor:
2.2 (2024);
5-Year Impact Factor:
2.3 (2024)
Latest Articles
Learning from Hospital Financial Distress Associated with Negative Cash Reserves
Int. J. Financial Stud. 2026, 14(6), 152; https://doi.org/10.3390/ijfs14060152 (registering DOI) - 5 Jun 2026
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This study introduces a multivariate distance-based framework for analyzing hospital liquidity stress using three financial indicators: cash reserves, days with negative cash, and accounts receivable. Using Definitive Healthcare data from 2020–2025, the study applies principal component analysis (PCA), Mahalanobis distance, Aitchison distance, and
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This study introduces a multivariate distance-based framework for analyzing hospital liquidity stress using three financial indicators: cash reserves, days with negative cash, and accounts receivable. Using Definitive Healthcare data from 2020–2025, the study applies principal component analysis (PCA), Mahalanobis distance, Aitchison distance, and ternary plots to characterize structural relationships among these liquidity variables. The results show that the first two principal components explain more than 94% of the variation in the transformed variables, indicating that the joint financial structure can be represented in a lower-dimensional space. Beginning in 2023, accounts receivable became more geometrically separated from the cash-based variables, suggesting that revenue-cycle dynamics may have become a more independent dimension of hospital liquidity stress. Importantly, this manuscript does not directly predict hospital closure or bankruptcy because verified event/non-event outcome data are not available in the analytic file. Instead, its contribution is methodological and exploratory: it demonstrates how distance-based and compositional methods can identify structural liquidity instability and potential early warning signals that warrant further validation with longitudinal closure, bankruptcy, or severe-distress outcomes.
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Open AccessArticle
State-Dependent Value of News Sentiment in S&P 500 Direction Forecasting
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Prabin Bajgai and Zhaoxian Zhou
Int. J. Financial Stud. 2026, 14(6), 151; https://doi.org/10.3390/ijfs14060151 (registering DOI) - 5 Jun 2026
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Next-day S&P 500 direction forecasting matters for allocation, hedging, and risk management because broad-index movements transmit quickly across portfolios. Does structured news sentiment help predict next-day S&P 500 direction? We test four feature sets over 2008–2023 in an ablation sequence: technical indicators only
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Next-day S&P 500 direction forecasting matters for allocation, hedging, and risk management because broad-index movements transmit quickly across portfolios. Does structured news sentiment help predict next-day S&P 500 direction? We test four feature sets over 2008–2023 in an ablation sequence: technical indicators only (Set A), with FinBERT headline sentiment (Set B), with BERTopic topic-linked sentiment (Set C), and with realized-volatility weighting (Set D). This design makes two contributions: it separates the incremental value of increasingly structured sentiment features, and it tests whether sentiment value is state-dependent across volatility regimes. CatBoost, XGBoost, LightGBM, LSTM, and GRU are evaluated under walk-forward cross-validation, nested cross-validation, and formal statistical tests. On the full sample, sentiment does not deliver a measurable forecasting edge. Walk-forward AUCs sit near 0.50 for every feature set, and pairwise tests find no significant differences. However, this average masks a consistent pattern. Sentiment becomes more informative during high-volatility periods, suggesting that its value is state-dependent rather than uniform. Rolling AUC swings from 0.28 to 0.71 depending on the market period. When we split by VIX regime, Set D reaches 0.5684 AUC during high-volatility episodes ( , permutation ) while adding almost nothing in calm markets. Set D also has the lowest fold-to-fold variance and the shallowest drawdown in trading simulations. These results imply that the relevant question is not whether sentiment works in general, but when it does. Sentiment does not help on average; whether it helps during stress is suggestive but unconfirmed and needs more crisis-period data to settle.
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Open AccessFeature PaperArticle
Equity Market Structure and Trading Diversification: Insights from Panel Data, Clustering, and Machine Learning
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Angelo Leogrande, Fabio Anobile, Alberto Costantiello, Carlo Drago and Massimo Arnone
Int. J. Financial Stud. 2026, 14(6), 150; https://doi.org/10.3390/ijfs14060150 - 4 Jun 2026
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This paper studies the topic that has been rather less explored until now—the internal diversification of trading. Unlike looking at aggregate measures of financial development such as market capitalization and liquidity, the study focuses on trading diversification, defined as the portion of trading
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This paper studies the topic that has been rather less explored until now—the internal diversification of trading. Unlike looking at aggregate measures of financial development such as market capitalization and liquidity, the study focuses on trading diversification, defined as the portion of trading volume attributed to firms other than the ten most actively traded (VTX). The empirical analysis is based on the World Bank’s Global Financial Development database. It covers an unbalanced cross-country dataset of 2004–2021. Due to limited data availability, the resulting database became smaller and has an unbalanced panel structure. Four main independent variables in the core regression specification are related to financial structure (bank deposits) and financial integration (remittances, international public debt), as well as external measures of financial development (market capitalization, excluding firms within VTX). A broad range of control variables are introduced into the model to account for macroeconomic conditions, financial development, market size, liquidity, and participation. Lagged regressors are introduced to address persistence, delays, and potential endogeneity issues. The methodology relies on panel data econometrics, hierarchical clustering, and machine learning. The findings show that market structure and remittances positively affect trading diversification, whereas banks’ dominance and international public debt contribute to its concentration. The results persist across alternative specifications and robustness tests. The country-level analysis shows a core–periphery pattern, while machine learning demonstrates the critical importance of market structure.
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Open AccessReview
Two Decades of Research on Sustainability and Sovereign Ratings: Trends, Research Puzzles and Future Directions
by
Insaf Arfa, Wided Khiari, Houssein Ballouk and Foued Ben Said
Int. J. Financial Stud. 2026, 14(6), 149; https://doi.org/10.3390/ijfs14060149 - 4 Jun 2026
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The article provides a systematic and bibliometric review of the literature on the relationship between countries’ ESG scores and their sovereign ratings. The study, which follows the PRISMA 2020 guidelines, employs a bibliometric methodology using a sample of 168 peer-reviewed articles sourced from
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The article provides a systematic and bibliometric review of the literature on the relationship between countries’ ESG scores and their sovereign ratings. The study, which follows the PRISMA 2020 guidelines, employs a bibliometric methodology using a sample of 168 peer-reviewed articles sourced from Scopus and WoS (2006–2024) to analyze the progression of research in this growing area. The analysis shows that academic output is concentrated in Europe (35% of publications) and North America (12% of publications), and that there is increasing interest in incorporating ESG factors into sovereign risk assessment, especially since 2019. The bibliometric mapping shows that themes concerning ESG integration into sovereign risk assessment, rating methodologies, and sustainability-driven financial risk pricing are predominant. Four main research streams are revealed through co-occurrence and clustering analyses, which also highlight an expanding yet disjointed body of knowledge. The results also suggest large differences in how ESG ratings are calculated, which brings into question how comparable and reliable they are for use in empirical studies. This study helps structure the field and proposes a more coherent research agenda by identifying four key research puzzles. Future research should concentrate on enhancing ESG measurement, breaking down its components, and crafting tailored approaches for emerging markets.
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Horizon- and Regime-Dependent Performance of GARCH-Type Models: Evidence from Volatility Forecasting in a Frontier Market
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Abraham Kisembe Wawire, Christine Nanjala Simiyu, Munene Laiboni and Rogers Ochenge
Int. J. Financial Stud. 2026, 14(6), 148; https://doi.org/10.3390/ijfs14060148 - 4 Jun 2026
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In frontier markets, financial volatility exhibits long-memory properties and regime-dependent asymmetries that standard linear models do not capture. This leads to inaccuracies in forecasting risk when a single model is applied across regimes. This study investigates the horizon- and regime-dependent performance of volatility
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In frontier markets, financial volatility exhibits long-memory properties and regime-dependent asymmetries that standard linear models do not capture. This leads to inaccuracies in forecasting risk when a single model is applied across regimes. This study investigates the horizon- and regime-dependent performance of volatility models within a horizon- and regime-sensitive evaluation framework that applies single-regime Generalized Autoregressive Conditional Heteroscedasticity (GARCH) variants alongside a Hidden Markov Model (HMM). We evaluate the predictive accuracy of GARCH, Exponential GARCH (EGARCH), Glosten-Jagannathan-Runkle GARCH (GJR-GARCH), Asymmetric Power ARCH (APARCH), Fractionally Integrated GARCH (FIGARCH), and an HMM. Diebold–Mariano test statistics reveal that predictive superiority is sensitive to the chosen benchmark. When EGARCH is the benchmark, results highlight the importance of leverage effects, whereas a FIGARCH benchmark demonstrates that short-memory models are rejected as horizons increase. While short-memory models capture immediate clustering, FIGARCH maintains stable performance via hyperbolic decay. HMM provides a superior in-sample fit by capturing transitions between calm and turbulent regimes. Economic validation through Value-at-Risk (VaR) and Expected Shortfall (ES) backtesting indicates that FIGARCH and APARCH offer more reliable coverage for early warning systems during market stress. The findings emphasize that forecasting in a frontier market requires asset-specific approaches where benchmark selection dictates the interpretation of model superiority.
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Open AccessArticle
The Role of Financial Development in Economic Complexity: An Analysis of Asymmetry and Nonlinearity Perspectives
by
Clement Olalekan Olaniyi
Int. J. Financial Stud. 2026, 14(6), 147; https://doi.org/10.3390/ijfs14060147 - 3 Jun 2026
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This study enhances the knowledge base by providing an empirical inquiry into the asymmetric sensitivity of economic complexity (ECI) to changes in financial development (FD), using data from 30 African countries for the period of 1995–2023. To deliver robust estimates in the face
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This study enhances the knowledge base by providing an empirical inquiry into the asymmetric sensitivity of economic complexity (ECI) to changes in financial development (FD), using data from 30 African countries for the period of 1995–2023. To deliver robust estimates in the face of econometric pitfalls, this study employs estimators such as Hatemi-J data decomposition procedures, robust standard-error regression of Driscoll and Kraay, Feasible Generalised Least Squares, Lewbel’s IV-Two-Stage Least Squares, and Quantile regression via moments. The findings from the linear model indicate that FD enhances ECI upgrades in Africa. The findings provide robust evidence of asymmetric structures in ECI’s sensitivity to changes in FD. It highlights that both positive and negative change components (financial sector expansionary and contractionary policies, respectively) in the FD significantly contribute to ECI upgrades. These findings reveal the obscure aspects of how FD change components contribute differently to ECI upgrades in African countries. These findings highlight that expansionary financial sector policies aid the development of knowledge-based productivity, technology diffusion, and manufacturing capabilities, enabling the production of a chain of high-tech, high-quality, and globally competitive products for export. On the other hand, contractionary financial sector policies in African countries spur cumulative reductions in the channelling of financial resources and other technical support to ECI-impeding initiatives, thereby making more resources available to fund ECI-enhancing initiatives that aid the manufacturing of quality, competitive products for exports. This study draws and outlines relevant policy implications of the findings.
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(This article belongs to the Special Issue Advances in Financial Econometrics)
Open AccessArticle
Financial Fraud Detection Based on an Explainable Multi-Layer Framework
by
Hui Xia, Yilong Huang, Shanshan Fang, Qin Wang and Jinyu Shen
Int. J. Financial Stud. 2026, 14(6), 146; https://doi.org/10.3390/ijfs14060146 - 3 Jun 2026
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Financial information plays a critical role in decision-making for stakeholders, including investors, regulators, and corporate managers. However, financial data is susceptible to deliberate manipulation, where some firms may distort disclosures to mislead stakeholders and potentially engage in fraudulent activities. With the rapid expansion
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Financial information plays a critical role in decision-making for stakeholders, including investors, regulators, and corporate managers. However, financial data is susceptible to deliberate manipulation, where some firms may distort disclosures to mislead stakeholders and potentially engage in fraudulent activities. With the rapid expansion of capital markets and advancements in information technology, financial fraud has grown increasingly sophisticated and concealed. As a result, conventional detection methods often struggle to identify emerging fraud patterns, rendering fraud prevention increasingly complex and less effective. In this paper, we propose a novel multi-layer architecture model that integrates business, internal control, and strategic features. Our framework leverages multi-layer neural networks for effective feature extraction and concatenates their outputs for classification. Furthermore, we develop this framework by incorporating explainable artificial intelligence (XAI) techniques to enhance interpretability. Empirical results show that the proposed framework provides competitive discriminatory ability and produces conservative, low-false-alarm fraud warnings under the full multi-layer feature setting while also offering interpretable insights for the diverse needs of stakeholders. This study contributes to the development of fraud detection tools that are both operationally useful and interpretable.
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Open AccessFeature PaperArticle
Spatiotemporal Return Decomposition and Multi-Strategy Performance Analysis in Dow Jones Industrial Average Constituents: A 20-Year Empirical Investigation
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Sarthak Pattnaik, Chhayank Jain and Eugene Pinsky
Int. J. Financial Stud. 2026, 14(6), 145; https://doi.org/10.3390/ijfs14060145 - 3 Jun 2026
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This paper presents a comprehensive spatiotemporal decomposition of equity returns for nine top-weighted constituents of the Dow Jones Industrial Average (DJIA) over a twenty-year period spanning January 2004 through December 2023, encompassing 5033 trading days and multiple market regimes, including the Global Financial
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This paper presents a comprehensive spatiotemporal decomposition of equity returns for nine top-weighted constituents of the Dow Jones Industrial Average (DJIA) over a twenty-year period spanning January 2004 through December 2023, encompassing 5033 trading days and multiple market regimes, including the Global Financial Crisis (2008–2009), the COVID-19 crash and recovery (2020), and the Federal Reserve tightening cycle (2022–2023). Daily price movements are systematically partitioned into two orthogonal sessions: the open-to-close (OTC, or daytime) session, capturing within-session price discovery, and the close-to-open (CTO, or overnight) session, capturing the accumulated information arrival and liquidity dynamics between market closes and subsequent opens. Within this bipartite return framework, we construct and rigorously evaluate 24 distinct trading strategies, spanning directional (long/short), neutral (cash), momentum (inertia), and contrarian (reversal) approaches, applied independently to each session or in combinatorial cross-session configurations. Each strategy is evaluated under three transaction cost regimes (0, 1, and 2 basis points per trade) using an initial investment of $100, and assessed using annualized return, annualised volatility, Sharpe ratio, Sortino ratio, and maximum drawdown. The study universe—comprising UnitedHealth Group (UNH), Goldman Sachs (GS), Microsoft (MSFT), Home Depot (HD), Caterpillar (CAT), Amgen (AMGN), McDonald’s (MCD), Salesforce (CRM), and Honeywell (HON)—captures cross-sector heterogeneity across Healthcare, Financials, Technology, Consumer Discretionary, Industrials, Biotech, and Consumer Staples. The universe is selected from the top-weighted DJIA constituents as of early 2026; the paper is, therefore, best read as a focused, in-depth case study of index-representative large-cap names rather than a general cross-sectional statement about all U.S. equities. The principal findings are threefold. First, the overnight session consistently delivers superior risk-adjusted performance: seven of nine stocks record higher Sharpe ratios during the overnight period versus the daytime period, with the mean overnight Sharpe ratio (0.662) substantially exceeding the mean daytime Sharpe ratio (0.357), a statistically and economically significant overnight premium. Second, the hybrid Strategy #18—Long Overnight coupled with Daytime Reversal—emerges as the dominant cross-asset configuration, generating portfolio values as high as $8464 from a $100 initial investment (AMGN; Sharpe: 0.991) over the 20-year horizon. Third, Trajectory Change Analysis reveals (i) Lévy-stable tails with a mean stability index across all constituents, substantially below the Gaussian benchmark of ; (ii) Hurst exponents clustering below 0.5 ( ), confirming dominant mean-reverting dynamics; and (iii) positive rolling CAPM alpha in 51–79% of rolling windows, indicating persistent risk-adjusted outperformance above the S&P 500 benchmark. These findings provide a rigorous empirical foundation for session-aware algorithmic trading system design and challenge the prevailing assumption of temporal homogeneity in equity return processes.
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Environmental Performance, Digital Integration and Default Risk: Evidence from European Firms
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Majdi Anwar Quttainah and Imen Ayadi
Int. J. Financial Stud. 2026, 14(6), 144; https://doi.org/10.3390/ijfs14060144 - 3 Jun 2026
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This study examines the relationship between environmental performance, digital integration, information asymmetry, and default risk among European firms. It seeks to understand how sustainability and digitalization jointly enhance corporate financial stability. The sample comprises 1303 non-financial firms from 20 European countries over the
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This study examines the relationship between environmental performance, digital integration, information asymmetry, and default risk among European firms. It seeks to understand how sustainability and digitalization jointly enhance corporate financial stability. The sample comprises 1303 non-financial firms from 20 European countries over the period 2016–2023. This study uses a Thomson Reuters sample composed of European publicly listed companies with ESG (environmental, social, and governance) ratings. Europe represents an ideal setting for this analysis due to its dual green and digital transition, supported by some of the most advanced regulatory policies in the world. Methodologically, the analysis employs a dynamic panel model estimated using the two-step system GMM approach, complemented by a robustness check based on 2SLS-IV estimation to address potential endogeneity concerns. The empirical findings reveal that both environmental performance and digital integration significantly reduce default risk whereas information asymmetry increases it. Moreover, sustainability and digital transformation attenuate the adverse effect of information asymmetry on financial stability, confirming their complementary role as resilience-enhancing mechanisms. These results underscore the critical importance of transparency, innovation, and organizational capabilities in mitigating financial risk. Overall, the study makes an original contribution to the literature on sustainable governance by demonstrating that environmental performance and digital integration are not merely regulatory requirements but constitute strategic intangible assets that strengthen financial soundness and reduce default risk within the European context.
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(This article belongs to the Special Issue Advancing Financial Stability and Performance Through AI and Digital Transformation)
Open AccessArticle
Climate Risk, CSR, and Financial Performance: An Interaction Perspective on Corporate Resilience in Europe
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Salma Zaiane, Fatma Ben Moussa, Souhir Masmoudi and Rahma Louati
Int. J. Financial Stud. 2026, 14(6), 143; https://doi.org/10.3390/ijfs14060143 - 2 Jun 2026
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This paper explores the impact of climate risk on corporate social responsibility engagement and examines whether CSR moderates the relationship between climate risk and financial performance among European firms. The study is based on a panel of 3304 observations relating to companies in
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This paper explores the impact of climate risk on corporate social responsibility engagement and examines whether CSR moderates the relationship between climate risk and financial performance among European firms. The study is based on a panel of 3304 observations relating to companies in the STOXX Europe 600. We use two-stage least squares estimation with instrumental variables (2SLS-IV) to account for endogeneity issues. The results show that the companies most exposed to climate risk increase their CSR commitments, a relationship that remains particularly robust for environmentally sensitive firms. More importantly, the empirical results show a significant interaction between climate risk and CSR; while climate risk negatively affects financial performance, CSR engagement serves as a critical moderating mechanism that reduces this adverse effect. The strength of this moderating effect significantly increases following the Paris Agreement.
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(This article belongs to the Special Issue Corporate Financial Performance and Sustainability Practices)
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Normative Lean Performance Score Model Based on Financial and Accounting Metrics
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Attila Bányai, Judit Bárczi and Gergő Thalmeiner
Int. J. Financial Stud. 2026, 14(6), 142; https://doi.org/10.3390/ijfs14060142 - 2 Jun 2026
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This paper introduces the Normative Lean Performance Score (NLPS) model designed to evaluate lean operational performance using publicly available financial and accounting metrics, without requiring advanced analytics for practical implementation. The study applies an empirical research design based on a longitudinal dataset, where
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This paper introduces the Normative Lean Performance Score (NLPS) model designed to evaluate lean operational performance using publicly available financial and accounting metrics, without requiring advanced analytics for practical implementation. The study applies an empirical research design based on a longitudinal dataset, where firms are first classified into lean-oriented groups, followed by logistic regression to identify significant indicators and Random Forest models to estimate their relative importance. The resulting index provides an objective, interpretable, and easily implementable performance measure suitable for cross-firm benchmarking and managerial decision support. Empirical testing using automotive manufacturers demonstrates strong alignment with lean classification and efficiency outcomes, providing evidence for the model’s relevance as an accounting-based benchmarking tool. In addition to its practical applicability, the framework contributes to lean performance measurement by translating machine learning insights into a reproducible index that can be applied in data-constrained environments. This approach ensures that the resulting index remains both empirically grounded and practically interpretable, while avoiding reliance on arbitrary or expert-assigned weighting schemes and qualitative assessment-based approaches. The model therefore offers a scalable and transparent alternative for practitioners, analysts, and researchers seeking robust lean performance evaluation when advanced modelling resources are unavailable. The study contributes a transparent, accounting-based normative index that reframes lean performance as a financial configuration rather than an operational maturity construct. The empirical analysis uses quarterly financial data from 17 publicly listed automotive manufacturers over the period 1994Q1–2024Q4.
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Open AccessArticle
Heterogeneous Adjustment in Monetary Transmission: Short-Run Evidence from an Emerging Market on Bank Equity Valuations, Balance Sheets, and Inflation
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Adil Boutfssi, Youssef Zizi and Mehdi Bensouda
Int. J. Financial Stud. 2026, 14(6), 141; https://doi.org/10.3390/ijfs14060141 - 2 Jun 2026
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This paper examines the short-run dynamics of monetary policy transmission in a bank-dominated emerging economy, with a focus on the relative timing of adjustments across financial valuations, balance-sheet aggregates, and inflation. Using monthly data over the period 2018–2024, the analysis relies on a
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This paper examines the short-run dynamics of monetary policy transmission in a bank-dominated emerging economy, with a focus on the relative timing of adjustments across financial valuations, balance-sheet aggregates, and inflation. Using monthly data over the period 2018–2024, the analysis relies on a reduced-form VAR framework. The results indicate that monetary policy innovations are more rapidly reflected in bank equity valuations proxied by the MASI banking index at short horizons, while balance-sheet variables exhibit more limited and less persistent adjustments. Inflation dynamics remain difficult to identify clearly within the short-run horizon, consistent with the gradual nature of price adjustments. These findings suggest that financial variables react more quickly to monetary policy innovations, while credit and macroeconomic variables adjust more gradually due to institutional constraints, risk considerations, and nominal rigidities. This pattern reflects heterogeneous adjustment speeds across variables rather than a structurally identified transmission mechanism. This paper provides evidence on the timing of short-run adjustments across financial and macroeconomic variables, highlighting the importance of temporal dynamics in the analysis of monetary transmission.
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(This article belongs to the Special Issue Banking Stability, Credit Risk and Financial Resilience in Emerging Markets)
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Open AccessArticle
Financial Intermediation and Provincial Economic Activity in a Dollarised Economy: Panel VAR Evidence from Ecuador
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Félix Casares-Conforme, Ángel Maridueña-Larrea, Rocío González-Reyes, Javier Patricio Cadena-Silva and Patricio Álvarez-Muñoz
Int. J. Financial Stud. 2026, 14(6), 140; https://doi.org/10.3390/ijfs14060140 - 1 Jun 2026
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In dollarised economies, the absence of autonomous monetary policy shifts the burden of macroeconomic adjustment onto the banking system, where deposits and credit constitute the principal channel through which liquidity is conveyed to commercial activity. The literature has documented this relationship using aggregate
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In dollarised economies, the absence of autonomous monetary policy shifts the burden of macroeconomic adjustment onto the banking system, where deposits and credit constitute the principal channel through which liquidity is conveyed to commercial activity. The literature has documented this relationship using aggregate national data, yet its behaviour at the monthly provincial scale remains underexplored for Latin America, particularly in fully dollarised economies and over recent periods marked by severe shocks. This article addresses that gap for Ecuador using a monthly panel of its 24 provinces over 2019–2025, estimated as a Panel VAR by two-step GMM, with monthly sales declared to the Internal Revenue Service used as a high-frequency indicator of provincial economic activity. The pandemic is incorporated as an exogenous control. The theoretical framework combines the supply-leading hypothesis, the credit-channel literature on transmission lags arising from financial frictions, and financial intermediation theory on liquidity and asset transformation. The system exhibits a predominantly supply-leading dynamic: deposits and credit retain predictive capacity over provincial sales, with no robust evidence of reverse feedback. Transmission speed is heterogeneous across channels. Deposits affect sales with a one-period lag, whereas credit requires an additional period—a pattern consistent with the differential role of each channel in banks’ asset-transformation function. The provincial-scale evidence for a dollarised economy shows that the macroeconomic relevance of financial intermediation depends on the heterogeneous transmission speeds of its components, with implications for territorial policy.
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(This article belongs to the Special Issue Financial Markets: Risk Forecasting, Dynamic Models and Data Analysis)
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Open AccessArticle
Knowledge, Actionable Digital Skills, and Old-Age Anxiety: Evidence from Digital Financial Literacy Components Among Japanese Retail Investors
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Jargalmaa Amarsanaa, Honoka Nabeshima and Yoshihiko Kadoya
Int. J. Financial Stud. 2026, 14(6), 139; https://doi.org/10.3390/ijfs14060139 - 1 Jun 2026
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Rapid digitalization has reshaped financial decision-making, and anxiety about later life is an important concern among middle-aged and older investors. Yet it remains unclear whether the traditional Big Three financial knowledge component captures the aspects of financial capability most closely associated with lower
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Rapid digitalization has reshaped financial decision-making, and anxiety about later life is an important concern among middle-aged and older investors. Yet it remains unclear whether the traditional Big Three financial knowledge component captures the aspects of financial capability most closely associated with lower anxiety in digital financial environments. This study examines the association between old-age anxiety and digital financial literacy (DFL) components among digitally active Japanese retail investors aged 40–64. Using data from a large-scale survey of 94,695 investors, we estimate ordered probit models to examine overall DFL and its eight subdimensions. While overall DFL is negatively associated with anxiety about life after age 65, decomposing the index reveals substantial heterogeneity across components. The traditional Big Three financial knowledge component does not show a robust independent negative association with old-age anxiety once actionable and protective digital competencies are accounted for. In contrast, practical know-how, positive financial attitude, and self-protection are more consistently associated with lower anxiety. Supplementary heterogeneity analyses suggest that the positive conditional association between financial knowledge and anxiety is most visible among men aged 50–59, although these subgroup patterns should be interpreted cautiously. These findings do not imply that financial knowledge is unimportant. Rather, they suggest that Big Three financial knowledge alone may be an insufficient proxy for the dimensions of financial capability associated with lower self-reported old-age anxiety in digital financial environments. Given the cross-sectional design, the findings are interpreted as conditional associations rather than causal effects.
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(This article belongs to the Special Issue Behavioral Insights into Financial Decision Making)
Open AccessArticle
Firm Performance and Corporate Social Responsibility: The Moderating Role of Board Skills
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Rihem Soussi Fathallah, Hamza Nizar, Houssam Bouzgarrou and Abdulrahman Alomair
Int. J. Financial Stud. 2026, 14(6), 138; https://doi.org/10.3390/ijfs14060138 - 1 Jun 2026
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Purpose: This study examines the association between firm profitability and corporate social responsibility (CSR), with a particular focus on the moderating role of board skills, specifically those with financial and industry expertise. Design/methodology/approach: Based on a sample of 42,623 observations from 2002 to
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Purpose: This study examines the association between firm profitability and corporate social responsibility (CSR), with a particular focus on the moderating role of board skills, specifically those with financial and industry expertise. Design/methodology/approach: Based on a sample of 42,623 observations from 2002 to 2021, we use panel regression analysis with robust standard errors are clustered at the firm level. Findings: The results show that firm profitability is positively associated with CSR performance. However, the positive effect is less likely in the presence of board with financial and industry expertise. Indeed, boards dominated by financially and industry experienced directors tend to prioritize short-term financial returns over long-term CSR initiatives. Originality/value: This study offers a novel contribution to stakeholder and legitimacy theory perspectives by show that the association between financial performance and CSR depends significantly on the expertise embedded within the boardroom. While financial and industry expertise can bring valuable oversight, it may not adequately support CSR initiatives. In this regard, firms may need directors with additional skills and perspectives—such as sustainability or stakeholder management expertise—to better address CSR issues and balance financial objectives with long-term societal and legitimacy concerns. Practical implications: Policy and decision makers should carefully consider the composition of the board when seeking to align profitability with corporate social responsibility (CSR) outcomes. While financial and industry expertise are valuable for oversight, an overrepresentation of such skills on the board may inadvertently undermine long-term CSR commitments.
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Open AccessArticle
AI for Financial Advice, Fraud Loss, and the Moderating Effect of Financial Knowledge Miscalibration
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Isha Chawla, Mindy Joseph, Kenneth White and Chasity Winder Scantling
Int. J. Financial Stud. 2026, 14(6), 137; https://doi.org/10.3390/ijfs14060137 - 1 Jun 2026
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There is growing interest in using AI for financial advice, yet fraud and related financial losses remain widespread. While previous research has examined fraud victimization in general, there has been less focus on the losses resulting from fraud. Additionally, there is a limited
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There is growing interest in using AI for financial advice, yet fraud and related financial losses remain widespread. While previous research has examined fraud victimization in general, there has been less focus on the losses resulting from fraud. Additionally, there is a limited understanding of whether individuals’ willingness to use AI for financial advice is linked to these losses. This study utilizes data from the 2024 National Financial Capability Study (NFCS) and is grounded in Routine Activity Theory and Bounded Rationality. It examines the relationship between the willingness to use AI for financial advice and the likelihood of experiencing loss due to fraud. Furthermore, the study examines the moderating effect of financial knowledge miscalibration (overconfidence). Results from multivariate logistic regression models indicate a statistically significant interaction between the willingness to use AI and financial knowledge miscalibration. Specifically, overconfidence was positively associated with the likelihood of experiencing loss due to fraud among individuals who were willing to use AI for financial advice, whereas this association was not observed among those who were not willing to use AI. These findings have important implications for financial professionals and stakeholders involved in preventing fraud.
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Open AccessArticle
The Relationship Between Geopolitical Risk and Asset Market Co-Movement: Evidence from South Africa
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Mpho Sephetho and Fabian Moodley
Int. J. Financial Stud. 2026, 14(6), 136; https://doi.org/10.3390/ijfs14060136 - 29 May 2026
Abstract
Periods of geopolitical uncertainty have increasingly shaped the performance of global financial markets, yet the extent to which these risks influence the co-movement of asset markets in South Africa remains unclear. Although co-movement has emerged as a crucial factor for investors seeking portfolio
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Periods of geopolitical uncertainty have increasingly shaped the performance of global financial markets, yet the extent to which these risks influence the co-movement of asset markets in South Africa remains unclear. Although co-movement has emerged as a crucial factor for investors seeking portfolio diversification, existing studies present mixed findings, with some suggesting that geopolitical risk strengthens financial integration, defined as the extent to which markets move together in response to global shocks, while others find that it weakens these linkages by triggering market segmentation. Against this backdrop, this study examines the impact of geopolitical risk’s influence on the co-movement of South African asset markets, focusing on how shifts in global uncertainty interact with local market dynamics. Using time-series monthly data from December 2004 to January 2025, the study applies a dual-method approach. The multivariate generalised autoregressive conditional heteroskedasticity asymmetric dynamic conditional correlation (MGARCH-ADCC) model is first employed to estimate time-varying correlations across the equity, bond, and property markets. Thereafter, the autoregressive distributed lag (ARDL) model is used to assess both the short- and long-run effects of geopolitical risk on these co-movement patterns. The results indicate that geopolitical risk significantly increases co-movement between South African asset markets in both the short and long run, thereby diminishing the traditional benefits of diversification. These findings reinforce the view that market participants respond collectively to uncertainty rather than fundamentals. Overall, the study contributes to the empirical understanding of market integration under geopolitical stress and highlights the need for investors and policymakers to incorporate geopolitical risk indicators into investment and policy frameworks to strengthen market resilience.
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(This article belongs to the Special Issue Advances in Financial Risk Management)
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Open AccessEditor’s ChoiceArticle
Measuring Financial Repression in CFA Franc Zones: Index Construction and Implications for Investment Activity
by
Amirreza Kazemikhasragh
Int. J. Financial Stud. 2026, 14(6), 135; https://doi.org/10.3390/ijfs14060135 - 26 May 2026
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This study develops a composite index of financial repression to overcome persistent gaps and inconsistencies in financial data across the CFA franc zones. The index aggregates proxies such as interest rate spreads, real interest rates, domestic credit to the private sector as a
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This study develops a composite index of financial repression to overcome persistent gaps and inconsistencies in financial data across the CFA franc zones. The index aggregates proxies such as interest rate spreads, real interest rates, domestic credit to the private sector as a percentage of GDP, broad money supply as a percentage of GDP, and bank liquid-reserves-to-assets ratio, with inversion applied to align higher values with greater repression. Fixed-effects panel regressions reveal a significant negative impact of repression on gross capital formation, indicating a 2.8 percentage point reduction per unit increase, robust to controls including GDP per capita growth, trade openness, population growth, public debt, and inflation. Findings underscore repression’s role in impeding investment activity in CFA franc zones, where centralized controls crowd out private allocation amid fiscal dependencies. Policy implications advocate for gradual liberalization to enhance intermediation, while future research could extend to dynamic interdependencies via vector autoregression. This contribution advances repression measurement in African contexts, bridging theoretical distortions with empirical evidence for sustainable growth.
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Open AccessArticle
The Effect of IFRS Adoption on Foreign Investment in the Japanese Equity Market
by
Yoshitaka Kubota and Fumiko Takeda
Int. J. Financial Stud. 2026, 14(5), 134; https://doi.org/10.3390/ijfs14050134 - 21 May 2026
Abstract
This study investigates the effects of International Financial Reporting Standards (IFRS) adoption on foreign investment in the Japanese equity market. Previous research suggests that a positive relationship between IFRS adoption and foreign investment typically emerges when a country meets specific conditions, such as
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This study investigates the effects of International Financial Reporting Standards (IFRS) adoption on foreign investment in the Japanese equity market. Previous research suggests that a positive relationship between IFRS adoption and foreign investment typically emerges when a country meets specific conditions, such as a strong regulatory environment or credible improvements in reporting uniformity. We contribute to this literature by re-examining the effects of voluntary IFRS adoption on the foreign shareholding ratio of Japanese companies from 2010 to 2023. Our analysis explicitly controls for the impact of reduced cross-shareholdings and increased share buybacks—structural factors likely to affect the capacity for foreign ownership. Using a difference-in-differences approach combined with propensity score matching to mitigate endogeneity, we compare 168 voluntary IFRS adopters against a control group of non-adopters. Unlike previous studies that reported no significant relationship in Japan—largely attributable to different denominator constructions for foreign shareholding ratios or shorter observation periods—our approach demonstrates that IFRS adoption significantly increases foreign investment over an extended horizon.
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(This article belongs to the Special Issue Stock Market Developments and Investment Implications)
Open AccessEditor’s ChoiceArticle
A Design Science Approach to Predicting ESG Performance Using Ensemble Machine Learning
by
Yara Ibrahim, Khaled Hussainey and Taghred Mokhtar Sayed Moawad
Int. J. Financial Stud. 2026, 14(5), 133; https://doi.org/10.3390/ijfs14050133 - 19 May 2026
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Environmental, Social, and Governance (ESG) metrics have become a cornerstone to sustainable finance, yet their measurement and predictability remain constrained by data heterogeneity, methodological divergence, and disclosure bias. This study develops a comprehensive ESG prediction framework grounded in the Design Science Research paradigm,
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Environmental, Social, and Governance (ESG) metrics have become a cornerstone to sustainable finance, yet their measurement and predictability remain constrained by data heterogeneity, methodological divergence, and disclosure bias. This study develops a comprehensive ESG prediction framework grounded in the Design Science Research paradigm, integrating advanced machine learning techniques with rigorous data preprocessing, feature selection, and temporal validation. Using firm-level data from Refinitiv and Bloomberg, the analysis distinguishes between ESG composite performance and disclosure-based robustness, addressing a critical gap in the literature. Ensemble learning models, including Random Forest and XGBoost, are evaluated alongside deep learning architectures using multiple sampling strategies and rolling-window validation. The results demonstrate that ESG performance is moderately forecastable, with ensemble methods consistently outperforming neural networks in structured datasets. In contrast, disclosure robustness exhibits lower predictability, reflecting its dependence on discretionary strategic reporting and institutional factors. The findings highlight the importance of data quality, model selection, and validation design in ESG analytics, while emphasizing the limitations of deep learning in tabular financial contexts. The integration of explainable artificial intelligence further enhances interpretability by identifying key predictors of ESG outcomes. Overall, the study contributes to the literature by providing a robust, interpretable, and methodologically rigorous framework for ESG prediction, with implications for investors, regulators, and corporate decision-making.
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