Search Results (4,094)

This study examines the relationship between price discovery and trading performance across different investor types in Taiwan’s active order-driven market. Using five-second intraday data, we construct a stock-trader-direction information share (IS) measure and link it to trading performance. Our results reveal several key findings: institutional investors have a higher IS per order, reflecting greater contributions to price discovery, and they outperform individual investors in trading performance. While higher IS is associated with better contemporaneous outcomes, it does not predict long-term performance. Determinants of price discovery include investor type, price aggressiveness, trade size, herding behavior, firm characteristics, and macroeconomic conditions. Robustness tests, covering one-minute IS, high-volatility periods, earnings announcements, and macroeconomic influences, support these conclusions. Full article

This study examines whether fiscal decentralization operates as a strategic macroeconomic risk-management instrument and whether its effectiveness depends on institutional quality. Using a balanced panel of 27 European Union member states over 2008–2023, a composite fiscal decentralization index combining expenditure and revenue autonomy is constructed, and a dynamic specification is estimated using a two-step System-GMM estimator. Output volatility is measured as a five-year rolling standard deviation of real GDP growth. The results indicate that fiscal decentralization exhibits a statistically significant effect on volatility whose direction depends on governance quality. Institutional quality directly reduces volatility, and the interaction between decentralization and institutional quality is negative and highly significant. A critical institutional threshold of 1.865 (WGI estimate scale), above which decentralization reduces output volatility, is identified. These findings indicate that decentralization functions as a conditional risk-management mechanism embedded within institutional capacity. The results provide policy-relevant insights into EU fiscal architecture design in an era of recurrent macroeconomic shocks. Full article

Manipulation-detection models calibrated in developed markets are routinely applied to frontier economies without validation, yet the institutional conditions that make such tools function as pricing signals are rarely present in those settings. This study provides the first systematic test of the Beneish M-Score and Dechow F-Score as return predictors in Vietnam, a frontier market navigating staged International Financial Reporting Standards (IFRS) convergence. Apparent negative associations between manipulation scores and excess returns under System Generalized Method of Moments (System GMM) do not survive panel fixed effects, Fama–MacBeth, or between-firm estimation. Persistent second-order serial correlation confirms that the GMM signal reflects frontier-market return momentum rather than manipulation pricing. By contrast, Big Four audit quality generates a robust cross-sectional return premium, establishing audit credibility as the operative governance channel where regulatory enforcement is absent. Survival analysis further shows that high-risk firms face substantially elevated exit hazards, demonstrating that reporting risk shapes long-run viability even where short-run pricing is absent. These findings constitute an IFRS paradox: Vietnam has adopted the institutional form of international reporting standards while lacking the informational infrastructure to support detection models that function as reliable pricing signals. Governance infrastructure, not standards convergence, is the operative condition for market discipline in frontier settings. Full article

Recent advances in large language models (LLMs) have raised questions about their potential role in portfolio allocation beyond traditional sentiment analyses. This study investigated whether LLMs, when prompted directly, can autonomously generate portfolio weights that compete with classical optimization and AI-enhanced strategies. We evaluated seven medium-sized open-source LLMs—Gemma-7B, Mistral-7B, Jansen Adapt-Finance-Llama2-7B, DeepSeek-R1-8B, QuantFactory Llama-3-8B-Instruct-Finance, Qwen-7B, and Llama2-7B—using systematic prompt engineering and temperature tuning. Portfolios were constructed from financial news headlines for S&P 500 equities and benchmarked against mean–variance optimization (MVO), the Black–Litterman model, AI-driven optimizers, and naive diversification strategies. The results show that, while LLM-generated portfolios outperformed naive diversification (Sharpe ratio up to 0.741), they lagged behind AI-optimized benchmarks (Sharpe ratio up to 1.361). A transaction cost analysis revealed that low-turnover LLM strategies retain their competitiveness post-costs, surpassing cap-weighted benchmarks. Statistical tests confirmed significant performance differences ($p\le 0.01$). These findings highlight the ability of LLMs to extract signals from unstructured text, but also their limitations without explicit optimization. Future research should explore hybrid frameworks that combine LLM reasoning with quantitative optimization for cost-sensitive environments. Full article

Global commodity shocks are associated with volatility in frontier financial markets, affecting exchange rates and equity indices. This study examined volatility transmission from global commodity shocks to Kenya’s USD/KES exchange rate and the NSE 20 Share Index using daily data from November 1997 to December 2024. While GARCH specifications capture clustering, they are sensitive to structural breaks and regime changes, which distort persistence and weaken risk measures. Machine learning approaches provide alternatives capable of capturing nonlinear dependencies, abrupt volatility bursts, and regime-independent dynamics. Empirical evidence demonstrates that the 2008 Global Financial Crisis and COVID-19 induced permanent volatility regime changes. This study examined volatility transmission from global commodity shocks to a frontier financial market, focusing on the USD/KES and the NSE 20 Share Index. Structural break-detection was integrated through the Iterative Cumulative Sum of Squares algorithm, alongside APARCH, FIGARCH models and ML architectures (XGBoost, LSTM). In Kenya volatility is characterized by strong persistence and long-memory dynamics, with limited evidence of leverage effects. Break-adjusted models improve inference by correcting spurious persistence, while machine learning approaches demonstrate superior tracking of volatility during stress regimes. Volatility transmission is nonlinear, break-sensitive, and state-dependent; hybrid ML–econometric methods enhance crisis forecasting and regime-sensitive financial stability analysis. Full article

Understanding how financial management practices translate into firm-level financial sustainability remains an important yet insufficiently explored issue. This study examines how financial discipline and financial risk management contribute to financial sustainability through financial resilience capacity. Drawing on a resilience-based perspective, financial resilience capacity is conceptualized as the firm’s ability to absorb financial shocks and adapt to uncertainty. The study employs a quantitative, survey-based research design using firm-level data collected during 2024–2025 from 217 respondents in financial and managerial roles. Financial discipline and financial risk management are operationalized through multi-item Likert-scale proxies capturing cost control, financial policy discipline, risk identification, diversification, and strategic financial planning, while financial sustainability is measured through indicators reflecting long-term financial stability and the ability to meet financial obligations. The relationships are tested using covariance-based structural equation modeling (SEM), including mediation analysis. The results show that both financial discipline and financial risk management significantly enhance financial resilience capacity, which in turn exerts a strong positive effect on financial sustainability. Financial resilience capacity acts as the primary mechanism linking financial practices to sustainable outcomes. While financial discipline has both direct and indirect effects, the impact of financial risk management operates fully through financial resilience capacity. These findings highlight the critical role of resilience in translating financial practices into long-term financial sustainability. Full article

This study examines the interplay between CEO overconfidence, strategic risk-taking, and financial performance within Indonesian digital banks. Grounded in Upper Echelons Theory and behavioral corporate finance, we investigate whether strategic risk-taking serves as an organizational pathway through which CEO overconfidence is more likely to be associated with specific financial outcomes. We analyzed a census-based, longitudinal dataset of seven Indonesian digital banks from 2014 to 2024. Using Partial Least Squares Structural Equation Modeling (PLS-SEM), we tested a moderated mediation framework incorporating CEO age and gender as contextual characteristics. The empirical results reveal a nuanced pattern: while CEO overconfidence is positively associated with strategic risk-taking, such risk-taking tends to correlate negatively with financial performance. Since these direct and indirect pathways operate in opposite directions, the total association between overconfidence and performance is not statistically significant. This structure suggests that strategic risk-taking represents a primary channel through which the potential downside of CEO overconfidence may be translated into financial outcomes. Furthermore, this negative association appears more pronounced under male leadership, while CEO age exhibits no significant moderating association. Overall, the findings suggest that while CEO overconfidence may align with strategic ambition, its financial implications appear contingent upon the specific risk posture through which it is expressed. Full article

The debate around corporate sustainability has become increasingly heated, with opinions ranging from denial of climate change to fatalistic acceptance of an impending collapse of civilization. This study examines how multinational enterprises, which contribute to nearly half of global GDP, internalize knowledge of planetary boundaries and take action within the organization or externally with investors, industry bodies, and policymakers. The research is grounded in empirical analysis of longitudinal data on two large samples of multinationals. It is found that, despite warnings from scientists about breaching planetary boundaries, multinationals, at best, follow an incremental approach to sustainability initiatives that collectively fail to drive positive global change. This well-entrenched practice remains unquestioned by external stakeholders, such as regulators, investors, and lenders. The research explains this behavior through (a) our inability to link global scientific findings to non-financial performance imperatives for individual businesses, and (b) our reliance on traditional enterprise risk management models that are less effective in a non-ergodic world. Full article

This study examines how market leadership in Indian equities has structurally shifted away from foreign institutional investors (FIIs) toward domestic institutional investors (DIIs) and mutual funds (MFs), and it evaluates the systemic risks created by this rebalancing. Using monthly transaction data from April 2007 to January 2026, we analyze evolving investment patterns among FIIs, DIIs, and MFs by employing trend analysis, Pearson’s and Spearman’s correlation analyses, phase decomposition, stationarity tests, Granger causality analysis, ARIMA modelling, and GARCH volatility estimation. Since 2021, FIIs have recorded cumulative net outflows exceeding ₹8.68 lakh crore (US$95.36 billion), while DIIs mainly led by mutual funds financed largely through Systematic Investment Plans (SIPs) have made net purchases of over ₹19.37 lakh crore (US$212.67 billion), effectively absorbing FII selling and helping to maintain elevated index levels. The trend continues with SENSEX having remained above 80,000 points through 2025 despite persistent FII disengagement. The DII share of total market purchases rose from approximately 39% in 2017 to over 54% by January 2026, documenting a structural shift in market composition. The results show that DII flows have stayed positively and significantly correlated with SENSEX, with FII flows being significantly negatively correlated. Granger causality tests suggest market-responsive rather than market-driving behavior by domestic institutions. Drawing upon Minsky’s financial instability hypothesis and behavioral finance frameworks, we interpret that prolonged domestic absorption of FII exists where direct fundamental evidence is unavailable. The Minsky-type fragility interpretation is offered as a structured hypothesis for future empirical investigation. The findings carry important implications for retail investors, fund managers, and regulators. Full article

Understanding how information flows across financial segments during global crises is crucial for analyzing complex and highly interconnected markets. This study investigated the dynamic information flow between cryptocurrencies, commodities, stock market indices of G10 countries, five-year sovereign CDS spreads, ten-year government bond yields, foreign exchange market variables, and technology company stocks using daily return data spanning from 1 January 2018 to 24 March 2026. Transfer Entropy is estimated using two alternative approaches: directly from the original variables and from independent components obtained via Independent Component Analysis (ICA), which reduces noise and uncovers latent relationships. A sliding-window framework is employed to capture time-varying directional information flow and to assess changes across major global events, including the COVID-19 pandemic, the Russia–Ukraine conflict, and the Middle East tensions. The results indicate that the magnitude and direction of information flow change significantly during crisis periods, revealing an event-sensitive and dynamically evolving connectivity structure between financial segments. Overall, the integration of ICA and Transfer Entropy provides a clearer and more reliable representation of directional interactions in multidimensional financial systems under the conditions of heightened uncertainty. Full article

Exchange rate volatility has intensified in recent decades, yet its systematic implications for banking-sector stability remain contested. This study investigates whether exchange rate volatility constitutes a meaningful source of financial fragility using a global panel of 103 countries over the period 2000–2021. Financial stability is proxied by the banking-sector Z-score, while exchange rate volatility is estimated using a EGARCH-based framework to capture time-varying uncertainty. To address cross-sectional dependence, heterogeneity, and endogeneity, the analysis employs Driscoll–Kraay fixed effects, two-step system GMM, and quantile regressions. The results reveal that exchange rate volatility exerts a statistically and economically significant negative effect on banking stability, reducing Z-scores across countries and income groups. The findings remain robust across alternative specifications and estimators. Bank-level fundamentals—capitalisation, liquidity, and credit—enhance stability, whereas higher non-performing loans and risk exposure amplify fragility. Macroeconomic conditions also matter, with stronger growth, institutional quality and external balances supporting resilience, while inflation, economic policy uncertainty and expansionary government spending weaken stability. By integrating time-varying volatility modelling with dynamic panel techniques in a large cross-country setting, this study provides new global evidence that exchange rate volatility is not merely a macroeconomic fluctuation but a structural source of banking-sector risk. The findings carry important implications for macroprudential policy, foreign-exchange management, and coordinated monetary–fiscal responses aimed at safeguarding financial stability in open economies. Full article

This article examines whether a high-performing fraud detection model can also meet the demands of auditability, documentation, and regulatory transparency. Using the publicly available European credit card fraud dataset of 284,807 transactions, including 492 fraudulent cases, this study compares weighted logistic regression with XGBoost under severe class imbalance. Model performance is assessed through precision, recall, F1 score, ROC AUC, and precision–recall AUC, with particular attention to alert burden and fraud capture. Results show that XGBoost materially outperforms logistic regression in operational terms. While logistic regression achieves slightly higher recall, XGBoost raises precision from 0.061 to 0.562, improves PR AUC from 0.719 to 0.863, and reduces false positives from 1386 to 67. The PR AUC of 0.863 refers to the cross-validated average reported in the model comparison, while the holdout test result reported later in this paper is 0.852. It cuts the review queue from 1476 alerts to 153 while still identifying 86 of 98 fraud cases in the test set. Explainability is then introduced through SHAP, which provides both global feature attribution and transaction-level reasoning. The findings show that SHAP makes the boosted model readable at the level of both overall model behaviour and individual fraud flags, thereby supporting audit review, model validation, and regulatory scrutiny. The article argues that the combination of XGBoost and SHAP offers a stronger fit for auditing than either a weaker but transparent linear model or a stronger opaque classifier. One limit remains, since the dataset contains anonymised principal components rather than original business variables, which restricts semantic interpretation. Even so, the workflow provides a practical bridge between predictive fraud analytics and the demands of explainable, reviewable, and accountable AI in auditing. Full article

Corporate social performance has increasingly become a strategic source of competitive advantage, requiring examination of both firm-level leadership factors and broader institutional contexts. Drawing on upper echelons and institutional theories, this study examines how observable CEO demographic (age, gender, nationality) and cognitive (education, tenure, multiple directorships) characteristics influence corporate social performance and how institutional governance conditions moderate these relationships. Using panel data analysis, the study empirically examines the relationship between CEO characteristics and social performance outcomes, while assessing the moderating role of governance quality. In addition, the analysis explores potential differences by industry sensitivity through a subsample approach, where firms are divided into sensitive and non-sensitive industries to test heterogeneous effects. The results indicate that the influence of CEO characteristics varies across institutional and industry contexts. In sensitive industries, demographic characteristics exert a stronger and more consistent influence on social performance, particularly under stronger governance conditions, while cognitive traits also contribute in a less stable and more governance-dependent manner. In non-sensitive industries, both demographic and cognitive characteristics exhibit a relatively similar influence on social performance, suggesting a more balanced role of CEO traits in less scrutinized environments. The findings suggest that executive influence on sustainability outcomes is not uniform but depends on the interaction between leadership attributes, institutional governance, and industry sensitivity, highlighting the importance of contextual factors in understanding variations in corporate social performance. Full article

Disruptions caused by disasters, pandemics, and systemic crises have increased the complexity and vulnerability of global supply chains, highlighting the need for advanced analytical approaches to risk and resilience management. In this context, artificial intelligence (AI) has emerged as a promising analytical capability for improving risk assessment and decision-making in disrupted supply chains. The study follows PRISMA 2020 reporting guidelines adapted for bibliometric research and presents a bibliometric and knowledge-mapping analysis of artificial intelligence applications in disaster supply chain risk and resilience management. Using the Web of Science Core Collection, a dataset of 288 peer-reviewed publications was analyzed through keyword co-occurrence, bibliographic coupling, citation analysis, and collaboration network mapping. The findings indicate a rapidly expanding research field in which AI supports predictive risk assessment, real-time monitoring, and resilience-oriented decision-making in disaster-prone supply networks. The analysis identifies dominant thematic clusters, emerging research directions, and opportunities for integrating AI-enabled analytics into supply chain risk management frameworks. The mapped literature also suggests secondary interpretive implications for financial risk exposure and supply chain finance, rather than indicating a separately operationalized finance-specific bibliometric subfield. To enhance interpretive depth, an AI-assisted analytical layer was applied to refine thematic clusters and detect emerging trends. However, this layer operates as a complementary interpretive tool and is subject to methodological limitations, including sensitivity to keyword semantics, dependence on bibliometric outputs, and potential interpretive bias in AI-assisted thematic labeling. Consequently, the AI-assisted analysis is used to support, rather than replace, bibliometric findings. Overall, this study contributes to the emerging literature on artificial intelligence in disaster supply chain risk management and highlights future research opportunities, including improved methodological integration and enhanced analytical transparency in AI-assisted bibliometric research. Full article

The escalating prominence of environmental, social, and governance (ESG) criteria within contemporary corporate practice has generated a proliferation of measurement and reporting frameworks, creating substantial challenges regarding comparability and standardisation. This study aimed to critically analyse and synthesise the scholarly literature on ESG measurement and reporting frameworks from an accounting perspective. A systematic review adhering to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines was conducted, searching Scopus, ScienceDirect, and Web of Science databases for the period 2020–2025, yielding a final corpus of 50 peer-reviewed articles. Findings reveal that Stakeholder Theory and Institutional Theory constitute the predominant conceptual underpinnings, with three framework categories identified: measurement, reporting, and integrated approaches. The analysis evidenced persistent methodological heterogeneity among ESG metrics and considerable variation in achieved comparability levels. Notably, the governance dimension remains underdeveloped relative to environmental and social dimensions, and small and medium-sized enterprises continue to be underrepresented despite their economic significance. This review contributes by providing a classification of ESG frameworks and their theoretical foundations, whilst identifying gaps that delineate avenues for future inquiry. Full article