This study examines whether Turkish banks with greater pre-existing reliance on foreign-currency deposits experienced different funding adjustments during the FX-protected deposit (Kur Korumalı Mevduat, KKM) era. Using quarterly statements for 18 deposit banks over 2018Q1–2025Q4, it relates funding changes to banks’ average FX-deposit shares over 2020Q1–2021Q3. A one-standard-deviation higher exposure is associated with a 4.02-percentage-point larger post-versus-pre change in non-deposit funding relative to assets. The differential becomes more pronounced during 2024–2025, connecting the finding to the evolution from KKM expansion and liraization toward monetary normalization. Complementary funding measures yield positive but less precise estimates, leaving uncertainty about the extent of adjustment in funding stocks. The evidence links the initial currency composition of the deposit franchise to subsequent differences in broader liability structures. Initial exposure is closely related to other bank characteristics, however, and the association weakens under bank-specific trends. Together with the sensitivity of pre-period diagnostics, these findings support an interpretation of differential funding adjustment during the wider KKM era without identifying the program’s causal effect.
Stock-market prediction is a difficult, multidisciplinary problem because financial time series are nonlinear, volatile and non-stationary. This study presents a Systematic Literature Review (SLR) of artificial-intelligence techniques for stock-price prediction, covering statistical, machine-learning, deep-learning, continual-learning, reinforcement-learning, multimodal and ensemble methods, through the lens of Responsible, Explainable and Fair AI (REF-AI). Following PRISMA 2020, 140 studies published between 2018 and July 2026 were included (139 peer-reviewed publications and one arXiv preprint) and coded by method family, task, data modality, market and REF-AI dimension. The review finds rapid growth in model complexity but concentration on a few markets, heavy reliance on price data and price-level accuracy metrics, and limited reporting of explainability (20 studies), uncertainty (11) and fairness (6). A case study then evaluates 23 models on eight stocks, two from each of the United States, Pakistan, India and Malaysia, using next-day return forecasts, an expanding-window walk-forward design, out-of-sample R^2 , directional-accuracy, Diebold–Mariano, Clark–West and Model Confidence Set tests, and a transaction-cost-adjusted trading exercise. Out-of-sample return predictability is weak for all model families: median out-of-sample R^2 values are slightly negative, directional accuracy is close to 50 R^2 above 0.97. The main exception is a short-horizon reversal in the two Malaysian stocks, whose trading value is sensitive to costs. Conformal intervals are close to nominal coverage overall but under-cover in bear regimes, and SHAP, integrated-gradients and attention analyses reveal stock-specific attributions that are informative only where a predictive signal exists. Because each market is represented by two stocks, the case study is illustrative and not a general test of developed versus emerging markets. The findings indicate that no model family is universally superior, that predictive and economic claims require return-based, benchmark-relative and cost-adjusted evidence, and that explainability, uncertainty quantification and regime-disaggregated evaluation are essential for responsible deployment.
Although the biodiversity risk (BDR) in sustainable finance has gained increasing attention, a comprehensive consolidation of existing insights at this interface remains limited. This study systematically reviews prior studies to synthesize the state of knowledge, emerging thematic trajectories and delineate future research agendas for BDR and financial decision-making. Employing the PRISMA protocol, 63 peer-reviewed journal articles published between 2012 and 2025 were retrieved from Scopus and Web of Science. The findings reveal an intensifying scholarly emphasis on the multifaceted implications of BDR and its integration into ESG and sustainable finance frameworks, underscoring the critical need for disclosure, biodiversity accounting, institutional mechanisms for green investment, biodiversity policy, and a valuation ecosystem aligned with the global biodiversity conservation goal. This study offers implications for future research and for policymakers, regulatory bodies, and academics.
Stock price volatility (PV) is an important concern in capital markets and the association between dividend policy (DP) and PV may vary with investor composition. This study examines the relationship between DP and PV by incorporating institutional ownership (INST) as a moderating factor among 862 non-financial firms listed on Bursa Malaysia’s Main Market during 2022–2025, comprising 2,990 firm-year observations. Findings from fixed-effects models indicate that dividend yield (DY) is positively associated with PV, while dividend payout ratio (DPR) shows a weaker and less consistent relationship. INST is negatively associated with contemporaneous PV, and the positive contemporaneous association between DY and PV is modestly stronger at higher levels of institutional ownership. However, quantile regression results provide supplementary evidence that the estimated associations vary across volatility regimes. Timing-based robustness analyses using one-year-lagged dividend measures show that the interaction between lagged DY and INST is statistically insignificant, indicating that the moderation pattern is sensitive to temporal specification. Hence, the study contributes evidence that inferences about DP, INST and PV depend on the dividend measure, temporal specification, and volatility regime in an emerging-market setting.
This study examines how digital finance (DF) and digital trade (DT) influence Green Low-Carbon Circular Development (GLCD) across 21 emerging Asian economies from 2008 to 2022, using international datasets from the World Bank and OECD. A composite GLCD index is constructed via principal component analysis (PCA). To capture long-run dynamics and address potential endogeneity, we employ Two-Stage Least Squares (2SLS) and the Difference Generalised Method of Moments (GMM). The empirical analysis shows that the effects of DF on GLCD are statistically significant and positive, whereas the effect of DT is statistically significant and negative. Additionally, technological innovation (TI) partially mediates the relationship between DF and GLCD, and human capital (HC) enhances the sustainability contribution of digital finance. These findings underscore the importance of financial digitalisation, cross-border digital trade, and workforce capabilities in driving environmentally responsible growth. They offer empirical guidance for policymakers seeking to design inclusive, digitally enabled green growth strategies in the Asia–Pacific region.
