Abstract: This paper draws on survey data from 585 family farms in Jiangsu Province, China, in 2023. It endeavors to examine how farmers' utilization of information and communication technologies (ICT) in agricultural production and management affects their access to agricultural production credit. The results demonstrate that farmers who apply ICT more comprehensively in agricultural production and management are more inclined to obtain agricultural production credit. Intriguingly, these outcomes persist resilient even when taking into account selection bias and endogeneity issues.In terms of transmission mechanisms, agricultural digital transformation can facilitate farmers' access to agricultural production credit. Specifically, it does so by reducing the credit transaction costs related to bank loans and enhancing the efficiency of agricultural resource allocation. Furthermore, the heterogeneity analysis reveals that agricultural digital transformation is more conducive for smallholder farmers to acquire agricultural production credit from large banks. Finally, it is evident that the application of ICT in areas such as agricultural product sales and the management of agricultural digital equipment is more beneficial for farmers in attaining agricultural production credit.

Abstract: Climate transition risk is emerging as a critical determinant of value in real estate finance as cities adopt increasingly stringent decarbonization policies, adding to the pressure of physical risk. New York City’s Local Law 97 (LL97), which imposes binding emissions caps and financial penalties on large buildings, offers a salient case to examine how capital markets respond when building-sector climate regulation becomes financially consequential. This paper investigates whether and how U.S. equity Real Estate Investment Trusts (REITs) with exposure to New York City assets are impacted by climate transition policies like LL97. Using a standard event study framework, the analysis examines abnormal returns around two key milestones: the policy’s approval as part of the Climate Mobilization Act in April 2019 and the onset of its enforcement phase in January 2024. Results show that the initial announcement generated statistically insignificant cumulative abnormal returns, suggesting that investors did not price LL97’s long-term horizon implications at the time of the vote. By contrast, the enforcement milestone coincided with economically meaningful negative abnormal returns across most sampled REITs, particularly those with substantial New York City office exposure, although these effects are not statistically significant and can be attributed to broader sectoral stress. Cross-sectional tests reveal no significant differences between highly and moderately exposed groups. Overall, while isolating the impact of transition risk alone is empirically challenging, the findings suggest that climate-related transition risk is priced gradually, potentially non-material in the short term and can become more salient as implementation approaches.

Abstract: This study investigates the role of market volatility, proxied by the CBOE Volatility Index (VIX), as a potential amplifier of corporate leverage risk within the S&P 100. Addressing the limitations of traditional financial distress models in capturing non-linear and regime-dependent dynamics, we employ XGBoost combined with SHAP-based explainable AI (XAI) on a longitudinal dataset spanning 2000-2025. The results show that total debt remains the dominant predictor of financial distress, while the influence of risk-related variables such as the VIX and equity returns increases during crises periods. Monetary policy indicators become more important during pandemic conditions, whereas inflation dominates in stable environment. This finding highlights the regime-dependent nature of financial risk drivers and demonstrates the value of explainable machine learning in developing interpretable early warning systems. By integrating predictive accuracy with interpretability, this study provides new insights into the interaction between firm-level leverage and external market volatility.

Abstract: The study sought to examine the intervening influence of financial development on the relationship between sustainability practices and sustainable development of the Sub-Saharan African countries. The study used a longitudinal panel design and incorporated both the descriptive and explanatory elements. The study adopted a positivist research philosophy. It examined data from 49 Sub-Saharan African countries over a 24-year period from 2000 to 2023 to analyse sustainability practices, financial development and their influence on sustainable development. The study relied on secondary data from the World Bank Data Bank, UNDP and Sustainable Development Reports. Descriptive analysis and regression models were used for analysis. The study found that financial development does not serve as an effective transmission channel through which sustainability practices influence sustainable development outcomes. The research concluded that policy interventions should include developing sustainable banking regulations, creating green finance incentives, establishing sustainability-linked lending criteria, and strengthening financial inclusion policies that target sustainable development sectors.

Abstract: This paper has presented a combined empirical framework for measuring the risk-return profile of listed real-estate securities in a non-Gaussian market situation. By leveraging daily data for 30 U.S. and international listed real estate securities from 2021 to 2024, we probe how portfolio outcomes vary according to the optimization criterion and distributional aspects of returns obscured by conventional mean-variance summaries. We build long-only and long-short portfolios using classical Markowitz mean–variance optimization methods and conditional value-at-risk (CVaR) optimization techniques, and compare their realized dynamics cumulative growth and efficient frontiers under alternative risk-free benchmarks. By applying extreme value theory, we quantify extreme-risk exposure via generalized Pareto modeling and Hill tail-index estimation, which is compared to the broader behavior of the equity market. We analyze portfolio stability and reward efficiency using volatility, Sharpe, Sortino, and Rachev ratios, as well as maximum drawdown and information ratio. In addition, robust single-factor regressions are estimated on a sector benchmark, and residual diagnostics are analyzed to define common factor dependence while minimizing the impact of outliers. To introduce a forward-looking dimension, we calibrate the double-subordinated normal inverse Gaussian specification and extract NDIG model-implied option prices and volatility surfaces. We also investigate volatility persistence through ARFIMA-GARCH modeling to assess whether listed real-estate security returns exhibit long-memory features beyond standard volatility clustering. Results indicate that listed real-estate security returns exhibit heavy tails, pronounced downside sensitivity, and persistent volatility, supporting the use of tail-aware optimization, robust estimation, and long-memory-consistent volatility diagnostics beyond standard Gaussian benchmarks.

Abstract: The Ghanaian foreign exchange (FX) market has experienced substantial transformation over the past decade, marked by rising trading volumes and several episodes of exchange rate turbulence. Building on the pioneering work of Duffour et al. (2011) on order flow and exchange rate dynamics in Ghana, this study employs high‑frequency daily data from 2018 to 2023—capturing both stable and volatile market conditions. Using the BK‑18 spillover index, our findings show that order flows and exchange rates are tightly interconnected through bidirectional causality. Moreover, the EUR/GHS exchange rate emerges as a dominant transmitter of shocks within the multivariate system of order flows and exchange rates. These insights carry important implications for foreign exchange (FX) policy design, regulatory oversight, market monitoring, and trading strategies in Ghana.

Abstract: This study empirically explores the digital determinants of financial account ownership of 152 economies using the Global Findex Database 2025, which is the most recent cross-country repository on financial inclusion produced by the World Bank. By using ordinary least squares (OLS) regressions, together with descriptive, regional and bivariate analytical procedures, the authors examine the extent to which digital financial access, ownership of a mobile money account and the use of the internet predict financial account ownership at country level. The results show that digital financial access is the strongest and statistically significant determinant of financial account ownership in the world with a coefficient of 0.911 (p < 0.001), while the full model explains 76.7% of the cross-country variation in account ownership. Mobile money account ownership is added to the model with a negative and statistically significant coefficient of -0.236 (p = 0.006) indicating a substitution effect in economies where mobile money becomes the primary avenue to financial access in lieu of the traditional infrastructure of a banking sector. While internet usage is positively correlated with account ownership in bivariate tests, in the multivariate model, internet usage is not found statistically significant in driving financial inclusion without the presence of functioning digital financial infrastructure alongside with it. The regional analysis shows significant disparities in the results of financial inclusion. High income economies have an average account ownership rate of 92.03%, compared with 42.41% in the Middle East and North Africa and 55.60% in Sub-Saharan Africa. These results have some interesting implications for policymakers and development-finance institutions interested in designing inclusive digital financial ecosystems; they highlight the importance of investing in digital financial infrastructure in a targeted way rather than simply expanding internet access as a tool of financial inclusion. This study is the first cross-country empirical analysis of the factors driving digital financial inclusion based on the Global Findex 2025 dataset and offers timely and original evidence that can be relevant for policy formulation and practice concerning digital financial inclusion.

Abstract: This study investigates the dynamics of market efficiency and herding behavior in four leading precious metals - gold, silver, platinum, and palladium - during and after the COVID-19 pandemic using 5-day-week data from December 9, 2019 to March 1, 2026. Employing Multifractal Detrended Fluctuation Analysis (MFDFA) to estimate generalized Hurst exponent (GHE), magnitude of long memory (MLM), and the predictability of return, the findings reveal heightened fractal spectrum and increased herding tendencies during the post-pandemic period for only gold. While the fractal dimension improves for these metals, excluding for gold, they all exhibited weaker long-memory characteristics and thereby an upward trend in the degrees of inefficiencies, implying resistance to persistent shocks, estimated by the MLM approach. Within this framework, the returns of silver, platinum, and palladium became more predictable during the post-pandemic period, implying lower volatility. These results highlight structural shifts in investor behavior and the patterns of market efficiency during crisis periods, providing important insights for enhancing market resilience and volatility management.

Abstract: This study examined the comparative performance of GARCH family models including GARCH, EGARCH, GJR GARCH, APARCH, and FIGARCH within a horizon and regime aware framework to assess forecasting accuracy. Using daily prices of equity and foreign exchange markets in Kenya covering 1997–2024, volatility was modelled and validated through Value at Risk and Expected Shortfall back tests to establish economic relevance. The results reveal strong horizon and regime dependence: EGARCH performs well in capturing short run volatility in the equity market and turbulent foreign exchange re-gimes, while FIGARCH dominates in calm equity markets and at medium term horizons. Risk validation confirms that FIGARCH delivers reliable tail risk forecasts for equities, whereas EGARCH excels in turbulent foreign exchange markets. Unlike prior comparative studies that focus on efficient markets with stable volatility structures, this study applied GARCH family models to a frontier market, comparing forecasting accuracy across vary-ing horizons and regimes. The study advanced beyond best fit evaluations by linking forecasting performance to horizon length, asset type, and regime shifts, thereby contrib-uting new evidence on modelling volatility in African frontier markets and offering in-sights relevant for regulators, institutional investors, and policymakers concerned with financial stability.

Abstract: Traditional financial early warning models often rely on the independent and identically distributed (IID) assumption, failing to adequately capture cross-sectional spatial contagion effects and temporal dynamic mutations, and are susceptible to the over-smoothing problem when processing highly imbalanced graph networks. To address these limitations, this study proposes a micro-manifold-based identity-preserving spatiotemporal graph neural network framework (Micro-STAGNN). In the spatial dimension, an identity-preserving graph convolutional operator (IP-GCN) is constructed. By hard-coding a self-preservation coefficient (λ=0.8), it quantifies peer risk spillover while mitigating feature dilution, ensuring the transmission of heterogeneous default signals. In the temporal dimension, Long Short-Term Memory networks are cascaded with a temporal attention mechanism to capture the nonlinear temporal inflection points that trigger financial distress. The empirical study utilizes a sample of China's A-share market from 2015 to 2025, evaluating the model using an Out-of-Time Validation protocol and Focal Loss. Results indicate that under a highly imbalanced distribution with a positive-to-negative sample ratio of approximately 1:50, Micro-STAGNN achieves an OOT ROC-AUC of 0.9095, a minority class default recall of 89%, and reduces the missed detection rate to 11%, outperforming traditional nonlinear cross-sectional models such as XGBoost. Furthermore, temporal attention weights provide explainable support for the early warning results.

Abstract: Earnings management continues to attract attention in accounting research because of its implications on financial reporting credibility and investor confidence. Increasing stakeholder pressure regarding environmental accountability has shifted scholarly focus toward the association between environmental performance and financial reporting behaviour, however empirical evidence remains inconclusive in developing countries and carbon intensive firms. This study examines the relationship between carbon-related environmental performance and earnings management among companies listed on the Johannesburg Stock Exchange. The study employs fixed and system generalized method of moment model to evaluate how carbon-related environmental performance shape earnings management among 53 carbon-intensive companies over the period 2015-2024. The findings report a statistically significant negative relationship between carbon-related environmental performance and earnings management, indicating that companies demonstrating stronger carbon emission reduction tend to exhibit lower levels of earnings management. These results suggest that environmental accountability may reinforce financial reporting discipline rather than facilitating opportunistic reporting behaviour in carbon-intensive firms. These results provide valuable insights for investors assessing firms’ sustainability and financial reporting credibility, while inspiring firms to reinforce environmental performance as part of responsible governance and transparent financial reporting.

Abstract: Global commodity shocks are associated with volatility in frontier financial markets, affecting exchange rates and equity indices. 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 regimeindependent dynamics. Empirical evidence demonstrates that 2008 Global Financial Crisis and COVID19 induced permanent volatility regime changes. This study examined volatility transmission from global commodity shocks to a frontier financial market, focusing on the USD/KES exchange rate 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 longmemory dynamics, with limited evidence of leverage effects. Breakadjusted models improve inference by correcting spurious persistence, while machine learning approaches demonstrate superior tracking of volatility during stress regimes. We show that volatility transmission from global commodity shocks to a frontier market is nonlinear, break-sensitive, and state-dependent, and that hybrid ML-econometric methods improve forecasting during crisis-period. Findings highlight persistence distortion, horizondependent performance, and relevance of regimesensitive modelling frameworks for financial stability in structurally evolving economies.

Abstract: The U.S. residential real estate market represents a significant component of national wealth, yet investment decision-making in this sector remains largely dependent on heuristic judgment rather than systematic, data-driven analysis. This study presents a multi-dimensional analytical framework that integrates multiple Zillow housing market indices to evaluate residential real estate investment opportunities across the United States. Using publicly available data from the Zillow Home Value Index (ZHVI), Zillow Observed Rent Index (ZORI), days-on-market metrics, and regional listing data spanning Q1 2018 through Q1 2023, the research conducts six interconnected analyses: (1) identification of high-growth regions in the single-family home segment, (2) rental market trend analysis across major metropolitan areas, (3) short-term property value forecasting, (4) market liquidity assessment through days-on-market analysis, (5) return on investment (ROI) distribution for rental strategies, and (6) ROI distribution for sale strategies. The framework employs Python-based data manipulation and visualization techniques to synthesize these indices into actionable investment intelligence. Key findings indicate that the top 3% of U.S. regions exhibited disproportionately high single-family home price appreciation between December 2022 and March 2023, rental indices demonstrated a consistent upward trajectory across major metropolitan areas, and property values were projected to increase by approximately 1.6% through March 2024. Furthermore, state-level variation in days-on-market and significant regional disparities in ROI distributions for both rental and sale strategies were identified, with notable outlier regions offering substantially higher returns. The proposed framework demonstrates the practical utility of integrating publicly available housing market indices for systematic investment evaluation, offering a reproducible methodology that can inform both individual and institutional decision-making in the residential real estate sector.

Abstract: Large language models (LLMs) are increasingly used by retail traders to interpret information and design complex strategies, yet existing adoption constructs do not capture the decision-time experience of being cognitively scaffolded by an LLM. We define Perceived Cognitive Assistance (PCA) as the trader’s felt expansion of cognitive capability at the moment of trading decision when an LLM is available, and we report initial content validation of a PCA item pool. Study 1 specified the PCA content domain using a two-tier qualitative corpus (8 interviews and 44 YouTube narratives on LLM-assisted trading, plus 24 qualitative and mixed-method studies on robo-advice and social trading). Reflexive thematic analysis yielded five facilitative assistance facets and one adjacent risk facet (over-reliance), and these were translated into a 16-item PCA pool. Study 2 used a naïve-judge sort-and-rate task with 48 retail traders to test whether items show definitional correspondence to PCA and definitional distinctiveness from similar constructs: perceived usefulness, perceived ease of use, trust in the LLM, and trading self-efficacy. The resulting 9 item set is ready for subsequent factor-analytic and predictive validation. This study advances our understanding of how large language models shape retail trading behaviour by identifying and empirically grounding Perceived Cognitive Assistance as the decision-time psychological experience through which LLMs cognitively scaffold traders, clarifying how LLM use differs from generic technology adoption, trust, or self-efficacy effects.

Abstract: This study explains cryptocurrency investment decisions by integrating personality traits, influencer credibility, and social influence within the Stimulus–Organism–Response (SOR) framework. Openness, extraversion, conscientiousness, influencer credibility, and social influence are positioned as stimuli; heuristic bias and herding behaviour as organism states; and cryptocurrency investment decision as the response, with risk tolerance acting as a serial mediating mechanism. Data were collected from 367 Indonesian retail cryptocurrency investors through an online survey and analysed using SEM-PLS. The measurement model demonstrates adequate reliability and convergent validity, while discriminant validity is supported by HTMT values below the recommended threshold. The results indicate that personality traits significantly influence heuristic bias, while influencer credibility and social influence increase herding behaviour. Heuristic bias and herding behaviour both positively affect risk tolerance and cryptocurrency investment decisions, with heuristic bias showing the stronger effect. Risk tolerance also positively influences investment decisions and mediates the effects of heuristic bias and herding behaviour. The model explains a substantial portion of the variance in cryptocurrency investment decisions (Adjusted R² = 0.623). These findings extend the SOR framework to cryptocurrency markets by highlighting how psychological traits and social cues shape risk tolerance and ultimately influence investment behaviour in volatile digital asset environments.

Abstract: Research Purpose: This study investigates the role of digital investor behavior, meas-ured through Google Trends, alongside technical indicators such as RSI and Bollinger Bands, in forecasting volatility in the Dubai Financial Market. The aim is to develop a hybrid analytical framework that integrates behavioral and technical dimensions to enhance predictive accuracy in emerging markets. Study Methodology: Weekly data from 2020 to 2025 were collected, covering both cri-sis and post-crisis periods. Digital attention was quantified using Google Trends search indices, while technical indicators included RSI and Bollinger Bands calculated over a 7-day horizon. Volatility was modeled using ARCH, GARCH, and EGARCH frame-works, with Max Drawdown employed as a complementary risk metric to capture ex-treme market movements. Findings: The results reveal that digital investor attention significantly contributes to volatility forecasting, particularly when combined with technical indicators. Models incorporating both behavioral and technical variables demonstrated superior predic-tive performance compared to traditional approaches. The EGARCH model high-lighted the asymmetric impact of negative shocks, while Max Drawdown provided additional insights into risk exposure during periods of heightened market stress. Scientific value: This study positions digital attention as a leading indicator in volatil-ity modeling, moving beyond conventional approaches that treat behavioral signals as supplementary. By integrating Google Trends with technical analysis, the research in-troduces a hybrid forecasting framework that can be adapted to other emerging mar-kets. Practical Implications: The findings offer practical value for policymakers and inves-tors. Regulators can use digital attention measures as early-warning signals to antici-pate instability, while investors can integrate behavioral and technical indicators to improve risk management and trading strategies. From a foresight perspective, the study contributes to building more resilient financial systems by embedding behavioral data into predictive tools.

Abstract: Short-horizon equity return forecasting presents fundamental challenges due to regime shifts and nonlinear market dynamics. Traditional approaches either employ interpretable probabilistic models lacking predictive power or deploy opaque deep learning architectures sacrificing economic interpretability. We propose the Neural Regime-Switching Model with Markov-Informed Attention (NRSM-MIA), an end-to-end differentiable architecture that jointly learns discrete market regimes, Markov transition dynamics, and regime-conditioned temporal patterns. The framework introduces differentiable regime inference via Gumbel-Softmax relaxation, regime-conditioned multi-head attention, a mixture-of-experts decoder, and built-in uncertainty quantification. Following cross-sectional learning, we train on pooled data from 15 U.S. equities across five sectors. Experiments demonstrate that NRSM-MIA achieves the best Mean Absolute Error (1.1104), Directional Accuracy (52.86\%), and Sharpe Ratio (0.6765) compared to LSTM, Transformer, and HMM-GBR baselines. Crucially, the model maintains interpretable regime assignments with a balanced distribution across volatility states, validating that prediction performance and interpretability can be achieved simultaneously through staged training with diversity regularization.

Abstract: This paper employs machine learning techniques based on market volatility to identify and construct trading signals for both short-term and long-term Time Series Momentum (TSM) strategies. Through a comparative study of China's CSI 300 Index and the U.S. S&amp;P 500 Index, we conduct an empirical analysis from a cross-market perspective. The findings reveal that the performance of time series momentum strategies is jointly determined by their signal responsiveness and the prevailing market volatility regime. Using the Random Forest algorithm, this study effectively identifies critical thresholds for regime switching between low-volatility and high-volatility states in index futures markets. The empirical results demonstrate that during high-volatility periods, short-term TSM strategies significantly outperform their long-term counterparts, whereas the opposite holds true in low-volatility environments. Further analysis indicates that the short-term momentum alpha can be attributed to market timing ability. Our findings provide important theoretical and practical implications for optimizing trend-following strategies in commodity and financial futures markets through machine learning approaches.

Abstract: Purpose: This paper investigates the moderation role of digital finance in the relationship among inclusive finance, banking sector development, and economic development in Africa. Design/Methodology/Approach: We construct indices through the Principal Component Analysis to measure inclusive finance, digital finance, and banking sector development in a panel of 39 selected African countries over the period, 2004-2022. Subsequently, we employ the two-stage system Generalized Method of Moments estimator to examine the moderating effect of digital finance on the key variables. Findings: The results showed persistence of economic development with its lag recording positive and statistically significant relationship with the dependent variable. This result suggests that earlier levels of economic development serve as positive and significant drivers of the current levels of economic development. The results also indicate that digital finance can spike economic development in the presence of inclusive finance and banking sector development. We document evidence of negative impact of increasing population growth on economic development. Originality: This paper provides the first empirical evidence of the moderating role of digital finance on the effect of inclusive finance and banking sector development on economic development from the African perspective. Practical Implication: The promotion of digital finance applications will improve inclusion and banking development which will enhance economic development, while inclusion policy should be able to address the bottlenecks such as inadequate digital infrastructure. Social Implication: Greater access to finance through digital finance can promote banking sector development which can substantially support poverty alleviation and long-term development resulting into economic development.

Abstract: Nigeria entered the 2020 COVID-19–related oil price downturn without the fiscal buffers that numerous resource-rich economies had built over time. Despite heavy dependence on petroleum revenues, the country has made limited use of stabilization tools such as structured hedging programs, sovereign savings mechanisms, or strategic reserves, leaving public finances exposed to external shocks. Drawing on political choice theory and the resource governance literature, this study examines how institutional conditions shaped crisis management during the 2020 oil price collapse and the COVID-19 pandemic. The study combines qualitative institutional analysis with stochastic counterfactual simulations, comparing Nigeria’s policy approach with those of oil-producing countries including Mexico, Saudi Arabia, the United Arab Emirates, Angola, and Ghana, using data from the IMF, World Bank, Afreximbank, and peer-reviewed sources. The analysis identifies institutional gaps in Nigeria’s use of hedging, sovereign savings, and reserve infrastructure. Counterfactual modeling indicates that even a modest oil hedging strategy could have mitigated the 2020 downturn, reducing GDP contraction by an estimated 0.54 percentage points. These findings suggest that governance constraints contributed to fiscal vulnerability. The study proposes a four-pillar framework centered on risk hedging, revenue savings, strategic investment, and institutional reform to strengthen fiscal stability and resilience to external shocks.