Abstract: This study investigates the long-run relationship between green finance and the ecological footprint in 13 countries with the highest levels of green financial development, while also examining the roles of green growth, economic growth, financial globalization, and capital formation. Using panel data from 1992 to 2020, the analysis applies advanced econometric techniques, including the Augmented Mean Group (AMG), Fully Modified Ordinary Least Squares (FMOLS), and Dynamic Ordinary Least Squares (DOLS) estimators to identify long-term effects. In addition, the Dumitrescu-Hurlin panel bootstrap causality test is used to explore the direction of relationships among variables. The results confirm the existence of cointegration among all variables. Green finance and green growth are found to reduce the ecological footprint, indicating their effectiveness in mitigating environmental degradation. In contrast, economic growth, financial globalization, and capital formation contribute positively to the ecological footprint, suggesting a link to increased environmental pressure. The causality analysis reveals a bidirectional relationship between green growth and ecological footprint, while green finance, economic growth, financial globalization, and capital are found to be causal factors of ecological footprint. The findings highlight the importance of promoting green finance and sustainable growth strategies while ensuring that financial and capital flows support environmental objectives.

Abstract: The paper investigates the ability of asset pricing models to explain the cross section of average stock returns of anomaly portfolios. A large sample of 286 anomaly portfolios are employed. We perform out-of-sample cross-sectional regression tests of both prominent asset pricing models and a relatively new model dubbed the ZCAPM. Empirical tests strongly support the lesser known ZCAPM but not other multifactor models. Further analyses of out-of-sample mispricing errors of the models reveal that the ZCAPM provides much more accurate pricing of anomaly portfolios than other models. We conclude that anomalies are anomalous to popular multifactor models but not the ZCAPM. By implication, the efficient market hypothesis is supported.

Abstract: Fraud remains a challenge for various organizations. One type of fraud with significant economic impact is Financial Statement Fraud (FSF). This study empirically investigates the influence of audit committee diversity on FSF, emphasizing four attributes: education level, gender, tenure, and age. The sample comprises 89 banks in Indonesia observed over 15 years (2009–2023). Data were analyzed using Random-effect GLS regression. The findings indicate that banks with audit committee members exhibiting high diversity in gender and educational levels can reduce FSF. However, high age diversity leads to communication and coordination issues, thus diminishing oversight quality and subsequently increasing FSF. We found no impact of tenure diversity among audit committee members on FSF. The results remain robust after controlling for majority ownership factors that may cause agency conflict between majority and minority ownership. This study provides valuable insights for bank owners when selecting audit committee members. It emphasizes the importance of considering gender, education level, and age backgrounds, as these factors significantly impact the effectiveness of audit committee members in fulfilling their responsibilities. Additionally, the bank regulators in enhancing corporate governance, highlighting the need to regulate the gender, age, and education backgrounds of audit committee members to reduce instances of financial statement fraud (FSF).

Abstract: This paper establishes a foundational framework for the application of Generative AI in financial risk management by providing a comprehensive overview and review of essential quantitative techniques. We illustrate the quantitative aspect of these models in equity, fixed income, and mortgage-backed securities markets, emphasizing their proposed Gen AI enhancement in enhancing risk management practices. Furthermore, this paper examines the transformative potential of Generative AI, specifically Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), in reshaping risk modeling and stress testing. We bridge classical quantitative techniques—including Monte Carlo methods, Value-at-Risk, and stochastic processes—with modern generative models like GANs and VAEs. The study first reviews foundational models for market, credit, and liquidity risk, demonstrating their application across fixed income and mortgage-backed securities markets. We then propose novel AI-enhanced methodologies for: (1) liquidity risk quantification through augmented Monte Carlo simulations, (2) dynamic credit risk modeling using agentic frameworks, and (3) stress testing with synthetic scenario generation. A key contribution is the development of hybrid architectures that combine traditional risk metrics with data-driven generative AI, implemented through practical Python workflows. The paper concludes with empirical results showing improved accuracy in Expected Liquidity Shortfall (ELS) estimation and Value-at-Risk calculations, providing a pathway for next-generation risk management systems. By laying this quantitative foundation, we aim to facilitate the adoption of advanced Gen AI techniques, offering a forward-looking perspective on the future of quant-data-driven financial risk management.

Abstract: The global transition to renewable energy (RE) requires substantial investment amidst complex interactions between Environmental, Social, and Governance (ESG) factors and Geopolitical Risk (GPR). This study investigates these dynamics across 44 countries from 2008-2023 using an integrated panel dataset combining IRENA investment data, World Bank ESG indicators, and the GPR index. Panel data regression models (Pooled OLS, Random Effects, Fixed Effects) with robust clustered standard errors were estimated after addressing multicollinearity via VIF reduction and performing appropriate model selection tests. The Fixed Effects (Entity) model was preferred based on a significant Hausman test (p=0.0000). Results indicated that within-country changes in GPR were not significantly associated with annual RE investment changes (p=0.43). Specific social equity metrics (income share of lowest 20%, Gini index) showed significant associations with investment shifts, while changes in selected governance and environmental indicators did not. Investment composition by technology (e.g., wind, solar, hydro) and financing type (specifically grants, p=0.005) were significant predictors. The findings suggest foundational stability and social equity considerations are critical alongside targeted financial mechanisms for accelerating RE investment, while short-term GPR volatility showed limited direct impact within countries during this period.

Abstract: The purpose of the study was to understand the impact of environmental, social, and corporate governance on the financial performance of JSE-listed firms in South Africa. The study utilised the JSE Top 40 firms for the period from 2002 to 2022. Furthermore, the study employed a two-step system Generalised Method of Moments, to estimate the impact of total ESG and individual dimensions of ESG on firm financial performance. Additionally, the study examined the moderating effects of firm size on the relationship between financial performance and ESG. The results revealed a positive and significant relationship between total ESG and firm financial performance. However, the findings regarding individual ESG dimensions and firm performance are mixed. Firm size has a moderating effect on the relationship between ESG and firm financial performance. The implication of these findings for South Africa is increased foreign direct investment from green investors and listed firms seriously considering ESG in their operations.

Abstract: The Price-to-Earnings (P/E) ratio, though widely used in stock valuation, suffers from significant limitations: it is static, backward-looking, and fails to account for critical variables such as earnings growth, risk, and the time value of money. This paper introduces a new and robust alternative — the Stock Internal Rate of Return Including Price Appreciation (SIRRIPA). Derived from a refined version of the P/E ratio called the Potential Payback Period (PPP), SIRRIPA integrates both earnings accumulation and capital appreciation over time, expressed as a forward-looking, risk-adjusted, compound annual return. It is directly comparable to a bond’s Yield to Maturity (YTM), enabling a unified and rational framework for cross-asset valuation. Grounded in financial theory and supported by intuitive formulas, SIRRIPA offers investors and analysts a superior metric for assessing the intrinsic value and total return potential of equity investments.

Abstract: This paper explores the extent to which robo-advisors influence financial literacy and systematic investment behavior among retail investors in the United Arab Emirates. While robo-advisory platforms are widely adopted for their convenience and cost-efficiency, their actual contribution to investor knowledge and long-term financial discipline remains contested. Using a descriptive-correlational research design supported by thematic qualitative analysis, data were collected through a structured survey of 150 investors. The findings indicate that financial literacy significantly predicts investment behavior, while robo-advisor usage alone does not yield statistically significant behavioral improvement. Correlation analysis confirms that income and financial literacy are more strongly associated with disciplined investing than technological adoption. Thematic insights reveal that trust, usability, and the perceived lack of human interaction shape investor attitudes toward automated advice. These results suggest that financial technology, though valuable in access and automation, must be complemented by behavioral reinforcement mechanisms such as educational modules, timely nudges, and user feedback to foster meaningful financial behavior change. The study contributes to the literature on financial technology by emphasizing the need for integrative robo-advisory designs that align cognitive engagement with behavioral outcomes.

Abstract: This paper examines the relationship between stock prices, energy prices and climate policy uncertainty using 11 sectoral stocks in the US market. Evidence confirms that rising prices of energy commodities positively affect not only the energy & oil sector stocks but also create spillover effects across other sectors. Notably, all sectoral stocks, except those in real estate, show resilience to increases in crude oil and gasoline, suggesting potential hedging benefits. The findings reveal that sectoral stock returns are generally negatively affected by several types of uncertainty, including climate policy uncertainty, economic policy uncertainty, and oil price uncertainty, as well as energy & environmental regulation induced equity market volatility and the energy uncertainty index. These adverse effects hold across sectors, with few exceptions. Evidence reveals that the feedback effect between climate policy uncertainty changes and oil price changes produces an adverse impact on stock returns. Omitting these uncertainty factors from analyses could lead to biased estimates in the relationship between stock and energy prices.

Abstract: In recent years, the coordinated development of digital finance has significantly influenced corporate financing behavior and shadow banking activities. However, the relationship between digital finance and shadow banking, particularly under the influence of environmental regulation, remains underexplored. This study utilizes data from non-financial firms listed on the Shanghai and Shenzhen Stock Exchanges in China from 2012 to 2022, constructing an index of the coordinated development of digital finance using a coupling coordination model. It investigates the impact mechanism of coordinated development of digital finance on corporate shadow banking activities under environmental regulation. The results reveal that the coordinated development of digital finance significantly promotes shadow banking activities of non-financial firms, demonstrating a "U-shaped" nonlinear relationship. Environmental regulation moderates this effect. Under low-intensity environmental regulation, an increase in regulation strengthens the promoting impact of digital finance on shadow banking activities and weakens its nonlinear influence. In contrast, under high-intensity environmental regulation, digital finance's "U-shaped" effect on shadow banking activities becomes less pronounced. Furthermore, heterogeneity analysis shows that the promoting effect is more significant for firms facing higher financing constraints, state-owned firms, and regions with higher marketization. This study provides empirical evidence on the dynamic evolution of shadow banking activities and offers policy recommendations for optimizing environmental regulations and promoting the development of digital finance.

Abstract: This study focused on investigating the factors that drive reward-based crowdfunding in Africa, particularly considering the increasing limitations entrepreneurs face in accessing traditional financial resources globally, by analysing 215 crowdfunding projects from prominent platforms like Kickstarter, IndieGoGo, and Fundraised, the research aimed to identify the key drivers of crowdfunding success. The results from an econometric logistic regression analysis revealed that while images, longer campaign durations, and videos positively influenced crowdfunding, they did not significantly contribute to achieving success. In contrast, the number of backers showed a positive and significant impact on outcomes, whereas the targeted funding amount was associated negatively and significantly with success. Notably, the presence of spelling errors was found to have a positive, though statistically insignificant, relationship with crowdfunding success. These findings enhance the existing literature on crowdfunding and offer valuable insights into concepts such as information asymmetry and signalling theory within the context of reward-based crowdfunding.

Abstract: This study examines the economic and financial performance of a sample of popular Italian banks that maintained their mutualistic structure after the 2015 reform that imposed the conversion of the largest banks into joint-stock companies. The analysis covers 2013-2023 and employs two financial ratios, profit margin and Tier 1 ratio, to assess the impact of structural transformation and pandemic crisis. First, the size trend is quantified by assessing the asset trend. The methodology integrates balance sheet analysis, variance analysis (ANOVA) and the Tukey-Kramer test to detect significant differences between geographical areas (North, Central and South Italy). The results partially confirm the hypothesis that cooperative banks have grown despite macroeconomic challenges. The Tier 1 ratio confirms the financial stability of the cooperative banks that have remained so. The profit margin, on the other hand, shows territorial variability, suggesting a correlation between bank performance and local socio-economic conditions. These findings contribute to the debate on the sustainability of cooperative banking models. Future research could extend this analysis with additional financial indicators and apply machine learning techniques to improve predictive modelling in performance evaluation.

Abstract: In this work, we explore Conjecture 1 proposed in the newly released pioneering paper “Volatility transformers: an optimal transport-inspired approach to arbitrage-free shaping of implied volatility surfaces” by Zetocha Valer, which posits that the “One-X” property is both a necessary and sufficient condition for convex order between non-negative continuous strictly increasing distributions with the same mean. We provide a counterexample demonstrating that the conjecture, as originally stated, does not hold. By examining stochastic orders, particularly the increasing convex order, and the “One-X” property, we propose an enhanced version of the conjecture which is shown to be valid. Furthermore, we discuss the implications of these findings in the context of equity implied volatility fitting and the construction of implied volatility surfaces, highlighting the challenges posed by real-market conditions. In the end, we propose several applications in implied volatility surface construction or simulation. We also discussed the connection between TP2, RR2 and the one-X property.

Abstract: The study examines the response of equity market returns to swings in investor sentiment in emerging equity markets of Brazil, Indonesia, India, Russia, South Africa, China, and Pakistan. Data is collected from 2001 to 2020, and Investor Sentiment Index is constructed by applying the Principal Component Analysis. This index is then subdivided into three stages termed moderate, extremely optimistic, and extremely pessimistic, in order to examine the impact of these sentiment swings on equity returns, Auto Regressive and Dynamic Panel Data analysis are conducted at the country-level and group level respectively. The results revealed that equity markets significantly respond to the moderate, extremely optimistic, and extremely pessimistic stage of investor sentiment swings in the selected emerging markets. Investor sentiment swings show a divergent impact in the emerging equity markets, therefore, be vigilant about the idiosyncratic behavior of equity markets.

Abstract: Financial time series forecasting remains a focalpoint of research in finance due to its crucial role in investmentdecision-making and risk management. However, the highlynonlinear and non-stationary characteristics of financial marketspose significant challenges for prediction. This paper introducesa Frequency-Adaptive Normalized (FAN) time series predictionmodel that enhances forecasting accuracy through an innovativefrequency domain analysis approach. The model employs adual-path architecture, incorporating frequency-adaptive nor-malization mechanisms and residual learning strategies, whicheffectively captures both the periodic patterns of time series andaccurately models fine-grained market fluctuations. Experimentsconducted on the TSLA stock dataset demonstrate that theFAN model achieves substantial improvements in both predictiveaccuracy and computational efficiency compared to traditionalmethods. Notably, the model exhibits robust performance whenforecasting during periods of intense volatility. Ablation studiesfurther validate the necessity of each model component, providingnew research directions for financial time series prediction.

Abstract: Objective: Budget transparency is one of the fundamental components of good governance, which helps increase accountability, enhance citizen participation in government financial decision-making, build public trust, and reduce corruption. In inflationary conditions, rising prices and eco-nomic volatility can lead to a reduction in budget transparency and subse-quently weaken financial sustainability. This study leverages global da-tasets to examine the relationship between budget transparency and finan-cial sustainability, with a particular emphasis on how inflation acts as a moderating variable. Method: This research has been carried out within the economic contexts of some selected Asian and European countries. The dependent variable in this respect is the OBI provided by the IBP, whereas explanatory variables are various financial sustainability indicators. Besides, the inflation rate was also taken as the moderating variable. The research aims will be achieved by analyzing data from selected Asian and European countries, comprising 15 observations for each country, using multiple regression techniques from 2010 to 2021. Findings: The findings of this study reveal that the Open Budget Index (OBI), which serves as a benchmark for assessing budget transparency across coun-tries, plays a pivotal role in enhancing financial sustainability within both Asian and European economies. Nevertheless, the analysis highlights that inflation weakens the beneficial association between budget transparency and financial sustainability. Additionally, the results derived from the Fisher test underscore significant differences in the OBI-financial sustainability relationship between these two regions, with the influence of OBI being notably stronger in European contexts compared to Asian ones. These findings, combined with the need for budget transparency, indicate that the areas of financial policy reforms, inflation control, and mechanisms for public oversight of financial activities require further follow-up. The result will be a marked improvement in the sustainability of fi-nance, and hence, countries will realize more sustainable financial and economic management.

Abstract: Artificial Intelligence (AI) is transforming financial risk management by enhancing predictive accuracy, automating processes, and mitigating risks. This paper explores the challenges such as ethical concerns, data privacy, and systemic risks. Drawing on recent literature, we analyze the benefits and limitations of AI adoption in finance and propose recommendations for future research and policy frameworks. This paper explores the applications, benefits, risks, and ethical considerations associated with AI in finance. The findings highlight the potential of AI to enhance efficiency while underscoring challenges related to systemic risks, data privacy, and governance. We delve into the benefits of AI, including improved accuracy, automation, and real-time insights, while also addressing the inherent risks and ethical considerations, such as algorithmic bias, data privacy, and systemic risk. Furthermore, we discuss the evolving regulatory landscape and the challenges financial institutions face in effectively managing AI-related risks. Through a systematic review of academic literature, industry reports, and regulatory documents, we identify three core dimensions of AI's impact: (1) operational enhancements including 15-40\% improvements in risk detection and \$1.2B annual fraud prevention savings; (2) systemic risks such as 20\% increased market volatility from model homogeneity; and (3) ethical concerns including 30\% bias rates in credit scoring models. The study develops a lifecycle risk framework spanning development (data biases, adversarial vulnerabilities), deployment (compliance failures, overreliance), and monitoring phases (model drift, cybersecurity threats). We propose a tripartite control matrix—remedial (algorithmic audits, human oversight), curative (explainable AI, diverse data sourcing), and compensative (insurance products, hybrid systems)—to address these challenges. The analysis reveals significant research gaps, including longitudinal performance studies (absent in 80\% of literature) and quantum AI integration (addressed by only 2 papers). Regulatory fragmentation between EU and US approaches emerges as a key governance challenge. The paper concludes with actionable recommendations for financial institutions, including continuous model auditing protocols, stress-testing standards for AI systems, and ethical AI certification frameworks. These findings contribute to both academic discourse and industry practice by providing evidence-based strategies for responsible AI adoption in finance.

Abstract: This paper examines the impact of various uncertainty channels on stock market returns in Saudi Arabia, with a focus on the Tadawul All Share Index (TASI). It examines factors such as Saudi-specific Geopolitical Risk, Global Oil Price Uncertainty, Climate Policy Uncertainty, and U.S. Monetary Policy Uncertainty. Using monthly data and the Local Projections (LP) methodology, the study examines how these uncertainties impact market returns across various time horizons, taking into account potential structural breaks and non-linear dynamics. Our findings indicate significant variations in the market's response to the uncertainty measures across two distinct periods. During the first period, geopolitical risks have a strong positive impact on market returns. Conversely, the second period reveals a reversal, with negative cumulative effects, suggesting a shift in risk-return dynamics. Oil price uncertainty consistently exhibits a negative impact in both periods, highlighting the changing nature of oil dependency in the Saudi market. Additionally, climate policy uncertainty is becoming more significant, reflecting increased market sensitivity to global environmental policy changes. Our analysis reveals significant asymmetries in the effects of various uncertainty shocks, with monetary policy uncertainty exhibiting non-linear effects that peak at intermediate horizons, while commodity-related uncertainties exhibit more persistent impacts. These findings, which remain robust across various tests, offer critical insights for portfolio management, policy formulation, and risk assessment in emerging markets undergoing substantial economic changes.

Abstract: This study investigates the effect of board gender diversity on firm performance in Saudi Arabia, an emerging market undergoing significant economic and social reforms as part of Vision 2030. By analyzing a dataset of 180 firms, we evaluate key performance indicators—including Return on Assets (ROA), Return on Equity (ROE), Return on Invested Capital (ROIC), and Tobin’s Q—and identify a consistent positive relationship between the presence of women on boards and improved financial performance. Although only 20% of boards have female directors, our results suggest that gender diversity is a valuable governance mechanism that can enhance profitability, particularly in terms of market valuation. High foreign ownership and larger firm size both appear to create conditions under which the managerial and strategic benefits of a diverse board are more likely to translate into improved firm performance. The findings provide actionable insights for policymakers, corporate leaders, and investors, suggesting that initiatives to increase female representation on boards may enhance corporate governance and support broader economic diversification and modernization efforts within the region. These results are consistent across multiple methodological approaches and robustness checks, enhancing their credibility and practical relevance.

Abstract: This study examines the evolution and challenges of sustainable investing in emerging markets, focusing on the integration of sustainability, social and governance (ESG) principles. Through a systematic review of the literature in Scopus, following the Preferred Elements for Systematic Review Reporting and Meta-Analysis (PRISMA) method, the most relevant research of the last decade is analyzed. The study highlights the exponential increase in scientific production in the field since 2019, underlining its interdisciplinary, collaborative and international nature, driven by the recognition of sustainability as a strategic axis for competitiveness and sustainable development. It highlights that emerging economies still face significant challenges at both the systemic and firm levels. In this regard, it emphasizes the need to update methodologies for evaluating socially responsible investment (SRI), strengthen and homogenize regulatory frameworks, integrate ecological practices into urban planning and the financial sector, strengthen financial education, and the need for a proactive role for public policy makers and other key actors. In addition, it suggests future research on the role of sustainable financial instruments, AI-mediated ESG innovation, and resilient business models with the aim of offering a comprehensive vision that optimizes the effectiveness of sustainable investments and promotes inclusive and environmentally responsible economic development in developing economies.