This study investigates the determinants and predictability of stock return volatility by integrating firm-specific financial characteristics with advanced econometric and volatility modeling techniques. Using an unbalanced panel dataset comprising 1596 firms and 19,752 firm-year observations from MENA stock markets over the period 2010–2024, the analysis employs fixed-effects panel regression models, conditional volatility models, and machine learning-based forecasting approaches. Following extensive diagnostic testing, including tests for heteroskedasticity, serial correlation, cross-sectional dependence, and model specification, a two-way fixed-effects model with Driscoll–Kraay standard errors is adopted as the preferred estimation framework. The results indicate that liquidity ratio, cash ratio, sales growth, firm age, lagged volatility, and lagged returns are significant determinants of stock return volatility, whereas leverage, tangibility, board independence, firm size, Tobin’s Q, and profitability do not exhibit statistically significant effects after controlling for firm-specific and time-specific heterogeneity. The volatility analysis reveals substantial persistence in stock return volatility, with the EGARCH-t specification providing the best fit among the competing GARCH-family models according to the Akaike Information Criterion. The estimated asymmetry parameters indicate that volatility responds differently to positive and negative shocks, supporting the presence of asymmetric volatility dynamics and the suitability of asymmetric volatility models. The forecasting analysis shows that advanced machine learning and deep learning models achieve competitive predictive performance; however, differences in predictive accuracy across models are generally modest.

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.

This study analyzes the relationship between sustainability reporting quality (SRQ) and earnings management (EM) in an emerging economy and investigates the moderating role of ethical leadership. Analyzing 531 firm‐year observations from mandatory reports (2022–2024), SRQ is measured via a 51‐indicator index, while EM is proxied by the modified Jones and Kothari models. Using firm fixed effects and 2SLS to address endogeneity, the results show a positive, simultaneous correlation between SRQ and EM, supporting the view that greenwashing disclosures mask short‐term manipulation. Conversely, lagged specifications reveal a negative correlation, suggesting that consistent quality fosters long‐term discipline. Additionally, ethical leadership acts as an internal control mechanism, reversing the positive association and inhibiting the symbolic exploitation of sustainability. By shifting focus from disclosure quantity to quality and employing an objective financial measure, this research provides vital insights for regulators and investors regarding reporting integrity and ethical conduct in emerging markets.

This study examines whether carbon exposure is incorporated into corporate borrowing costs within the Middle East and North Africa (MENA) region. Using a panel of 771 firm‐year observations from publicly listed non‐financial firms between 2016 and 2023, the analysis investigates the relationship between carbon intensity and firms' cost of debt using a comprehensive empirical framework that combines two‐way fixed effects estimation, instrumental‐variable regression, Generalized Method of Moments (GMM), quantile regression, and machine learning techniques. The baseline fixed effects results show that carbon intensity does not exert a statistically significant effect on the cost of debt. This finding remains robust across alternative carbon measures and industry‐year specifications. Governance quality partially moderates the relationship between emissions exposure and borrowing costs, particularly for Scope 3 emissions. System GMM estimation confirms the persistence of borrowing costs through a significant lagged dependent variable coefficient, whereas carbon intensity remains insignificant. Machine learning results reveal substantial nonlinear predictive relationships. Random Forest achieves the strongest out‐of‐sample performance, followed by XGBoost, whereas Artificial Neural Network (ANN) performs considerably weaker. Shapley Additive Explanations (SHAP) analysis further identifies leverage and carbon intensity as among the most influential predictors of borrowing costs. Overall, the findings suggest that climate transition risk is not yet systematically priced into MENA debt markets, with institutional structures and governance quality shaping how environmental exposure influences financing conditions.

This study examines whether ESG‐related profitability differences arise from observable firm characteristics or from structural differences in how firms convert those characteristics into performance outcomes. Using an unbalanced panel data design, the final sample comprises 727 firm‐year observations from 178 publicly listed non‐financial firms across 11 MENA countries and 10 economic sectors, covering the fiscal years from 2016 to 2023. The analysis combines fixed‐effects estimation, system GMM, Blinder–Oaxaca decomposition, and a cohort‐based pseudo‐panel framework. The results show that the positive cross‐sectional relationship between ESG and profitability disappears after controlling for firm‐fixed effects, indicating that ESG premiums are primarily driven by persistent firm characteristics rather than within‐firm ESG improvements. Dynamic panel and lagged specifications provide limited evidence of a causal effect of ESG on profitability. Blinder–Oaxaca decomposition results indicate that observable firm characteristics explain only a small share of the profitability gap between high‐ and low‐ESG firms, while most of the gap reflects differences in the estimated returns to those characteristics rather than differences in firm composition. Overall, the findings suggest that ESG‐related profitability differences are predominantly structural rather than compositional.

Sovereign Financial Distress, conceptualised in this study as a financial manifestation of sovereign risk involving pressures on a government’s capacity to meet its financial obligations, service its debt, and avoid default, has attracted sustained scholarly attention because of its implications for financial stability, borrowing costs, investment, and economic resilience. This study maps the intellectual and thematic development of the broader sovereign risk literature on sovereign financial distress through a bibliometric analysis of 2030 English-language, peer-reviewed journal articles indexed in Scopus and published between 1974 and 2026. We systematically screened 2444 records against pre-specified criteria, yielding a final analytical corpus after excluding 385 non-article records and 29 non-English articles. We used performance analysis, science mapping, and a rule-based protocol linking bibliometric evidence to five theoretical perspectives. Publication activity increased substantially after 2008, coinciding with the Global Financial Crisis and the European sovereign debt crisis, and one-third of output is concentrated in a Bradford core of 20 journals. Reference publication year spectroscopy (RPYS) identified 1981, 1989, 2012, and 2013 as prominent intellectual reference points. Keyword clustering revealed three themes: sovereign bond markets and policy; pricing, ratings, and contagion; and sovereign debt, country risk, and emerging markets. The theory-mapping results are consistent with a broadening rather than a replacement of the explanatory apparatus: contagion-related signals rise after the crises, whereas institutional and governance-related signals do not, contradicting one limb of the pre-specified sequence. Sustainability does not form an independent thematic cluster in any configuration tested. Because the absence of a keyword cluster cannot establish the absence of theory, the gap is stated narrowly: the findings point to an emerging theoretical frontier on directly incorporating climate and geopolitical exposures into sovereign repayment decisions within limited-commitment default models.