Purpose: This paper aims to examine the relationship between sustainability practices and financial performance, using accounting and market-based measures to integrate findings from diverse research while identifying the underlying reasons for discrepancies in results. Design/methodology/approach: This study conducts a comprehensive meta-analysis of empirical studies published from 2010 to 2024, sourced from four major databases. The data set used 233,390 observations, the analysis uses combined effect sizes, publication bias detection, subgroup analyses and moderation regression analyses to synthesize research findings. Findings: Descriptive and quantitative analyses confirm a positive significant correlation between sustainability practices and financial performance. However, introducing economic status as a moderator reveals that this correlation diminishes. Specifically, for every unit increase in economic development, the positive association between sustainability and financial performance decreases by 0.08, highlighting the nuanced nature of this relationship. Originality/value: This paper offers a significant contribution by integrating economic status as a moderator in the sustainability–financial performance relationship. Unlike prior research, it uniquely analyzes the interaction between sustainability practices in diverse economic contexts. It provides actionable insights for businesses, enabling them to better align sustainability strategies with specific contextual factors to enhance financial outcomes. Furthermore, the findings are highly relevant for policymakers, as they reveal the nuanced effects of economic dynamics on sustainability’s financial impact, guiding the development of tailored regulatory frameworks. On a societal level, this research underscores the importance of sustainable business practices in fostering environmental conservation, improving social equity and driving economic development, thereby promoting a more inclusive and sustainable global economy.

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 examines how assurance level and assurer type jointly influence non-professional investors’ reactions to sustainability disclosures in an emerging market context. It employs a controlled 2 × 2 mixed-design experiment that manipulates assurance level (limited vs. reasonable) and assurer type (audit firm vs. non-audit firm). Data were collected from MBA and DBA students in Egypt as proxies for non-professional investors. Investor reaction is captured through multiple measures, including perceived sustainability performance, reliance on sustainability information, investment intention, stock valuation, and decision confidence. Non-parametric statistical techniques are used to test hypotheses, complemented by exploratory machine learning using SHAP values. The results provide strong and consistent evidence that the assurance level is the dominant factor shaping investor reactions. Reasonable assurance significantly enhances investor judgments across all key measures, whereas the type of assurer does not have a statistically significant independent effect. Additional analyses reveal that reasonable assurance from a non-audit firm elicits more favorable reactions than limited assurance from an audit firm, underscoring the primacy of assurance strength over provider identity. Exploratory findings further indicate that assurance influences investment decisions primarily through perceived sustainability performance and reliance on information. This study contributes to the literature by clarifying the relative roles of assurance level and assurer type and providing novel evidence from an emerging market setting (i.e., Egypt). The findings offer important implications for firms, assurance providers, and regulators seeking to enhance the credibility and decision usefulness of sustainability reporting.

This study investigates the interconnected relationship between credit risk, profitability, and financial stability within the context of emerging market banking systems, using panel data from 10 listed Egyptian commercial banks over the period 2013–2023. Utilizing panel regression models with clustered standard errors to address heteroskedasticity and serial correlation, the analysis evaluates both the direct and mediating effects of credit risk on bank performance. Profitability, proxied by Return on Assets (ROA), is examined as a potential channel through which credit risk influences financial resilience, measured by Z-scores. The findings reveal that while capital adequacy significantly enhances profitability, credit risk does not exert a statistically significant influence on ROA. Furthermore, profitability does not significantly predict financial stability in the Egyptian context, challenging the linear transmission mechanisms proposed in traditional models. However, credit risk demonstrates a marginally significant negative effect on financial stability, reinforcing concerns about the structural vulnerabilities in credit portfolios. These results underscore the heterogeneous nature of bank behavior in emerging economies and highlight the limitations of earnings-based buffers in weak institutional environments. The study contributes to the literature by integrating risk management, income generation, and stability outcomes within a single empirical framework, offering context-specific insights that extend beyond conventional models developed for advanced markets. Practical implications are offered for policymakers and regulators seeking to strengthen provisioning practices and risk-sensitive performance metrics.