This study investigates whether annual report readability influences the project cost of equity capital (COE) in the emerging market of Egypt. To reassess this relationship within a project evaluation context, the research develops a novel, explainable heterogeneous ensemble model capable of capturing complex nonlinear interactions among financial and textual determinants affecting COE estimation. The proposed ensemble integrates Gradient Boosting Regression (GBR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Elastic Net using an average-voting strategy. Across multiple evaluation metrics, the ensemble model outperforms linear regression benchmarks, homogeneous bagging and boosting ensembles, and its individual base learners. Specifically, the model achieves superior predictive performance, with an R² of 0.2538, Mean Squared Error (MSE) of 0.0078, Mean Absolute Error (MAE) of 0.0656, and Root Mean Squared Error (RMSE) of 0.0885, significantly outperforming both linear regression and state-of-the-art machine learning alternatives. Feature importance analysis using RF shows that the market-to-book ratio (MTB) and return on equity (ROE) contribute most to predictive accuracy (18.1% and 18.7%, respectively), highlighting the dominant role of financial fundamentals in project COE estimation. Conversely, readability measures exhibit minimal influence. Shapley Additive Explanations (SHAP) further confirm that annual report readability does not exert a statistically meaningful impact on COE within the Egyptian context. By leveraging advanced machine learning and explainability techniques, this study enhances understanding of COE determinants and offers evidence-based insights to improve project appraisal, financial planning, and strategic decision-making.

This study investigates the role of frontier operational efficiency in predicting financial performance within Egypt’s emerging market. Data Envelopment Analysis (DEA) quantifies operational efficiency, and its predictive power is assessed within a machine learning (ML) framework, extending beyond traditional financial ratios. A Super Learner ensemble is developed, integrating Random Forest (RF) and Categorical Gradient Boosting (CatBoost) with a linear regression meta-learner. The Super Learner enhances accuracy and robustness by dynamically weighting and combining predictions from diverse base models, using a meta-learner to minimize error, reduce overfitting, and improve generalization. Empirical results demonstrate that incorporating DEA significantly improves predictive performance, increasing R² by 3.8% (t = 5.45, p < 0.01). The Super Learner achieves an R² of 0.612, with an RMSE of 0.061 and MAE of 0.046, outperforming both linear regression and state-of-the-art ML models. Feature importance analysis (via CatBoost) identifies net working capital (11.5%) and DEA efficiency (10.0%) as the top predictors. SHapley Additive exPlanations (SHAP) and partial dependence analyses further indicate that DEA efficiency, net working capital, and cash holdings exhibit positive but nonlinear associations with financial performance, while leverage demonstrates a concave, nonlinear relationship. These findings provide practical implications for investors, managers, and policymakers, highlighting the strategic value of operational efficiency. Additionally, the study introduces a scalable, interpretable framework combining frontier efficiency metrics with explainable ML, offering a robust tool for financial decision-making.

This study develops an explainable ensemble machine learning framework for sustainable credit risk assessment in peer-to-business (P2B) lending, a rapidly expanding FinTech model that enhances access to financing for small and medium-sized enterprises (SMEs). The increasing reliance on algorithmic decision-making underscores the need for transparent and interpretable credit evaluation, particularly in environments characterized by information asymmetry and reliance on borrower-provided disclosures. To address these challenges, a heterogeneous ensemble model is proposed, integrating Random Forest (RF), Light Gradient Boosting Machine (LGBM), and deep learning classifiers within a soft-voting architecture. Feature selection and class balancing are guided by RF importance scores and resampling techniques, resulting in a compact and interpretable 12-feature set comprising pricing, contractual, and transaction-level variables derived from borrower disclosures. Using real-world transaction-level data from a UK platform, the proposed model achieves improved predictive performance (ROC-AUC = 0.767) compared to a neural network baseline (ROC-AUC = 0.717) under severe class imbalance. SHAP-based explainability analysis identifies Maturity Days, Annualised Gross Yield, Advance Rate, and Discount Rate as the most influential predictors, highlighting how disclosed information is translated into pricing and contractual terms in digital lending markets. The findings demonstrate that disclosure-informed features can enhance both predictive accuracy and interpretability, supporting more transparent, robust, and accountable credit risk assessment in FinTech lending environments.

This study investigates the impact of cash holdings and capital structure on firm valuation in Egypt's emerging market, examining how COVID-19 altered investor perceptions. The research employs explainable machine learning (ML) to uncover non-linear financial thresholds that traditional valuation models overlook. Egyptian listed firms from 2015 to 2022 are analyzed using a Super Learner ensemble (Extremely Randomized Trees, Extreme Gradient Boosting, and a Linear Regression meta-learner) alongside SHapley Additive exPlanations (SHAP) and partial dependence analysis, with the Super Learner's performance compared against conventional methods in assessing financial policy effects on Tobin's Q. Three key findings emerge: (1) Leverage exhibits a non-linear relationship with valuation, where extreme levels (LEV > 1.2) unexpectedly enhance firm value, challenging trade-off theory; (2) Cash holdings demonstrate threshold effects, with optimal value at ~40% of assets and sharply increasing marginal benefits beyond this point; and (3) COVID-19 amplified these dynamics, elevating the liquidity premium while penalizing excessive debt. The Super Learner significantly outperformed traditional statistical and ML models (R² = 0.572 vs. 0.19-0.47). Practical implications suggest that investors and managers in emerging markets should adopt dynamic cash-debt optimization to avoid undervaluation, while policymakers can use ML-driven thresholds to design crisis-responsive regulations. This study contributes to the literature by (1) identifying non-linear thresholds that extend trade-off and pecking order theories, (2) introducing explainable ML to valuation research to balance accuracy and interpretability, and (3) providing novel evidence of COVID-19's structural impact on investor behavior in emerging economies.