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 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.