Enhancing project financial performance prediction: An explainable machine learning framework integrating frontier efficiency and super learner

Abstract

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.

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