Forecasting Sovereign Debt Distress in Egypt Using a Machine Learning-Based Early Warning System

In the era of rising global sovereign risk, this study designs and validates A Machine Learning-Based Early Warning System for sovereign debt distress specific to Egypt. Going beyond conventional econometric models, we advance machine learning techniques suited to the idiosyncratic nature of the Egyptian economy. Upon rigorous analysis of a comprehensive dataset (1990–2023), the XGBoost algorithm (an implementation of Gradient Boosting showed excellent forecasting power (the AUC-ROC being 0.92), significantly surpassing that of traditional benchmarks. Interpretation using the SHAP framework (Shapley Additive explanations, rooted in cooperative game theory) identified that foreign reserve adequacy (with a critical non-linear threshold below 3.0 months of import cover), exchange rate misalignment, and the public debt-to-GDP ratio are the top risk predictors. The practical validity of this framework is demonstrated by its successful forecast of the 2016 crisis and its strong explanatory power for the 2023 distress, predicted using data available up to 2022, serving as a rigorous ex-post validation. This provides policymakers with a truly novel, transparent, and operational tool for forward-looking risk identification and management

Bad debt · Boosting (machine learning · Credit risk · Debt · Early warning system · Explanatory power · Foreign-exchange reserves · Identification (biology · Sovereign debt · Warning system · Credit Risk and Financial Regulations · Fiscal Policies and Political Economy · Global Financial Crisis and Policies