Panagiotis Andreas Zavos

This article investigates the predictive performance of several machine learning models for sovereign bond credit ratings. Given that previous methods suffer from certain deficiencies, this work compares ensemble and non-parametric learners, including XGBoost, AdaBoost, Random Forest and K-nearest neighbours, with benchmark classifiers previously used in the sovereign-rating literature through a repeated grouped 10-fold cross-validation framework.