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Sovereign credit ratings summarize the creditworthiness of countries. Theseratings have a large influence on the economy and the yields at whichgovernments can issue new debt. This paper investigates the use of a MultilayerPerceptron (MLP), Classification and Regression Trees (CART), Support VectorMachines (SVM), Na\"ive Bayes (NB), and an Ordered Logit (OL) model for theprediction of sovereign credit ratings. We show that MLP is best suited forpredicting sovereign credit ratings, with a random cross-validated accuracy of68%, followed by CART (59%), SVM (41%), NB (38%), and OL (33%). Investigationof the determining factors shows that there is some heterogeneity in theimportant variables across the models. However, the two models with the highestout-of-sample predictive accuracy, MLP and CART, show a lot of similarities inthe influential variables, with regulatory quality, and GDP per capita ascommon important variables. Consistent with economic theory, a higherregulatory quality and/or GDP per capita are associated with a higher creditrating.

Modelling Sovereign Credit Ratings: Evaluating the Accuracy and Driving Factors using Machine Learning Techniques