Modelling Sovereign Credit Ratings: Evaluating the Accuracy and Driving Factors using Machine Learning Techniques
This work addresses the problem of assessing country creditworthiness for economists and policymakers, but it is incremental as it applies existing methods to this domain.
The paper tackled predicting sovereign credit ratings using machine learning techniques, finding that a Multilayer Perceptron achieved the highest accuracy at 68%, with regulatory quality and GDP per capita as key influential factors.
Sovereign credit ratings summarize the creditworthiness of countries. These ratings have a large influence on the economy and the yields at which governments can issue new debt. This paper investigates the use of a Multilayer Perceptron (MLP), Classification and Regression Trees (CART), Support Vector Machines (SVM), Naïve Bayes (NB), and an Ordered Logit (OL) model for the prediction of sovereign credit ratings. We show that MLP is best suited for predicting sovereign credit ratings, with a random cross-validated accuracy of 68%, followed by CART (59%), SVM (41%), NB (38%), and OL (33%). Investigation of the determining factors shows that there is some heterogeneity in the important variables across the models. However, the two models with the highest out-of-sample predictive accuracy, MLP and CART, show a lot of similarities in the influential variables, with regulatory quality, and GDP per capita as common important variables. Consistent with economic theory, a higher regulatory quality and/or GDP per capita are associated with a higher credit rating.