  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), 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 an accuracy of
68%, followed by CART (59%) and OL (33%). Investigation of the determining
factors shows that roughly the same explanatory variables are important in all
models, with regulatory quality, GDP per capita and unemployment rate as common
important variables. Consistent with economic theory, a higher regulatory
quality and/or GDP per capita are associated with a higher credit rating, while
a higher unemployment rate is associated with a lower credit rating.
