A note comparing support vector machines and ordered choice models’ predictions of international banks’ ratings

We find that support vector machines can produce notably better predictions of international bank ratings than the standard method currently used for this purpose, ordered choice models. This appears due to the support vector machine's ability to estimate a large number of country dummies unrestrictedly, which was not possible with the ordered choice models due to the low sample size.

Research highlights
► SVM for regression can produce notably better in-sample and out-of-sample predictions than the standard method.
► The crucial importance of modelling country effects.
► The applied methodology can be successfully applied by ratings agencies.