On the Prediction of Credit Ratings

Abstract

The prediction of credit ratings is of interest to many market participants. Portfolio risk managers often need to predict credit ratings for unrated issuers. Issuers may seek a preliminary estimate of what their rating might be prior to entering the capital markets. For that matter, the rating agencies themselves may seek objective benchmarks as an initial input in the rating process. This paper suggests a new approach to predicting credit ratings and evaluates its performance against conventional approaches, such as linear regression and ordered probit models. We find that by essentially every measure, the new technology outperforms, often dramatically, these other models. While this new approach is more complicated to estimate, it is not much more complicated to apply. The new model has additional advantages in its interpretation as a structural ratings model. Its output includes implied ratings from each individual credit metric and the appropriate weights to attach to those implied ratings, which sometimes can be matters of interest themselves. This makes analysis and counterfactual testing nearly transparent.