On the Prediction of Credit Ratings

24 Pages Posted: 31 Aug 2007

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.

Keywords: Credit Ratings, Credit Prediction, Credit Ratios, Forecasting

JEL Classification: C15, C41, C60, G29

Suggested Citation

Metz, Albert, On the Prediction of Credit Ratings. Available at SSRN: https://ssrn.com/abstract=1008551 or http://dx.doi.org/10.2139/ssrn.1008551

Albert Metz (Contact Author)

The Brattle Group ( email )

44 Brattle Street
3rd Floor
Cambridge, MA 02138-3736
United States

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
537
Abstract Views
1,883
rank
71,494
PlumX Metrics