Synthetic Credit Ratings and the Inefficiency of Agency Ratings

Abstract

This study develops and evaluates a model that generates synthetic credit ratings using accounting and market based information. The model performs very well in explaining agency ratings, suggesting that fitted values for unrated companies are likely to be reasonably precise. In addition, the synthetic credit ratings help explain cross sectional differences in CDS spreads, even after controlling for contemporaneous agency ratings. The incremental information provided by agency ratings relative to the synthetic ratings has declined substantially in recent years, possibly due to new SEC regulation that limits rating agencies’ ability to obtain confidential information from rated companies. Consistent with the finding that agency ratings do not fully impound the information in the synthetic credit ratings, differences between the synthetic and agency ratings predict changes in agency ratings in subsequent months, especially for small companies. This relationship is very significant, both statistically and economically, and while it monotonically declines over the forecasting horizon, the difference between the synthetic and agency ratings predicts changes in agency ratings as far as 24 months later. There is no evidence of substantial improvement over the last thirty years in the timeliness of agency ratings with respect to the information in synthetic ratings. Investors, in contrast, appear to process the synthetic rating information in a timely fashion, as the difference between the synthetic and agency ratings does not predict changes in CDS spreads or in stock prices.