A Point-in-Time Perspective on Through-the-Cycle Ratings

The role and performance of credit-rating agencies are currently under debate. Several surveys conducted in the United States reveal that most investors believe rating agencies are too slow in adjusting their ratings to changes in corporate creditworthiness. It is well known that agencies achieve rating stability by their through-the-cycle methodology. This study provides quantitative insight into this methodology from an investor's point-in-time perspective and quantifies the effects of the methodology on three, somewhat conflicting, objectives: rating stability, rating timeliness, and performance in predicting defaults. The results can guide the search for an optimal balance among these three objectives. The role and performance of credit-rating agencies are currently under debate. Several surveys conducted in the United States reveal that most investors believe the rating agencies are too slow in adjusting their ratings to changes in corporate creditworthiness. At the same time, investors want to keep their portfolio rebalancing to a minimum and desire some level of rating stability. They do not want ratings to be changed to reflect small changes in financial condition. Apparently, investors want both stable and timely ratings, which are likely to be two conflicting objectives.Agencies achieve rating stability by their through-the-cycle methodology. This methodology has two aspects: (1) a focus on the permanent component of default risk and (2) a prudent "migration" (rating-change) policy. In an earlier article, we focused primarily on the modeling of the through-the-cycle methodology, especially the prudent migration policy. In this article, we emphasize thequantitative consequences of the through-the-cycle methodology for rating stability, timeliness, and default prediction—from an investor's point-in-time perspective. To proxy the investor's perception as closely as possible, we carefully formulated and estimated credit-scoring models for default prediction with various time horizons. We found that these credit-scoring models could serve as credible benchmarks because their long-term default prediction was comparable to that of agency ratings.In this benchmark study, we compared the properties of agency ratings with ratings based on credit scores. We used data on agency ratings from the July 2002 version of Standard & Poor's CreditPro Database for the January 1981–July 2002 period. We linked corporate ratings at the end of each calendar quarter to stock price data and accounting data, which are used to estimate default prediction models and an agency rating prediction model. Scores of these benchmark credit-scoring models represent a range of point-in-time perspectives with different time horizons and different sensitivities to the temporary component of default risk. After conversion of credit model scores to credit model ratings, equivalent to agency ratings, actual agency ratings were benchmarked on rating stability, rating timeliness, and default prediction performance.We came to the following conclusions. Rating stability is enhanced primarily by the prudent migration policy, not by the focus on the permanent component of credit risk. Through-the-cycle rating procedures delay migration in agency ratings, on average, by 1/2 year on the downgrade side and 3/4 year on the upgrade side. From the perspective of an investor's one-year horizon, through-the-cycle rating significantly affects default prediction. In this case, the advantage in information quality that the agencies have over credit scoring is more than offset by the agencies' use of the through-the-cycle methodology. Long-term default predictions are less affected by the methodology than are short-term predictions.Our results can guide the search for an optimal balance among rating stability, rating timeliness, and default prediction. We suggest that incorporating the agencies' “rating outlook” and “watchlist” information in future analysis would improve the accuracy of the agency ratings.