Good news, bad news and rating announcements: An empirical investigation

In this paper we employ a new approach to test the contribution of information in rating announcements. This is the first study to test and corroborate how the CDS market responds to rating actions after controlling for the presence of concurrent public and private information. We show that since the clustering of rating announcements characterizes economically significant developments, the common practice of using “uncontaminated” samples underestimates market response. As in previous studies, we find that the market response to bad news is stronger than to good news. Nevertheless, bad news and negative rating announcements tend to cluster. Therefore, the residual contribution of negative rating announcements is small and in some cases insignificant. Positive rating announcements are less frequent and less clustered, though their residual contribution is still significant.     

Predicting Default More Accurately: To Proxy or Not to Proxy for Default?

Previous studies targeting accuracy improvement of default models mainly focused on the choice of the explanatory variables and the statistical approach. We alter the focus to the choice of the dependent variable. We particularly explore whether the common practice (in the literature) of using proxies for default events (bankruptcy or delisting) to increase sample size indeed improves accuracy. We examine four definitions of financial distress and show that each definition carries considerably different characteristics. We discover that rating agencies effort to measure correctly the timing of default is valuable. Our main conclusion is that one cannot improve default prediction by making use of other distress events.     

Bailouts and the modeling of bank distress

In this article, we develop a model for predicting distress events among large banks. We show that a bailout possibility induces different behaviors among small and large banks, and the proposed failure prediction model for large banks is thus considerably different from that for small banks. Major bank-level fundamentals show opposite conjecture directions for large versus small banks. The Tier 1 capital ratio, which is under the scrutiny of regulators and investors, has almost no distress prediction power among large banks. However, banks rescued by governments tend to maintain a lower Tier 1 ratio. The cost of funding in large banks is negatively correlated with the probability of failure, reflecting the fact that lenders internalize the too-big-to-fail (TBTF) policy and demand a lower interest rate from TBTF banks.