We examine the information content of quarterly earnings announcements in the syndicated bank loan market, a hybrid public/private debt market that is exclusively comprised of informed institutional participants. In contrast to the literature on equity price reactions to earnings announcements, we find that bank loan returns experience no significant response on earnings announcement dates. However, we do find significant price movements in the secondary loan market four weeks prior to earnings announcement dates, around the time of the monthly covenant reports to members of the syndicate. Moreover, we find that the information content in syndicated bank loan prices is most pronounced for borrowers with predominantly intangible assets that experience declining earnings. Thus, we find evidence that when earnings announcements convey relevant information about the borrowing firm (i.e., for informationally opaque firms with declining creditworthiness), the syndicated bank loan market expeditiously incorporates that information into prices. JEL Classifications: G14, M41

No abstract

No abstract

We propose to estimate Value at Risk (VaR) using quantile regression and provide a risk analysis for defaultable bond portfolios. Design/Methodology/approach: The method we propose is based on quantile regression pioneered by Koenker and Bassett (1978). The quantile regression approach allows for a general treatment on the error distribution and is robust to distributions with heavy tails. Findings: We provide a risk analysis for defaultable bond portfolios using quantile regression method. In the proposed model we use information variables such as short term interest rates and term spreads as covariates to improve the estimation accuracy. We also find that confidence intervals constructed around the estimated VaRs can be very wide under volatile market conditions, making the estimated VaRs less reliable when their accurate measurement is most needed. Original/value: Provide a risk analysis for defaultable bond using quantile regression approach.