[We analyze the empirical power and specification of test statistics designed to detect abnormal bond returns in corporate event studies, using monthly and daily data. We find that test statistics based on frequently used methods of calculating abnormal monthly bond returns are biased. Most methods implemented in monthly data also lack power to detect abnormal returns. We also consider unique issues arising when using the newly available daily bond data, and formulate and test methods to calculate daily abnormal bond returns. Using daily bond data significantly increases the power of the tests, relative to the monthly data. Weighting individual trades by size while eliminating noninstitutional trades from the TRACE data also increases the power of the tests to detect abnormal performance, relative to using all trades or the last price of the day. Further, value-weighted portfolio-matching approaches are better specified and more powerful than equal-weighted approaches. Finally, we examine abnormal bond returns to acquirers around mergers and acquisitions to demonstrate how the abnormal return model and use of daily versus monthly data can affect inferences.]

Ang, Hodrick, Xing, and Zhang (2006a) show that stocks with high idiosyncratic return volatility tend to have low future returns. This paper further documents that idiosyncratic volatility is inversely related to future earning shocks, and more importantly, that the return-predictive power of idiosyncratic volatility is induced by its information content about future earnings. We examine various explanations of the triangular relation among idiosyncratic volatility, future earning shocks, and future stock returns. Our results show that the idiosyncratic volatility anomaly is not a simple manifestation of previously documented market anomalies related to excessive extrapolation on firm growth, over-investment tendency, accounting accruals, or investor underreaction to earnings news. On the other hand, there is evidence that the idiosyncratic volatility anomaly is related to corporate selective disclosure, and the anomaly is stronger among stocks with a less sophisticated investor base.

We analyze the empirical power and specification of test statistics designed to detect abnormal bond returns in corporate event studies, using monthly and daily data. We find that test statistics based on frequently used methods of calculating abnormal monthly bond returns are biased. Most methods implemented in monthly data also lack power to detect abnormal returns. We also consider unique issues arising when using the newly available daily bond data, and formulate and test methods to calculate daily abnormal bond returns. Using daily bond data significantly increases the power of the tests, relative to the monthly data. Weighting individual trades by size while eliminating noninstitutional trades from the TRACE data also increases the power of the tests to detect abnormal performance, relative to using all trades or the last price of the day. Further, value-weighted portfolio-matching approaches are better specified and more powerful than equal-weighted approaches. Finally, we examine abnormal bond returns to acquirers around mergers and acquisitions to demonstrate how the abnormal return model and use of daily versus monthly data can affect inferences.