Dynamic Risk Adjustment in Long-Run Event Study Tests

Abstract

This study applies a rolling estimation window approach to adjust for time-varying risk parameters in asset pricing models to compute long-run abnormal returns after major corporate events. Abnormal returns are defined as realized returns minus predicted returns on each day in a five-year, post-event period. A variety of asset pricing models are employed to compute out-of-sample predicted returns in different estimation windows for seasoned equity offerings (SEOs) and mergers and acquisitions (M&As). We find that, after an initial significant return response in the month or two after corporate announcements, abnormal returns thereafter disappear over a five-year, post-event period. Robustness checks corroborate our results: (1) with or without matched and random control samples, (2) for different asset pricing models including the CAPM market model, and (3) in robustness tests of share repurchases (SRs) as well as different subperiods. We conclude that, after dynamic risk adjustment, long-run abnormal returns are not evident after these major corporate actions.