Consequences of Outlier Returns for Event Studies: A Methodological Investigation and Treatment

Abstract

Stock returns are decomposed into their regular and outlier components using a maximum likelihood outlier-resistant estimation method. Analytical results depicting the impact of outliers on the ordinary least square (OLS) estimated models and cumulative abnormal return (CAR) statistics are derived and validated using Monte Carlo simulations. The implications of outliers for past event studies are investigated using samples drawn randomly from the universe of stocks in the CRSP database. The OLS-CAR statistics fail to forecast about 37% of the negative-impact and 43% of the positive-impact events. These results raise serious concerns about the validity of conclusions of past event studies, especially those that rejected the hypothesis of significant-impact events.