Robust Methods in Event Studies: Empirical Evidence and Theoretical Implications

We apply methodology robust to outliers to an existing event study of the eect of U.S. nancial reform on the stock markets of the 10 largest world economies, and obtain results that dier from the original OLS results in important ways. This nding underlines the importance of han- dling outliers in event studies. We further review closely the population of outliers identied using Cook's distance and nd that many of the out- liers lie within the event windows. We acknowledge that those data points lead to inaccurate regression tting; however, we cannot remove them since they carry valuable information regarding the event eect. We study further the residuals of the outliers within event windows and nd that the resid- uals change with application of M-estimators and MM-estimators; in most cases they became larger, meaning the main prediction equation is pulled back towards the main data population and further from the outliers and indicating more proper tting. We support our empirical results by pseudo- simulation experiments and nd signicant improvement in determination of both types of the event eect abnormal returns and change in systematic risk. We conclude that robust methods are important for obtaining accurate measurement of event eects in event studies.