Journal of Data Science

Abstract: We apply methodology robust to outliers to an existing event study of the effect of U.S. financial reform on the stock markets of the 10 largest world economies, and obtain results that differ from the original OLS results in important ways. This finding underlines the importance of han dling outliers in event studies. We further review closely the population of outliers identified using Cook’s distance and find that many of the out liers lie within the event windows. We acknowledge that those data points lead to inaccurate regression fitting; however, we cannot remove them since they carry valuable information regarding the event effect. We study further the residuals of the outliers within event windows and find 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 fitting. We support our empirical results by pseudo simulation experiments and find significant improvement in determination of both types of the event effect − abnormal returns and change in systematic risk. We conclude that robust methods are important for obtaining accurate measurement of event effects in event studies.