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.
This paper empirically investigates the impact of the government bailout on analysts’ forecast optimism regardingfirms in the automotive industry. We compare the results from M- and MM-robust methodologies to the results from OLS regression in an event study context and find that inferences change. When M- and MM-robust estimation methods are used to estimate the same model, the results for key control variables fall directly in line with those of similar previous studies. Furthermore, an analysis of residuals indicates that the application of M- and MM estimation methods pulls the main prediction equation towards the main sample data, suggesting a more rigorous fit. Based on robust methods, we observe changes in analyst optimism during the announcement period of the bailout, as evidenced by the significantly positive variable of interest. We support our empirical results with simulations and confirm significant improvements in estimation accuracy when robust regression methods are applied to the samples contaminated by outliers.