Event Studies for Merger Analysis: An Evaluation of the Effects of Non-Normality on Hypothesis Testing
27 Pages Posted: 23 Aug 2006
Date Written: August 2006
Abstract
Financial event studies using daily stock returns are frequently employed in the analysis of mergers to estimate the sign and magnitude of stock movements to particular merger announcements. A common method of conducting the event study is least squares regression with dummy variables. Daily stock returns, however, are typically non-normally distributed, potentially rendering the hypothesis tests on the least squares coefficients invalid if based on asymptotic critical values. We present evidence on the non-normality of daily stock returns and the consequences of it on critical values using a bootstrap technique. We find that non-normality can lead to substantial departures from the asymptotic critical values and large asymmetries. Both under- and over-rejection of the null hypothesis are possible depending on the particular form of the non-normality.
Keywords: Event Study, Normal Distribution, Hypothesis Testing, Bootstrap, Generalized Bootstrap
JEL Classification: C12, C14, C15
Suggested Citation: Suggested Citation
Do you have a job opening that you would like to promote on SSRN?
Recommended Papers
-
Alternative Methods for Robust Analysis in Event Study Applications
-
By Scott E. Hein and Peter Westfall
-
Conducting Event Studies on a Small Stock Exchange
By Jan Bartholdy, Dennis Olson, ...
-
On the Statistical Significance of Event Effects on Unsystematic Volatility
By Jimmy E. Hilliard and Robert Savickas
-
Conducting Event Studies With Asia-Pacific Security Market Data
By Charles J. Corrado and Cameron Truong
-
HAC Standard Errors and the Event Study Methodology: A Cautionary Note
By George S. Ford, John D. Jackson, ...