Event Studies for Merger Analysis: An Evaluation of the Effects of Non-Normality on Hypothesis Testing
George S. Ford
Phoenix Center for Advanced Legal & Economic Public Policy Studies
Audrey D. Kline
University of Louisville College of Business
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.
Number of Pages in PDF File: 27
Keywords: Event Study, Normal Distribution, Hypothesis Testing, Bootstrap, Generalized Bootstrap
JEL Classification: C12, C14, C15working papers series
Date posted: August 23, 2006
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