Event Studies for Merger Analysis: An Evaluation of the Effects of Non-Normality on Hypothesis Testing

27 Pages Posted: 23 Aug 2006

See all articles by George S. Ford

George S. Ford

Phoenix Center for Advanced Legal & Economic Public Policy Studies

Audrey D. Kline

University of Louisville College of Business

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

Ford, George S. and Kline, Audrey D., Event Studies for Merger Analysis: An Evaluation of the Effects of Non-Normality on Hypothesis Testing (August 2006). Available at SSRN: https://ssrn.com/abstract=925953 or http://dx.doi.org/10.2139/ssrn.925953

George S. Ford (Contact Author)

Phoenix Center for Advanced Legal & Economic Public Policy Studies ( email )

5335 Wisconsin Avenue, NW
Suite 440
Washington, DC 20015
United States

Audrey D. Kline

University of Louisville College of Business ( email )

Louisville, KY 40292
United States
502-852-4839 (Phone)
502-852-7557 (Fax)

HOME PAGE: http://business.louisville.edu

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