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Banking on event studies : statistical problems, a bootstrap solution, and an application to failed-bank acquisitions

University of British Columbia

A variety of both parametric and nonparametric test statistics have been employed in the finance literature for the purpose of conducting hypothesis tests in event studies. This thesis begins by formally deriving the result that these statistics may not follow their conventionally assumed distribution in finite samples and in some cases even asymptotically. Thus, standard event study test statistics can exhibit a statistically significant bias to size in practice, a result which I document extensively. The bias typically arises due to commonly observed stock return traits, including non-normality, which violate basic assumptions underlying the event study test statistics. In this thesis, I develop an unbiased and powerful alternative: conventional test statistics are normalized in a straightforward manner, then their distribution is estimated using the bootstrap. This bootstrap approach allows researchers to conduct powerful and unbiased event study inference. I adopt the approach in an event study which makes use of a unique data set of failed-bank acquirers in the United States. By employing the bootstrap approach, instead of more conventional and potentially misleading event study techniques, I overturn the past finding of significant gains to failed-bank acquirers. This casts doubt on the common belief that the federal deposit insurance agency's failed-bank auction procedures over-subsidize the acquisition of failed banks.

Thesis/Dissertation

application/pdf

2009-06-02

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http://hdl.handle.net/2429/8591

Business Administration

University of British Columbia

UBCV

DSpace