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.
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:BVAU.2429/8591 |
| Date | 05 1900 |
| Creators | Kramer, Lisa Andria |
| Source Sets | Library and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada |
| Language | English |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Relation | UBC Retrospective Theses Digitization Project [http://www.library.ubc.ca/archives/retro_theses/] |
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