Thesis (M.E.)--University of Canterbury, 2007. / Typescript (photocopy). Includes bibliographical references (leaves 178-189). Also available via the World Wide Web.

Thesis (Ph.D.)--University of Delaware, 2007. / Principal faculty advisor: Ardashpal S. Sethi, Dept. of Computer & Information Sciences. Includes bibliographical references.

Thesis (Ph. D.)--University of Hong Kong, 2008. / Includes bibliographical references (leaf 96-100) Also available in print.

Thesis (M.S.)--West Virginia University, 2009. / Title from document title page. Document formatted into pages; contains xiii, 113 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 105-110).

Thesis (M.S.)--Virginia Polytechnic Institute and State University, 1992. / Vita. Abstract. Includes bibliographical references (leaves 73-74). Also available via the Internet.

Thesis (M.S.)--Mississippi State University. Department of Electrical and Computer Engineering. / Title from title screen. Includes bibliographical references.

Thesis (Ph. D.)--University of Washington, 2005. / Vita. Includes bibliographical references (leaves 123-134).

M.Phil.

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. / Business, Sauder School of / Graduate

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