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dc.contributor.advisorLawson, Kenneth Allen, 1952-en
dc.creatorYarger, Stephen A., 1974-en
dc.date.accessioned2011-08-01T20:11:52Zen
dc.date.available2011-08-01T20:11:52Zen
dc.date.issued2010-12en
dc.date.submittedDecember 2010en
dc.identifier.urihttp://hdl.handle.net/2152/ETD-UT-2010-12-2058en
dc.descriptiontexten
dc.description.abstractFDA decisions and late-stage clinical trial results regarding new pharmaceutical approvals can cause extreme moves in the share price of small biopharmaceutical companies. Throughout the clinical trial process, many potential investors are exposed to market-moving information before such information is made available to the investing public. An investor who wished to profit from advance knowledge about clinical trial results may use the publicly traded options markets in order to increase leverage and maximize profits. This research examined options data surrounding the public release of information pertaining to the efficacy of clinical trials and approval decisions made by the FDA. Events were identified for small pharmaceutical companies with fewer than three currently approved drugs in an attempt to isolate the effect of individual clinical trial and FDA-related events on the share price of the underlying company. Option data were analyzed using logistic regression models in an attempt to predict phase II and III clinical trial outcome results and FDA new drug approval decisions. Implied volatility, open interest, and option contract delta values were the primary independent variables used to predict positive or negative event outcomes. The dichotomized version of a predictor variable designed to estimate total investment exposure incorporating open interest, option contract delta values, and the underlying stock price was a significant predictor of negative pharmaceutical related events. However, none of ii the variables examined in this research were significant predictors of positive drug research related events. The estimated total investment exposure variable used in this research can be applied to the prediction of future clinical trial and FDA decision related events when this predictor variable shows a negative signal. Additional research would help confirm this finding by increasing the sample size of events that potentially follow the same pattern as those examined in this research.en
dc.format.mimetypeapplication/pdfen
dc.language.isoengen
dc.subjectStock market optionsen
dc.subjectClinical trialsen
dc.subjectFood and Drug Administrationen
dc.subjectFDA decisionsen
dc.subjectLeading indicatoren
dc.subjectpredictive modelen
dc.subjectEvent predictionen
dc.subjectInsider tradingen
dc.subjectInformed investoren
dc.subjectDrug researchen
dc.subjectDrug trialsen
dc.subjectNew drug applicationen
dc.subjectPharmaceuticalsen
dc.titleThe information content of options data applied to the prediction of clinical trial resultsen
dc.date.updated2011-08-01T20:12:00Zen
dc.identifier.slug2152/ETD-UT-2010-12-2058en
dc.contributor.committeeMemberRascati, Karenen
dc.contributor.committeeMemberStrassels, Scotten
dc.contributor.committeeMemberGarlappi, Lorenzoen
dc.contributor.committeeMemberLeslie, Ryanen
dc.description.departmentPharmacyen
dc.type.genrethesisen
thesis.degree.departmentPharmacyen
thesis.degree.disciplinePharmacyen
thesis.degree.grantorUniversity of Texas at Austinen
thesis.degree.levelDoctoralen
thesis.degree.nameDoctor of Philosophyen


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