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dc.contributor.advisorLucey, Brian
dc.contributor.authorPerez Alzueta, Julian
dc.date.accessioned2019-11-14T11:44:37Z
dc.date.available2019-11-14T11:44:37Z
dc.date.issued2010
dc.identifier.citationJulian Perez Alzueta, 'A dynamic study of the characteristics, predictive power and profitable opportunities underlying takeover prediction models : evidence from the UK and the USA', [thesis], Trinity College (Dublin, Ireland). Trinity Business School, 2010, pp 263
dc.identifier.otherTHESIS 9079
dc.description.abstractTakeover prediction models can be defined as a cross-sectional regression using a firms corporate accounting information in order to measure its likelihood of becoming an acquisition target. Given the theoretical and empirical evidence that these models are unstable both over time and across economies, the research objectives of this thesis are to (i) determine persistent takeover characteristics over a ten-year time span, (ii) analyze the model's predictive accuracy and (iii) investigate whether long-term abnormal returns can be earned by investing in realistic portfolios based on the model's predictions and whether the model's profitability is correlated with its predictive ability.
dc.format1 volume
dc.language.isoen
dc.publisherTrinity College (Dublin, Ireland). Trinity Business School
dc.relation.isversionofhttp://stella.catalogue.tcd.ie/iii/encore/record/C__Rb14628610
dc.subjectBusiness, Ph.D.
dc.subjectPh.D. Trinity College Dublin.
dc.titleA dynamic study of the characteristics, predictive power and profitable opportunities underlying takeover prediction models : evidence from the UK and the USA
dc.typethesis
dc.type.supercollectionthesis_dissertations
dc.type.supercollectionrefereed_publications
dc.type.qualificationlevelDoctoral
dc.type.qualificationnameDoctor of Philosophy (Ph.D.)
dc.rights.ecaccessrightsopenAccess
dc.format.extentpaginationpp 263
dc.description.noteTARA (Trinity’s Access to Research Archive) has a robust takedown policy. Please contact us if you have any concerns: rssadmin@tcd.ie
dc.identifier.urihttp://hdl.handle.net/2262/90528


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