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dc.contributor.authorFescioglu-Unver, N.en_US
dc.contributor.authorTanyeri B.en_US
dc.date.accessioned2016-02-08T09:39:46Z
dc.date.available2016-02-08T09:39:46Z
dc.date.issued2013en_US
dc.identifier.issn0266-4763
dc.identifier.urihttp://hdl.handle.net/11693/21020
dc.description.abstractA merger proposal discloses a bidder firm's desire to purchase the control rights in a target firm. Predicting who will propose (bidder candidacy) and who will receive (target candidacy) merger bids is important to investigate why firms merge and to measure the price impact of mergers. This study investigates the performance of artificial neural networks and multinomial logit models in predicting bidder and target candidacy. We use a comprehensive data set that covers the years 1979-2004 and includes all deals with publicly listed bidders and targets. We find that both models perform similarly while predicting target and non-merger firms. The multinomial logit model performs slightly better in predicting bidder firms.en_US
dc.language.isoEnglishen_US
dc.source.titleJournal of Applied Statisticsen_US
dc.relation.isversionofhttp://dx.doi.org/10.1080/02664763.2012.750717en_US
dc.subjectartificial neural network modelsen_US
dc.subjectmergersen_US
dc.subjectmultinomial logistic modelsen_US
dc.titleA comparison of artificial neural network and multinomial logit models in predicting mergersen_US
dc.typeArticleen_US
dc.departmentDepartment of Managementen_US
dc.citation.spage712en_US
dc.citation.epage720en_US
dc.citation.volumeNumber40en_US
dc.citation.issueNumber4en_US
dc.identifier.doi10.1080/02664763.2012.750717en_US
dc.publisherRoutledgeen_US


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