Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/6059
Title: A comparison of artificial neural network and multinomial logit models in predicting mergers
Authors: Fescioğlu, Ünver, Nilgün
Tanyeri, Başak
Keywords: mergers
artificial neural network models
multinomial logistic models
Issue Date: 2013
Publisher: Taylor & Francis Ltd
Abstract: A 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 19792004 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.
URI: https://doi.org/10.1080/02664763.2012.750717
https://hdl.handle.net/20.500.11851/6059
ISSN: 0266-4763
1360-0532
Appears in Collections:Endüstri Mühendisliği Bölümü / Department of Industrial Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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