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  5. Takeover prediction and portfolio strategies: A multinomial approach
 
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Takeover prediction and portfolio strategies: A multinomial approach

Author(s)
Powell, Ronan  
Uri
http://hdl.handle.net/10197/7504
Date Issued
2004
Date Available
2016-02-15T13:48:43Z
Abstract
This paper uses a multinomial framework to develop several takeover prediction models. The motivation for this approach lies with Morck, Shleifer and Vishny (1988), who note that separate considerations are appropriate for predicting which firms are subject to hostile (disciplinary) and friendly (synergistic) takeovers in the USA. In a typical binomial setting, in which takeover targets are treated as belonging to one homogenous group, differences between hostile and friendly targets are ignored. This may result in biased takeover probabilities and poor predictive performance. Using UK data, the results from this paper show that the characteristics of hostile and friendly targets do differ, particularly in terms of firm size. The multinomial models also have higher significance and explanatory power when compared to the binomial models. Furthermore, when the models are tested in an investment portfolio setting, the results suggest that a strategy of predicting hostile targets only, beats a benchmark control portfolio of firms of a similar size and market-to-book.
Type of Material
Journal Article
Publisher
Global Business Publications
Journal
Multinational Finance Journal
Volume
8
Issue
1 and 2
Start Page
35
End Page
72
Copyright (Published Version)
2004 Multinational Finance Society
Subjects

Multinomial logit

Takeover prediction

Abnormal returns

Size effect

Classification
G14
G34
Web versions
http://www.mfsociety.org/modules/modDashboard/uploadFiles/journals/googleScholar/732.html
Language
English
Status of Item
Peer reviewed
This item is made available under a Creative Commons License
https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
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Owning collection
Business Research Collection

Item descriptive metadata is released under a CC-0 (public domain) license: https://creativecommons.org/public-domain/cc0/.
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