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Takeover Prediction Models and Portfolio Strategies: A Multinomial Approach

Ronan Powell

Dublin City University; UNSW Australia Business School, School of Banking and Finance; Financial Research Network (FIRN)

Multinational Finance Journal, Forthcoming

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.

Number of Pages in PDF File: 38

Keywords: Multinomial logit, takeover prediction, abnormal returns, size effect

JEL Classification: G34

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Date posted: November 23, 2004  

Suggested Citation

Powell, Ronan, Takeover Prediction Models and Portfolio Strategies: A Multinomial Approach. Multinational Finance Journal, Forthcoming. Available at SSRN: http://ssrn.com/abstract=622101

Contact Information

Ronan G. Powell (Contact Author)
Dublin City University ( email )
Ireland 9
Dublin 9, leinster 9
UNSW Australia Business School, School of Banking and Finance ( email )
Sydney, NSW 2052

Financial Research Network (FIRN) ( email )
C/- University of Queensland Business School
St Lucia, 4071 Brisbane

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References:  37
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