A note on takeover success prediction
A takeover success prediction model attempts to use information that is publicly available at the time of the announcement in order to predict the probability that a takeover attempt will succeed. This paper develops a takeover success prediction model by comparing two techniques: the traditional logistic regression model and the artificial neural network technology. To alleviate the problem of bias from the sampling variation, we validate our results through re-sampling. Our empirical results indicate that 1). Arbitrage spread, target resistance, deal structure and transaction size are the dominating factors that have impacts on the outcome of a takeover attempt. 2). Neural network model outperforms logistic regression in predicting failed takeover attempts and performs as well as logistic regression in predicting successful takeover attempts.
Year of publication: |
2008
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Authors: | Branch, Ben ; Wang, Jia ; Yang, Taewon |
Published in: |
International Review of Financial Analysis. - Elsevier, ISSN 1057-5219. - Vol. 17.2008, 5, p. 1186-1193
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Publisher: |
Elsevier |
Keywords: | Takeover success prediction Artificial neural network Logistic regression |
Saved in:
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