A note on takeover success prediction

Ben Branch, Jia Wang and Taewon Yang

International Review of Financial Analysis, 2008, vol. 17, issue 5, 1186-1193

Abstract: 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.

Keywords: Takeover; success; prediction; Artificial; neural; network; Logistic; regression (search for similar items in EconPapers)
Date: 2008
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Citations: View citations in EconPapers (14)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:finana:v:17:y:2008:i:5:p:1186-1193

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