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.
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- Officer, Micah S., 2003. "Termination fees in mergers and acquisitions," Journal of Financial Economics, Elsevier, vol. 69(3), pages 431-467, September.
- Walkling, Ralph A., 1985. "Predicting Tender Offer Success: A Logistic Analysis," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 20(04), pages 461-478, December.
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NBER Working Papers
7085, National Bureau of Economic Research, Inc.
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- Kar Yan Tam & Melody Y. Kiang, 1992. "Managerial Applications of Neural Networks: The Case of Bank Failure Predictions," Management Science, INFORMS, vol. 38(7), pages 926-947, July.
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