A note on takeover success prediction
AbstractA 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.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
Bibliographic InfoArticle provided by Elsevier in its journal International Review of Financial Analysis.
Volume (Year): 17 (2008)
Issue (Month): 5 (December)
Contact details of provider:
Web page: http://www.elsevier.com/locate/inca/620166
Takeover success prediction Artificial neural network Logistic regression;
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- 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.
- 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.
- G. William Schwert, 2000.
"Hostility in Takeovers: In the Eyes of the Beholder?,"
Journal of Finance,
American Finance Association, vol. 55(6), pages 2599-2640, December.
- G. William Schwert, 1999. "Hostility in Takeovers: In the Eyes of the Beholder?," NBER Working Papers 7085, National Bureau of Economic Research, Inc.
- Officer, Micah S., 2003. "Termination fees in mergers and acquisitions," Journal of Financial Economics, Elsevier, vol. 69(3), pages 431-467, September.
- Samuelson, William & Rosenthal, Leonard, 1986. " Price Movements as Indicators of Tender Offer Success," Journal of Finance, American Finance Association, vol. 41(2), pages 481-99, June.
- Zhang, Jianhong & Zhou, Chaohong & Ebbers, Haico, 2011. "Completion of Chinese overseas acquisitions: Institutional perspectives and evidence," International Business Review, Elsevier, vol. 20(2), pages 226-238, April.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Zhang, Lei).
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.