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A note on takeover success prediction

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  • Branch, Ben
  • Wang, Jia
  • Yang, Taewon

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.

Suggested Citation

  • Branch, Ben & Wang, Jia & Yang, Taewon, 2008. "A note on takeover success prediction," International Review of Financial Analysis, Elsevier, vol. 17(5), pages 1186-1193, December.
    • Handle: RePEc:eee:finana:v:17:y:2008:i:5:p:1186-1193
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    References listed on IDEAS

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    Cited by:

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    2. Wolfgang Bessler & Colin Schneck, 2015. "Excess premium offers and bidder success in European takeovers," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 5(1), pages 23-62, June.
    3. Rexford Attah‐Boakye & Yilmaz Guney & Elvis Hernandez‐Perdomo & Johnathan Mun, 2021. "Why do some merger and acquisitions deals fail? A global perspective," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(3), pages 4734-4776, July.
    4. Tugce Karatas & Ali Hirsa, 2021. "Predicting Status of Pre and Post M&A Deals Using Machine Learning and Deep Learning Techniques," Papers 2110.09315, arXiv.org.
    5. 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.
    6. Basnet, Anup & Davis, Frederick & Walker, Thomas & Zhao, Kun, 2021. "The effect of securities class action lawsuits on mergers and acquisitions," Global Finance Journal, Elsevier, vol. 48(C).
    7. Ioannis Anagnostopoulos & Anas Rizeq, 2021. "Conventional and neural network target‐matching methods dynamics: The information technology mergers and acquisitions market in the USA," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 28(2), pages 97-118, April.
    8. Lee, Kangbok & Joo, Sunghoon & Baik, Hyeoncheol & Han, Sumin & In, Joonhwan, 2020. "Unbalanced data, type II error, and nonlinearity in predicting M&A failure," Journal of Business Research, Elsevier, vol. 109(C), pages 271-287.
    9. Braun, Declan & Han, Yue & Wang, Heng Emily, 2023. "The application of feed forward neural networks to merger arbitrage: A return-based analysis," Finance Research Letters, Elsevier, vol. 58(PB).
    10. Deepak Kumar & Keya Sengupta & Mousumi Bhattacharya, 2023. "M&A Negotiations: Role of Negotiation Process, Ownership and Advisors on Deal Completion," Group Decision and Negotiation, Springer, vol. 32(5), pages 1083-1115, October.
    11. Md. Salamun Rashidin & Sara Javed & Lingming Chen & Wang Jian, 2020. "Assessing the Competitiveness of Chinese Multinational Enterprises Development: Evidence From Electronics Sector," SAGE Open, , vol. 10(1), pages 21582440198, January.
    12. He, Xinming & Zhang, Jianhong, 2018. "Emerging market MNCs' cross-border acquisition completion: Institutional image and strategies," Journal of Business Research, Elsevier, vol. 93(C), pages 139-150.

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