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The application of feed forward neural networks to merger arbitrage: A return-based analysis

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  • Braun, Declan
  • Han, Yue
  • Wang, Heng Emily

Abstract

This study examines the effectiveness and applicability of a trending machine learning algorithm, the feed forward neural networks (FFNNs) in making merger arbitrage investment decisions. Using a sample of attempted takeovers, 24 deal-specific, target-specific, and macroeconomic factors serve as input variables for the proposed FFNNs model. The resulting failure probabilities are utilized by a simulated hedge fund in evaluating merger arbitrage opportunities. By comparing other funds employing simplistic or commonplace predictive models and investment decision rules, our findings reveal the power of machine learning in takeover failure prediction and the use of FFNN can increase risk-standardized deal returns on average.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:finlet:v:58:y:2023:i:pb:s1544612323007638
    DOI: 10.1016/j.frl.2023.104391
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    References listed on IDEAS

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    1. Ye, Pengfei, 2014. "Does the Disposition Effect Matter in Corporate Takeovers? Evidence from Institutional Investors of Target Companies," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 49(1), pages 221-248, February.
    2. Martin Bugeja & David Gallagher, 2015. "The impact of target firm financial distress in Australian takeovers," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 55(2), pages 361-396, June.
    3. 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.
    4. David A. Becher & Jonathan B. Cohn & Jennifer L. Juergens, 2015. "Do Stock Analysts Influence Merger Completion? An Examination of Postmerger Announcement Recommendations," Management Science, INFORMS, vol. 61(10), pages 2430-2448, October.
    5. C. N. V. Krishnan & Ronald W. Masulis, 2013. "Law Firm Expertise and Merger and Acquisition Outcomes," Journal of Law and Economics, University of Chicago Press, vol. 56(1), pages 189-226.
    6. 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.
    7. Bereskin, Fred & Byun, Seong K. & Officer, Micah S. & Oh, Jong-Min, 2018. "The Effect of Cultural Similarity on Mergers and Acquisitions: Evidence from Corporate Social Responsibility," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 53(5), pages 1995-2039, October.
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    More about this item

    Keywords

    M&A; Arbitrage; Machine learning; FFNN;
    All these keywords.

    JEL classification:

    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • G34 - Financial Economics - - Corporate Finance and Governance - - - Mergers; Acquisitions; Restructuring; Corporate Governance

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