A Deep Learning Method for Predicting Mergers and Acquisitions: Temporal Dynamic Industry Networks

• URL: http://arxiv.org/abs/2404.07298v3
• Date: Thu, 17 Oct 2024 18:48:12 GMT
• Title: A Deep Learning Method for Predicting Mergers and Acquisitions: Temporal Dynamic Industry Networks
• Authors: Dayu Yang,
• Abstract summary: An effective M&A predictive model should offer deal-level predictions without requiring ad-hoc feature engineering or data rebalancing. Due to the sparsity of M&A events, existing models require data rebalancing, which introduces bias and limits their real-world applicability. We propose a Temporal Dynamic Industry Network (TDIN) model, leveraging temporal point processes and deep learning to capture complex M&A interdependencies.
• Score: 0.0
• License:
• Abstract: Merger and Acquisition (M&A) activities play a vital role in market consolidation and restructuring. For acquiring companies, M&A serves as a key investment strategy, with one primary goal being to attain complementarities that enhance market power in competitive industries. In addition to intrinsic factors, a M&A behavior of a firm is influenced by the M&A activities of its peers, a phenomenon known as the "peer effect." However, existing research often fails to capture the rich interdependencies among M&A events within industry networks. An effective M&A predictive model should offer deal-level predictions without requiring ad-hoc feature engineering or data rebalancing. Such a model would predict the M&A behaviors of rival firms and provide specific recommendations for both bidder and target firms. However, most current models only predict one side of an M&A deal, lack firm-specific recommendations, and rely on arbitrary time intervals that impair predictive accuracy. Additionally, due to the sparsity of M&A events, existing models require data rebalancing, which introduces bias and limits their real-world applicability. To address these challenges, we propose a Temporal Dynamic Industry Network (TDIN) model, leveraging temporal point processes and deep learning to capture complex M&A interdependencies without ad-hoc data adjustments. The temporal point process framework inherently models event sparsity, eliminating the need for data rebalancing. Empirical evaluations on M&A data from January 1997 to December 2020 validate the effectiveness of our approach in predicting M&A events and offering actionable, deal-level recommendations.

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