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

Dayu Yang

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.

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

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.
Paper Structure (27 sections, 37 equations, 9 figures, 2 tables)

This paper contains 27 sections, 37 equations, 9 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Literature Review
  3. M&A Predictive Works
  4. Temporal Point Processes
  5. Graph Neural Network
  6. Key Novelties of Our Study
  7. Method
  8. Problem Formulation
  9. Temporal Dynamic Industry Network (TDIN)
  10. Timing Module
  11. Choice Module
  12. Empirical Evaluation
  13. Data
  14. Parameter Estimation
  15. Evaluation
  16. ...and 12 more sections

Figures (9)

  • Figure 1: An example of merger and acquisition events
  • Figure 2: The Overall Structure of Proposed Temporal Dynamic Industry Network M&A Predictive Model
  • Figure 3: The relationship among $\mathbb{V}$, $\mathbb{D}$, and $\mathbb{V}^t$
  • Figure 4: Intrinsic and Extrinsic Factors of Merger and Acquisition Events: An Example
  • Figure 5: The $e_d^t$ Embedding Network
  • ...and 4 more figures