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

ArXiv ID: 2404.07298 “View on arXiv”

Authors: Unknown

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.

Keywords: Mergers and Acquisitions, Temporal Point Processes, Deep Learning, Network Analysis, Equities

Complexity vs Empirical Score

• Math Complexity: 7.0/10
• Empirical Rigor: 8.5/10
• Quadrant: Holy Grail
• Why: The paper employs advanced mathematics, including temporal point processes and deep learning architectures for network modeling, indicating high complexity. It also demonstrates high empirical rigor with a large-scale 24-year dataset, available code, and specific metrics (AUC-ROC, F1, RMSE) for deal-level predictions.

  flowchart TD
    A["Research Goal: Predict M&A deals<br>without ad-hoc feature engineering"] --> B["Methodology: Temporal Dynamic Industry Network<br>Deep Learning + Temporal Point Processes"]
    B --> C["Data: M&A events & firm attributes<br>Jan 1997 - Dec 2020"]
    C --> D["Computation: Network embedding<br>modeling peer effects & event sparsity"]
    D --> E["Outcomes: Deal-level predictions<br>Actionable recommendations for bidders & targets"]