Title: Predicting Mergers and Acquisitions: Temporal Dynamic Industry Networks

Abstract: M&A activities are pivotal for market consolidation, enabling firms to augment market power through strategic complementarities. Existing research often overlooks the peer effect, the mutual influence of M&A behaviors among firms, and fails to capture complex interdependencies within industry networks. Common approaches suffer from reliance on ad-hoc feature engineering, data truncation leading to significant information loss, reduced predictive accuracy, and challenges in real-world application. Additionally, the rarity of M&A events necessitates data rebalancing in conventional models, introducing bias and undermining prediction reliability. We propose an innovative M&A predictive model utilizing the Temporal Dynamic Industry Network (TDIN), leveraging temporal point processes and deep learning to adeptly capture industry-wide M&A dynamics. This model facilitates accurate, detailed deal-level predictions without arbitrary data manipulation or rebalancing, demonstrated through superior evaluation results from M&A cases between January 1997 and December 2020. Our approach marks a significant improvement over traditional models by providing detailed insights into M&A activities and strategic recommendations for specific firms.