Predicting Mergers and Acquisitions: Temporal Dynamic Industry Networks
<|reference_start|>Predicting Mergers and Acquisitions: Temporal Dynamic Industry Networks: 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.<|reference_end|>
@article{yang2024a,
    title={A Deep Learning Method for Predicting Mergers and Acquisitions: Temporal
  Dynamic Industry Networks},
    author={Dayu Yang},
    journal={arXiv preprint arXiv:2404.07298},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.07298},
    primaryClass={q-fin.ST cs.LG cs.SI q-fin.GN}
}