Understanding Trends, Patterns, and Dynamics in Global Company Acquisitions: A Network Perspective
<|reference_start|>Understanding Trends, Patterns, and Dynamics in Global Company Acquisitions: A Network Perspective: Studying acquisitions offers invaluable insights into startup trends, aiding informed investment decisions for businesses. However, the scarcity of studies in this domain prompts our focus on shedding light in this area. Employing Crunchbase data, our study delves into the global network of company acquisitions using diverse network analysis techniques. Our findings unveil an acquisition network characterized by a primarily sparse structure comprising localized dense connections. We reveal a prevalent tendency among organizations to acquire companies within their own country and industry, as well as those within the same age bracket. Furthermore, we show that the country, region, city, and category of the companies can affect the formation of acquisition relationships between them. Our temporal analysis indicates a growth in the number of weakly connected components of the network over time, accompanied by a trend toward a sparser network. Through centrality metrics computation in the cross-city acquisition network, we identify New York, London, and San Francisco as pivotal and central hubs in the global economic landscape. Finally, we show that the United States, United Kingdom, and Germany are predominant countries in international acquisitions. The insights from our research assist policymakers in crafting better regulations to foster global economic growth, and aid businesses in deciding which startups to acquire and which markets to target for expansion.<|reference_end|>
@article{kalhor2024understanding,
    title={Understanding Trends, Patterns, and Dynamics in Global Company
  Acquisitions: A Network Perspective},
    author={Ghazal Kalhor and Behnam Bahrak},
    journal={EPJ Data Science 13.1 (2024): 64},
    year={2024},
    doi={10.1140/epjds/s13688-024-00503-z},
    archivePrefix={arXiv},
    eprint={2402.03910},
    primaryClass={cs.SI}
}