The community structure identification for the Chinese merger and acquisition network

Xin-Yu Guo, Kai Yang, Xian-Ming Wu and Qiang Guo

Physica A: Statistical Mechanics and its Applications, 2019, vol. 526, issue C

Abstract: The community structures of the mergers and acquisitions (M&As) could bring deeply insight on the company complex system from the viewpoint of macroscopic level. Firstly, we construct the directed merger and acquisition network (MAN) based on the M&A events which deal from 2000 to 2017 initiated by Chinese listed companies, where the nodes represent the companies and the links denote the relationship of M&As. Regarding the fact that the M&A network is a directed network, by using the Infomap algorithm, we investigate the community structures of the M&A network, and find that the network has the clear community structures with the modularity Q=0.8757. Furthermore, we present a parameter η defined as the ratio of the number of companies which belong to the same industry to the total number of companies in the community for analyzing the company industry characteristics within community. The empirical results show that there are a large proportion of companies belonging to one industry for each community, which illustrates that the characteristic of M&As is that M&As generally occur between the same industry within communities. Finally, we calculate the clustering coefficient of directed networks to analyze the clustering properties of the network with the community structures. The clustering coefficient of the network indicates that the number of triangular configuration is very few, which illustrates that the relationship of a company’s neighbors is weak. This work provides insight to structural properties of directed networks based on the M&As from the perspective of complex systems.

Keywords: Mergers and acquisitions; Complex networks; Directed networks; Community structure (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:526:y:2019:i:c:s0378437119305011

DOI: 10.1016/j.physa.2019.04.133

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