Statistical properties of Chinese merger and acquisition network

Xin-Yu Guo, Kai Yang, Xian-Ming Wu and Jian-Guo Liu

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

Abstract: Mergers and acquisitions (M&As) have important implications for the long-term development and profits of companies. In this paper, from the viewpoint of network science, we investigate the evolution patterns of M&As for Chinese companies. Firstly, by taking into account the M&A flows of Chinese company’s M&As for the period 2000–2017, we construct temporal directed M&A networks (MAN), then the temporal MAN are integrated into one global network (IMAN). The empirical statistical results show that the IMAN has a scale-free feature with a power-law degree distribution, is a low density and heterogeneous network. For the largest connected component (LCC), the company centrality for the M&A behaviors is calculated based on the degree, betweenness, closeness and PageRank (PR) measurements. Then we find that the correlations between the node importance and the amount of money for a company’s M&As are 0.4653 and 0.3319 for the out-degree and PR indices respectively, which indicates that the out-degree and PR measurements could be used to predict the M&A price. Finally, we introduce a multiple linear regression model to analyze the impact of these structural factors on M&As. The experimental results show that the out-degree and PR measurements are significantly related to the company’s M&As and the significance coefficient p values are 0.000 and 0.007 respectively, which illustrates that the centrality of a company could be provided with reference to make decision for managers. This work provides a way to analyze the M&As from the viewpoints of complex systems.

Keywords: Mergers and acquisitions; Complex networks; Multiple linear regression; Centrality measurements (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:526:y:2019:i:c:s0378437119305916

DOI: 10.1016/j.physa.2019.04.219

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