Undirected and Directed Network Analysis of the Chinese Stock Market

Binghui Li and Yuehan Yang ()
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Binghui Li: Central University of Finance and Economics
Yuehan Yang: Central University of Finance and Economics

Computational Economics, 2022, vol. 60, issue 3, No 14, 1155-1173

Abstract: Abstract To study the characteristics of Chinese stock market, this paper analyses the undirected and directed stock market networks of the constituent stocks of CSI 300. We first apply the spectral clustering on the ratio of eigenvectors (SCORE) (Jin in Ann Stat 43(1):57–89, 2015) to detect the community structure of the undirected market network. Four communities are found and analysed in detail: “Financial industry (Securities category) community”, “Real estate industry community”, “Financial industry (Bank category) community” and “Heavy industry and Manufacturing industry community”. We then test the stability of the undirected stock network by analysing its topological stability, showing that the network is stable to random attacks but vulnerable to the particular type of deliberate attacks. We establish a directed market network to further analyse the characteristics of the Chinese stock market, exploring the characteristics of stocks with high in-degrees and out-degrees. During the network analysis, we describe the characteristics of the Chinese stock market from the perspective of network analysis, analysing the key companies and providing suggestions for the researchers and investors. Stock market network analysis also provides an effective practice and expansion of the statistical clustering algorithm. Our findings shed light on trends and topological patterns of stock market networks.

Keywords: Stock market; Undirected network; Directed network; Community detection; SCORE (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s10614-021-10183-w

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