Abstract
Purpose: In order to investigate community structure of the component stocks of SSE (Shanghai Stock Exchange) 180-index, a stock correlation network is built to find the intra-community and inter-community relationship. Design/methodology/approach: The stock correlation network is built taking the vertices as stocks and edges as correlation coefficients of logarithm returns of stock price. It is built as undirected weighted at first. GN algorithm is selected to detect community structure after transferring the network into un-weighted with different thresholds. Findings: The result of the network community structure analysis shows that the stock market has obvious industrial characteristics. Most of the stocks in the same industry or in the same supply chain are assigned to the same community. The correlation of the internal stock prices’ fluctuation is closer than in different communities. The result of community structure detection also reflects correlations among different industries. Originality/value: Based on the analysis of the community structure in Shanghai stock market, the result reflects some industrial characteristics, which has reference value to relationship among industries or sub-sectors of listed companies.
Highlights
Complex networks are the abstract of complex systems, such as computer network, biology network, social network and transportation network
The research of complex network gradually expanded from mathematics, physics and biology to sociology and economics
GN algorithm is applied to un-weighted network, so we determine a threshold to turn the network into un-weighted
Summary
Complex networks are the abstract of complex systems, such as computer network, biology network, social network and transportation network. Li, He, Zhuang and Shi (2011) studied the impact of stocks on Chinese inter-bank network stability from the perspective of complex networks In these researches, complex networks revealed some obvious statistical characteristics, including small-world properties (Milgram, 1967), scale-free property (Pool & Kochen, 1987) and so on. The power-law distribution is known as the scale-free distribution Another important characteristic of complex networks is community structure (Albert, Jeong & Barabasi, 1999). Han and Wang (2010) utilized improving CNM algorithm and Li and Chen (2013) utilized multi-gene method, they both found obvious community structure of stock market The results of these researches are beneficial for economic forecast and financial supervision. The analysis of industrial characteristics of stocks has reference value to relationship among industries or sub-sectors of listed companies
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