Analysis of chinese stock market by using the method of visibility graph

Abstract

Many researchers studied the complexity of financial markets based on complex networks methods. The conversion of financial markets into complex networks is an open issue. In this paper, the data of 2571 stock companies in 2012 and the data of 2578 stock companies in 2013 are collected from Chinese stock market. Every year, data of these stock companies are randomly arranged. These data are then converted into some complex networks based on the visibility graph method. For these complex networks, degree distribution and clustering coefficient are considered. Our results show that the complex networks have the powerlaw distribution and small-world properties. The Pareto principle is also testified by the rank of average degree of companies in Chinese stock.