Network Analysis of the Shanghai Stock Exchange Based on Partial Mutual Information

Abstract

Analyzing social systems, particularly financial markets, using a complex network approach has become one of the most popular fields within econophysics. A similar trend is currently appearing within the econometrics and finance communities, as well. In this study, we present a state-of-the-art method for analyzing the structure and risk within stock markets, treating them as complex networks using model-free, nonlinear dependency measures based on information theory. This study is the first network analysis of the stock market in Shanghai using a nonlinear network methodology. Further, it is often assumed that markets outside the United States and Western Europe are inherently riskier. We find that the Chinese stock market is not structurally risky, contradicting this popular opinion. We use partial mutual information to create filtered networks representing the Shanghai stock exchange, comparing them to networks based on Pearson's correlation. Consequently, we discuss the structure and characteristics of both the presented methods and the Shanghai stock exchange. This paper provides an insight into the cutting edge methodology designed for analyzing complex financial networks, as well as analyzing the structure of the market in Shanghai and, as such, is of interest to both researchers and financial analysts.

The impact of financial contagion on real economy-An empirical research based on combination of complex network technology and spatial econometrics model

“…See for example, Kenett et al (2010;2012a;2012b; [10][11][12][13], [14] and Wang et al (2018) [15]. Other method adopts other correlation to measure the network, such as mutual information-based network [1,16,17], connectedness-based network [18,19], cointegration-based network [20][21][22], and entropy-based network [23][24][25].…”

Topological Structure of Stock Market Networks during Financial Turbulence: Non-Linear Approach

“…Many researchers apply Pearson's correlation coefficient, which is linear inherently; therefore, it prevents us from capturing the nonlinear behavior of financial networks [14]. For this reason, Fiedor seeks mutual information-based approaches to Warsaw stock exchange and New York stock exchange, [6,7], also, Tao et al deploy information-theoretic distances to analyze the structure of Shanghai stock exchange [24]. Moreover, a well-accepted principle in the financial area obtain the significant role of traded volume on stock price movement; therefore, we decided to use traded volume alongside log-returns.…”

The Relation between Degree and Strength in the Complex Network Derived from an Individual Stock

“…In fact, it is almost impossible to put forward a standard coarse-graining process which is fit for all kinds of data because of the subjectivity of the process. Practically, coarse-grain methods which are used to construct network from the financial time series, such as those mentioned in [21,28], are similar: symbolize segments by specified thresholds. How to find out proper thresholds is still unclear.…”

“…Chen et al suggest that centrality and modularity of a complex network based on correlation are used to detect the effect of interconnection on stock returns and industries [17]. See also [18][19][20][21][22].…”