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

Journal of Risk and Financial Management

Tao You²  Paweł Fiedor¹  Artur Ho𔋚¹ 

[1] Cracow University of Economics, Rakowicka 27, Kraków 31-510, Poland; E-Mail:;School of Statistics and Mathematics, Central University of Finance and Economics, Beijing 100081, China; E-Mail:

关键词: dependency networks;    financial markets;    complex systems;    mutual information;   

DOI  :  10.3390/jrfm8020266

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.