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

Abstract

In this paper, researchers utilize mutual information and distance covariance to establish the minimum spanning tree of the financial network of log-returns and trading volumes of the top 96 companies of the United States stock market listed on S&P 100 index. Researchers analyze the United States stock market's turbulence during 2015-2016, employing the data from January 2012 to July 2018. For investigating the turbulence, researchers construct three minimum spanning trees of the pre-turbulence, turbulence and post-turbulence. The findings represent that the degree distribution follows the power law and the minimum spanning tree of pre-turbulence contains a notable difference in topological characteristics and network's measures such degree ratio, betweenness, closeness, eigenvector centrality, node eccentricity, node strength, node domination compared with turbulence and post-turbulence minimum spanning trees. Moreover, the minimum spanning trees constructed by two methods of mutual information and distance covariance are different in topological characteristics and the network's behavior. Besides, the pre-turbulence and post-turbulence networks are robust against nodes attack, and turbulence network is tenuous against it.