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

Arash Sioofy Khoojine,Han Dong

doi:10.13189/ujaf.2019.070403

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.

Highlights

1.1 Brief Literature ReviewIn the real world, most of the complex systems have been represented by complex networks [19]
0.1 scores are smaller than other periods; the average closeness of most of the sectors in the turbulence period is greater than others; companies during the turbulence period are close together
Results in Table. 2 show that the size of the maximal connected component of the Minimum Spanning Tree (MST) of the pre-turbulence and post-turbulence with the mutual information and distance covariance methods have a slight change after removing a different percentage of stocks compared with turbulence period

Summary

Introduction

Most of the complex systems have been represented by complex networks [19]. The stock market has been explained as a complex system. There exist an intricate relationship between stocks, which causes price oscillation. During the last two decades, researchers have scrutinized stock markets by forming the stock correlation networks, of which the nodes represent stocks and edges between nodes are price oscillation relationships of stocks [18]. Scrutinizing financial systems, stock price markets, using the complex networks perspective has become one of the most widespread fields within econophysics. A similar tendency is nowadays coming into sight within the econometrics and finance community researchers

Methods

Results

Conclusion