Complexities in Financial Network Topological Dynamics: Modeling of Emerging and Developed Stock Markets

Abstract

Policy makings and regulations of financial markets rely on a good understanding of the complexity of financial markets. There have been recent advances in applying data-driven science and network theory into the studies of social and financial systems. Financial assets and institutions are strongly connected and influence each other. It is essential to study how the topological structures of financial networks could potentially influence market behaviors. Network analysis is an innovative method to enhance data mining and knowledge discovery in financial data. With the help of complex network theory, the topological network structures of a market can be extracted to reveal hidden information and relationships among stocks. In this study, two major markets of the most influential economies, China and the United States, are systematically studied from the perspective of financial network analysis. Results suggest that the network properties and hierarchical structures are fundamentally different for the two stock markets. The patterns embedded in the price movements are revealed and shed light on the market dynamics. Financial policymakers and regulators can gain inspiration from these findings for applications in policy making, regulations design, portfolio management, risk management, and trading.

A Tail Dependence-Based MST and Their Topological Indicators in Modeling Systemic Risk in the European Insurance Sector

“…Based on the trees thus obtained ( = 1 … ) we determine the time series of the following topological network indicators: It should be mentioned that in the literature the minimum spanning trees that evolve in time are also monitored by many other topological network indicators such as the Eigenvector Centrality (Tang et al 2018), MOL (Mean Occupation Layer) (Onnela et al 2002(Onnela et al , 2003, Normalized Tree Length (Onnela et al 2003), Tree Half-life (Onnela et al 2003); Survival Ratio of the edges (Onnela et al 2002, Sensoy andTabak 2014); and Agglomerative Coefficient (Matesanz and Ortega 2015).…”

Comparative Analysis of Financial Network Topology for the Russian, Chinese and US Stock Markets

“…Network analysis methods have proved to be successful in studying various individual major financial markets such as US market [27,28,29], Germany [30,4], EU [31,32,33], Italian [34,35]. Some research papers have been investigate developing markets such as China [29,36,12,37], Brazil [38,39], Korea [40], Russia [21], and Mexico [41,42]. Moreover, some papers employed network approach to examine global markets [43,44].…”

“…The paper [29] employs various methods of network analysis to investigate financial networks of both in China and the United States and to study how the two markets behave differently. In this paper, we will present a similar analysis of Russian stock market.…”

“…In this paper we compare topological properties of the networks constructed for the US and China stock markets in [29] with the properties of corresponding networks constructed for the Russian stock market using a dataset spanning over nine years.…”

“…Section 3 presents the market network properties using market graph approach proposed in [18,11]. Section 4 examines the hierarchical structures of the Russian stock network using HT, and MST approaches and compares them with findings of the paper [29]. Finally, conclusions are presented in Section 5.…”