Query: Complex network stock market

title: The Evolution Characteristics of Systemic Risk in China's Stock Market Based on a Dynamic Complex Network
abstract: The stock market is a complex system with unpredictable stock price fluctuations. When the positive feedback in the market amplifies, the systemic risk will increase rapidly. During the last 30 years of development, the mechanism and governance system of China's stock market have been constantly improving, but irrational shocks have still appeared suddenly in the last decade, making investment decisions risky. Therefore, based on the daily return of all a shares in China, this paper constructs a dynamic complex network of individual stocks, and represents the systemic risk of the market using the average weighting degree, as well as the adjusted structural entropy, of the network. In order to eliminate the influence of disturbance factors, empirical mode decomposition (EMD) and grey relational analysis (GRA) are used to decompose and reconstruct the sequences to obtain the evolution trend and periodic fluctuation of systemic risk. The results show that the systemic risk of China's stock market as a whole shows a downward trend, and the periodic fluctuation of systemic risk has a long term equilibrium relationship with the abnormal fluctuation of the stock market. Further, each rise of systemic risk corresponds to external factor shocks and internal structural problems.

title: Simulation of stock market investor behavior based on bayesian learning and complex network
abstract: The increasing complexity of the financial system has increased the uncertainty of the market, which has led to the complexity of the evolution of limited rational investor behavior decisions. Moreover, it also has a negative effect on the market and affects the development of the real economy and social stability. In view of the interconnected characteristics of various elements presented in financial complexity, based on complex network theory, Bayesian learning theory and social learning theory, this study systematically describes the behavioral decision making mechanism of individual investors and institutional investors from the perspective of network learning. In addition, this study builds an evolutionary model of investor behavior based on Bayesian learning strategies. According to the results of the horizontal and vertical bidirectional studies simulated by experiments, we can see that the method proposed in this study has a certain effect on the evaluation and decision support of stock market investment.

title: Do Stocks Stalk Other Stocks in Their Complex Network? A Complex Networks Approach to Stock Market Dynamics
abstract: This article presents a complex network approach to analyze the dynamics of the US stock market. The authors first introduce a machine learning technique to learn a latent space representation of the financial instruments using a complex networks approach to analyze the dynamics of the stock market. They show that the clustering of co moving assets and breaking of already existing clusters have interesting connections to regime changes in the market. The authors also present some preliminary ideas on incorporating the effects of news and announcements in the dynamics of the assets.

title: An analysis of the clustering effect of a jump risk complex network in the Chinese stock market
abstract: With the development of Chinese financial market, the correlation between stock price jump risks cannot be ignored. This study uses the complex network method to analyze the clustering effect of stock price jumps. Taking a sample of stocks from the CSI 300 Index, the realized jumps are extracted from the 5 minute high frequency data using the MinRV method. The authors use the Minimum Spanning Tree algorithm to construct a complex network of stock price jumps. It is found that there is a clear correlation among stocks in the entire jump network. The jump in manufacturing industry stocks plays the most important role in the network. The Modular Q function and the Fast Unfolding algorithm are used to divide the entire complex network and study the differences in jump correlation between different communities. The result shows that the correlation among the financial industry stocks is stronger, and a large fluctuation in the price of one financial stock can cause the price of another financial stock to fluctuate significantly.

title: Structural Change and Dynamics of Pakistan Stock Market during Crisis: A Complex Network Perspective
abstract: We studied the cross correlations in the daily closing prices of 181 stocks listed on the Pakistan stock exchange (PSX) covering a time period of 2007 2017 to compute the threshold networks and minimum spanning trees. In addition to the full sample analysis, our study uses three subsamples to examine the structural change and topological evolution before, during, and after the global financial crisis of 2008. We also apply Shannon entropy on the overall sample to measure the volatility of individual stocks. Our results find substantial clustering and a crisis like less stable overall market structure, given the external and internal events of terrorism, political, financial, and economic crisis for Pakistan. The subsample results further reveal hierarchal scale free structures and a reconfigured metastable market structure during a postcrisis period. In addition, time varying topological measures confirm the evidence of the presence of several star like structures, the shrinkage of tree length due to crisis related shocks, and an expansion in the recovery phase. Finally, changes of the central node of minimum spanning trees (MSTs) the volatile stock recognition using Shannon entropy, and the topology of threshold networks will help local and international investors of Pakistan Stock Exchange limited (PSX) to manage their portfolios or regulators to monitor the important nodes to achieve stability and to predict an upcoming crisis.

title: Sectoral Growth and Centrality in Stock Market in Iran: Application of Complex Network Analysis

title: A NEW INDEX FOR REFLECTION OF STOCK MARKET BEHAVIOR USING BY COMPLEX NETWORK ANALYSIS APPROACH

title: Analysis of Shareholders Structure in Chinese Stock Market from a Complex Network Perspective
abstract: In modern financial system, the focus arises on the relationship among listed companies and the relationship between listed companies and shareholders. Existing studies are mainly based on the correlation of stock price of listed company. In this paper, we propose to use shareholder structure data rather than the price data to analyze the relationship between the listed companies and its major shareholders by complex network theory and tools. For each stock, we collected shareholders structure data from companies' quarterly announcement. Then the bipartite networks and its projection networks are established from the raw data that are collected. The complex system formed by the network is analyzed with statistics and some basic characteristics of complex network were obtained, such as "small world" "no scale" and so on. It is found that the networks have aggregation characteristics through the community detection algorithm. Through a series of observations and analysis, the practical implications of these network characteristics in the financial system are found.

title: A complex network for studying the transmission mechanisms in stock market
abstract: This paper introduces a new complex network to describe the volatility transmission mechanisms in stock market. The network can not only endogenize stock market's volatility but also figure out the direction of volatility spillover. In this model, we first use BEKK GARCH to estimate the volatility spillover effects among Chinese 18 industry sectors. Then, based on the ARCH coefficients and GARCH coefficients, the directional shock networks and variance networks in different stages are constructed separately. We find that the spillover effects and network structures changes in different stages. The results of the topological stability test demonstrate that the connectivity of networks becomes more fragile to selective attacks than stochastic attacks.

title: Global energy investment structure from the energy stock market perspective based on a Heterogeneous Complex Network Model
abstract: Energy is not only an important international commodity but is also an important financial product. Many studies address the global energy structure by considering energy as commodity, but few study it based on energy's financial attributes. In this paper, we analyzed the global energy investment structure based on the shareholding relationships between listed energy companies and their shareholders by taking the frontier approach to complex network theory and treating these relationships as a heterogeneous complex network from a global angle. We constructed a primitive investment network of listed energy companies and their shareholders (two sets of actors) Then, based on the two mode affiliation relationships between two sets of actors and countries, we constructed a derivative investment network of 112 countries (and regions) By calculating different topological features, we quantitatively analyzed the national diversity of outward and inward energy investment, the energy investment preference, the strength of the bilateral energy investment relationship between countries (and regions) and the most powerful group of energy investing countries based on the shareholding relationships of global listed energy companies. It is found that the vast majority of outward and inward investment relationships are still in the hands of a few countries: traditional developed countries, emerging countries, and a few particular island countries (and regions) that are tax havens. This study is helpful for analyzing the roles and investment relationships of nations as well as the global energy investment structure taking the perspective of the energy stock market.