Stock Market Network Research Articles

Impact of US-China Trade War on the Network Topology Structure of Chinese Stock Market

Sustainable environment is needed for a progressive stock market. Simultaneously, international events of crisis exert chaos among investors and affect progress of the stock market. It is observed that a mature stock market can resist external crisis effectively. On March 23 2018, a trade war broke out between China and the United States. As a representative of emerging economies, how will the network structure of China's stock market change when China and the United States fueled a trade war? We use Pearson's coefficient to construct the correlation between stocks, and set up a network for the Chinese stock market in three periods (before trade war, during trade war, and the overall period) through the threshold network for analysis. We find that recent trade war between China and US changes the topological structure of China's stock market network, making it denser and more likely heading towards a crisis. At the same time, we find that after 30 years of development, China's stock market has become more mature and its ability to resist external risks has improved. This study will be of a great significance for the government to regulate the stock market and for others to study the stock market of an emerging economy.

The Global Stock Network Connected and Resonance Effect Based on the Time-zone VAR Model with LASSO

We propose the LASSO VAR model with the time-zone effect to investigate how stock markets worldwide interact with each other. Our approach overcomes the dimensional problem in the VAR model and characterises connections within financial markets systematically. Using the daily data of 36 national stock indexes from 2006 to 2009, we construct the global stock market network and discover a strong regional structure in which national stock markets behave significantly distinct before, during, and after the 2008 subprime crisis. Besides, the lagged returns in the United States lead a large number of international stock returns.

Thermodynamic Entropy in Quantum Statistics for Stock Market Networks

The stock market is a dynamical system composed of intricate relationships between financial entities, such as banks, corporations, and institutions. Such a complex interactive system can be represented by the network structure. The underlying mechanism of stock exchange establishes a time‐evolving network among companies and individuals, which characterise the correlations of stock prices in the time sequential trades. Here, we develop a novel technique in quantum statistics to analyse the financial market evolution. We commence from heat bath analogy where the normalised Laplacian matrix plays the role of the Hamiltonian operator of the network. The eigenvalues of the Hamiltonian specify energy states of the network. These states are occupied by either indistinguishable bosons or fermions with corresponding Bose‐Einstein and Fermi‐Dirac statistics. Using the relevant partition functions, we develop the thermodynamic entropy to explore dynamic network characterisations. We conduct the experiments to apply this novel method to identify the significant variance in network structure during the financial crisis. The thermodynamic entropy provides an excellent framework to represent the variations taking place in the stock market.

The stability of Chinese stock network and its mechanism

Based on the multi-fractal properties of financial time series, Value-at-Risk (VaR) and price fluctuation correlation, we construct China’s stock market networks and analyze empirically three networks’ topological features, their stability and their relationship with international stock market indices. The results show that: (1) In the three types of networks, the stock price network does not have scale-free features, multi-scale network and risk network both have small-world and scale-free characteristics; (2) The stock market volatility and the network stability coefficient are Granger causality, the early changes of the stock market volatility can effectively explain the changes of the network stability coefficient, the network connectivity and clustering coefficient are negatively correlated with the stock market volatility, the volatility of the international stock index in Hong Kong, Japan, and the United States has a positive effect on the network stability coefficient, and the international gold or crude oil markets has a negative effect on it; (3) By applying the probit binary selection model test, we found that the network structure is more capable of explaining abnormal fluctuations than international market factors and that the stock network with higher network stability coefficient and higher eigenvector centrality of financial institutions indicates that downward fluctuation of stock price will intensify in the future. From the empirical results, we see that the risk network is more robust and provides a reference for the analysis of the short-term risks and stability of the Chinese stock market. In order to maintain the stability of the stock market, the regulatory authorities should pay a close attention to internal and external factors, increase network connectivity and integration, and actively guard against the risk spillover effects of financial institutions.

A Network Analysis of the Stock Market in Malaysia, Singapore and Indonesia

In this study, we investigate the stock market network among the stocks traded in Malaysia, Singapore and Indonesia using the minimal spanning tree approach. Based on the market capitalization, the monthly adjusted closing prices from 2016 until 2017 of 10 companies for each stock market are chosen to construct the network, and the most influential stocks between Malaysia, Singapore and Indonesia stock markets are identified. Findings of this study show that 3 out of 30 companies are identified as the most influential in the Malaysia, Singapore and Indonesia stock market.

Dynamic features of China's photovoltaic listed companies in different periods: Based on partial Granger causality network

As an important kind of renewable energy, China's photovoltaic (PV) industry has experienced many challenges in different historical stages. Many PV enterprises emerge in the stock market, correlate with each other, and form China's PV stock market network. However, the mutual influence between any two companies in the stock market is impacted by other enterprises and some external factors, which is not considered in the previous studies. In order to study the direct interdependence among China's PV stock markets, we propose the partial Granger causality network (PGCN) model. It is a first practice in the model to apply partial Granger causality to quantify the direct interactions between stock returns. The daily closing prices of 79 China's PV enterprises are selected, and the whole research period 2007.10.2–2016.10.3 is divided into four sub-periods according to three important time nodes. The PGCN in the overall period and four sub-networks are also constructed. Combined with the dynamic behaviors of networks' topological properties, the distribution of enterprise's influence, the conductive force of enterprises, and the stability of the stock market are analyzed. Meanwhile, the regional agglomeration development pattern is revealed, and top 10 influential enterprises are identified.

Geodetic convex boundary curvatures of the communities in stock market networks

We analyze the daily returns of stock market indices of NASDAQ and S&P 500 over the period of 2004–2015. We build network models by using a threshold method that captures strong relations amongst the agents. We also subdivide the whole time scale into 20 parts to capture structural changes in networks throughout the global economic crisis of 2008. To determine the dynamics of the structural changes, we use the mimetic discretization of the curvatures on boundaries of communities. We observe different geometric behavior of the boundary throughout the economic crisis. Our findings suggest that the mimetic discretization of boundary curvature is an efficient tool to capture granular structural changes in financial networks.

Risk prediction and evaluation of transnational transmission of financial crisis based on complex network

In this paper, the transmission characteristics of financial crisis in stock market are studied based on complex network. The influences factor and propagation model of financial crisis in international stock market are qualitatively analyzed through comprehensive application of the qualitative and quantitative analysis method by taking complex network and communication theory of financial crisis as theoretical basis. Meanwhile, the transmission mechanism, measurement of transmission effect, transmission path and immunization strategy of financial crisis in stock market network are empirically analyzed.

Comparison between global financial crisis and local stock disaster on top of Chinese stock network

The science of complex network theory can be usefully applied in many important fields, one of which is the finance. In these practical cases, a massive dataset can be represented as a very large network with certain attributes associated with its nodes and edges. As one of the most important components of financial market, stock market has been attracting more and more attention. In this paper, we propose a threshold model to build Chinese stock market networks and study the topological properties of these networks. To be specific, we compare the effects of different crises, namely the 2008 global crisis and the stock market disaster in 2015, on the threshold networks. Prices of the stocks belonging to the Shanghai and Shenzhen 300 index are considered for three periods: the global crisis, common period and the stock market disaster. We find the probability distribution of the cross-correlations of the stocks during the stock market disaster is fatter than that of others. Besides, the thresholds of cross-correlations are assigned to obtain the threshold networks and the power-law of degree distribution in these networks are observed in a certain range of threshold values. The networks during the stock market disaster also appear to have larger mean degree and modularity, which reveals the strong correlations among these stock prices. Our findings to some extent crosscheck the liquidity shortage reason which is believed to result in the outbreak of the stock market disaster. Moreover, we hope that this paper could give us a deeper understanding of the market’s behavior and also lead to interesting future research about the problems of modern finance theory.

Yeni Bir Nicel Yaklaşımının Hisse Senedi Piyasalarına Uygulanması: Minimum Geren Ağaç

The systems involving interacting agents such as food networks, scientific citations, social networks, communication networks, the Internet, and the companies interacting in stock portfolios have long been studied by many researchers under the concept of complex systems. Such systems are expressed in terms of weighted networks. The dense connections and entwined relations amongst the agents play important roles for forecasting or risk analysis. In this study we present a novel approach to determine hierarchical structure of Industrial sector in the globally operating stock market network. By using the subdominant ultra-metric topology emerge from the minimum spanning tree of the stock market network; it becomes possible to extract the important properties of this complex system. Moreover, we use the dynamic time warping distance to determine the taxonomy and to measure similarity between time series of the operating Industrial sectors. It is found that United States, United Kingdom, Netherlands and Denmark are the most dominant stock exchanges in Industrials sector. We also find three hierarchical clusters and then topologically analyze the structure of considered clusters.

The network of the Italian stock market during the 2008–2011 financial crises

We build the network of the top 190 Italian quoted companies during the two financial crises of 2008-2009 (US credit crisis) and 2010-2011 (European sovereign debt crisis) and compare its structure to the pre-crises years, using both minimum spanning trees and the full network with thresholds. We also analyze the centrality and compactness of industry sectors. We find a general contraction of the network during the crises, both numerically due to stronger correlation as well as topologically, with the appearance of central dominant companies which attract the other ones into a very large cluster, dominated by financial institutions (commercial banks and insurance companies). In particular, we note the role of insurance behemoth Assicurazioni Generali, which rises from a pre-crises subordinate role to become the central company in the minimum spanning tree after the crises period. The few sectors which maintain compactness before and during the crises are utilities, publishing, and construction.

Structure Characteristics of the International Stock Market Complex Network in the Perspective of Whole and Part

International stock market forms an abstract complex network through the fluctuation correlation of stock price index. Past studies of complex network almost focus on single country’s stock market. Here we investigate the whole and partial characteristics of international stock market network (ISMN) (hereinafter referred to as ISMN). For the analysis on the whole network, we firstly determine the reasonable threshold as the basic of the following study. Robustness is applied to analyze the stability of the network and the result shows that ISMN has robustness against random attack but intentional attack breaks the connection integrity of ISMN rapidly. In the partial network, the sliding window method is used to analyze the dynamic evolution of the relationship between the Chinese (Shanghai) stock market and the international stock market. The connection between the Chinese stock market and foreign stock markets becomes increasingly closer, and the links between them show a significant enhancement especially after China joined the WTO. In general, we suggest that transnational investors pay more attention to some significant event of the stock market with large degree for better risk-circumvention.

Dynamical evolution of trading behavior on anti-coordination game in complex networks

Purpose The purpose of this paper is to explore the evolvement of investors’ behavior in stock market dynamically on the basis of non-cooperative strategy applied by investors in complex networks. Design/methodology/approach Using modeling and simulation research method, this study designs and conducts a mathematical modeling and its simulation experiment of financial market behavior according to research’s basic norms of complex system theory and methods. Thus the authors acquire needed and credible experimental data. Findings The conclusions drawn in this paper are as follows. The dynamical evolution of investors’ trading behavior is not only affected by the stock market network structure, but also by the risk dominance degree of certain behavior. The dynamics equilibrium of trading behavior’s evolvement is directly influenced by the risk dominance degree of certain behavior, connectivity degree and the heterogeneity of the stock market networks. Research limitations/implications This paper focuses on the dynamical evolvement of investors’ behavior on the basis of the hypothesis that common investors prefer to mimic their network neighbors’ behavior through different analysis by the strategy of anti-coordination game in complex network. While the investors’ preference and the beliefs among them are not easy to quantify, that is deterministic or stochastic as the environment changes, and is heterogeneous definitely. Thus, these limitations should be broken through in the future research. Originality/value This paper aims to address the dynamical evolvement of investors’ behavior in stock market networks on the principle of non-cooperative represented by anti-coordination game in networks for the first time, considering that investors prefer to mimic their network neighbors’ behavior through different analysis by the strategy of differential choosing in every time step. The methodology designed and used in this study is a pioneering and exploratory experiment.

A network approach to portfolio selection

Abstract In this study, a financial market is conceived as a network where the securities are nodes and the links account for returns' correlations. We theoretically prove the negative relationship between the centrality of assets in this financial market network and their optimal weights under the Markowitz framework. Therefore, optimal portfolios overweight low-central securities to avoid the large variances that result when highly influential stocks are included in the investor's opportunity set. Next, we empirically investigate the major financial and market determinants of stock's centralities. The evidence indicates that highly central nodes tend to coincide with older, larger-cap, cheaper and financially riskier securities. Finally, we explore by means of in-sample and out-of-sample analysis the extent to which the structure of the stock market network can be employed to improve the portfolio selection process. We propose a network-based investment strategy that outperforms well-known benchmarks while presenting positive and significant Carhart alphas. The major contribution of the paper is to employ the financial market network as a useful device to improve the portfolio selection process by targeting a group of assets according to their centrality.

Can Network Linkage Effects Determine Return? Evidence from Chinese Stock Market.

This study used the dynamic conditional correlations (DCC) method to identify the linkage effects of Chinese stock market, and further detected the influence of network linkage effects on magnitude of security returns across different industries. Applying two physics-derived techniques, the minimum spanning tree and the hierarchical tree, we analyzed the stock interdependence within the network of the China Securities Index (CSI) industry index basket. We observed that that obvious linkage effects existed among stock networks. CII and CCE, CAG and ITH as well as COU, CHA and REI were confirmed as the core nodes in the three different networks respectively. We also investigated the stability of linkage effects by estimating the mean correlations and mean distances, as well as the normalized tree length of these indices. In addition, using the GMM model approach, we found inter-node influence within the stock network had a pronounced effect on stock returns. Our results generally suggested that there appeared to be greater clustering effect among the indexes belonging to related industrial sectors than those of diverse sectors, and network comovement was significantly affected by impactive financial events in the reality. Besides, stocks that were more central within the network of stock market usually had higher returns for compensation because they endured greater exposure to correlation risk.

Interdependencies and Causalities in Coupled Financial Networks.

We explore the foreign exchange and stock market networks for 48 countries from 1999 to 2012 and propose a model, based on complex Hilbert principal component analysis, for extracting significant lead-lag relationships between these markets. The global set of countries, including large and small countries in Europe, the Americas, Asia, and the Middle East, is contrasted with the limited scopes of targets, e.g., G5, G7 or the emerging Asian countries, adopted by previous works. We construct a coupled synchronization network, perform community analysis, and identify formation of four distinct network communities that are relatively stable over time. In addition to investigating the entire period, we divide the time period into into “mild crisis,” (1999–2002), “calm,” (2003–2006) and “severe crisis” (2007–2012) sub-periods and find that the severe crisis period behavior dominates the dynamics in the foreign exchange-equity synchronization network. We observe that in general the foreign exchange market has predictive power for the global stock market performances. In addition, the United States, German and Mexican markets have forecasting power for the performances of other global equity markets.

Are stock market networks non-fractal? Evidence from New York Stock Exchange

In this paper, we investigate the fractal (non-fractal) property of stock market network by using the edge-covering with simulated annealing method. We choose the daily closing price of 2109 stocks traded on the NYSE during the period from 2011 to 2014 as dataset and construct the network by using minimal spanning tree (MST). The empirical results show that the degree of stocks obeys power-law distribution and the highly connected stocks connect with each other directly, i.e., the stock market network is non-fractal. Our work provides a new perspective on risk management, which can be used in other network-based financial systems.

Soft Vibrational Force on Stock Market Networks

Stock market networks commonly involve uncertainty, and the theory of soft sets emerges as a powerful tool to handle it. In this study, we present a soft analogue of the differential of a vibrational potential function acting on a stock market network as vibrational force. For this purpose, we first study the vibrational potential function operating on each vertex by using the Laplacian of the neighborhood graph, then applied the soft approximator for the soft sets where the data points are embedded to Euclidean n space. We used the data of the globally operating leading stock markets of 17 countries and presented the results respect to them.

Topology of the South African stock market network across the 2008 financial crisis

This study uses the cross-correlations in the daily closing prices of the South African Top 100 companies listed on the JSE All share index (ALSI) from June 2003 to June 2013 to compute minimum spanning tree maps. In addition to the full sample, the analysis also uses three sub-periods to investigate the topological evolution before, during, and after the 2008 financial crisis. The findings show that although there is substantial clustering and homogeneity on the JSE, the most connected nodes are in the financial and resources sectors. The sub-sample results further reveal that the JSE network tree shrank in the run-up to, and during the financial crisis, and slowly expanded afterwards. In addition, the different clusters in the network are connected by various nodes that are significantly affected by diversification and credit market dynamics.

Network analysis of returns and volume trading in stock markets: The Euro Stoxx case

This study applies network analysis to analyze the structure of the Euro Stoxx market during the long period from 2002 up to 2014. The paper generalizes previous research on stock market networks by including asset returns and volume trading as the main variables to study the financial market. A multidimensional generalization of the minimal spanning tree (MST) concept is introduced, by adding the role of trading volume to the traditional approach which only includes price returns. Additionally, we use symbolization methods to the raw data to study the behavior of the market structure in different, normal and critical, situations. The hierarchical organization of the network is derived, and the MST for different sub-periods of 2002–2014 is created to illustrate how the structure of the market evolves over time. From the structural topologies of these trees, different clusters of companies are identified and analyzed according to their geographical and economic links. Two important results are achieved. Firstly, as other studies have highlighted, at the time of the financial crisis after 2008 the network becomes a more centralized one. Secondly and most important, during our second period of analysis, 2008–2014, we observe that hierarchy becomes more country-specific where different sub-clusters of stocks belonging to France, Germany, Spain or Italy are found apart from their business sector group. This result may suggest that during this period of time financial investors seem to be worried most about country specific economic circumstances.