Stock Market Network Research Articles

Research on Stock Market Risk Contagion of Major Debt Crises Based on Complex Network Models—The Case of Evergrande in China

After a major debt crisis occurs in a listed company, the stock prices of related enterprises may also fluctuate sharply, resulting in the spread of debt risk to more enterprises. Taking the stocks of listed companies as network nodes, we constructed a complex stock market network over three periods of time through the logarithmic return rate of stocks for the three periods of prophase, metaphase, and anaphase of the debt crisis. We studied the topological characteristics of the network and destructiveness over the three periods. Finally, the minimum spanning tree was used to construct a network and the community structure of the network. The empirical analysis took the debt crisis of the China Evergrande Company as an example to analyze the impact of its major debt crisis on the Chinese stock market. The research findings were as follows: First, the debt crisis increased the inter-industry connections within the network, that is, the correlations between enterprises in different industries was enhanced. Second, the closeness of the cross-industry connections increased the connectivity efficiency of the network, but compared with the other two periods, the debt crisis in the metaphase was less stable. Third, community research showed that in the metaphase of the debt crisis, the enterprises became the core nodes of the network.

Systemic risk prediction using machine learning: Does network connectedness help prediction?

The global financial crisis not only highlights the important role of financial network connectedness, but also urges regulators pay more attention to systemic risk. We study whether network connectedness helps systemic risk prediction by using machine learning techniques, including k-nearest neighbor (KNN), support vector regression (SVR), extreme gradient boosting (XGB), and feedforward neural network (FNN). Based on prediction results, we use the fingerprint model to deliver interpretability evaluations of financial variables and network connectedness. We find that (1) the linear and nonlinear effects of stock market volatility and network connectedness effectively interpret systemic risk; (2) the interaction effects between stock market volatility and other drivers have a stronger ability to predict system risk in SVR, KNN, and XGB approaches, while network connectedness performs more profoundly in FNN approach; and (3) the interaction effect between stock market volatility and network connectedness is evident in all approaches.

On the Dynamic Changes in the Global Stock Markets’ Network during the Russia–Ukraine War

Analysis of the relationships among global stock markets is crucial for international investors, regulators, and policymakers, particularly during a crisis. Complex network theory was applied to analyze the relationship between global stock markets during the Russia–Ukraine war. Daily data from 55 stock markets from 6 August 2021 to 23 September 2023 were retrieved and used to investigate the changes in global stock market networks. The sample period was divided into 22 subsamples, using a 100-day rolling window rolled forward a trading month, and then long-range correlations based on distance matrices were calculated. These distance matrices were utilized to construct stock market networks. Moreover, minimum spanning trees (MSTs) were extracted from these financial networks for analytical purposes. Based on topological and structural analysis, we identified important/central nodes, distinct communities, vulnerable/stable nodes, and changes thereof with the escalation of war. The empirical findings reveal that the Russia–Ukraine war impacted the global stock markets’ network. However, its intensity varied with changes in the region and the passage of time due to the level of stock market integration and stage of war escalation, respectively. Stock markets of France, Germany, Canada, and Austria remained the most centrally connected within communities; surprisingly, the USA’s stock market is not on this list.

US and European Stock Markets in Response to Exogenous Shocks: Cross-Regional Analysis of Dynamic Networks During the COVID-19 Pandemic

This study aims to analyse the impact of force majeure events or exogenous shocks, characterized by a high level of uncertainty, on the US and European stock market behaviour through cross-regional and inter-industrial comparisons. Based on the sample of 832 largest stock-listed companies publicly traded in both regions, the authors investigate whether specific regional and industrial differences in the stock market reaction to exogenous shocks—as exemplified by the ongoing COVID-19 pandemic—and their recovery paths to be determined. The novelty of the research conducted by the authors is underpinned by the application of a dynamic network analysis method—supported by the exponential random graph modelling—for a data sample of publicly traded companies, encompassing major players and industrial leaders in both regions. Furthermore, the methods are applied quarterly for an identical set of industries in cross-market comparison, within a time span of periods before, during and beyond the exogenous shock. The results reveal significant differences in the reaction of US and European stock markets to exogenous shocks, despite globally integrated financial markets. The connectivity of the largest listed companies on the US market is higher than the European ones. Thus, publicly traded companies in the United States are more likely to be closely connected to their industry peers. As demonstrated by a cross-market comparison of the European and US stock market network configuration, the US network reveals higher probabilities of intra-industrial connections of its market participants. In contrast, the European stock market exhibits fewer connections and a less dense network of market participants in times of uncertainty or exogenous shocks. Except for the European financial industry, which behaves similarly to its US counterpart, the overall interconnectedness of European companies is weaker within the uncertainty-related timespan. The latest could be partially explained by numerous national regulations being implemented at a country level, which are partly deviating from the EU single-market policies. In contrast to a relatively homogeneous setting of the US market, a low level of interconnectedness of publicly traded companies in Europe could be further argued by the application of non-harmonized industrial policies, with the latest being expanded during the ongoing crisis in selected European locations. This difference could also be explained by the absence of a mutual strategy for alleviating the effects of the COVID-19 pandemic in Europe. The obtained results could be valuable for academics, conducting similar thematic research; for portfolio investors, and policymakers in forecasting, reacting and assessing stock market behaviour overall—and at the level of industrial sectors in particular—in response to events characterized as force majeure, exogenous shocks or periods of uncertainty.

Community detection and clustering characteristics analysis of the stock market

AbstractIn this paper, we study and analyze the community detection algorithms and the related evaluation metrics to explore the clustering characteristics of China's stock market. We build the stock market network by the constituent companies of the CSI 300 index and study the tightness between nodes. We first choose an appropriate adjacency matrix, which is obtained by the relation coefficient. Then, based on the singular value decomposition (SVD) of the adjacency matrix, we analyze the optimal network based on the directed spectral clustering on the ratio of eigenvectors and the spectral clustering followed by local refinement. The optimal network is chosen based on the best clustering effect according to the clustering effectiveness indexes Calinski–Harabasz and Davies–Bouldin. We explore the community structure in the stock market and study the influence of each community. Through the index out‐degree and in‐degree, we also obtain the most important companies in the market. Different from the traditional strategies that are always based on qualitative methods and undirected networks, this paper explores the characteristics of how to detect China's stock market. Specifically, we offer suitable strategies and measurements to evaluate the clustering results objectively and fairly. The qualities of community detection are measured too. Based on the above analysis, we further provide valuable insights by exploring the characteristics of the stock market.

Connectedness of International Stock Market at Major Public Events: Empirical Study via Dynamic Time Warping-Based Network

Several public events have drawn renewed attention to the connectedness of the international stock market since the financial crisis of 2008. We investigate systemic and regional connectedness among stock markets around the world at major public events by constructing correlation networks for 46 markets based on the dynamic time-warping method. We find that (i) geographic regionalization is typically observed in the stock market network, in which France is dominant, (ii) Europe has the greatest and the Middle East and Africa the least within-region connectedness, (iii) the correlation network structure is highly integrated and compact at major public events, and global events influence the international stock market more significantly than regional events do, and (iv) the importance of China reaches its peak during the era of Sino-US trade friction, showing that public events have enormous impacts on the countries involved.

The dynamical relation between price changes and trading volume: A multidimensional clustering analysis.

This paper introduces a new method to describe and analyse multidimensional time series based on wavelets. The methodology considers the time series as observations of a functional random variable. 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. The methodology is applied to examine the dynamics and structure of the Nasdaq-100 stock market during the pandemic period 2019/12-2021/12 considering both asset returns and volume trading to model the behaviour of different assets that are part of the index, applying an algorithm that offers better performance than others applied in the clustering literature. The study detects four clusters of firms corresponding with companies sharing common economic activities. The structure of the network reveals a nonlinear relationship between the variables, and the study shows that the main macroeconomic events during the period affect each cluster with different intensity. The change in the patterns of returns and risks and the redistribution of wealth in a highly changing environment are emerging phenomena, which must necessarily be carefully analyzed by public policies, in order to avoid the appearance of bubbles and systemic shocks.

The impact of COVID-19 on the topological properties of the Moroccan stock market network

This paper investigates the topological evolution of the Casablanca Stock Exchange (СSE) from the perspective of the Coronavirus 2019 (COVID-19) pandemic. Cross-correlations between the daily closing prices of the Moroccan most active shares (MADEX) index stocks from March 1, 2016 to February 18, 2022 were used to compute the minimum spanning tree (MST) maps. In addition to the whole sample, the analysis also uses three sub-periods to investigate the topological evolution before, during, and after the first year of the COVID-19 pandemic in Morocco. The findings show that, compared to other periods, the mean correlation coefficient increased remarkably through the crisis period; inversely, the mean distance decreased in the same period. The MST and its related tree length support the evidence of the star-like structure, the shrinkage of the MST in times of market turbulence, and an expansion in the recovery period. Besides, the CSE network was less clustered and homogeneous before and after the crisis than in the crisis period, where the banking sector held a key role. The degree and betweenness centrality analysis showed that Itissalat Al-Maghrib and Auto Hall were the most prominent stocks before the crisis. On the other hand, Attijariwafa Bank, Banque Populaire, and Cosumar were the leading stocks during and after the crisis. Indeed, the results of this study can be used to assist policymakers and investors in incorporating subjective judgment into the portfolio optimization problem during extreme events.

Sudden shock and stock market network structure characteristics: A comparison of past crisis events

Studying the correlation structure of the stock market is crucial for systemic risk and portfolio optimization. We construct the price volatility network of the Shanghai and Shenzhen 300 index components in China's stock market in the period 2007–2020. We select three representative major emergencies: the global financial crisis in 2008, the stock disaster in 2015, and the COVID-19 epidemic in 2020. First, we find that when the stock market is impacted by the major events, the network shows a cluster phenomenon. The cluster effect of the financial crisis events is smallest, while that of the epidemic events occurs most rapidly. Second, the key nodes in the stock market network have greater risk transmission ability. The manufacturing plays a crucial role during the later stages of events, while the financial industry plays an important role during the epidemic's recovery period. Third, the network structure of the stock market has an indicator effect on the systemic risk contributions. Generally, the greater a stock's eigenvector centrality, the greater its systemic risk contribution, while its closeness centrality and clustering coefficient have opposite effects. The study has important enlightenment significance for market regulators to prevent risk diffusion and reduce portfolio risk for market participants.

Stock market network based on bi-dimensional histogram and autoencoder

In this study, we propose a deep learning related framework to analyze S&P500 stocks using bi-dimensional histogram and autoencoder. The bi-dimensional histogram consisting of daily returns of stock price and stock trading volume is plotted for each stock. Autoencoder is applied to the bi-dimensional histogram to reduce data dimension and extract meaningful features of a stock. The histogram distance matrix for stocks are made of the extracted features of stocks, and stock market network is built by applying Planar Maximally Filtered Graph(PMFG) algorithm to the histogram distance matrix. The constructed stock market network represents the latent space of bi-dimensional histogram, and network analysis is performed to investigate the structural properties of the stock market. we discover that the structural properties of stock market network are related to the dispersion of bi-dimensional histogram. Also, we confirm that the autoencoder is effective in extracting the latent feature of the bi-dimensional histogram. Portfolios using the features of bi-dimensional histogram network are constructed and their investment performance is evaluated in comparison with other benchmark portfolios. We observe that the portfolio consisting of stocks corresponding to the peripheral nodes of bi-dimensional histogram network shows better investment performance than other benchmark stock portfolios.

Volatility correlation structure, dynamic network and portfolio implications of Chinese stock market

This study combines complex networks and sliding window technology to construct a static and dynamic network of volatility of the stocks in CSI 300 index using the COVID-19 epidemic as an example to analyze the impact of public health emergencies on the correlation structure of stock volatility, as an extended application to mine low-risk stock portfolios that are more resistant to risks under the "mean-variance" framework. Research shows three implications. (i) During the outbreak period, the density of the stock market volatility network was significantly higher than before and after the outbreak, and the network structure was more intense during the outbreak period. The leading industries are the manufacturing and financial industries, and the source of market risk transmission comes from the key nodes of the two industries. (ii) The dynamic network shows that under the impact of the epidemic, the correlation structure of stock market volatility has undergone abrupt changes and the overall market risk is time-changing, which indicates that the sudden impact of degeneration breaks the original structure and triggers new information connections in the stock market. (iii) The degree of stock centrality affects investment portfolio returns, which means that core stock portfolios with greater network centrality during the relatively stable market period and the upward period perform better, and the peripheral stock portfolio has an advantage in the period when the market fluctuates due to sudden external shocks. Interestingly, peripheral stock portfolios with lower centrality are more resistant to risks under sudden shocks. The results of this paper can provide important enlightenment for stock market supervision and investment portfolio risk management.

The structure of the South African stock market network during COVID-19 hard lockdown

This study investigates the topology of the South African stock market network pre, during, and post the level 5-lockdown period. We use the daily closing price of the 134 companies in the all-share index (ALSI) from 01 October 2019 to 30 October 2020. We construct the minimum spanning tree using the cross-correlation of the returns computed from the closing price data. The research findings show that the South African stock market network forms clusters and is homogeneous, and the finance industry plays a central role. Specifically, the results show an expansion of MST during the level 5 lockdown and shrinkage of the MST post level 5 lockdown. The average correlation coefficient decreases through all sub-periods; conversely, the average distance increases in all sub-periods. Post-level 5 lockdown period, stocks in the Health Care Equipment & Services sector form a small cluster that did not exist before the lockdown period. JSE node degree distribution for all sub-periods follows the power law.

The Impact of COVID-19 on the Dynamic Topology and Network Flow of World Stock Markets

Studies examining the impact of COVID-19 using network dynamics are scant and tend to evaluate a specific local stock market. We present a thorough investigation of 58 world stock market networks using a complex network approach spanning across the uncertain times that have resulted from the coronavirus outbreak. First, we use the daily closing prices of the world stock market indices to construct dynamic complex networks and sixteen minimum spanning tree (MST) maps for the period from December 2019 to March 2021. Second, we present the topological evolution properties of time-varying MSTs by applying normalized tree length, diameter, average path length, and centrality measures. Moreover, the empirical results suggest that (1) the highest correlation among the world stock markets is observed during the first wave of the COVID-19 pandemic in the months of February–March 2020; (2) most of the MSTs appear lower in hierarchy, and many chain-like structures are formed due to the sheer impact of pandemic-related crises; (3) Germany remained a hub node in many of the MSTs; and (4) the tree severely contracted during the first wave of the COVID-19 outbreak (during the months of February and March 2020) and expanded slightly afterwards. Moreover, the results obtained from this study can be used for the development of financial stability policies and stock market regulations worldwide.

The impact of COVID-19 pandemic on the volatility connectedness network of global stock market

This paper investigates how the COVID-19 pandemic affects the connectedness network of stock market volatility in 19 economies around the world. Our method builds on the Diebold-Yilmaz volatility network model to construct the volatility spillover index, and uses lag sparse group LASSO to accommodate the high-dimensional system. We find that the outbreak of the COVID-19 pandemic strengthens the overall volatility connectedness, and the global connectedness level remains high throughout 2020. In particular, connections across different continents have become stronger during this period. However, China is shown to be disconnected from the global volatility connectedness network until late November 2020. We find evidence that China is not the main source of volatility spillover during the COVID-19 pandemic.

Sectoral Analysis of the US Stock Market through Complex Networks

Purpose: This study was carried out to analyze the structure of the aggregated network at the level of economic sectors and to reveal the central/peripheral sectors. Design/Methodology/Approach: The study uses the method of complex networks, with the two-step procedure employed to construct the network of economic sectors. First, the MST approach is utilized based on the cross-correlation of 496 stock price returns of the S&P500 Index. Then, the network is aggregated at the level of economic sectors. In addition, to analyze the graph, the network theory, multi-dimensional scaling (MDS), and relative importance approaches are employed. Findings: The results indicate that the sectoral network has a core/periphery structure. Based on the centrality measures, the ranking of sectors is provided. Of the 11 sectors, 3 are classified as central nodes, 4 as peripheral nodes, and the remaining 4 are classified as intermediate. In addition, the network configuration analysis demonstrates that the graph consists of two parts with a star-like structure, connected through the industrials sector. Practical Implications: An analysis of the cross-correlation network of aggregated assets at the level of economic sectors can be applied to ascertain the direction of stock price movements in the stock market. The division of sectors in the network into central and peripheral nodes has important implications for the management of an optimal portfolio of stocks. Originality/value: This study contributes to complex network theory and portfolio strategy design. A unique procedure is proposed to construct the network of economic sectors using the MST-based approach. Detection of the stock market network structure is vital for investors and regulators alike.

Statistical mechanical analysis for unweighted and weighted stock market networks

Financial markets are time-evolving complex systems containing different financial entities, such as banks, corporations and institutions that interact through transactions and respond to external economic and political events. They can be conveniently represented as a network structure. In this paper, we analyse the unweighted and weighted market networks from a statistical mechanical perspective. In particular, we propose a novel thermodynamic analogy to characterise the dynamic structural properties of time-evolving networks. The intricate pattern of edge connections in the network is modelled by using a heat bath analogy in which particles occupy the energy states according to the Boltzmann distribution. According to this analogy the occupation of the energy states is determined by the temperature of the heat bath, and the spectrum of energy states of the network is determined by the number of nodes and edges. For unweighted networks, the binary representation of the elements in the adjacency matrix can be modelled as a statistical ensemble, using the corresponding partition function to compute thermodynamic network characterisations. For weighted networks, on the other hand, the derived thermodynamic quantities together with their distribution of fluctuations identify the salient structure in the network evolution. We conduct experiments on time-evolving stock exchanges using data for the S&P500 Index Stock Exchanges over the past decade. The thermodynamic characterisations provide an excellent framework to identify epochs in which there is significant variance in network structure during financial crises induced by economic and political events.

Understanding the Impact of COVID–19 on Global Financial Network Using Graph Based Algorithm: Minimum Spanning Tree Approach

Abstract In this paper effects of COVID–19 pandemic on stock market network are analyzed by an application of operational research with a mathematical approach. For this purpose two minimum spanning trees for each time period namely before and during COVID–19 pandemic are constructed. Dynamic time warping algorithm is used to measure the similarity between each time series of the investigated stock markets. Then, clusters of investigated stock markets are constructed. Numerical values of the topology evaluation for each cluster and time period is computed.

Thermodynamic motif analysis for directed stock market networks

In this paper, we present a novel thermodynamically based analysis method for directed networks, and in particular for time-evolving networks in the finance domain. Based on an analogy with a dilute gas in statistical mechanics, we develop a partition function for a network composed of directed motifs. The method relies on the decomposition of directed networks into a series of frequently occurring graphlets, or motifs. According to the connection between a directed network and the dilute gas, the network motifs have the same topological structure as the low-order interactions between particles in the gas. This means that we can use the so-called cluster expansion from statistical mechanics to develop a partition function for the motif decomposition. In prior work, we have reported a detailed analysis of the cluster expansion for the case of undirected graphs, and showed how the resulting motif entropy can be used to analyse time evolving networks [1]. In this paper we extend this work to the case of directed graphs to compute thermodynamic quantities including energy, entropy and temperature for the directed network. The three thermodynamic quantities constitute the thermodynamic framework for the analysis of directed network evolution. We apply our thermodynamic framework to the financial and biological domains to represent real world complex systems as time-varying directed networks. Experimental results successfully demonstrate the effectiveness of the thermodynamic framework in representing the evolution of directed network structure and anomalous event detection.

Effectiveness of Price Limit on Stock Market Network: A Time-Migrated DCCA Approach

In this paper, we investigated the effectiveness of price limit on stock market with the correlation study and complex network technology. We proposed a time-migrated DCCA cross-correlation coefficient which is beneficial to detect the asynchronous correlations of nonstationary time series. The stock market network is constructed with the threshold method based on time-migrated DCCA. The effectiveness of the price limit during the stock market crash period is studied based on the time-migrated DCCA stock market network. The results indicate that the time-migrated DCCA ensures more relevant results than the equal-time DCCA method. An interesting finding is that the price limit has different effects on the stock market network at different stages of dynamic evolution. Market stabilization will be lowered and the systemic risk will be increased if the price limit is enhanced. Such studies are relevant for a better understanding of the stock market and have a significant contribution to the stock market in reality.

Построение доверительного множества связанных акций фондового рынка

The problem of analysis of pairwise connections between stocks of financial market by observations on stock returns is considered. Such problem arise in stock market network analysis. It is assumed that joint distribution of stock returns belongs to the wide class of elliptical distributions. Classical Pearson correlation, Fechner correlation and Kendall correlation are used as measure of dependence. The construction problems of sets of stocks with strong connections between its returns are investigated. The construction problems of sets of stocks with strong connections between its returns are investigated. To construct such sets the multiple hypotheses testing procedures on values of correlations are used. The properties of these statistical procedures are investigated by simulations. The simulation results show that procedures based on individual Fechner and Kendall tests lead to such sets of stocks with given confidence probability unlike procedure based on Pearson individual tests which do not control the confidence probability. At the same time it is emphasized that for Student distribution the constructed set is nearly the same to the confidence set. The procedure of consistency testing with elliptical model is proposed and exemplified. The peculiarities of the model are discussed.