Stock market as temporal network

Zhao, Longfeng; Wang, Gang‐Jin; Wang, Mingang; Bao, Weiqi; Li, Wei; Stanley, H. Eugene

doi:10.1016/j.physa.2018.05.039

Cited by 73 publications

(40 citation statements)

References 43 publications

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“…We extend the financial network literature by studying the impact of general elections on the network topology of Pakistan's stock market. Network-based methods are widely applied by researchers to study interdependency and the evolution of stock markets, such as minimum spanning tree (MST) (Mantegna 1999;Onnela et al 2003b;Zhao et al 2018;Yao and Memon 2019), threshold networks (CT) (Boginski et al 2005;Lee and Nobi 2018), planar maximally filtered graphs (PMFG) (Tumminello et al 2005;Yan et al 2015;Musmeci et al 2016), wavelet (Wang et al 2017), and multiple criteria decision making (MCDM) (Kou et al 2014). We have chosen MST, a main network mapping methodology extensively used to analyze various financial crisis (Wiliński et al 2013;Majapa and Gossel 2016;Coletti and Murgia 2016;Xia et al 2018;Memon and Yao 2019;Kou et al 2019), currency crisis (Jang et al 2011;Sultornsanee et al 2013), sovereign debt crisis events (Dias 2012), as well as macroeconomic phases (Zhang et al 2011).…”

Section: Introductionmentioning

confidence: 99%

General election effect on the network topology of Pakistan’s stock market: network-based study of a political event

2020

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To examine the interdependency and evolution of Pakistan's stock market, we consider the cross-correlation coefficients of daily stock returns belonging to the blue chip Karachi stock exchange (KSE-100) index. Using the minimum spanning tree network-based method, we extend the financial network literature by examining the topological properties of the network and generating six minimum spanning tree networks around three general elections in Pakistan. Our results reveal a star-like structure after the general elections of 2018 and before those in 2008, and a tree-like structure otherwise. We also highlight key nodes, the presence of different clusters, and compare the differences between the three elections. Additionally, the sectorial centrality measures reveal economic expansion in three industrial sectors-cement, oil and gas, and fertilizers. Moreover, a strong overall intermediary role of the fertilizer sector is observed. The results indicate a structural change in the stock market network due to general elections. Consequently, through this analysis, policy makers can focus on monitoring key nodes around general elections to estimate stock market stability, while local and international investors can form optimal diversification strategies.

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Section: Introductionmentioning

confidence: 99%

General election effect on the network topology of Pakistan’s stock market: network-based study of a political event

2020

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“…Huang et al [48] put forward an algorithm-namely, a backward temporal diffusion process-to calculate the shortest temporal distance to the transmission source. Zhao et al [49] characterized the time-evolving correlation-based networks of stock markets through the temporal network framework, in order to highlight the instability of the underlying market by portfolio selection in the evolution of the topology structure of the financial networks. Qu et al [13] investigated the dynamic hedging performance of the highfrequency data of the CSI 300 index futures by designing the minimum-variance hedge ratio (RMVHR) approach.…”

Section: Complexitymentioning

confidence: 99%

“…Lyócsa et al [50] studied the connectedness of a sample of 40 stock markets across five continents using daily closing prices and return spillovers based on Granger causality based on the network model. Zhao et al [51] utilized the temporal network framework to characterize the time-evolving correlation-based networks of stock markets.…”

Section: Complexitymentioning

confidence: 99%

Stock Market Temporal Complex Networks Construction, Robustness Analysis, and Systematic Risk Identification: A Case of CSI 300 Index

Wan

Zhang

et al. 2020

Complexity

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The Chinese stock 300 index (CSI 300) is widely accepted as an overall reflection of the general movements and trends of the Chinese A-share markets. Among the methodologies used in stock market research, the complex network as the extension of graph theory presents an edged tool for analyzing internal structure and dynamic involutions. So, the stock data of the CSI 300 were chosen and divided into two time series, prepared for analysis via network theory. After stationary test and coefficients calculated for daily amplitudes of stock, two “year-round” complex networks were constructed, respectively. Furthermore, the network indexes, including out degree centrality, in degree centrality, and betweenness centrality, were analyzed by taking negative correlations among stocks into account. The first 20 stocks in the market networks, termed “major players,” “gatekeeper,” and “vulnerable players,” were explored. On this basis, temporal networks were constructed and the algorithm to test robustness was designed. In addition, quantitative indexes of robustness and evaluation standards of network robustness were introduced and the systematic risks of the stock market were analyzed. This paper enriches the theory on temporal network robustness and provides an effective tool to prevent systematic stock market risks.

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“…Recently, Memon and Yao (2019) applied threshold and MST methods on 181 stocks of Pakistan stock market and found a crisis-like less stable overall market structure due to the external and internal events of crisis for Pakistan. Zhao et al (2018) analyzed the time evolution of the three major stock markets in the US, UK and China through the application of different network based methods.…”

Section: Introductionmentioning

confidence: 99%

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

Yao

Lü

Memon

2019

Journal of Asian Business Strategy

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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.Contribution/ Originality: To the best of our knowledge, this is first paper that examines China stock market network structure from the perspective of US-China trade war. Additionally, this study performs comparative approach by dividing the overall 10 years long data period into two subperiods of before, and during trade war.

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Stock market as temporal network

Cited by 73 publications

References 43 publications

General election effect on the network topology of Pakistan’s stock market: network-based study of a political event

General election effect on the network topology of Pakistan’s stock market: network-based study of a political event

Stock Market Temporal Complex Networks Construction, Robustness Analysis, and Systematic Risk Identification: A Case of CSI 300 Index

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

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