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Research on energy stock market associated network structure based on financial indicators

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  • Xi, Xian
  • An, Haizhong

Abstract

A financial market is a complex system consisting of many interacting units. In general, due to the various types of information exchange within the industry, there is a relationship between the stocks that can reveal their clear structural characteristics. Complex network methods are powerful tools for studying the internal structure and function of the stock market, which allows us to better understand the stock market. Applying complex network methodology, a stock associated network model based on financial indicators is created. Accordingly, we set threshold value and use modularity to detect the community network, and we analyze the network structure and community cluster characteristics of different threshold situations. The study finds that the threshold value of 0.7 is the abrupt change point of the network. At the same time, as the threshold value increases, the independence of the community strengthens. This study provides a method of researching stock market based on the financial indicators, exploring the structural similarity of financial indicators of stocks. Also, it provides guidance for investment and corporate financial management.

Suggested Citation

  • Xi, Xian & An, Haizhong, 2018. "Research on energy stock market associated network structure based on financial indicators," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 1309-1323.
    • Handle: RePEc:eee:phsmap:v:490:y:2018:i:c:p:1309-1323
    DOI: 10.1016/j.physa.2017.08.114
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    References listed on IDEAS

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    Cited by:

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    2. Dong, Zhiliang & An, Haizhong & Liu, Sen & Li, Zhengyang & Yuan, Meng, 2020. "Research on the time-varying network structure evolution of the stock indices of the BRICS countries based on fluctuation correlation," International Review of Economics & Finance, Elsevier, vol. 69(C), pages 63-74.
    3. Fenghua Wen & Yujie Yuan & Wei‐Xing Zhou, 2021. "Cross‐shareholding networks and stock price synchronicity: Evidence from China," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(1), pages 914-948, January.
    4. Liu, Wei & Ma, Qianting & Liu, Xiaoxing, 2022. "Research on the dynamic evolution and its influencing factors of stock correlation network in the Chinese new energy market," Finance Research Letters, Elsevier, vol. 45(C).
    5. Huang, Chuangxia & Zhao, Xian & Deng, Yunke & Yang, Xiaoguang & Yang, Xin, 2022. "Evaluating influential nodes for the Chinese energy stocks based on jump volatility spillover network," International Review of Economics & Finance, Elsevier, vol. 78(C), pages 81-94.
    6. Cerqueti, Roy & Deffains-Crapsky, Catherine & Storani, Saverio, 2022. "Similarity-based heterogeneity and cohesiveness of networked companies issuing minibonds," Chaos, Solitons & Fractals, Elsevier, vol. 164(C).
    7. Xi, Xian & Gao, Xiangyun & Zhou, Jinsheng & Zheng, Huiling & Ding, Jiazheng & Si, Jingjian, 2021. "Uncovering the impacts of structural similarity of financial indicators on stock returns at different quantile levels," International Review of Financial Analysis, Elsevier, vol. 76(C).
    8. Fenghua Wen & Yujie Yuan & Wei-Xing Zhou, 2019. "Cross-shareholding networks and stock price synchronicity: Evidence from China," Papers 1903.01655, arXiv.org.

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