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Complex networks analysis in Iran stock market: The application of centrality

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  • Esmaeilpour Moghadam, Hadi
  • Mohammadi, Teymour
  • Feghhi Kashani, Mohammad
  • Shakeri, Abbas

Abstract

A big data set can often be illustrated by the nodes and edges of a big network. A large volume of data is generally produced by the stock market, and complex networks can be used to reflect the stock market behavior. The correlation of stock prices can be examined by analyzing the stock market based on complex networks. This paper uses the stock data of Tehran Stock Exchange from March 21, 2014, to March 21, 2017, to construct its stock correlation network using the threshold method. With an emphasis on centrality in complex networks, this article addresses key economic and financial implications that can be derived from stock market centrality. Central industries and stocks are thus identified. The results of the analysis of stock centrality suggest that stocks with a higher market capitalization, a greater risk, a higher volume of transactions and a lower debt ratio (i.e. greater liquidity) are more central. These stocks attract more customers due to their attractive investment features and thus have a greater market influence. The review of the relationship between centrality and the growth of industries shows that an industry or a sector with greater economic growth has a higher centrality value and is positioned more centrally in the stock market network.

Suggested Citation

  • Esmaeilpour Moghadam, Hadi & Mohammadi, Teymour & Feghhi Kashani, Mohammad & Shakeri, Abbas, 2019. "Complex networks analysis in Iran stock market: The application of centrality," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 531(C).
    • Handle: RePEc:eee:phsmap:v:531:y:2019:i:c:s0378437119310581
    DOI: 10.1016/j.physa.2019.121800
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    Cited by:

    1. Hao, Xiaoqing & An, Haizhong, 2022. "Comparative study on transmission mechanism of supply shortage risk in the international trade of iron ore, pig iron and crude steel," Resources Policy, Elsevier, vol. 79(C).
    2. Vidal-Tomás, David, 2021. "Transitions in the cryptocurrency market during the COVID-19 pandemic: A network analysis," Finance Research Letters, Elsevier, vol. 43(C).
    3. Xiaoying Zhai & Huiping Ma & Yongmin Zhang, 2022. "Can high-performance funds be built and managed by improving their network locations? –- evidence from entrepreneurship in Chinese fund managers," International Entrepreneurship and Management Journal, Springer, vol. 18(1), pages 383-407, March.
    4. Lin, Hai & Wang, Jingcheng, 2022. "Pinning synchronization of complex networks with time-varying outer coupling and nonlinear multiple time-varying delay coupling," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 588(C).
    5. Niu, Xiaojian & Niu, Xiaoli & Wu, Kexing, 2021. "Implicit government guarantees and the externality of portfolio diversification: A complex network approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 572(C).
    6. Mbatha, Vusisizwe Moses & Alovokpinhou, Sedjro Aaron, 2022. "The structure of the South African stock market network during COVID-19 hard lockdown," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 590(C).
    7. Liwen Sun & Ying Han, 2022. "Spatial Correlation Network Structure and Influencing Factors of Two-Stage Green Innovation Efficiency: Evidence from China," Sustainability, MDPI, vol. 14(18), pages 1-22, September.
    8. de Pontes, Lucca Siebra & Rêgo, Leandro Chaves, 2022. "Impact of macroeconomic variables on the topological structure of the Brazilian stock market: A complex network approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).
    9. Zhu, Mingxue & Zhang, Hua & Xing, Wanli & Zhou, Xuanru & Wang, Lu & Sun, Haoyu, 2023. "Research on price transmission in Chinese mining stock market: Based on industry," Resources Policy, Elsevier, vol. 83(C).
    10. Jaroonchokanan, Nawee & Termsaithong, Teerasit & Suwanna, Sujin, 2022. "Dynamics of hierarchical clustering in stocks market during financial crises," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).
    11. Dariusz Siudak, 2021. "Sectoral Analysis of the US Stock Market through Complex Networks," European Research Studies Journal, European Research Studies Journal, vol. 0(3B), pages 951-966.
    12. Tao, Chen & Zhong, Guang-Yan & Li, Jiang-Cheng, 2023. "Dynamic correlation and risk resonance among industries of Chinese stock market: New evidence from time–frequency domain and complex network perspectives," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 614(C).
    13. Evangelos Ioannidis & Iordanis Sarikeisoglou & Georgios Angelidis, 2023. "Portfolio Construction: A Network Approach," Mathematics, MDPI, vol. 11(22), pages 1-24, November.

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