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Ranking influential and influenced stocks over time using transfer entropy networks

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  • Neto, José de Paula Neves
  • Figueiredo, Daniel Ratton

Abstract

Influence is a concept found in nature and society and is related to the interdependence among a set of objects. In the context of a stock market, the price variation of stocks can affect the price of other stocks leading to an influence between stocks. This work leverages the notion of information flow measured by transfer entropy to build networks of stocks where directed edges indicate influence. Network centrality metrics such as Pagerank and node weight are used to rank the nodes in order to determine the top ranked influential and influenced stocks. The proposed methodology is applied to a dataset comprising of a 32-year period of the Brazilian stock market exchange. Results indicate that the top ranking of influential and influenced stocks is very dynamic under different ranking metrics, while top ranking of stocks based on financial indicators is relatively stable. Results also indicate that rankings based on financial indicators have little correlation to rankings based on influence, motivating the need for specific metrics to assess influence.

Suggested Citation

  • Neto, José de Paula Neves & Figueiredo, Daniel Ratton, 2023. "Ranking influential and influenced stocks over time using transfer entropy networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 630(C).
    • Handle: RePEc:eee:phsmap:v:630:y:2023:i:c:s037843712300674x
    DOI: 10.1016/j.physa.2023.129119
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    References listed on IDEAS

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    1. Peng Yue & Qing Cai & Wanfeng Yan & Wei-Xing Zhou, 2020. "Information flow networks of Chinese stock market sectors," Papers 2004.08759, arXiv.org.
    2. Park, Sangjin & Jang, Kwahngsoo & Yang, Jae-Suk, 2021. "Information flow between bitcoin and other financial assets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 566(C).
    3. Namaki, A. & Shirazi, A.H. & Raei, R. & Jafari, G.R., 2011. "Network analysis of a financial market based on genuine correlation and threshold method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(21), pages 3835-3841.
    4. He, Jiayi & Shang, Pengjian, 2017. "Comparison of transfer entropy methods for financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 482(C), pages 772-785.
    5. Boginski, Vladimir & Butenko, Sergiy & Pardalos, Panos M., 2005. "Statistical analysis of financial networks," Computational Statistics & Data Analysis, Elsevier, vol. 48(2), pages 431-443, February.
    6. Oleg Shirokikh & Grigory Pastukhov & Vladimir Boginski & Sergiy Butenko, 2013. "Computational study of the US stock market evolution: a rank correlation-based network model," Computational Management Science, Springer, vol. 10(2), pages 81-103, June.
    7. Tabak, Benjamin M. & Serra, Thiago R. & Cajueiro, Daniel O., 2010. "Topological properties of stock market networks: The case of Brazil," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(16), pages 3240-3249.
    8. Okyu Kwon & Jae-Suk Yang, 2008. "Information flow between stock indices," Papers 0802.1747, arXiv.org.
    9. Jan Korbel & Xiongfei Jiang & Bo Zheng, 2017. "Transfer entropy between communities in complex networks," Papers 1706.05543, arXiv.org.
    Full references (including those not matched with items on IDEAS)

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