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Analysis of weakly correlated nodes in market network

Author

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  • Dmitry Semenov

    (National Research University Higher School of Economics)

  • Alexander Koldanov

    (National Research University Higher School of Economics)

  • Petr Koldanov

    (National Research University Higher School of Economics)

Abstract

The aim of the article is to analyze graphs of weakly correlated stocks. Characteristics of these graphs such as number of edges, histogram of vertices degrees, degrees distribution, hubs and cliques are investigated. Pearson correlation and Kendall correlation are used to construct these graphs. Graphs constructed by the traditional procedure and by Holm procedure are compared. Obtained results are exemplified on the data of French stock market. In particular it is shown that reliable maximum cliques contain very few nodes despite the large number of edges in the graph of weakly correlated stocks.

Suggested Citation

  • Dmitry Semenov & Alexander Koldanov & Petr Koldanov, 2024. "Analysis of weakly correlated nodes in market network," Computational Management Science, Springer, vol. 21(1), pages 1-18, June.
    • Handle: RePEc:spr:comgts:v:21:y:2024:i:1:d:10.1007_s10287-023-00499-3
    DOI: 10.1007/s10287-023-00499-3
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    References listed on IDEAS

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    1. Kalyagin, V.A. & Koldanov, A.P. & Koldanov, P.A., 2022. "Reliability of maximum spanning tree identification in correlation-based market networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 599(C).
    2. Millington, Tristan & Niranjan, Mahesan, 2021. "Construction of minimum spanning trees from financial returns using rank correlation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 566(C).
    3. Chang, Jinyuan & Qiu, Yumou & Yao, Qiwei & Zou, Tao, 2018. "Confidence regions for entries of a large precision matrix," Journal of Econometrics, Elsevier, vol. 206(1), pages 57-82.
    4. 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.
    5. 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.
    6. Chang, Jinyuan & Qiu, Yumou & Yao, Qiwei & Zou, Tao, 2018. "Confidence regions for entries of a large precision matrix," LSE Research Online Documents on Economics 87513, London School of Economics and Political Science, LSE Library.
    7. Andrea Lancichinetti & Filippo Radicchi & José J Ramasco & Santo Fortunato, 2011. "Finding Statistically Significant Communities in Networks," PLOS ONE, Public Library of Science, vol. 6(4), pages 1-18, April.
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    Cited by:

    1. Panos Pardalos & Valery Kalyagin & Mario R. Guarracino, 2024. "Editorial," Computational Management Science, Springer, vol. 21(1), pages 1-5, June.

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