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Finding Weakly Correlated Nodes in Random Variable Networks

Author

Listed:
  • Petr Koldanov

    (National Research University Higher School of Economics)

  • Alexander Koldanov

    (National Research University Higher School of Economics)

  • Dmitry Semenov

    (National Research University Higher School of Economics)

Abstract

The issue of identifying sets of weakly correlated stocks is explored. Four distinct methods for constructing these sets are compared: the traditional approach using Pearson correlation, the traditional approach using Kendall correlation, and multiple hypothesis testing methods, which apply both Pearson and Kendall correlations. To derive specific findings, we analyze daily returns of a selection of stocks listed on the Frankfurt (FWB), London (LSE), and Paris (Euronext Paris) stock exchanges. Our results reveal a significant difference between the identified sets of weakly correlated stocks in Pearson and Kendall correlation networks. Notably, this difference is more substantial in the statistically significant sets of weakly correlated stocks derived from multiple hypothesis testing methods than in those obtained through traditional procedures. We recommend for the use of multiple hypothesis testing methods based on Kendall correlation for analyzing market data.

Suggested Citation

  • Petr Koldanov & Alexander Koldanov & Dmitry Semenov, 2024. "Finding Weakly Correlated Nodes in Random Variable Networks," SN Operations Research Forum, Springer, vol. 5(4), pages 1-14, December.
    • Handle: RePEc:spr:snopef:v:5:y:2024:i:4:d:10.1007_s43069-024-00401-0
    DOI: 10.1007/s43069-024-00401-0
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    References listed on IDEAS

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    1. Millington, Tristan & Niranjan, Mahesan, 2021. "Stability and similarity in financial networks—How do they change in times of turbulence?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 574(C).
    2. 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).
    3. Kalyagin, V.A. & Koldanov, A.P. & Koldanov, P.A. & Pardalos, P.M. & Zamaraev, V.A., 2014. "Measures of uncertainty in market network analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 413(C), pages 59-70.
    4. J.-P. Onnela & K. Kaski & J. Kertész, 2004. "Clustering and information in correlation based financial networks," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 38(2), pages 353-362, March.
    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. 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).
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