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An Application of Correlation Clustering to Portfolio Diversification

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  • Hannah Cheng Juan Zhan
  • William Rea
  • Alethea Rea

Abstract

This paper presents a novel application of a clustering algorithm developed for constructing a phylogenetic network to the correlation matrix for 126 stocks listed on the Shanghai A Stock Market. We show that by visualizing the correlation matrix using a Neighbor-Net network and using the circular ordering produced during the construction of the network we can reduce the risk of a diversified portfolio compared with random or industry group based selection methods in times of market increase.

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  • Hannah Cheng Juan Zhan & William Rea & Alethea Rea, 2015. "An Application of Correlation Clustering to Portfolio Diversification," Papers 1511.07945, arXiv.org.
    • Handle: RePEc:arx:papers:1511.07945
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    4. R. Mantegna, 1999. "Hierarchical structure in financial markets," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 11(1), pages 193-197, September.
    5. Rea, Alethea & Rea, William, 2014. "Visualization of a stock market correlation matrix," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 400(C), pages 109-123.
    6. Dale L. Domian & David A. Louton & Marie D. Racine, 2007. "Diversification in Portfolios of Individual Stocks: 100 Stocks Are Not Enough," The Financial Review, Eastern Finance Association, vol. 42(4), pages 557-570, November.
    7. Fama, Eugene F & French, Kenneth R, 1992. "The Cross-Section of Expected Stock Returns," Journal of Finance, American Finance Association, vol. 47(2), pages 427-465, June.
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    Cited by:

    1. Adam Korniejczuk & Robert Ślepaczuk, 2024. "Statistical arbitrage in multi-pair trading strategy based on graph clustering algorithms in US equities market," Working Papers 2024-09, Faculty of Economic Sciences, University of Warsaw.
    2. Fazlollah Soleymani & Mahdi Vasighi, 2022. "Efficient portfolio construction by means of CVaR and k‐means++ clustering analysis: Evidence from the NYSE," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(3), pages 3679-3693, July.
    3. Paolo Giudici & Gloria Polinesi & Alessandro Spelta, 2022. "Network models to improve robot advisory portfolios," Annals of Operations Research, Springer, vol. 313(2), pages 965-989, June.
    4. Giuseppe Genovese & Ashkan Nikeghbali & Nicola Serra & Gabriele Visentin, 2022. "Universal approximation of credit portfolio losses using Restricted Boltzmann Machines," Papers 2202.11060, arXiv.org, revised Apr 2023.
    5. Gautier Marti & Frank Nielsen & Miko{l}aj Bi'nkowski & Philippe Donnat, 2017. "A review of two decades of correlations, hierarchies, networks and clustering in financial markets," Papers 1703.00485, arXiv.org, revised Nov 2020.
    6. Wenpin Tang & Xiao Xu & Xun Yu Zhou, 2021. "Asset Selection via Correlation Blockmodel Clustering," Papers 2103.14506, arXiv.org, revised Aug 2021.

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    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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