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Asset Selection via Correlation Blockmodel Clustering

Author

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  • Wenpin Tang
  • Xiao Xu
  • Xun Yu Zhou

Abstract

We aim to cluster financial assets in order to identify a small set of stocks to approximate the level of diversification of the whole universe of stocks. We develop a data-driven approach to clustering based on a correlation blockmodel in which assets in the same cluster are highly correlated with each other and, at the same time, have the same correlations with all other assets. We devise an algorithm to detect the clusters, with theoretical analysis and practical guidance. Finally, we conduct an empirical analysis to verify the performance of the algorithm.

Suggested Citation

  • Wenpin Tang & Xiao Xu & Xun Yu Zhou, 2021. "Asset Selection via Correlation Blockmodel Clustering," Papers 2103.14506, arXiv.org, revised Aug 2021.
    • Handle: RePEc:arx:papers:2103.14506
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    References listed on IDEAS

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    1. Nicolo Musmeci & Tomaso Aste & Tiziana Di Matteo, 2014. "Relation between Financial Market Structure and the Real Economy: Comparison between Clustering Methods," Papers 1406.0496, arXiv.org, revised Jan 2015.
    2. 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.
    3. Musmeci, Nicoló & Aste, Tomaso & Di Matteo, T., 2015. "Relation between financial market structure and the real economy: comparison between clustering methods," LSE Research Online Documents on Economics 61644, London School of Economics and Political Science, LSE Library.
    4. repec:dau:papers:123456789/4688 is not listed on IDEAS
    5. Nicoló Musmeci & Tomaso Aste & T Di Matteo, 2015. "Relation between Financial Market Structure and the Real Economy: Comparison between Clustering Methods," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-24, March.
    6. Nandita Das, 2003. "Hedge Fund Classification using K-means Clustering Method," Computing in Economics and Finance 2003 284, Society for Computational Economics.
    7. Hannah Cheng Juan Zhan & William Rea & Alethea Rea, 2014. "An Application of Correlation Clustering to Portfolio Diversification," Working Papers in Economics 14/11, University of Canterbury, Department of Economics and Finance.
    8. Michael Ho & Zheng Sun & Jack Xin, 2015. "Weighted Elastic Net Penalized Mean-Variance Portfolio Design and Computation," Papers 1502.01658, arXiv.org, revised Oct 2015.
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