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Community detection and portfolio optimization

Author

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  • Longfeng Zhao
  • Chao Wang
  • Gang-Jin Wang
  • H. Eugene Stanley
  • Lin Chen

Abstract

Community detection methods can be used to explore the structure of complex systems. The well-known modular configurations in complex financial systems indicate the existence of community structures. Here we analyze the community properties of correlation-based networks in worldwide stock markets and use community information to construct portfolios. Portfolios constructed using community detection methods perform well. Our results can be used as new portfolio optimization and risk management tools.

Suggested Citation

  • Longfeng Zhao & Chao Wang & Gang-Jin Wang & H. Eugene Stanley & Lin Chen, 2021. "Community detection and portfolio optimization," Papers 2112.13383, arXiv.org.
    • Handle: RePEc:arx:papers:2112.13383
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    File URL: http://arxiv.org/pdf/2112.13383
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    References listed on IDEAS

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    1. Tola, Vincenzo & Lillo, Fabrizio & Gallegati, Mauro & Mantegna, Rosario N., 2008. "Cluster analysis for portfolio optimization," Journal of Economic Dynamics and Control, Elsevier, vol. 32(1), pages 235-258, January.
    2. Jihui Han & Wei Li & Zhu Su & Longfeng Zhao & Weibing Deng, 2016. "Community detection by label propagation with compression of flow," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 89(12), pages 1-11, December.
    3. Gang-Jin Wang & Chi Xie & Shou Chen, 2017. "Multiscale correlation networks analysis of the US stock market: a wavelet analysis," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 12(3), pages 561-594, October.
    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. Fabio Caccioli & Susanne Still & Matteo Marsili & Imre Kondor, 2013. "Optimal liquidation strategies regularize portfolio selection," The European Journal of Finance, Taylor & Francis Journals, vol. 19(6), pages 554-571, July.
    6. Shi-Min Cai & Yan-Bo Zhou & Tao Zhou & Pei-Ling Zhou, 2010. "Hierarchical Organization And Disassortative Mixing Of Correlation-Based Weighted Financial Networks," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 21(03), pages 433-441.
    7. Carlo Acerbi & Dirk Tasche, 2002. "Expected Shortfall: A Natural Coherent Alternative to Value at Risk," Economic Notes, Banca Monte dei Paschi di Siena SpA, vol. 31(2), pages 379-388, July.
    8. Zhao, Longfeng & Wang, Gang-Jin & Wang, Mingang & Bao, Weiqi & Li, Wei & Stanley, H. Eugene, 2018. "Stock market as temporal network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 1104-1112.
    9. Ya-Chun Gao & Yong Zeng & Shi-Min Cai, 2015. "Influence network in Chinese stock market," Papers 1503.00823, arXiv.org.
    10. Dong-Ming Song & Michele Tumminello & Wei-Xing Zhou & Rosario N. Mantegna, 2011. "Evolution of worldwide stock markets, correlation structure and correlation based graphs," Papers 1103.5555, arXiv.org.
    11. Peralta, Gustavo & Zareei, Abalfazl, 2016. "A network approach to portfolio selection," Journal of Empirical Finance, Elsevier, vol. 38(PA), pages 157-180.
    12. Gang-Jin Wang & Chi Xie & Kaijian He & H. Eugene Stanley, 2017. "Extreme risk spillover network: application to financial institutions," Quantitative Finance, Taylor & Francis Journals, vol. 17(9), pages 1417-1433, September.
    13. Jihui Han & Wei Li & Longfeng Zhao & Zhu Su & Yijiang Zou & Weibing Deng, 2017. "Community detection in dynamic networks via adaptive label propagation," PLOS ONE, Public Library of Science, vol. 12(11), pages 1-16, November.
    14. Dror Y Kenett & Michele Tumminello & Asaf Madi & Gitit Gur-Gershgoren & Rosario N Mantegna & Eshel Ben-Jacob, 2010. "Dominating Clasp of the Financial Sector Revealed by Partial Correlation Analysis of the Stock Market," PLOS ONE, Public Library of Science, vol. 5(12), pages 1-14, December.
    15. Fabio Caccioli & Imre Kondor & Matteo Marsili & Susanne Still, 2016. "Liquidity Risk And Instabilities In Portfolio Optimization," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 19(05), pages 1-28, August.
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