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Efficient portfolio construction by means of CVaR and k‐means++ clustering analysis: Evidence from the NYSE

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  • Fazlollah Soleymani
  • Mahdi Vasighi

Abstract

The major target of this article is to build a machine learning model furnishing an efficient and quick analysis for a large portfolio of stocks. Towards constructing such an efficient portfolio, we employ the Value‐at‐Risk (VaR) and Conditional Value‐at‐Risk (CVaR) as tools of well‐consolidated use for controlling the anomalies' presence of potential danger to the financial stability of the portfolio. It is shown how the well‐resulted k‐means++ clustering technique is employed to cluster financial returns for the stocks of a system and then the risk measures of VaR and CVaR are obtained for the clusters to find the most and least riskiest groups of stocks. The proposed procedure is fast for clustering a financial large set of data by providing many features for each cluster.

Suggested Citation

  • 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.
    • Handle: RePEc:wly:ijfiec:v:27:y:2022:i:3:p:3679-3693
    DOI: 10.1002/ijfe.2344
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    References listed on IDEAS

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    1. Lu, Ya-Nan & Li, Sai-Ping & Zhong, Li-Xin & Jiang, Xiong-Fei & Ren, Fei, 2018. "A clustering-based portfolio strategy incorporating momentum effect and market trend prediction," Chaos, Solitons & Fractals, Elsevier, vol. 117(C), pages 1-15.
    2. Di Lascio, F. Marta L. & Giammusso, Davide & Puccetti, Giovanni, 2018. "A clustering approach and a rule of thumb for risk aggregation," Journal of Banking & Finance, Elsevier, vol. 96(C), pages 236-248.
    3. Kocheturov, Anton & Batsyn, Mikhail & Pardalos, Panos M., 2014. "Dynamics of cluster structures in a financial market network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 413(C), pages 523-533.
    4. Moien Nikusokhan, 2018. "GJR-Copula-CVaR Model for Portfolio Optimization: Evidence for Emerging Stock Markets," Iranian Economic Review (IER), Faculty of Economics,University of Tehran.Tehran,Iran, vol. 22(4), pages 990-1015, Autumn.
    5. M. Tumminello & T. Di Matteo & T. Aste & R. N. Mantegna, 2007. "Correlation based networks of equity returns sampled at different time horizons," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 55(2), pages 209-217, January.
    6. Rama Cont & Romain Deguest & Giacomo Scandolo, 2010. "Robustness and sensitivity analysis of risk measurement procedures," Post-Print hal-00413729, HAL.
    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. Laurent Laloux & Pierre Cizeau & Marc Potters & Jean-Philippe Bouchaud, 2000. "Random Matrix Theory And Financial Correlations," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 3(03), pages 391-397.
    9. Rama Cont & Romain Deguest & Giacomo Scandolo, 2010. "Robustness and sensitivity analysis of risk measurement procedures," Quantitative Finance, Taylor & Francis Journals, vol. 10(6), pages 593-606.
    10. Álvaro Cartea & Sebastian Jaimungal & Damir Kinzebulatov, 2016. "Algorithmic Trading With Learning," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 19(04), pages 1-30, June.
    11. Fei Ren & Ya-Nan Lu & Sai-Ping Li & Xiong-Fei Jiang & Li-Xin Zhong & Tian Qiu, 2017. "Dynamic Portfolio Strategy Using Clustering Approach," PLOS ONE, Public Library of Science, vol. 12(1), pages 1-23, January.
    12. Nicholas L. Georgakopoulos, 2018. "Illustrating Finance Policy with Mathematica," Quantitative Perspectives on Behavioral Economics and Finance, Palgrave Macmillan, number 978-3-319-95372-4, February.
    13. Heiberger, Raphael H., 2014. "Stock network stability in times of crisis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 393(C), pages 376-381.
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