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An analysis of machine learning risk factors and risk parity portfolio optimization

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  • Liyun Wu
  • Muneeb Ahmad
  • Salman Ali Qureshi
  • Kashif Raza
  • Yousaf Ali Khan

Abstract

Many academics and experts focus on portfolio optimization and risk budgeting as a topic of study. Streamlining a portfolio using machine learning methods and elements is examined, as well as a strategy for portfolio expansion that relies on the decay of a portfolio’s risk into risk factor commitments. There is a more vulnerable relationship between commonly used trademarked portfolios and neural organizations based on variables than famous dimensionality decrease strategies, as we have found. Machine learning methods also generate covariance and portfolio weight structures that are more difficult to assess. The least change portfolios outperform simpler benchmarks in minimizing risk. During periods of high instability, risk-adjusted returns are present, and these effects are amplified for investors with greater sensitivity to chance changes in returns R.

Suggested Citation

  • Liyun Wu & Muneeb Ahmad & Salman Ali Qureshi & Kashif Raza & Yousaf Ali Khan, 2022. "An analysis of machine learning risk factors and risk parity portfolio optimization," PLOS ONE, Public Library of Science, vol. 17(9), pages 1-19, September.
    • Handle: RePEc:plo:pone00:0272521
    DOI: 10.1371/journal.pone.0272521
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    References listed on IDEAS

    as
    1. DeMiguel, Victor & Martin-Utrera, Alberto & Nogales, Francisco J. & Uppal, Raman, 2017. "A Portfolio Perspective on the Multitude of Firm Characteristics," CEPR Discussion Papers 12417, C.E.P.R. Discussion Papers.
    2. Vaughn Gambeta & Roy Kwon, 2020. "Risk Return Trade-Off in Relaxed Risk Parity Portfolio Optimization," JRFM, MDPI, vol. 13(10), pages 1-28, October.
    3. Moura, Guilherme V. & Santos, André A.P. & Ruiz, Esther, 2020. "Comparing high-dimensional conditional covariance matrices: Implications for portfolio selection," Journal of Banking & Finance, Elsevier, vol. 118(C).
    4. Daniele Bianchi & Matthias Büchner & Tobias Hoogteijling & Andrea Tamoni, 2021. "Corrigendum: Bond Risk Premiums with Machine Learning [Bond risk premiums with machine learning]," The Review of Financial Studies, Society for Financial Studies, vol. 34(2), pages 1090-1103.
    5. William F. Sharpe, 1963. "A Simplified Model for Portfolio Analysis," Management Science, INFORMS, vol. 9(2), pages 277-293, January.
    6. Guanhao Feng & Stefano Giglio & Dacheng Xiu, 2020. "Taming the Factor Zoo: A Test of New Factors," Journal of Finance, American Finance Association, vol. 75(3), pages 1327-1370, June.
    7. repec:dau:papers:123456789/4688 is not listed on IDEAS
    8. Strub, Moris S. & Li, Duan & Cui, Xiangyu & Gao, Jianjun, 2019. "Discrete-time mean-CVaR portfolio selection and time-consistency induced term structure of the CVaR," Journal of Economic Dynamics and Control, Elsevier, vol. 108(C).
    9. Fama, Eugene F. & French, Kenneth R., 2015. "A five-factor asset pricing model," Journal of Financial Economics, Elsevier, vol. 116(1), pages 1-22.
    10. Hiroshi Konno & Hiroaki Yamazaki, 1991. "Mean-Absolute Deviation Portfolio Optimization Model and Its Applications to Tokyo Stock Market," Management Science, INFORMS, vol. 37(5), pages 519-531, May.
    11. Martin Lettau & Markus Pelger & Stijn Van Nieuwerburgh, 2020. "Factors That Fit the Time Series and Cross-Section of Stock Returns," The Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 2274-2325.
    12. D’Hondt, Catherine & De Winne, Rudy & Ghysels, Eric & Raymond, Steve, 2020. "Artificial Intelligence Alter Egos: Who might benefit from robo-investing?," Journal of Empirical Finance, Elsevier, vol. 59(C), pages 278-299.
    13. Xi Bai & Katya Scheinberg & Reha Tutuncu, 2016. "Least-squares approach to risk parity in portfolio selection," Quantitative Finance, Taylor & Francis Journals, vol. 16(3), pages 357-376, March.
    14. Thomas Conlon & John Cotter & Iason Kynigakis, 2021. "Machine Learning and Factor-Based Portfolio Optimization," Working Papers 202111, Geary Institute, University College Dublin.
    15. Laurent Callot & Mehmet Caner & A. Özlem Önder & Esra Ulaşan, 2021. "A Nodewise Regression Approach to Estimating Large Portfolios," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(2), pages 520-531, March.
    16. Helmut Mausser & Oleksandr Romanko, 2018. "Long-only equal risk contribution portfolios for CVaR under discrete distributions," Quantitative Finance, Taylor & Francis Journals, vol. 18(11), pages 1927-1945, November.
    17. David E. Rapach & Jack K. Strauss & Guofu Zhou, 2013. "International Stock Return Predictability: What Is the Role of the United States?," Journal of Finance, American Finance Association, vol. 68(4), pages 1633-1662, August.
    18. Giorgio Costa & Roy H. Kwon, 2020. "Generalized risk parity portfolio optimization: an ADMM approach," Journal of Global Optimization, Springer, vol. 78(1), pages 207-238, September.
    19. Banihashemi, Shokoofeh & Navidi, Sarah, 2017. "Portfolio performance evaluation in Mean-CVaR framework: A comparison with non-parametric methods value at risk in Mean-VaR analysis," Operations Research Perspectives, Elsevier, vol. 4(C), pages 21-28.
    20. Daniele Bianchi & Matthias Büchner & Andrea Tamoni, 2021. "Bond Risk Premiums with Machine Learning [Quadratic term structure models: Theory and evidence]," The Review of Financial Studies, Society for Financial Studies, vol. 34(2), pages 1046-1089.
    21. Jean-Charles Richard & Thierry Roncalli, 2019. "Constrained Risk Budgeting Portfolios: Theory, Algorithms, Applications & Puzzles," Papers 1902.05710, arXiv.org.
    22. Bruder, Benjamin & Roncalli, Thierry, 2012. "Managing risk exposures using the risk budgeting approach," MPRA Paper 37246, University Library of Munich, Germany.
    23. Gu, Shihao & Kelly, Bryan & Xiu, Dacheng, 2021. "Autoencoder asset pricing models," Journal of Econometrics, Elsevier, vol. 222(1), pages 429-450.
    24. Martin R. Young, 1998. "A Minimax Portfolio Selection Rule with Linear Programming Solution," Management Science, INFORMS, vol. 44(5), pages 673-683, May.
    25. Alan Moreira & Tyler Muir, 2017. "Volatility-Managed Portfolios," Journal of Finance, American Finance Association, vol. 72(4), pages 1611-1644, August.
    26. Xiaobing Huang & Xinxin Meng & Meng Chen & Xiaolian Liu, 2022. "The impact of administrative simplification on outward foreign direct investment: Evidence from a quasi-natural experiment in China," The Journal of International Trade & Economic Development, Taylor & Francis Journals, vol. 31(3), pages 375-393, April.
    27. Martin Lettau & Markus Pelger, 2020. "Factors That Fit the Time Series and Cross-Section of Stock Returns," Review of Finance, European Finance Association, vol. 33(5), pages 2274-2325.
    28. Haftor, Darek M. & Costa Climent, Ricardo & Lundström, Jenny Eriksson, 2021. "How machine learning activates data network effects in business models: Theory advancement through an industrial case of promoting ecological sustainability," Journal of Business Research, Elsevier, vol. 131(C), pages 196-205.
    29. Ran Ji & Miguel A. Lejeune, 2018. "Risk-budgeting multi-portfolio optimization with portfolio and marginal risk constraints," Annals of Operations Research, Springer, vol. 262(2), pages 547-578, March.
    30. Thorsten Poddig & Albina Unger, 2012. "On the robustness of risk-based asset allocations," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 26(3), pages 369-401, September.
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