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A more human-like portfolio optimization approach

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  • Silva, Thuener
  • Pinheiro, Plácido Rogério
  • Poggi, Marcus

Abstract

Black and Litterman proposed an improvement to the Markowitz portfolio optimization model. They suggested the construction of views to represent investor’s opinion about the future of stocks’ returns. However, conceiving these views can be quite confusing. It requires the investor to quantify several subjective parameters. In this article, we propose a new way of creating these views using Verbal Decision Analysis. Questionnaires were designed with the intent of making it easier for investors to express their vision about stocks. Following the ZAPROS methodology, the investor answers sets of questions allowing to determine a Formal Index of Quality (FIQ). The views are then derived from the resulting FIQ. Our approach was implemented and tested on data from the Brazilian Stocks. It allows investors to create a personal risk-return balanced portfolio without the help of an expert. The experiments show that the proposed method mitigates the impact of poor view estimation. Also, one must notice that the method is qualitative and its aim is to create a more efficient portfolio considering the investor’s vision.

Suggested Citation

  • Silva, Thuener & Pinheiro, Plácido Rogério & Poggi, Marcus, 2017. "A more human-like portfolio optimization approach," European Journal of Operational Research, Elsevier, vol. 256(1), pages 252-260.
    • Handle: RePEc:eee:ejores:v:256:y:2017:i:1:p:252-260
    DOI: 10.1016/j.ejor.2016.06.018
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    References listed on IDEAS

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    Cited by:

    1. Ahmadi-Javid, Amir & Fallah-Tafti, Malihe, 2019. "Portfolio optimization with entropic value-at-risk," European Journal of Operational Research, Elsevier, vol. 279(1), pages 225-241.
    2. Platanakis, Emmanouil & Sutcliffe, Charles & Ye, Xiaoxia, 2021. "Horses for courses: Mean-variance for asset allocation and 1/N for stock selection," European Journal of Operational Research, Elsevier, vol. 288(1), pages 302-317.
    3. Sahamkhadam, Maziar & Stephan, Andreas & Östermark, Ralf, 2022. "Copula-based Black–Litterman portfolio optimization," European Journal of Operational Research, Elsevier, vol. 297(3), pages 1055-1070.
    4. Tamara Teplova & Mikova Evgeniia & Qaiser Munir & Nataliya Pivnitskaya, 2023. "Black-Litterman model with copula-based views in mean-CVaR portfolio optimization framework with weight constraints," Economic Change and Restructuring, Springer, vol. 56(1), pages 515-535, February.
    5. Çela, Eranda & Hafner, Stephan & Mestel, Roland & Pferschy, Ulrich, 2021. "Mean-variance portfolio optimization based on ordinal information," Journal of Banking & Finance, Elsevier, vol. 122(C).
    6. Kocherlakota Satya Pritam & Trilok Mathur & Shivi Agarwal & Sanjoy Kumar Paul & Ahmed Mulla, 2022. "A novel methodology for perception-based portfolio management," Annals of Operations Research, Springer, vol. 315(2), pages 1107-1133, August.
    7. Platanakis, Emmanouil & Urquhart, Andrew, 2020. "Should investors include Bitcoin in their portfolios? A portfolio theory approach," The British Accounting Review, Elsevier, vol. 52(4).
    8. Newton, David & Platanakis, Emmanouil & Stafylas, Dimitrios & Sutcliffe, Charles & Ye, Xiaoxia, 2021. "Hedge fund strategies, performance &diversification: A portfolio theory & stochastic discount factor approach," The British Accounting Review, Elsevier, vol. 53(5).
    9. Li, Jianping & Yao, Xiaoyang & Sun, Xiaolei & Wu, Dengsheng, 2018. "Determining the fuzzy measures in multiple criteria decision aiding from the tolerance perspective," European Journal of Operational Research, Elsevier, vol. 264(2), pages 428-439.
    10. Leonardo José Silveira & Plácido Rogério Pinheiro & Leopoldo Soares de Melo Junior, 2021. "A Novel Model Structured on Predictive Churn Methods in a Banking Organization," JRFM, MDPI, vol. 14(10), pages 1-24, October.
    11. Kocuk, Burak & Cornuéjols, Gérard, 2020. "Incorporating Black-Litterman views in portfolio construction when stock returns are a mixture of normals," Omega, Elsevier, vol. 91(C).
    12. Barua, Ronil & Sharma, Anil K., 2023. "Using fear, greed and machine learning for optimizing global portfolios: A Black-Litterman approach," Finance Research Letters, Elsevier, vol. 58(PC).
    13. Amir Ahmadi-Javid & Malihe Fallah-Tafti, 2017. "Portfolio Optimization with Entropic Value-at-Risk," Papers 1708.05713, arXiv.org.

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