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Using medians in portfolio optimization

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  • Stefano Benati

    (Dipartimento di Sociologia e Ricerca Sociale, Università di Trento, Trento, Italy)

Abstract

Some new portfolio optimization models are formulated by adopting the sample median instead of the sample mean as the investment efficiency measure. The median is a robust statistic, which is less affected by outliers than the mean, and in portfolio models this is particularly relevant as data are often characterized by attributes such as skewness, fat tails and jumps, which may strongly bias the mean estimate. As in mean/variance optimization, the portfolio problems are formulated as finding the optimal weights, for example, wealth allocation, which maximize the portfolio median, with risk constrained by some risk measure, respectively, the Value-at-Risk, the Conditional Value-at-Risk, the Mean Absolute Deviation and the Maximum Loss, for a whole of four different models. All these models are formulated as mixed integer linear programming problems, which, at least for moderate sized problems, are efficiently solved by standard software. Models are tested on real financial data, compared to some benchmark portfolios, and found to give good results in terms of realized profits. An important feature is greater portfolio diversification than that obtained with other portfolio models.

Suggested Citation

  • Stefano Benati, 2015. "Using medians in portfolio optimization," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 66(5), pages 720-731, May.
  • Handle: RePEc:pal:jorsoc:v:66:y:2015:i:5:p:720-731
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    Citations

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    Cited by:

    1. Virginie Gabrel & Cécile Murat & Aurélie Thiele, 2018. "Portfolio optimization with pw-robustness," EURO Journal on Computational Optimization, Springer;EURO - The Association of European Operational Research Societies, vol. 6(3), pages 267-290, September.
    2. Platanakis, Emmanouil & Sakkas, Athanasios & Sutcliffe, Charles, 2019. "Harmful diversification: Evidence from alternative investments," The British Accounting Review, Elsevier, vol. 51(1), pages 1-23.
    3. Benítez-Peña, Sandra & Bogetoft, Peter & Romero Morales, Dolores, 2020. "Feature Selection in Data Envelopment Analysis: A Mathematical Optimization approach," Omega, Elsevier, vol. 96(C).
    4. Emmanouil Platanakis & Athanasios Sakkas & Charles Sutcliffe, 2017. "Should Portfolio Model Inputs Be Estimated Using One or Two Economic Regimes?," ICMA Centre Discussion Papers in Finance icma-dp2017-07, Henley Business School, University of Reading.
    5. Kerstens, Kristiaan & Mazza, Paolo & Ren, Tiantian & Van de Woestyne, Ignace, 2022. "Multi-Time and Multi-Moment Nonparametric Frontier-Based Fund Rating: Proposal and Buy-and-Hold Backtesting Strategy," Omega, Elsevier, vol. 113(C).
    6. Tuncer Yılmaz & Bülent Yıldız, 2022. "Yatırımcıların Risk İştahı Endeksi İle Korku Endeksleri Arasındaki İlişki: Türkiye’de ARDL İle Ampirik Bir Uygulama," Journal of Research in Economics, Politics & Finance, Ersan ERSOY, vol. 7(3), pages 646-676.
    7. Maria Cristina Arcuri & Gino Gandolfi & Fabrizio Laurini, 2023. "Robust portfolio optimization for banking foundations: a CVaR approach for asset allocation with mandatory constraints," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 31(2), pages 557-581, June.
    8. González-Díaz, Julio & González-Rodríguez, Brais & Leal, Marina & Puerto, Justo, 2021. "Global optimization for bilevel portfolio design: Economic insights from the Dow Jones index," Omega, Elsevier, vol. 102(C).
    9. Benati, S. & Conde, E., 2022. "A relative robust approach on expected returns with bounded CVaR for portfolio selection," European Journal of Operational Research, Elsevier, vol. 296(1), pages 332-352.

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