IDEAS home Printed from https://ideas.repec.org/a/inm/ormnsc/v44y1998i5p673-683.html
   My bibliography  Save this article

A Minimax Portfolio Selection Rule with Linear Programming Solution

Author

Listed:
  • Martin R. Young

    (University of Michigan School of Business, Department of Statistics and Management Science, Ann Arbor, Michigan 48109-1234)

Abstract

A new principle for choosing portfolios based on historical returns data is introduced; the optimal portfolio based on this principle is the solution to a simple linear programming problem. This principle uses minimum return rather than variance as a measure of risk. In particular, the portfolio is chosen that minimizes the maximum loss over all past observation periods, for a given level of return. This objective function avoids the logical problems of a quadratic (nonmonotone) utility function implied by mean-variance portfolio selection rules. The resulting minimax portfolios are diversified; for normal returns data, the portfolios are nearly equivalent to those chosen by a mean-variance rule. Framing the portfolio selection process as a linear optimization problem also makes it feasible to constrain certain decision variables to be integer, or 0-1, valued; this feature facilitates the use of more complex decision-making models, including models with fixed transaction charges and models with Boolean-type constraints on allocations.

Suggested Citation

  • Martin R. Young, 1998. "A Minimax Portfolio Selection Rule with Linear Programming Solution," Management Science, INFORMS, vol. 44(5), pages 673-683, May.
    • Handle: RePEc:inm:ormnsc:v:44:y:1998:i:5:p:673-683
    DOI: 10.1287/mnsc.44.5.673
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/mnsc.44.5.673
    Download Restriction: no
    File URL: https://libkey.io/10.1287/mnsc.44.5.673?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Elton, Edwin J & Gruber, Martin J & Padberg, Manfred W, 1976. "Simple Criteria for Optimal Portfolio Selection," Journal of Finance, American Finance Association, vol. 31(5), pages 1341-1357, December.
    2. Sharpe, William F., 1971. "A Linear Programming Approximation for the General Portfolio Analysis Problem," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 6(5), pages 1263-1275, December.
    3. Harry Markowitz, 1952. "Portfolio Selection," Journal of Finance, American Finance Association, vol. 7(1), pages 77-91, March.
    4. 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.
    5. Stone, Bernell K., 1973. "A Linear Programming Formulation of the General Portfolio Selection Problemâ€," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 8(4), pages 621-636, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Bai, Zhidong & Liu, Huixia & Wong, Wing-Keung, 2016. "Making Markowitz's Portfolio Optimization Theory Practically Useful," MPRA Paper 74360, University Library of Munich, Germany.
    2. Arenas Parra, M. & Bilbao Terol, A. & Rodriguez Uria, M. V., 2001. "A fuzzy goal programming approach to portfolio selection," European Journal of Operational Research, Elsevier, vol. 133(2), pages 287-297, January.
    3. Polak, George G. & Rogers, David F. & Sweeney, Dennis J., 2010. "Risk management strategies via minimax portfolio optimization," European Journal of Operational Research, Elsevier, vol. 207(1), pages 409-419, November.
    4. Mansini, Renata & Speranza, Maria Grazia, 1999. "Heuristic algorithms for the portfolio selection problem with minimum transaction lots," European Journal of Operational Research, Elsevier, vol. 114(2), pages 219-233, April.
    5. Li, Han-Lin & Tsai, Jung-Fa, 2008. "A distributed computation algorithm for solving portfolio problems with integer variables," European Journal of Operational Research, Elsevier, vol. 186(2), pages 882-891, April.
    6. Bai, Zhidong & Li, Hua & Wong, Wing-Keung, 2013. "The best estimation for high-dimensional Markowitz mean-variance optimization," MPRA Paper 43862, University Library of Munich, Germany.
    7. Zhihui Lv & Amanda M. Y. Chu & Wing Keung Wong & Thomas C. Chiang, 2021. "The maximum-return-and-minimum-volatility effect: evidence from choosing risky and riskless assets to form a portfolio," Risk Management, Palgrave Macmillan, vol. 23(1), pages 97-122, June.
    8. Patriksson, Michael, 2008. "A survey on the continuous nonlinear resource allocation problem," European Journal of Operational Research, Elsevier, vol. 185(1), pages 1-46, February.
    9. Castro, F. & Gago, J. & Hartillo, I. & Puerto, J. & Ucha, J.M., 2011. "An algebraic approach to integer portfolio problems," European Journal of Operational Research, Elsevier, vol. 210(3), pages 647-659, May.
    10. Aydın Ulucan, 2007. "An analysis of mean-variance portfolio selection with varying holding periods," Applied Economics, Taylor & Francis Journals, vol. 39(11), pages 1399-1407.
    11. Angelelli, Enrico & Mansini, Renata & Speranza, M. Grazia, 2008. "A comparison of MAD and CVaR models with real features," Journal of Banking & Finance, Elsevier, vol. 32(7), pages 1188-1197, July.
    12. Justin Dzuche & Christian Deffo Tassak & Jules Sadefo Kamdem & Louis Aimé Fono, 2021. "On two dominances of fuzzy variables based on a parametrized fuzzy measure and application to portfolio selection with fuzzy return," Annals of Operations Research, Springer, vol. 300(2), pages 355-368, May.
    13. Huang, Jinbo & Li, Yong & Yao, Haixiang, 2018. "Index tracking model, downside risk and non-parametric kernel estimation," Journal of Economic Dynamics and Control, Elsevier, vol. 92(C), pages 103-128.
    14. Mansini, Renata & Ogryczak, Wlodzimierz & Speranza, M. Grazia, 2014. "Twenty years of linear programming based portfolio optimization," European Journal of Operational Research, Elsevier, vol. 234(2), pages 518-535.
    15. P Xidonas & G Mavrotas & J Psarras, 2010. "A multiple criteria decision-making approach for the selection of stocks," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(8), pages 1273-1287, August.
    16. Jakobsons Edgars, 2016. "Scenario aggregation method for portfolio expectile optimization," Statistics & Risk Modeling, De Gruyter, vol. 33(1-2), pages 51-65, September.
    17. Nagurney, Anna & Ke, Ke, 2006. "Financial networks with intermediation: Risk management with variable weights," European Journal of Operational Research, Elsevier, vol. 172(1), pages 40-63, July.
    18. Amritansu Ray & Sanat Kumar Majumder, 2018. "Multi objective mean–variance–skewness model with Burg’s entropy and fuzzy return for portfolio optimization," OPSEARCH, Springer;Operational Research Society of India, vol. 55(1), pages 107-133, March.
    19. Panos Xidonas & George Mavrotas, 2014. "Comparative issues between linear and non-linear risk measures for non-convex portfolio optimization: evidence from the S&P 500," Quantitative Finance, Taylor & Francis Journals, vol. 14(7), pages 1229-1242, July.
    20. Jongbin Jung & Seongmoon Kim, 2017. "Developing a dynamic portfolio selection model with a self-adjusted rebalancing method," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(7), pages 766-779, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:inm:ormnsc:v:44:y:1998:i:5:p:673-683. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.