IDEAS home Printed from https://ideas.repec.org/p/mit/sloanp/37153.html
   My bibliography  Save this paper

Portfolio Rebalancing: A Test of the Markowitz-Van Dijk Heuristic

Author

Listed:
  • Kritzman, Mark
  • Page, Sébastien
  • Myrgren, Simon

Abstract

Institutional investors usually employ mean-variance analysis to determine optimal portfolio weights. Almost immediately upon implementation, however, the portfolio€ٳ weights become sub-optimal as changes in asset prices cause the portfolio to drift away from the optimal targets. In an idealized world without transaction costs investors would rebalance continually to the optimal weights. In the presence of transaction costs investors must balance the cost of sub-optimality with the cost of restoring the optimal weights. We apply a quadratic heuristic to address the asset weight drift problem, and we compare it to a dynamic programming solution as well as to standard industry heuristics. Our tests reveal that the quadratic heuristic provides solutions that are remarkably close to the dynamic programming solutions for those cases in which dynamic programming is feasible and far superior to solutions based on standard industry heuristics. In the case of five assets, in fact, it performs better than dynamic programming due to approximations required to implement the dynamic programming algorithm. Moreover, unlike the dynamic programming solution, the quadratic heuristic is scalable to as many as several hundreds assets.

Suggested Citation

  • Kritzman, Mark & Page, Sébastien & Myrgren, Simon, 2007. "Portfolio Rebalancing: A Test of the Markowitz-Van Dijk Heuristic," Working papers 37153, Massachusetts Institute of Technology (MIT), Sloan School of Management.
  • Handle: RePEc:mit:sloanp:37153
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/1721.1/37153
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Kroll, Yoram & Levy, Haim & Markowitz, Harry M, 1984. "Mean-Variance versus Direct Utility Maximization," Journal of Finance, American Finance Association, vol. 39(1), pages 47-61, March.
    2. Levy, H & Markowtiz, H M, 1979. "Approximating Expected Utility by a Function of Mean and Variance," American Economic Review, American Economic Association, vol. 69(3), pages 308-317, June.
    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. Markowitz, Harry, 2014. "Mean–variance approximations to expected utility," European Journal of Operational Research, Elsevier, vol. 234(2), pages 346-355.
    2. Johnson, Michael & O'Connor, Ian & Malcolm, Bill, 2006. "Agribusiness Assets in Investment Portfolios," 2006 Conference (50th), February 8-10, 2006, Sydney, Australia 139794, Australian Agricultural and Resource Economics Society.
    3. Guo, Xu & Lien, Donald & Wong, Wing-Keung, 2015. "Good Approximation of Exponential Utility Function for Optimal Futures Hedging," MPRA Paper 66841, University Library of Munich, Germany.
    4. M. Glawischnig & I. Seidl, 2013. "Portfolio optimization with serially correlated, skewed and fat tailed index returns," 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. 21(1), pages 153-176, January.
    5. Kassimatis, Konstantinos, 2021. "Mean-variance versus utility maximization revisited: The case of constant relative risk aversion," International Review of Financial Analysis, Elsevier, vol. 78(C).
    6. repec:bpj:pepspp:v:18:y:2012:i:3:p:3:n:9 is not listed on IDEAS
    7. Monica Billio & Bertrand Maillet & Loriana Pelizzon, 2022. "A meta-measure of performance related to both investors and investments characteristics," Annals of Operations Research, Springer, vol. 313(2), pages 1405-1447, June.
    8. Rose A. Nyikal & Willis O. Kosura, 2005. "Risk preference and optimal enterprise combinations in Kahuro division of Murang'a district, Kenya," Agricultural Economics, International Association of Agricultural Economists, vol. 32(2), pages 131-140, March.
    9. Giovanni Mastrobuoni & David A Rivers, 2019. "Optimising Criminal Behaviour and the Disutility of Prison," The Economic Journal, Royal Economic Society, vol. 129(619), pages 1364-1399.
    10. Simaan, Majeed & Simaan, Yusif & Tang, Yi, 2018. "Estimation error in mean returns and the mean-variance efficient frontier," International Review of Economics & Finance, Elsevier, vol. 56(C), pages 109-124.
    11. Emmanuel Jurczenko & Bertrand Maillet & Paul Merlin, 2008. "Efficient Frontier for Robust Higher-order Moment Portfolio Selection," Post-Print halshs-00336475, HAL.
    12. Phillips Peter J, 2012. "The lone wolf terrorist: sprees of violence," Peace Economics, Peace Science, and Public Policy, De Gruyter, vol. 18(3), pages 1-3, December.
    13. David Allen & Stephen Satchell & Colin Lizieri, 2024. "Quantifying the non-Gaussian gain," Journal of Asset Management, Palgrave Macmillan, vol. 25(1), pages 1-18, February.
    14. Penaranda, Francisco, 2007. "Portfolio choice beyond the traditional approach," LSE Research Online Documents on Economics 24481, London School of Economics and Political Science, LSE Library.
    15. Harry M. Markowitz, 2002. "Efficient Portfolios, Sparse Matrices, and Entities: A Retrospective," Operations Research, INFORMS, vol. 50(1), pages 154-160, February.
    16. Andrea Morone, 2008. "Comparison of Mean-Variance Theory and Expected-Utility Theory through a Laboratory Experiment," Economics Bulletin, AccessEcon, vol. 3(40), pages 1-7.
    17. Fatma Lajeri-Chaherli, 2016. "On The Concavity And Quasiconcavity Properties Of ( Σ , Μ ) Utility Functions," Bulletin of Economic Research, Wiley Blackwell, vol. 68(3), pages 287-296, April.
    18. Harry Markowitz & Joseph Blasi & Douglas Kruse, 2010. "Employee stock ownership and diversification," Annals of Operations Research, Springer, vol. 176(1), pages 95-107, April.
    19. Gomez-Limon, Jose A. & Arriaza, Manuel & Riesgo, Laura, 2003. "An MCDM analysis of agricultural risk aversion," European Journal of Operational Research, Elsevier, vol. 151(3), pages 569-585, December.
    20. Brauneis, Alexander & Mestel, Roland, 2019. "Cryptocurrency-portfolios in a mean-variance framework," Finance Research Letters, Elsevier, vol. 28(C), pages 259-264.
    21. Marc Baudry & Edouard Civel & Camille Tévenart, 2023. "Land allocation and the adoption of innovative practices in agriculture: a real option modelling of the underlying hidden costs," EconomiX Working Papers 2023-1, University of Paris Nanterre, EconomiX.

    More about this item

    Keywords

    finance; portfolio;

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    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:mit:sloanp:37153. 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: None (email available below). General contact details of provider: https://edirc.repec.org/data/ssmitus.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.