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A theoretical foundation of portfolio resampling

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  • Gabriel Frahm

Abstract

A portfolio-resampling procedure invented by Richard and Robert Michaud is a subject of highly controversial discussion and big scientific dispute. It has been evaluated in many empirical studies and Monte Carlo experiments. Apart from the contradictory findings, the Michaud approach still lacks a theoretical foundation. I prove that portfolio resampling has a strong foundation in the classic theory of rational behavior. Every noise trader could do better by applying the Michaud procedure. By contrast, a signal trader who has enough prediction power and risk-management skills should refrain from portfolio resampling. The key note is that in most simulation studies, investors are considered as noise traders. This explains why portfolio resampling performs well in simulation studies, but could be mediocre in real life. Copyright Springer Science+Business Media New York 2015

Suggested Citation

  • Gabriel Frahm, 2015. "A theoretical foundation of portfolio resampling," Theory and Decision, Springer, vol. 79(1), pages 107-132, July.
    • Handle: RePEc:kap:theord:v:79:y:2015:i:1:p:107-132
    DOI: 10.1007/s11238-014-9453-0
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    References listed on IDEAS

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    1. Frahm, Gabriel & Memmel, Christoph, 2010. "Dominating estimators for minimum-variance portfolios," Journal of Econometrics, Elsevier, vol. 159(2), pages 289-302, December.
    2. Gabriel Frahm & Tobias Wickern & Christof Wiechers, 2012. "Multiple tests for the performance of different investment strategies," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 96(3), pages 343-383, July.
    3. repec:hal:journl:peer-00741629 is not listed on IDEAS
    4. Hansen, Lars Peter, 1982. "Large Sample Properties of Generalized Method of Moments Estimators," Econometrica, Econometric Society, vol. 50(4), pages 1029-1054, July.
    5. Burton G. Malkiel, 2003. "The Efficient Market Hypothesis and Its Critics," Working Papers 111, Princeton University, Department of Economics, Center for Economic Policy Studies..
    6. Harry Markowitz, 1952. "Portfolio Selection," Journal of Finance, American Finance Association, vol. 7(1), pages 77-91, March.
    7. Bernd Scherer, 2004. "Resampled efficiency and portfolio choice," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 18(4), pages 382-398, December.
    8. Timmermann, Allan & Granger, Clive W. J., 2004. "Efficient market hypothesis and forecasting," International Journal of Forecasting, Elsevier, vol. 20(1), pages 15-27.
    9. Lucas, Robert E, Jr, 1978. "Asset Prices in an Exchange Economy," Econometrica, Econometric Society, vol. 46(6), pages 1429-1445, November.
    10. repec:pri:cepsud:91malkiel is not listed on IDEAS
    11. Pulley, Lawrence B., 1981. "A General Mean-Variance Approximation to Expected Utility for Short Holding Periods," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 16(3), pages 361-373, September.
    12. Kan, Raymond & Zhou, Guofu, 2007. "Optimal Portfolio Choice with Parameter Uncertainty," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 42(3), pages 621-656, September.
    13. Victor DeMiguel & Lorenzo Garlappi & Francisco J. Nogales & Raman Uppal, 2009. "A Generalized Approach to Portfolio Optimization: Improving Performance by Constraining Portfolio Norms," Management Science, INFORMS, vol. 55(5), pages 798-812, May.
    14. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
    15. Richard Bradley, 2007. "A Unified Bayesian Decision Theory," Theory and Decision, Springer, vol. 63(3), pages 233-263, November.
    16. MOSSIN, Jan, 1968. "Optimal multiperiod portfolio policies," LIDAM Reprints CORE 19, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    17. Kerry Back, 2010. "Martingale Pricing," Annual Review of Financial Economics, Annual Reviews, vol. 2(1), pages 235-250, December.
    18. 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.
    19. Bernd Scherer, 2006. "A note on the out-of-sample performance of resampled efficiency," Journal of Asset Management, Palgrave Macmillan, vol. 7(3), pages 170-178, September.
    20. Bernd Scherer, 2007. "Can robust portfolio optimisation help to build better portfolios?," Journal of Asset Management, Palgrave Macmillan, vol. 7(6), pages 374-387, March.
    21. Burton G. Malkiel, 2003. "The Efficient Market Hypothesis and Its Critics," Journal of Economic Perspectives, American Economic Association, vol. 17(1), pages 59-82, Winter.
    22. Ramsey, Frank P., 1926. "Truth and Probability," Histoy of Economic Thought Chapters, in: Braithwaite, R. B. (ed.),The Foundations of Mathematics and other Logical Essays, chapter 7, pages 156-198, McMaster University Archive for the History of Economic Thought.
    23. LeRoy, Stephen F, 1973. "Risk Aversion and the Martingale Property of Stock Prices," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 14(2), pages 436-446, June.
    24. Burton G. Malkiel, 2003. "The Efficient Market Hypothesis and Its Critics," Working Papers 111, Princeton University, Department of Economics, Center for Economic Policy Studies..
    25. Bade, Alexander & Frahm, Gabriel & Jaekel, Uwe, 2008. "A general approach to Bayesian portfolio optimization," Discussion Papers in Econometrics and Statistics 1/08, University of Cologne, Institute of Econometrics and Statistics.
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    Cited by:

    1. Gabriel Frahm, 2016. "Pricing And Valuation Under The Real-World Measure," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 19(01), pages 1-39, February.
    2. Gabriel Frahm, 2020. "Statistical properties of estimators for the log-optimal portfolio," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 92(1), pages 1-32, August.
    3. Mynbayeva, Elmira & Lamb, John D. & Zhao, Yuan, 2022. "Why estimation alone causes Markowitz portfolio selection to fail and what we might do about it," European Journal of Operational Research, Elsevier, vol. 301(2), pages 694-707.

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    More about this item

    Keywords

    Asset allocation; Mean–variance analysis; Noise trader; Out-of-sample performance; Portfolio resampling; Resampled efficiency; Signal trader; Primary G11; Secondary D81;
    All these keywords.

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty

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