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A Comparison Of Pre And Post Modern Portfolio Theory Using Resampling

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  • Giuseppe Galloppo

Abstract

This article introduces the Resampling approach to Portfolio modelling, targeted at reducing the effect of estimation error present in any practical implementation of a Portfolio Model. Resampling is a method used in portfolio modelling to try to obtain better out of sample performance for given input model parameters. In the real world, where the possibility of estimating errors for future model forecasts certainly exist, it is necessary to consider the error component in building portfolios. Resampling does this by recombining the input parameters required for a portfolio model. In this paper an application of Resampling is performed using a sample of equities from different stock markets. The results are presented for Tracking Error Minimization, Mean Absolute Deviation Minimization (MADM) and Shortfall Probability Minimization Models. The innovation in this study lies in the comparison made with different portfolio models. Unlike previous studies, the evidence shows that Resampling applied to the Markowitz model does not generate better out of sample performance. However, the benefits of Resampling applied to the Post Modern Theory model are remarkable.

Suggested Citation

  • Giuseppe Galloppo, 2010. "A Comparison Of Pre And Post Modern Portfolio Theory Using Resampling," Global Journal of Business Research, The Institute for Business and Finance Research, vol. 4(1), pages 1-16.
    • Handle: RePEc:ibf:gjbres:v:4:y:2010:i:1:p:1-16
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    References listed on IDEAS

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    1. Nadima El-Hassan & Paul Kofman, 2003. "Tracking Error and Active Portfolio Management," Australian Journal of Management, Australian School of Business, vol. 28(2), pages 183-207, September.
    2. Cesari, Riccardo & Cremonini, David, 2003. "Benchmarking, portfolio insurance and technical analysis: a Monte Carlo comparison of dynamic strategies of asset allocation," Journal of Economic Dynamics and Control, Elsevier, vol. 27(6), pages 987-1011, April.
    3. Gaivoronski, Alexei A. & Krylov, Sergiy & van der Wijst, Nico, 2005. "Optimal portfolio selection and dynamic benchmark tracking," European Journal of Operational Research, Elsevier, vol. 163(1), pages 115-131, May.
    4. Harry Markowitz, 1952. "Portfolio Selection," Journal of Finance, American Finance Association, vol. 7(1), pages 77-91, March.
    5. Ulrich Derigs & Nils-H. Nickel, 2004. "On a Local-Search Heuristic for a Class of Tracking Error Minimization Problems in Portfolio Management," Annals of Operations Research, Springer, vol. 131(1), pages 45-77, October.
    6. Ravi Jagannathan & Tongshu Ma, 2003. "Risk Reduction in Large Portfolios: Why Imposing the Wrong Constraints Helps," Journal of Finance, American Finance Association, vol. 58(4), pages 1651-1684, August.
    7. Rudolf, Markus & Wolter, Hans-Jurgen & Zimmermann, Heinz, 1999. "A linear model for tracking error minimization," Journal of Banking & Finance, Elsevier, vol. 23(1), pages 85-103, January.
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    Cited by:

    1. Duc Hong Vo, 2021. "Portfolio Optimization and Diversification in China: Policy Implications for Vietnam and Other Emerging Markets," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 57(1), pages 223-238, January.

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    More about this item

    Keywords

    Technical Analysis; Post Modern Portfolio Theory; Resampling;
    All these keywords.

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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