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Robust Portfolio Optimisation with Multiple Experts

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  • Schotman, Peter C
  • Lutgens, Frank

Abstract

We consider mean-variance portfolio choice of a robust investor. The investor receives advice from J experts, each with a different prior for the distribution of returns. Confronted with these multiple priors the investor follows a min-max portfolio strategy. We study the structure of the robust mean-variance portfolio and empirically compare its performance with a variety of alternative portfolio strategies. The empirical tests are based on bootstrap simulations on the 25 Fama-French portfolios and on 81 European country and value portfolios. We find that the robust portfolio performs well in both settings. Robust portfolios do not exhibit the extreme weights typically observed in naive mean-variance portfolios. Robust portfolios are also better diversified than portfolios that impose short-sell constraints to suppress the symptoms of extreme weights.

Suggested Citation

  • Schotman, Peter C & Lutgens, Frank, 2007. "Robust Portfolio Optimisation with Multiple Experts," CEPR Discussion Papers 6161, C.E.P.R. Discussion Papers.
    • Handle: RePEc:cpr:ceprdp:6161
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    References listed on IDEAS

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    7. Lorenzo Garlappi & Raman Uppal & Tan Wang, 2007. "Portfolio Selection with Parameter and Model Uncertainty: A Multi-Prior Approach," The Review of Financial Studies, Society for Financial Studies, vol. 20(1), pages 41-81, January.
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    Cited by:

    1. 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.

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    More about this item

    Keywords

    Mean-variance; Model uncertainty; Portfolio choice;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • D80 - Microeconomics - - Information, Knowledge, and Uncertainty - - - General

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