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1/N and Long Run Optimal Portfolios: Results for Mixed Asset Menus

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  • Carolina Fugazza
  • Massimo Guidolin
  • Giovanna Nicodano

Abstract

Recent research [e.g., DeMiguel, Garlappi and Uppal, (2009a), Rev. Fin. Studies] has cast doubts on the out-of-sample performance of optimizing portfolio strategies relative to a naive, equally-weighted ones. However, most of the existing results concern the simple case in which an investor has a one-month horizon and mean-variance preferences. In this paper, we examine whether this finding holds for longer investment horizons, when the asset menu includes bonds and real estate beyond stocks and cash, and when the investor is characterized by constant relative risk aversion preferences which are not locally mean-variance for long horizons. Our experiments indicates that power utility investors with horizons of one year and longer would have on average benefited, ex-post, from an optimizing strategy that exploits simple linear predictability in asset returns over the period January 1995 - December 2007. This result is insensitive to the degree of risk aversion, to the number of predictors being included in the forecasting model, and to the deduction of transaction costs from measured portfolio performance.

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  • Carolina Fugazza & Massimo Guidolin & Giovanna Nicodano, 2010. "1/N and Long Run Optimal Portfolios: Results for Mixed Asset Menus," Carlo Alberto Notebooks 190, Collegio Carlo Alberto.
  • Handle: RePEc:cca:wpaper:190
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    Cited by:

    1. Fabio C. Bagliano & Carolina Fugazza & Giovanna Nicodano, 2014. "Optimal Life-Cycle Portfolios for Heterogeneous Workers," Review of Finance, European Finance Association, vol. 18(6), pages 2283-2323.
    2. Bianchi, Daniele & Guidolin, Massimo, 2014. "Can long-run dynamic optimal strategies outperform fixed-mix portfolios? Evidence from multiple data sets," European Journal of Operational Research, Elsevier, vol. 236(1), pages 160-176.
    3. Marat Molyboga & Seungho Baek & John F. O. Bilson, 2017. "Assessing hedge fund performance with institutional constraints: evidence from CTA funds," Journal of Asset Management, Palgrave Macmillan, vol. 18(7), pages 547-565, December.
    4. Jonathan Fletcher, 2011. "An Examination of Dynamic Trading Stategies in UK and US Stock Returns," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 38(9-10), pages 1290-1310, November.
    5. Alice A. Melkumian, 2012. "The Opportunity Cost of Holding a “Naive” Portfolio," Journal of Economic Insight, Missouri Valley Economic Association, vol. 38(1), pages 23-42.
    6. Rohit Malhorta, 2016. "Demystifying Optimal Welfare Weights Controversy From A Social Strategist Perspective," Journal of Social and Economic Statistics, Bucharest University of Economic Studies, vol. 5(2), pages 33-48, DECEMBER.
    7. Daniele Bianchi & Massimo Guidolin, 2014. "Can Linear Predictability Models Time Bull and Bear Real Estate Markets? Out-of-Sample Evidence from REIT Portfolios," The Journal of Real Estate Finance and Economics, Springer, vol. 49(1), pages 116-164, July.

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    More about this item

    Keywords

    equally weighted portfolios; long investment horizon; real-time strategic asset allocation; public real estate vehicles; ex post performance; predictability; parameter uncertainty;
    All these keywords.

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • L85 - Industrial Organization - - Industry Studies: Services - - - Real Estate Services

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