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Optimal granularity for portfolio choice

Author

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  • Branger, Nicole
  • Lučivjanská, Katarína
  • Weissensteiner, Alex

Abstract

Many optimization-based portfolio rules fail to beat the simple 1/N rule out-of-sample because of parameter uncertainty. In this paper we suggest a grouping strategy in which we first form groups of equally weighted stocks and then optimize over the resulting groups only. This strategy aims at balancing the trade-off between the benefits from optimization and the losses from estimation risk. We rely on Monte-Carlo simulations to illustrate the performance of the strategy, and we derive the optimal group size for a simplified setup. Furthermore, we show that estimation risk also has an impact via the criterion by which the assets are sorted into groups (like the expected excess returns or betas), but does not negate the grouping approach. We relate our work to linear asset pricing models, and we conduct out of sample back-tests in order to confirm the validity of our grouping strategy empirically.

Suggested Citation

  • Branger, Nicole & Lučivjanská, Katarína & Weissensteiner, Alex, 2019. "Optimal granularity for portfolio choice," Journal of Empirical Finance, Elsevier, vol. 50(C), pages 125-146.
    • Handle: RePEc:eee:empfin:v:50:y:2019:i:c:p:125-146
    DOI: 10.1016/j.jempfin.2019.01.005
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    References listed on IDEAS

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    Cited by:

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    2. Thomas Conlon & John Cotter & Iason Kynigakis, 2021. "Machine Learning and Factor-Based Portfolio Optimization," Working Papers 202111, Geary Institute, University College Dublin.
    3. Azra Zaimovic & Adna Omanovic & Almira Arnaut-Berilo, 2021. "How Many Stocks Are Sufficient for Equity Portfolio Diversification? A Review of the Literature," JRFM, MDPI, vol. 14(11), pages 1-30, November.
    4. Lassance, Nathan & Vanderveken, Rodolphe & Vrins, Frédéric, 2022. "On the optimal combination of naive and mean-variance portfolio strategies," LIDAM Discussion Papers LFIN 2022006, Université catholique de Louvain, Louvain Finance (LFIN).
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    6. Andrea Rigamonti & Alex Weissensteiner, 2020. "Asset allocation under predictability and parameter uncertainty using LASSO," Computational Management Science, Springer, vol. 17(2), pages 179-201, June.
    7. Lassance, Nathan, 2021. "Maximizing the Out-of-Sample Sharpe Ratio," LIDAM Discussion Papers LFIN 2021013, Université catholique de Louvain, Louvain Finance (LFIN).

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    More about this item

    Keywords

    Mean–variance optimization; 1/N rule; Parameter uncertainty; Optimal portfolio granularity; Linear asset pricing;
    All these keywords.

    JEL classification:

    • G1 - Financial Economics - - General Financial Markets
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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