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Characteristics-based portfolio choice with leverage constraints

Author

Listed:
  • Manuel Ammann

    (HSG - University of St.Gallen)

  • Guillaume Coqueret

    (Groupe Sup de Co Montpellier (GSCM) - Montpellier Business School)

  • Jan-Philip Schade

    (HSG - University of St.Gallen)

Abstract

We show that the introduction of a leverage constraint improves the practical implementation of characteristics-based portfolios. The addition of the constraint leads to significantly lower transaction costs, to a reduction of negative portfolio weights, and to a decrease in volatility and misspecification risk. Furthermore, it allows investors to implement any desired level of leverage. In this study, we include 12 characteristics, thereby extending the classical size, book-to-market and momentum paradigm. We report several key indicators such as the proportion of negative weights, Sharpe ratio, volatility, transaction costs, the transaction cost-adjusted certainty equivalent returns, and the Herfindahl–Hirschman index. Analyzing the sensitivity of these key indicators to the choice of multiple combinations of the 12 characteristics, to risk aversion, and to estimation sample size, we show that constrained policies are much less sensitive to these parameters than their unconstrained counterparts. Finally, for quadratic utility, we derive a semi-closed analytical form for the portfolio weights. Overall, we provide a comprehensive extension of characteristics-based portfolio choice and contribute to a better understanding and implementation of the allocation process.

Suggested Citation

  • Manuel Ammann & Guillaume Coqueret & Jan-Philip Schade, 2016. "Characteristics-based portfolio choice with leverage constraints," Post-Print hal-02312221, HAL.
    • Handle: RePEc:hal:journl:hal-02312221
    DOI: 10.1016/j.jbankfin.2016.04.019
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    Citations

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    Cited by:

    1. Tony Guida & Guillaume Coqueret, 2019. "Ensemble Learning Applied to Quant Equity: Gradient Boosting in a Multifactor Framework," Post-Print hal-02311104, HAL.
    2. Guillaume Coqueret & Tony Guida, 2020. "Training trees on tails with applications to portfolio choice," Annals of Operations Research, Springer, vol. 288(1), pages 181-221, May.
    3. Eric Andr'e & Guillaume Coqueret, 2020. "Dirichlet policies for reinforced factor portfolios," Papers 2011.05381, arXiv.org, revised Jun 2021.
    4. Simon, Frederik & Weibels, Sebastian & Zimmermann, Tom, 2023. "Deep parametric portfolio policies," CFR Working Papers 23-01, University of Cologne, Centre for Financial Research (CFR).
    5. Guillaume Chevalier & Guillaume Coqueret & Thomas Raffinot, 2022. "Supervised portfolios," Post-Print hal-04144588, HAL.
    6. Mengting Li & Qifa Xu & Cuixia Jiang & Qinna Zhao, 2023. "The role of tail network topological characteristic in portfolio selection: A TNA‐PMC model," International Review of Finance, International Review of Finance Ltd., vol. 23(1), pages 37-57, March.
    7. Gehrig, Thomas & Sögner, Leopold & Westerkamp, Arne, 2018. "Making Parametric Portfolio Policies Work," CEPR Discussion Papers 13193, C.E.P.R. Discussion Papers.
    8. Guillaume Coqueret & Tony Guida, 2020. "Training trees on tails with applications to portfolio choice," Post-Print hal-04144665, HAL.
    9. Guillaume Coqueret, 2022. "Characteristics-driven returns in equilibrium," Papers 2203.07865, arXiv.org.
    10. Jiang, Chonghui & Du, Jiangze & An, Yunbi & Zhang, Jinqing, 2021. "Factor tracking: A new smart beta strategy that outperforms naïve diversification," Economic Modelling, Elsevier, vol. 96(C), pages 396-408.

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