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Revisiting the 1/N-strategy: a neural network framework for optimal strategies

Author

Listed:
  • Marcos Escobar-Anel

    (University of Western Ontario)

  • Lorenz Theilacker

    (Technical University of Munich)

  • Rudi Zagst

    (Technical University of Munich)

Abstract

This work has two main objectives. First, we design a data-driven neural network approach to portfolio optimization within expected utility theory. The methodology is inspired by Li and Forsyth (Insur Math Econ 86:189–204, 2019. https://doi.org/10.1016/j.insmatheco.2019.03.001 ), who worked on target based defined contribution plans. Our proposal and the architecture of the model is flexible enough to address a variety of specific portfolio problems, from standard optimal utility allocation with constraints, to optimal deviations from a benchmark. Using the celebrated 1/N-Strategy (see DeMiguel et al. in Rev Financ Stud 22(5):1915–1953, 2007. https://doi.org/10.1093/rfs/hhm075 ) as benchmark constitutes the second objective of the paper. We consider two assets on a single path of historical return data for an investor whose utility is represented by a constant relative risk aversion function. Across several levels of risk aversion, we revisit the literature claims that it is essentially impossible to significantly outperform 1/N. Using our advanced method, we confirm that this is only true for high levels of risk aversion, but the 1/N can be consistently outperformed for low and moderate risk aversion levels.

Suggested Citation

  • Marcos Escobar-Anel & Lorenz Theilacker & Rudi Zagst, 2023. "Revisiting the 1/N-strategy: a neural network framework for optimal strategies," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 46(2), pages 505-542, December.
    • Handle: RePEc:spr:decfin:v:46:y:2023:i:2:d:10.1007_s10203-023-00388-z
    DOI: 10.1007/s10203-023-00388-z
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    References listed on IDEAS

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    More about this item

    Keywords

    Dynamic portfolio optimization; Expected utility theory; Neural network architecture; Financial factors;
    All these keywords.

    JEL classification:

    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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