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

Marcos Escobar-Anel (), Lorenz Theilacker () and Rudi Zagst ()
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Marcos Escobar-Anel: University of Western Ontario
Lorenz Theilacker: Technical University of Munich
Rudi Zagst: Technical University of Munich
Authors registered in the RePEc Author Service: Marcos Escobar Anel ()

Decisions in Economics and Finance, 2023, vol. 46, issue 2, No 6, 505-542

Abstract: 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.

Keywords: Dynamic portfolio optimization; Expected utility theory; Neural network architecture; Financial factors (search for similar items in EconPapers)
JEL-codes: C58 C61 G11 (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10203-023-00388-z

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