Optimal asset allocation for outperforming a stochastic benchmark target

Chendi Ni, Yuying Li, Peter Forsyth and Ray Carroll

Quantitative Finance, 2022, vol. 22, issue 9, 1595-1626

Abstract: We propose a data-driven Neural Network (NN) optimization framework to determine the optimal multi-period dynamic asset allocation strategy for outperforming a general stochastic target. We formulate the problem as an optimal stochastic control with an asymmetric, distribution shaping, objective function. The proposed framework is illustrated with the asset allocation problem in the accumulation phase of a defined contribution pension plan, with the goal of achieving a higher terminal wealth than a stochastic benchmark. We demonstrate that the data-driven approach is capable of learning an adaptive asset allocation strategy directly from historical market returns, without assuming any parametric model of the financial market dynamics. The optimal adaptive strategy outperforms the benchmark constant proportion strategy, achieving a higher terminal wealth with a 90% probability, a 46% higher median terminal wealth, and a significantly more right-skewed terminal wealth distribution.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:taf:quantf:v:22:y:2022:i:9:p:1595-1626

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DOI: 10.1080/14697688.2022.2072233

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