Evolutionary multi-objective optimisation for large-scale portfolio selection with both random and uncertain returns

Weilong Liu, Yong Zhang, Kailong Liu, Barry Quinn, Xingyu Yang and Qiao Peng

No 2023/02, QBS Working Paper Series from Queen's University Belfast, Queen's Business School

Abstract: With the advent of Big Data, managing large-scale portfolios of thousands of securities is one of the most challenging tasks in the asset management industry. This study uses an evolutionary multi objective technique to solve large-scale portfolio optimisation problems with both long-term listed and newly listed securities. The future returns of long-term listed securities are defined as random variables whose probability distributions are estimated based on sufficient historical data, while the returns of newly listed securities are defined as uncertain variables whose uncertainty distribution are estimated based on experts' knowledge. Our approach defines security returns as theoretically uncertain random variables and proposes a three-moment optimisation model with practical trading constraints. In this study, a framework for applying arbitrary multi-objective evolutionary algorithms to portfolio optimisation is established, and a novel evolutionary algorithm based on large-scale optimisation techniques is developed to solve the proposed model. The experimental results show that the proposed algorithm outperforms state-of-the-art evolutionary algorithms in large-scale portfolio optimisation.

Keywords: Evolutionary computations; Portfolio optimisation; Large-scale investment; Uncertain random variable; Multi-objective optimisation (search for similar items in EconPapers)
Date: 2023
New Economics Papers: this item is included in nep-cmp and nep-rmg
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:qmsrps:202302

DOI: 10.2139/ssrn.4376779

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