A hybrid combinatorial approach to a two-stage stochastic portfolio optimization model with uncertain asset prices

Cui, Tianxiang; Bai, Ruibin; Ding, Shusheng; Parkes, Andrew J.; Qu, Rong; He, Fang; Li, Jingpeng

Abstract

© 2019, Springer-Verlag GmbH Germany, part of Springer Nature. Portfolio optimization is one of the most important problems in the finance field. The traditional Markowitz mean-variance model is often unrealistic since it relies on the perfect market information. In this work, we propose a two-stage stochastic portfolio optimization model with a comprehensive set of real-world trading constraints to address this issue. Our model incorporates the market uncertainty in terms of future asset price scenarios based on asset return distributions stemming from the real market data. Compared with existing models, our model is more reliable since it encompasses real-world trading constraints and it adopts CVaR as the risk measure. Furthermore, our model is more practical because it could help investors to design their future investment strategies based on their future asset price expectations. In order to solve the proposed stochastic model, we develop a hybrid combinatorial approach, which integrates a hybrid algorithm and a linear programming (LP) solver for the problem with a large number of scenarios. The comparison of the computational results obtained with three different metaheuristic algorithms and with our hybrid approach shows the effectiveness of the latter. The superiority of our model is mainly embedded in solution quality. The results demonstrate that our model is capable of solving complex portfolio optimization problems with tremendous scenarios while maintaining high solution quality in a reasonable amount of time and it has outstanding practical investment implications, such as effective portfolio constructions.

Citation

Cui, T., Bai, R., Ding, S., Parkes, A. J., Qu, R., He, F., & Li, J. (2020). A hybrid combinatorial approach to a two-stage stochastic portfolio optimization model with uncertain asset prices. Soft Computing, 24, 2809–2831. https://doi.org/10.1007/s00500-019-04517-y

• Journal Article Type: Article
• Acceptance Date: Nov 19, 2019
• Online Publication Date: Nov 19, 2019
• Publication Date: 2020-02
• Deposit Date: Jan 9, 2020
• Publicly Available Date: Nov 20, 2020
• Journal: Soft Computing
• Print ISSN: 1432-7643
• Electronic ISSN: 1433-7479
• Publisher: Springer Verlag
• Peer Reviewed: Peer Reviewed
• Volume: 24
• Pages: 2809–2831
• DOI: https://doi.org/10.1007/s00500-019-04517-y
• Keywords: Theoretical Computer Science; Software; Geometry and Topology
• Public URL: https://nottingham-repository.worktribe.com/output/3440503
• Publisher URL: https://link.springer.com/article/10.1007%2Fs00500-019-04517-y
• Additional Information: First Online: 19 November 2019; : ; : The authors declare that they have no conflict of interest.