Distributionally Robust Mean-CVaR Portfolio Optimization with Cardinality Constraint

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  • 1 School of Mathematical Sciences, Dalian University of Technology, Dalian 116024, Liaoning, China;
    2 Key Laboratory for Computational Mathematics and Data Intelligence of Liaoning Province, Dalian 116024, Liaoning, China

Received date: 2022-07-18

  Revised date: 2023-05-23

  Online published: 2026-03-16

Supported by

The research was partially supported by the Project of Huzhou Science and Technology (No.2023GZ68).

Abstract

For a mean-CVaR model with cardinality constraint, we consider the situation where the true distribution of underlying uncertainty is unknown. We develop a distributionally robust mean-CVaR model with cardinality constraint (DRMCC) and construct the ambiguity set by moment information. We propose a discretization approximation to the moment-based ambiguity set and present the stability analysis of the optimal values and optimal solutions of the resulting discrete optimization problems as the sample size increases. We reformulate the DRMCC model as a bilevel optimization problem. Moreover, we propose a modified bilevel cutting-plane algorithm to solve the DRMCC model. Finally, some preliminary numerical test results are reported. We give the in-sample performance and out-of-sample performance of the DRMCC model.

Cite this article

Shuang Wang, Li-Ping Pang, Shuai Wang, Hong-Wei Zhang . Distributionally Robust Mean-CVaR Portfolio Optimization with Cardinality Constraint[J]. Journal of the Operations Research Society of China, 2026 , 14(1) : 179 -209 . DOI: 10.1007/s40305-023-00512-1

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