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A sparse chance constrained portfolio selection model with multiple constraints

Author

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  • Zhiping Chen

    (Xi’an Jiaotong University
    Xi’an International Academy for Mathematics and Mathematical Technology)

  • Shen Peng

    (Xi’an Jiaotong University
    Xi’an International Academy for Mathematics and Mathematical Technology
    Université Paris Sud)

  • Abdel Lisser

    (Université Paris Sud)

Abstract

This paper presents a general sparse portfolio selection model with expectation, chance and cardinality constraints. For the sparse portfolio selection model, we derive respectively the sample based reformulation and distributionally robust reformulation with mixture distribution based ambiguity set. These reformulations are mixed-integer programming problem and programming problem with difference of convex functions (DC), respectively. As an application of the general model and its reformulations, we consider the sparse enhanced indexation problem with multiple constraints. Empirical tests are conducted on the real data sets from major international stock markets. The results demonstrate that the proposed model, the reformulations and the solution method can efficiently solve the enhanced indexation problem and our approach can generally achieve sparse tracking portfolios with good out-of-sample excess returns and high robustness.

Suggested Citation

  • Zhiping Chen & Shen Peng & Abdel Lisser, 2020. "A sparse chance constrained portfolio selection model with multiple constraints," Journal of Global Optimization, Springer, vol. 77(4), pages 825-852, August.
    • Handle: RePEc:spr:jglopt:v:77:y:2020:i:4:d:10.1007_s10898-020-00901-3
    DOI: 10.1007/s10898-020-00901-3
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