A sparse chance constrained portfolio selection model with multiple constraints

Zhiping Chen (), Shen Peng () and Abdel Lisser ()
Additional contact information
Zhiping Chen: Xi’an Jiaotong University
Shen Peng: Xi’an Jiaotong University
Abdel Lisser: Université Paris Sud

Journal of Global Optimization, 2020, vol. 77, issue 4, No 6, 825-852

Abstract: 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.

Keywords: Portfolio selection; Chance constraint; Distributionally robust optimization; Cardinality constraint; Enhanced indexation; 90C15; 90C90; 91G10 (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (4)

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DOI: 10.1007/s10898-020-00901-3

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