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Cardinality-constrained distributionally robust portfolio optimization
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DOI：10.1016/j.ejor.2023.01.037.png)
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Abstract
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This paper studies a distributionally robust portfolio optimization model with a cardinality constraint for limiting the number of invested assets. We formulate this model as a mixed-integer semidefinite optimization (MISDO) problem by means of the moment-based ambiguity set of probability distributions of asset returns. To exactly solve large-scale problems, we propose a specialized cutting-plane algorithm that is based on bilevel optimization reformulation. We prove the finite convergence of the algorithm. We also apply a matrix completion technique to lower-level SDO problems to make their problem sizes much smaller. Numerical experiments demonstrate that our cutting-plane algorithm is significantly faster than the state-of-the-art MISDO solver SCIP-SDP. We also show that our portfolio optimization model can achieve good investment performance compared with the conventional robust optimization model based on the ellipsoidal uncertainty set.(c) 2023 Elsevier B.V. All rights reserved.
Keywords:
Portfolio optimization
Mixed-integer semidefinite optimization
Distributionally robust optimization
Cutting-plane algorithm
Matrix completion
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