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Title: A linear programming model for selection of sparse high-dimensional multiperiod portfolios
Authors: Pun, Chi Seng
Wong, Hoi Ying
Keywords: Investment Analysis
High-dimensional Portfolio Selection
Issue Date: 2019
Source: Pun, C. S., & Wong, H. Y. (2019). A linear programming model for selection of sparse high-dimensional multiperiod portfolios. European Journal of Operational Research, 273(2), 754-771. doi:10.1016/j.ejor.2018.08.025
Series/Report no.: European Journal of Operational Research
Abstract: This paper studies the mean-variance (MV) portfolio problems under static and dynamic settings, particularly for the case in which the number of assets (p) is larger than the number of observations (n). We prove that the classical plug-in estimation seriously distorts the optimal MV portfolio in the sense that the probability of the plug-in portfolio outperforming the bank deposit tends to 50% for p ≫ n and a large n. We investigate a constrained ℓ1 minimization approach to directly estimate effective parameters that appear in the optimal portfolio solution. The proposed estimator is implemented efficiently with linear programming, and the resulting portfolio is called the linear programming optimal (LPO) portfolio. We derive the consistency and the rate of convergence for LPO portfolios. The LPO procedure essentially filters out unfavorable assets based on the MV criterion, resulting in a sparse portfolio. The advantages of the LPO portfolio include its computational superiority and its applicability for dynamic settings and non-Gaussian distributions of asset returns. Simulation studies validate the theory and illustrate its finite-sample properties. Empirical studies show that the LPO portfolios outperform the equally weighted portfolio and the estimated optimal portfolios using shrinkage and other competitive estimators.
ISSN: 0377-2217
Rights: © 2019 Elsevier. This is the author created version of a work that has been peer reviewed and accepted for publication by European Journal of Operational Research, Elsevier. It incorporates referee’s comments but changes resulting from the publishing process, such as copyediting, structural formatting, may not be reflected in this document. The published version is available at: [].
Fulltext Permission: embargo_20210308
Fulltext Availability: With Fulltext
Appears in Collections:SPMS Journal Articles

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