Cardinality Constrained Portfolio Optimization via Alternating Direction Method of Multipliers

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Detail(s)

Original languageEnglish
Number of pages9
Journal / PublicationIEEE Transactions on Neural Networks and Learning Systems
Online published27 Jul 2022
Publication statusOnline published - 27 Jul 2022

Abstract

Inspired by sparse learning, the Markowitz mean-variance model with a sparse regularization term is popularly used in sparse portfolio optimization. However, in penalty-based portfolio optimization algorithms, the cardinality level of the resultant portfolio relies on the choice of the regularization parameter. This brief formulates the mean-variance model as a cardinality (ℓ0-norm) constrained nonconvex optimization problem, in which we can explicitly specify the number of assets in the portfolio. We then use the alternating direction method of multipliers (ADMMs) concept to develop an algorithm to solve the constrained nonconvex problem. Unlike some existing algorithms, the proposed algorithm can explicitly control the portfolio cardinality. In addition, the dynamic behavior of the proposed algorithm is derived. Numerical results on four real-world datasets demonstrate the superiority of our approach over several state-of-the-art algorithms.

Research Area(s)

  • ℓ0-norm, alternating direction method of multipliers (ADMMs), Convex functions, Covariance matrices, Heuristic algorithms, Indexes, mean-variance model, Neural networks, Optimization, Portfolios, sparse portfolio