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Sparse portfolio optimization via l1 over l2 regularization
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DOI：10.1016/j.ejor.2024.07.017.png)
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摘要
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Sparse portfolio optimization, which significantly boosts the out-of-sample performance of traditional mean-variance methods, is widely studied in the fields of optimization and financial economics. In this paper, we explore the l(1)/l(2) fractional regularization constructed by the ratio of the l(1) and l(2) norms on the mean-variance model to promote sparse portfolio selection. We present an l(1)/l(2) regularized sparse portfolio optimization model and provide financial insights regarding short positions and estimation errors. Then, we develop an efficient alternating direction method of multipliers (ADMM) method to solve it numerically. Due to the nonconvexity and noncoercivity of the l(1)/l(2) term, we give the convergence analysis for the proposed ADMM based on the nonconvex optimization framework. Furthermore, we discuss an extension of the model to incorporate a more general l(1)/l(q) regularization, where q>1. Moreover, we conduct numerical experiments on four stock datasets to demonstrate the effectiveness and superiority of the proposed model in promoting sparse portfolios while achieving the desired level of expected return.
Keyword:
Portfolio optimization
Sparse portfolio selection
Alternating direction method of multipliers
Convergence analysis
Portfolio optimization
Sparse portfolio selection
l(1)/l(2) regularization
Alternating direction method of multipliers
Convergence analysis
期刊
IF:
6
论文数:
2.2W
被引数:
6.4W
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