Sparsity penalized mean-variance portfolio selection: analysis and computation

• 成果类型: Article
• 署名作者: Sen, Buse; Akkaya, Deniz; Pinar, Mustafa c.
• 署名单位: Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne; Ihsan Dogramaci Bilkent University
• 刊物名称: MATHEMATICAL PROGRAMMING
• ISSN/ISSBN: 0025-5610
• DOI: 10.1007/s10107-024-02161-5
• 发表日期: 2025
• 页码: 281-318
• 关键词: VARIABLE SELECTION; REGRESSION SHRINKAGE; optimization; regularization; approximation; formulations; relaxation; algorithms; view

摘要

We consider the problem of mean-variance portfolio selection regularized with an & ell;0\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\ell _0$$\end{document}-penalty term to control the sparsity of the portfolio. We analyze the structure of local and global minimizers and use our results in the design of a Branch-and-Bound algorithm coupled with an advanced start heuristic. Extensive computational results with real data as well as comparisons with an off-the-shelf and state-of-the-art (MIQP) solver are reported.