Fast algorithms for sparse portfolio selection considering industries and investment styles

doi:10.1007/s10898-020-00911-1

CSpace

	Fast algorithms for sparse portfolio selection considering industries and investment styles
	Dong, Zhi-Long 1; Xu, Fengmin 1; Dai, Yu-Hong 2
	2020-05-25
发表期刊	JOURNAL OF GLOBAL OPTIMIZATION
ISSN	0925-5001
页码	27
摘要	In this paper, we consider a large scale portfolio selection problem with and without a sparsity constraint. Neutral constraints on industries are included as well as investment styles. To develop fast algorithms for the use in the real financial market, we shall expose the special structure of the problem, whose Hessian is the summation of a diagonal matrix and a low rank modification. Specifically, an interior point algorithm taking use of the Sherman-Morrison-Woodbury formula is designed to solve the problem without any sparsity constraint. The complexity in each iteration of the proposed algorithm is shown to be linear with the problem dimension. In the occurrence of a sparsity constraint, we propose an efficient three-block alternating direction method of multipliers, whose subproblems are easy to solve. Extensive numerical experiments are conducted, which demonstrate the efficiency of the proposed algorithms compared with some state-of-the-art solvers.
关键词	Portfolio selection Industry classification Style investment ADMM Sparse optimization
DOI	10.1007/s10898-020-00911-1
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[11631013] ; National Natural Science Foundation of China[11991021] ; National Natural Science Foundation of China[11991020] ; National Natural Science Foundation of China[11971372] ; National Natural Science Foundation of China[11801433] ; National Natural Science Foundation of China[71501155] ; National Natural Science Foundation of China[11571271] ; Beijing Academy of Artificial Intelligence (BAAI)
WOS研究方向	Operations Research & Management Science ; Mathematics
WOS类目	Operations Research & Management Science ; Mathematics, Applied
WOS记录号	WOS:000535168700001
出版者	SPRINGER
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/51480
专题	中国科学院数学与系统科学研究院
通讯作者	Xu, Fengmin
作者单位	1.Xi An Jiao Tong Univ, Sch Econ & Finance, Xian 710061, Shaanxi, Peoples R China 2.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Dong, Zhi-Long,Xu, Fengmin,Dai, Yu-Hong. Fast algorithms for sparse portfolio selection considering industries and investment styles[J]. JOURNAL OF GLOBAL OPTIMIZATION,2020:27.
APA	Dong, Zhi-Long,Xu, Fengmin,&Dai, Yu-Hong.(2020).Fast algorithms for sparse portfolio selection considering industries and investment styles.JOURNAL OF GLOBAL OPTIMIZATION,27.
MLA	Dong, Zhi-Long,et al."Fast algorithms for sparse portfolio selection considering industries and investment styles".JOURNAL OF GLOBAL OPTIMIZATION (2020):27.