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Some new efficient mean–variance portfolio selection models

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成果类型:
期刊论文
作者:
Dai, Zhifeng*;Kang, Jie
通讯作者:
Dai, Zhifeng
作者机构:
[Dai, Zhifeng; Kang, Jie] Changsha Univ Sci & Technol, Dept Stat, Changsha 410114, Hunan, Peoples R China.
通讯机构:
[Dai, Zhifeng] C
Changsha Univ Sci & Technol, Dept Stat, Changsha 410114, Hunan, Peoples R China.
语种:
英文
关键词:
Mean–variance portfolio selection;L-1‐regularization;robust optimization;shrinkage method
期刊:
International Journal of Finance & Economics
ISSN:
1076-9307
年:
2022
卷:
27
期:
4
页码:
4784-4796
基金类别:
Hunan Provincial Education DepartmentHunan Provincial Education Department [19A007]; National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [71771030, 11303041]
机构署名:
本校为第一且通讯机构
摘要:
Abstract The poor out‐of‐sample performance of mean–variance portfolio model is mainly caused by estimation errors in the covariance matrix and the mean return, especially the mean return vector. Meanwhile, in financial practice, what most investors actually like is to hold a few stocks in their portfolio. The goal of this paper is to propose some new efficient mean–variance portfolio selection models by considering the following aspects: (i) use the L1‐regularization in objective function to obtain sparse portfolio; (ii) use the shrinkage...

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