PRINCIPAL COMPONENT ANALYSIS AND OPTIMAL PORTFOLIO

被引：0
|
作者：
Beliavsky G. [1 ]
Danilova N. [1 ]
Yao K. [1 ]
机构：
[1] Operations Research, Mathematics, Mechanics and Computer Sciences Institute of Southern Federal University, Milchakova St., 8a, Rostov-on-Don
来源：
Journal of Mathematical Sciences | 2023年 / 271卷 / 3期
关键词：
Mean-variance portfolio; Minimal variance portfolio; Online learning; Principal component;
D O I：
10.1007/s10958-023-06526-7
中图分类号：
学科分类号：
摘要：
In this paper, we try to apply the advantages of principal component analysis (PCA) and online learning to the calculation of optimal portfolio investments. Each of these methods is widely used separately. This is done in order to make it possible to use online learning algorithms for high-dimension portfolios. © 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG.
引用
收藏
页码：368 / 377
页数：9
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