A portfolio optimization model for minimizing soft margin-based generalization bound

被引：1
|
作者：
Ha, Minghu [1 ,2 ]
Yang, Yang [1 ]
Wang, Chao [2 ]
机构：
[1] Hebei Univ, Coll Management, Baoding 071002, Peoples R China
[2] Hebei Univ Engn, Sch Econ & Management, Handan 056038, Peoples R China
来源：
JOURNAL OF INTELLIGENT MANUFACTURING | 2017年 / 28卷 / 03期
基金：
中国国家自然科学基金;
关键词：
Portfolio optimization model; Soft margin-based generalization bound; Smoothed safety first; Machine learning; SAFETY; 1ST; PERFORMANCE; PRINCIPLE; UTILITY;
D O I：
10.1007/s10845-014-1011-7
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Roy's safety first (RSF) criterion aims to minimize the shortfall probability in portfolio selection. Smoothed safety first portfolio optimization model is a useful tool to realize RSF criterion by minimizing an approximation of the empirical shortfall probability. However, the generalization performance of the smoothed safety first portfolio optimization model may be poor when the number of the samples is finite. In this paper, a soft margin-based generalization bound on the shortfall probability is obtained firstly. Then, a portfolio optimization model is built by minimizing the soft margin-based generalization bound. Finally, the good generalization performance of the portfolio optimization model is verified by experiments.
引用
收藏
页码：759 / 766
页数：8
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