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

被引：0
|
作者：
Minghu Ha
Yang Yang
Chao Wang
机构：
[1] Hebei University,College of Management
[2] Hebei University of Engineering,School of Economics and Management
来源：
Journal of Intelligent Manufacturing | 2017年 / 28卷
关键词：
Portfolio optimization model; Soft margin-based generalization bound; Smoothed safety first; Machine learning;
D O I：
暂无
中图分类号：
学科分类号：
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.
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页码：759 / 766
页数：7
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