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

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• 作者：Minghu Ha ; Yang Yang ; Chao Wang
• 关键词：Portfolio optimization model ; Soft margin ; based generalization bound ; Smoothed safety first ; Machine learning
• 刊名：Journal of Intelligent Manufacturing
• 出版年：2017
• 出版时间：March 2017
• 年：2017
• 卷：28
• 期：3
• 页码：759-766
• 全文大小：
• 刊物类别：Business and Economics
• 刊物主题：Production; Manufacturing, Machines, Tools; Control, Robotics, Mechatronics;
• 出版者：Springer US
• ISSN：1572-8145
• 卷排序：28

文摘

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.