经济数学2017，Vol.34Issue(1)：11-17,7.
基于高斯核支持向量机和遗传算法的优化组合研究
Optimal Portfolio Research with Gaussian Kernel Support Vector Machine and Genetic Algorithm
马静 1李星野 1徐荣1
作者信息
- 1. 上海理工大学 管理学院 数量经济学专业, 上海200093
- 折叠
摘要
Abstract
Based on the Gaussian kernel support vector machine,a portfolio was formed in Shanghai stock market and was compared with the Back propagation neural network and Generalized regression neural network by the relative error figure and some evaluations.The results indicate that support vector machine has the added advantage at stock prediction.The daily price data from January 2008 to December 2015 were used to illustrate the application of support vector machine and genetic algorithm to construct the optimal portfolio in Shanghai stock market, which made a contrast with the benchmark of Shanghai composite index.The results turn out that genetic algorithm optimizes the investment portfolio, and the hybrid model is more effective than a single model.关键词
机器学习/高斯核支持向量机/遗传算法/投资组合Key words
machine learning/Gaussian kernel support vector machine/genetic algorithm/optimal portfolio分类
管理科学引用本文复制引用
马静,李星野,徐荣..基于高斯核支持向量机和遗传算法的优化组合研究[J].经济数学,2017,34(1):11-17,7.
