摘要
本文遵循价值投资理念,建立基于支持向量机的股票投资价值分类模型。首先随机抽取500支A股股票作为样本,并选取对股票投资价值影响显著的财务指标构造样本特征集,然后采用支持向量机方法建立股票投资价值分类模型,最后将其与BP神经网络和RBF神经网络相比较,结果表明支持向量机的分类效果和泛化能力最优。
According to the idea of value investment, the classification model of stock investment value was built based on SVM （ Support Vector Machines） in this paper. 500 A - share stocks was randomly selected as sample firstly, and the financial data which distinct influent the stock investment value was selected to construct the sample feature set. Then the classification model of stock investment value was built based on SVM, the comparison of BP neural network and RBF neural network was proposed in the end. The results showed the classification effect and generalization capability was the best for the SVM.
出处
《中国软科学》
CSSCI
北大核心
2008年第1期135-140,共6页
China Soft Science
基金
国家自然科学基金项目(70573030)
