Application Study of BP Neural Network on Stock Market Prediction
Aiming at the complexity of interior and variety of exterior structure of stock price system, this paper analyzes principles of stock prediction based on BP neural network, provides prediction model for stock market by utilizing three-layered feed forward neural networks, presents topology of network, principles of determining the number of hidden layers, selection and pretreatment of sample data and determination of preliminary parameters. In order to avoid local extremum and promote convergence speed, Levenberg-Marquardt BP algorithm has been adopted. Simulation experiment based on representative index from Shanghai stock exchange market, through training on selecting samples and prediction model, indicates that this algorithm can make efficient short-term prediction.
stock index prediction BP neural network number of hidden nodes strategy of sampling
Feng Li Cheng Liu
Economic Management Institute University of Science and Technology Beijing China, Beijing, 100083
国际会议
2009 Ninth International Conference on Hybrid Intelligent Systems(第九届混合智能系统国际会议 HIS 2009)
沈阳
英文
1-5
2009-08-12（万方平台首次上网日期，不代表论文的发表时间）