文献类型：会议论文

英文题名：Prediction of Stock Price Trend Based on Wavelet Neural Network and RS Attributes Reduction

作者：Wei Yanming[1];Lou Yuanwei[2];Lei Lei[3]

第一作者：Wei Yanming

通讯作者：Wei, YM[1]

机构：[1]XiJing Coll, Xian 710123, Shaanxi, Peoples R China;[2]Air Force Engn Univ, Air Traff Control & Nav Coll, Xian 710051, Shaanxi, Peoples R China;[3]Henan Univ Econ & Law, Sch Business Adm, Zhengzhou 050046, Henan, Peoples R China

第一机构：XiJing Coll, Xian 710123, Shaanxi, Peoples R China

通讯机构：[1]corresponding author), XiJing Coll, Xian 710123, Shaanxi, Peoples R China.

会议论文集：International Conference on Education, Economics and Management Research (ICEEMR)

会议日期：MAY 29-31, 2017

会议地点：Singapore, SINGAPORE

语种：英文

外文关键词：Wavelet Neural Network; Rough Set; Stock Price; Prediction

摘要：For the prediction problem of stock price, a Wavelet Neural Network (WNN) method based on Rough Set (RS) attribute reduction is proposed. First RS attribute reduction is applied to reduce the dimensions of feature index for stock price trend, then based on RS attribute reduction the structure of WNN is optimized to establish the prediction model of stock price trend on the basis of feature index reduction, finally the built model is applied to predict the stock price trend. The simulation results indicate that, by introducing the RS attributes reduction, the structure of WNN model can be simplified to a great extent for stock price trend with improvement of the performance. The direction symmetry of prediction corresponding to SSE Composite Index is 65.75% with 1.7s training time. The prediction result is better than that of other neural network and WNN models. This verifies the feasibility and effectiveness of the method in the prediction of stock price trend.