文献类型：期刊文献

英文题名：Wavelet Neural Network Prediction Method of Stock Price Trend Based on Rough Set Attribute Reduction

作者：Lei, Lei[1]

第一作者：雷蕾

通讯作者：Lei, L[1]

机构：[1]Henan Univ Econ & Law, Sch Business Adm, Zhengzhou 450046, Henan, Peoples R China

第一机构：河南财经政法大学工商管理学院

通讯机构：[1]corresponding author), Henan Univ Econ & Law, Sch Business Adm, Zhengzhou 450046, Henan, Peoples R China.|[104843]河南财经政法大学工商管理学院;[10484]河南财经政法大学;

年份：2018

卷号：62

起止页码：923-932

外文期刊名：APPLIED SOFT COMPUTING

收录：;EI(收录号：20174304305080);Scopus(收录号：2-s2.0-85038233272);WOS:【SCI-EXPANDED(收录号:WOS:000418333500061)】；

基金：The author gratefully acknowledges supports of the National Social Science Foundation of China (Grant No. 16CJY060).

语种：英文

外文关键词：Wavelet Neural Network; Rough Set; Attribute reduction; Stock price; Predictiona

摘要：To improve the prediction capacity of stock price trend, an integrated prediction method is proposed based on Rough Set (RS) and Wavelet Neural Network (WNN). RS is firstly introduced to reduce the feature dimensions of stock price trend. On this basis, RS is used again to determine the structure of WNN, and to obtain the prediction model of stock price trend. Finally, the model is applied to prediction of stock price trend. The simulation results indicate that, through RS attribute reduction, the structure of WNN prediction model can be simplified significantly with the improvement of model performance. The directional symmetry values of prediction, corresponding to SSE Composite Index, CSI 300 Index, All Ordinaries Index, Nikkei 225 Index and Dow Jones Index, are 65.75%, 66.37%, 65.97%, 65.52% and 66.75%, respectively. The prediction results are better than those obtained by other neural networks, SVM, WNN and RS- WNN, which verifies the feasibility and effectiveness of the proposed method of predicting stock price trend. (C) 2017 Elsevier B.V. All rights reserved.