Prediction of Stock Price Trend Based on Wavelet Neural Network and RS Attributes Reduction

DOI

10.2991/iceemr-17.2017.24How to use a DOI?

Keywords

Wavelet Neural Network, Rough Set, Stock Price, Prediction

Abstract

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.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Yanming Wei
AU  - Yuanwei Lou
AU  - Lei Lei
PY  - 2017/05
DA  - 2017/05
TI  - Prediction of Stock Price Trend Based on Wavelet Neural Network and RS Attributes Reduction
BT  - Proceedings of the 2017 International Conference on Education, Economics and Management Research (ICEEMR 2017)
PB  - Atlantis Press
SP  - 95
EP  - 98
SN  - 2352-5398
UR  - https://doi.org/10.2991/iceemr-17.2017.24
DO  - 10.2991/iceemr-17.2017.24
ID  - Wei2017/05
ER  -