Share Email Print

Proceedings Paper

Stock price forecasting using secondary self-regression model and wavelet neural networks
Author(s): Chi-I Yang; Kai-Cheng Wang; Kuei-Fang Chang
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

We have established a DWT-based secondary self-regression model (AR(2)) to forecast stock value. This method requires the user to decide upon the trend of the stock prices. We later used WNN to forecast stock prices which does not require the user to decide upon the trend. When comparing these two methods, we could see that AR(2) does not perform as well if there are no trends for the stock prices. On the other hand, WNN would not be influenced by the presence of trends.

Paper Details

Date Published: 6 July 2015
PDF: 8 pages
Proc. SPIE 9631, Seventh International Conference on Digital Image Processing (ICDIP 2015), 96312K (6 July 2015); doi: 10.1117/12.2196914
Show Author Affiliations
Chi-I Yang, Feng-Chia Univ. (Taiwan)
Kai-Cheng Wang, Feng-Chia Univ. (Taiwan)
Kuei-Fang Chang, Feng-Chia Univ. (Taiwan)

Published in SPIE Proceedings Vol. 9631:
Seventh International Conference on Digital Image Processing (ICDIP 2015)
Charles M. Falco; Xudong Jiang, Editor(s)

© SPIE. Terms of Use
Back to Top