Stock price forecasting using secondary self-regression model and wavelet neural networks

Chi-I Yang; Kai-Cheng Wang; Kuei-Fang Chang

doi:10.1117/12.2196914

6 July 2015 Stock price forecasting using secondary self-regression model and wavelet neural networks

Chi-I Yang, Kai-Cheng Wang, Kuei-Fang Chang

Proceedings Volume 9631, Seventh International Conference on Digital Image Processing (ICDIP 2015); 96312K (2015) https://doi.org/10.1117/12.2196914
Event: Seventh International Conference on Digital Image Processing (ICDIP15), 2015, Los Angeles, United States

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.

Citation Download Citation

Chi-I Yang, Kai-Cheng Wang, and Kuei-Fang Chang "Stock price forecasting using secondary self-regression model and wavelet neural networks", Proc. SPIE 9631, Seventh International Conference on Digital Image Processing (ICDIP 2015), 96312K (6 July 2015); https://doi.org/10.1117/12.2196914

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