Title: Forecasting opening stock prices by integrating signal processing and the artificial neural network: an exploratory study
Authors: Queen E. Booker; Carl. M. Rebman
Addresses: College of Business Administration, Minnesota State University, Mankato, 150 Morris Hall, Mankato, MN 56001, USA ' College of Business Administration, University of San Diego, Alcala West, Coronado 212, San Diego, CA 92110, USA ' College of Business Administration, Ball State University, Whitinger Business Building, Room 203, 2000 W. University Ave., Muncie, IN 47306, USA
Abstract: This study applies the use of an integrated signal processing technique with artificial neural network to evaluate its effectiveness in predicting individual stock pricing. The technique was previously used to predict stock market behaviour with an effective average prediction accuracy of 96.7%. We apply it to individual stocks to determine whether the model has similar predictive accuracy for individual stocks and to determine whether the accuracy matters based on the longevity of the stock availability. Archived data of Kohls, JCPenneys, Apple and Blackberry were used for training and testing the proposed model. The results strongly support the effectiveness of the proposed model with an overall average prediction accuracy of 79%. This accuracy suggests that with additional study, a better model may be possible. This exploratory study expands current forecasting research by applying a recent stock market forecasting method to individual stock price forecasting.
Keywords: artificial neural networks; ANNs; financial modelling; signal processing; stock price forecasting; opening stock prices; stock market behaviour; stock markets; individual stocks.
International Journal of Services and Standards, 2016 Vol.11 No.3, pp.261 - 274
Received: 21 Apr 2016
Accepted: 27 Jun 2016
Published online: 30 Oct 2016 *