An Efficient Neural Network Model with Taylor Series-Based Data Pre-Processing for Stock Price Forecast

Abstract:

This study adopts popular back-propagation neural network to make one-period-ahead prediction of the stock price. A model based on Taylor series by using both fundamental and technical indicators EPS and MACD as input data is built for an empirical study. Leading Taiwanese companies in non-hi-tech industry such as Formosa Plastics, Yieh Phui Steel, Evergreen Marine, and Chang Hwa Bank are picked as targets to analyze their reasonable prices and moving trends. The performance of this model shows remarkable return and high accuracy in making long/short strategies.

• Periodical: Applied Mechanics and Materials (Volumes 284-287)
• Pages: 3020-3024
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.284-287.3020
• Online since: January 2013
• Authors: Jung Bin Li, Chien Ho Wu
• Keywords: BPN, Neural Network (NN), Price Forecast, Taylor Series

References

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