MATEC Web Conf.
Volume 119, 2017The Fifth International Multi-Conference on Engineering and Technology Innovation 2016 (IMETI 2016)
|Number of page(s)||12|
|Published online||04 August 2017|
Stock prices forecasting based on wavelet neural networks with PSO
1 Ph.D. Program in Mechanical and Aeronautical Engineering, Feng-Chia University, Taichung 40724, Taiwan
2 Department of Applied Mathematics, Feng-Chia University, Taichung 40724, Taiwan
a Corresponding author : email@example.com
This research examines the forecasting performance of wavelet neural network (WNN) model using published stock data obtained from Financial Times Stock Exchange (FTSE) Taiwan Stock Exchange (TWSE) 50 index, also known as Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX), hereinafter referred to as Taiwan 50. Our WNN model uses particle swarm optimization (PSO) to choose the appropriate initial network values for different companies. The findings come with two advantages. First, the network initial values are automatically selected instead of being a constant. Second, threshold and training data percentage become constant values, because PSO assists with self-adjustment. We can achieve a success rate over 73% without the necessity to manually adjust parameter or create another math model.
© The Authors, published by EDP Sciences, 2017
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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