TITLE:
The Prediction of Stock Price Based on Improved Wavelet Neural Network
AUTHORS:
Qinglan Ye, Lianxin Wei
KEYWORDS:
WNN, Forecasting Stock Prices, Momentum, Learning Rate, Self-Adaptive
JOURNAL NAME:
Open Journal of Applied Sciences,
Vol.5 No.4,
April
15,
2015
ABSTRACT: To improve the accuracy of forecasting stock prices, a new method is proposed, which based on improved Wavelet Neural Network (WNN). Firstly, the Genetic Algorithm (GA) is used to optimize initial weights, stretching parameters and movement parameters. Then, comparing with traditional WNN, the momentum are added in parameters adjusting and learning of network, what’s more, learning rate and the factor of momentum are self-adaptive. The prediction system is tested using Shanghai Index data, simulation result shows that improved WNN performs very well.