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Fang, Y. and Fataliyev, K. (2014) Improving the Genetic-Algorithm-Optimized Wavelet Neural Network for Stock Market Prediction. 2014 International Joint Conference on Neural Networks (IJCNN), Beijing, 6-11 July 2014, 3038- 3042.

has been cited by the following article:

• 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.