The Prediction of Stock Price Based on Improved Wavelet Neural Network
Affiliation(s)
Department of Applied Mathematics, College of Science, University of Shanghai for Science and Technology, Shanghai, China.
Department of Applied Mathematics, College of Science, University of Shanghai for Science and Technology, Shanghai, China.
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.
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.
Cite this paper
Ye, Q. and Wei, L. (2015) The Prediction of Stock Price Based on Improved Wavelet Neural Network. Open Journal of Applied Sciences, 5, 115-120. doi: 10.4236/ojapps.2015.54012.
Ye, Q. and Wei, L. (2015) The Prediction of Stock Price Based on Improved Wavelet Neural Network. Open Journal of Applied Sciences, 5, 115-120. doi: 10.4236/ojapps.2015.54012.
References
[1] Gupta, S. and Wang, L.P. (2010) Stock Forecasting with Feed forward Neural Networks and Gradual Data Sub-Sampling. Journal of Intelligent Information Systems, 11. [2] Zhu, M. and Wang, L.P. (2010) Intelligent Trading Using Support Vector Regression and Multilayer Perceptrons Optimized with Genetic Algorithms. The 2010 International Joint Conference on Neural Networks (IJCNN), Barcelona, 18-23 July 2010, 1-5. [3] Cai, Z.X., Wu, C.H. and Zhang, D.X. (2002) Network Simulation FTP Business Prospect of Mathematical Modeling and Simulation Flow. Computer Simulation, 19, 104-106. [4] Yu, G.Q. and Zhang, C.S. (2004) Switching ARIMA Model Based Forecasting for Traffic Flow. ICASSP, 2, 429-432. [5] Vapnik, V.N. (1995) The Nature of Statistical Learning Theory. Springer-Verlag, New York. [6] Donoho, D.L. and Johnstone, I.M. (1994) Ideal Spatial Adaptation by Wavelet Shrinkage. Biometrika, 3, 425-455. [7] Zhao, Y., Zhang, Y. and Qi, C.J. (2008) Prediction Model of Stock Market Returns Based on Wavelet Neural Network. Computational Intelligence and Industrial Application, 1, 31-36.
http://dx.doi.org/10.1109/PACIIA.2008.46 [8] Oussar, Y. and Dreyfus, G.H. (2000) Initialization by Selection for Wavelet Network Training. Neurocomputing, 34, 131-143.
http://dx.doi.org/10.1016/S0925-2312(00)00295-2 [9] Donoho, D.L. (1995) Denoising by Soft-Thresholding. IEEE Transaction on Information, 3, 613-627. [10] Xu, Q. and Shu, L.C. (2010) Application of Optimized Wavelet Neural Network Based on Genetic Algorithm in Ground- water Level Prediction. Hydrology, 1, 27-30. [11] Zhou, H.R. and Wei, Y.H. (2010) Stocks Market Modeling and Forecasting Based on HGA and Wavelet Neural Networks. Natural Computation, 2, 620-625. [12] 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.
[1] Gupta, S. and Wang, L.P. (2010) Stock Forecasting with Feed forward Neural Networks and Gradual Data Sub-Sampling. Journal of Intelligent Information Systems, 11. [2] Zhu, M. and Wang, L.P. (2010) Intelligent Trading Using Support Vector Regression and Multilayer Perceptrons Optimized with Genetic Algorithms. The 2010 International Joint Conference on Neural Networks (IJCNN), Barcelona, 18-23 July 2010, 1-5. [3] Cai, Z.X., Wu, C.H. and Zhang, D.X. (2002) Network Simulation FTP Business Prospect of Mathematical Modeling and Simulation Flow. Computer Simulation, 19, 104-106. [4] Yu, G.Q. and Zhang, C.S. (2004) Switching ARIMA Model Based Forecasting for Traffic Flow. ICASSP, 2, 429-432. [5] Vapnik, V.N. (1995) The Nature of Statistical Learning Theory. Springer-Verlag, New York. [6] Donoho, D.L. and Johnstone, I.M. (1994) Ideal Spatial Adaptation by Wavelet Shrinkage. Biometrika, 3, 425-455. [7] Zhao, Y., Zhang, Y. and Qi, C.J. (2008) Prediction Model of Stock Market Returns Based on Wavelet Neural Network. Computational Intelligence and Industrial Application, 1, 31-36.
http://dx.doi.org/10.1109/PACIIA.2008.46 [8] Oussar, Y. and Dreyfus, G.H. (2000) Initialization by Selection for Wavelet Network Training. Neurocomputing, 34, 131-143.
http://dx.doi.org/10.1016/S0925-2312(00)00295-2 [9] Donoho, D.L. (1995) Denoising by Soft-Thresholding. IEEE Transaction on Information, 3, 613-627. [10] Xu, Q. and Shu, L.C. (2010) Application of Optimized Wavelet Neural Network Based on Genetic Algorithm in Ground- water Level Prediction. Hydrology, 1, 27-30. [11] Zhou, H.R. and Wei, Y.H. (2010) Stocks Market Modeling and Forecasting Based on HGA and Wavelet Neural Networks. Natural Computation, 2, 620-625. [12] 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.