Abstract
It is an important research issue to improve the generalization ability of the neural network in the research of artificial neural network. This paper proposes the Bayesian regularization method to optimize the training process of the back propagation (BP) neural network, so that the optimized BP neural network model can predict new data in the BP neural network to a larger extent. Based on the experiments in which Bayesian regularization BP neural network is employed to predict the stock price series, and through the establishment of the stock customer transaction model network structure, an experimental program is selected to make an empirical analysis of the closing price data of Shanghai Stock in 800 trading days, the results of which show that the Bayesian regularization method has a better generalization ability.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
** L, Kuang X, Huang H, Qin Z, Wang Y (2005) Study on the overfitting of the artificial neural network forecasting model. Acta Meteorol Sinica 19(2):216–225
Wang B, Zhang F (2005) Comparison of artificial neural network and time series model for forecasting stock prices. J Wuhan Automot Polytech Univ 27(6):69–73
Zhang B, Yuan S, Cheng L, Yuan J, Cong X (2004) Model for predicting crop water requirements by using L-M optimization algorithm BP neural network. Trans Chin Soc Agric Eng 20(6):73–76
Choudhary A, Rishi R (2011) Improving the character recognition efficiency of feed forward BP neural network. Int J Comput Sci Inf Technol (IJCSIT) 3(1)
Lü S (2011) Statistical learning algorithms for regression and regularized spectral clustering. University of Science and Technology of China, Hefei
Wu G, Tao Q, Wang J (2005) Support vector machines based on posteriori probability. J Comput Res Dev 42(2):196–202
Feng W, Pengfei S (2000) Client-transaction-behavior analysis using conceptual clustering. Microcomput Appl 16(5):107–110
Sun B, Li T, Wang B (2011) Neural network forecasting model based on stock market sensitivity analysis. Comput Eng Appl 47(1):26–31
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Sun, Q., Che, WG., Wang, HL. (2014). Bayesian Regularization BP Neural Network Model for the Stock Price Prediction. In: Sun, F., Li, T., Li, H. (eds) Foundations and Applications of Intelligent Systems. Advances in Intelligent Systems and Computing, vol 213. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37829-4_45
Download citation
DOI: https://doi.org/10.1007/978-3-642-37829-4_45
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-37828-7
Online ISBN: 978-3-642-37829-4
eBook Packages: EngineeringEngineering (R0)