Application of Innovations Feedback Neural Networks in the Prediction of Ups and Downs Value of Stock Market

Author

Li Zhang ; Zhong, Chongquan ; Zhang, Liyong ; Ma, Futing ; Zhang, Liqian

Author_Institution

Sch. of Electron. & Inf. Eng., Dalian Univ. of Technol.

Volume

1

fYear

0

fDate

0-0 0

Firstpage

4162

Lastpage

4166

Abstract

This paper presents how the idea of Kalman prediction can be used to the study of the prediction of neural networks and puts forward an innovations feedback neural networks (IFNN). IFNN adopts back propagation (BP) algorithm and is applied to the prediction of ups and downs value of the stock. The theoretical results and numerical experiment show that the predicted capability of IFNN is better than that of the normal feedforward networks. Finally, a relative completed stock prediction system is developed on Windows 2003/XP platform by OOP programming method and SQL server 2000 database. By practical operation in Resource Investment Consult Inc., it shows the system can improve prediction credibility of stock market effectively

Keywords

backpropagation; economic forecasting; recurrent neural nets; stock markets; Kalman prediction; OOP programming method; Windows 2003/XP platform; back propagation algorithm; feedforward networks; innovations feedback neural networks; sql server2000 database; stock market; stock prediction system; Databases; Feedforward neural networks; Investments; Kalman filters; Network servers; Neural networks; Neurofeedback; Stock markets; Technological innovation; Uninterruptible power systems; BP algorithm; feedback; innovations; neural networks; stock prediction;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on

Conference_Location

Dalian

Print_ISBN

1-4244-0332-4

Type

conf

DOI

10.1109/WCICA.2006.1713158

Filename

1713158