DocumentCode :
3108676
Title :
An Improved OIF Elman Neural Network Model with Direction Profit Factor and Its Applications
Author :
Li, Ming ; Wang, Limin ; Liu, Yang ; Liu, Ying ; Sun, Qian ; Han, Xuming
Author_Institution :
Dept. of Inf., Changchun Taxation Coll., Changchun, China
fYear :
2009
fDate :
28-30 Dec. 2009
Firstpage :
208
Lastpage :
211
Abstract :
Output-input feedback (OIF) Elman neural network is a dynamic feedback network. An improved model is proposed based on the OIF Elman neural network by introducing direction profit factor in this paper. Moreover, the proposed model is applied to forecast the composite index of stock. In addition, some comparisons are also made when the stock exchange is performed using prediction results from OIF Elman neural network. Simulation results show that the proposed model is feasible and effective in the finance field. It shows that the proposed model can not only improve the forecasting precision evidently and possess the characteristic of quick convergence but also provide a good reference tool for investors to obtain more profits.
Keywords :
profitability; recurrent neural nets; stock markets; direction profit factor; dynamic feedback network; output-input feedback Elman neural network; stock composite index forecasting; stock exchange; Application software; Artificial neural networks; Computer networks; Finance; Machine vision; Neural networks; Neurofeedback; Predictive models; State feedback; Stock markets; OIF Elman neural network; composite index of stock; direction profit factor; forecast;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Machine Vision, 2009. ICMV '09. Second International Conference on
Conference_Location :
Dubai
Print_ISBN :
978-0-7695-3944-7
Electronic_ISBN :
978-1-4244-5645-1
Type :
conf
DOI :
10.1109/ICMV.2009.39
Filename :
5381114
Link To Document :
بازگشت