DocumentCode
2310302
Title
Notice of Retraction
Short-term forecasting of stock price based on genetic-neural network
Author
Hua-Ning Hao
Author_Institution
Sch. of Sci., Xi´an Shi You Univ., Xi´an, China
Volume
4
fYear
2010
fDate
10-12 Aug. 2010
Firstpage
1838
Lastpage
1841
Abstract
Notice of Retraction
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
For short-term forecasting of stock price, this paper proposes a method of analyzing and forecasting the closing price of a stock based on the genetic-BP algorithm. Results show that, within the range of allowable error, the obtained data from the present model can well forecast the short-term trend of a stock price. Though stock market is a very complex nonlinear system, it has the better foreground to predict stock price using neural network. The studies in this paper also prove that genetic-neural algorithm can improve the speed and reliability of the foresting method.
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
For short-term forecasting of stock price, this paper proposes a method of analyzing and forecasting the closing price of a stock based on the genetic-BP algorithm. Results show that, within the range of allowable error, the obtained data from the present model can well forecast the short-term trend of a stock price. Though stock market is a very complex nonlinear system, it has the better foreground to predict stock price using neural network. The studies in this paper also prove that genetic-neural algorithm can improve the speed and reliability of the foresting method.
Keywords
backpropagation; forecasting theory; genetic algorithms; neural nets; pricing; stock markets; genetic-BP algorithm; genetic-neural network; short-term forecasting; stock market; stock price; Artificial neural networks; Forecasting; Genetics; Neurons; Prediction algorithms; Stock markets; Training; Genetic-Neural Network; Short-term Forecasting; Stock Price;
fLanguage
English
Publisher
ieee
Conference_Titel
Natural Computation (ICNC), 2010 Sixth International Conference on
Conference_Location
Yantai
Print_ISBN
978-1-4244-5958-2
Type
conf
DOI
10.1109/ICNC.2010.5584528
Filename
5584528
Link To Document