DocumentCode
2629283
Title
Application of neural sequential associator to long-term stock price prediction
Author
Matsuba, Ikuo
Author_Institution
Hitachi Ltd., Kawasaki, Japan
fYear
1991
fDate
18-21 Nov 1991
Firstpage
1196
Abstract
A neural sequential associator using feedback multilayer neural networks in duplicate is proposed to analyze the inherent structure in the sequence and to predict the future sequence based on this structure. It is shown that the present method gives a better performance than that of neural networks without feedback when applied to the prediction of long-term stock prices
Keywords
financial data processing; neural nets; stock markets; time series; feedback multilayer neural networks; future sequence prediction; long-term stock price prediction; neural sequential associator; time series data; Artificial neural networks; Data mining; Delay effects; Feature extraction; Laboratories; Multi-layer neural network; Neural networks; Neurons; Parameter estimation; Pattern recognition;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 1991. 1991 IEEE International Joint Conference on
Print_ISBN
0-7803-0227-3
Type
conf
DOI
10.1109/IJCNN.1991.170559
Filename
170559
Link To Document