Title :
Application of neural sequential associator to long-term stock price prediction
Author_Institution :
Hitachi Ltd., Kawasaki, Japan
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;
Conference_Titel :
Neural Networks, 1991. 1991 IEEE International Joint Conference on
Print_ISBN :
0-7803-0227-3
DOI :
10.1109/IJCNN.1991.170559
