Recurrent Neural Network with Kernel Feature Extraction for Stock Prices Forecasting

Author

Sun, Xiang ; Ni, Yong

Author_Institution

Sch. of Manage., Hefei Univ. of Technol.

Volume

1

fYear

2006

fDate

Nov. 2006

Firstpage

903

Lastpage

907

Abstract

A two-stage neural network architecture constructed by combining recurrent neural network (RNN) with kernel feature extraction is proposed for stock prices forecasting. In the first stage, kernel independent component analysis (KICA) and kernel principal component analysis (KPCA) are used as feature extraction. In the second stage, RNN with kernel feature extraction is used to regression estimation. By examining the stock prices data, it is shown that (1) RNN with feature extraction outperforms single RNN; (2) RNN with kernel performs better than those without kernel

Keywords

economic forecasting; feature extraction; independent component analysis; neural net architecture; pricing; principal component analysis; recurrent neural nets; regression analysis; stock markets; kernel feature extraction; kernel independent component analysis; kernel principal component analysis; neural network architecture; recurrent neural network; regression estimation; stock prices forecasting; Economic forecasting; Feature extraction; Independent component analysis; Kernel; Load forecasting; Machine learning; Predictive models; Principal component analysis; Recurrent neural networks; Technology forecasting;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Intelligence and Security, 2006 International Conference on

Conference_Location

Guangzhou

Print_ISBN

1-4244-0605-6

Electronic_ISBN

1-4244-0605-6

Type

conf

DOI

10.1109/ICCIAS.2006.294269

Filename

4072222