A new computational method of input selection for stock market forecasting with neural networks

Authors:
Wei Huang;Shouyang Wang;Lean Yu;Yukun Bao;Lin Wang
Affiliations:
School of Management, Huazhong University of Science and Technology, WuHan, China;Institute of Systems Science, Academy of Mathematics and Systems Sciences, Chinese Academy of Sciences, Beijing, China;Institute of Systems Science, Academy of Mathematics and Systems Sciences, Chinese Academy of Sciences, Beijing, China;School of Management, Huazhong University of Science and Technology, WuHan, China;School of Management, Huazhong University of Science and Technology, WuHan, China
Venue:
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part IV
Year:
2006

Citing 4
Cited 1

A Comparison of Neural Networks with Time Series Models for Forecasting Returns on a Stock Market Index

IEA/AIE '02 Proceedings of the 15th international conference on Industrial and engineering applications of artificial intelligence and expert systems: developments in applied artificial intelligence
Neural Networks in Business Forecasting

Neural Networks in Business Forecasting
Forecasting stock market movement direction with support vector machine

Computers and Operations Research
Select the size of training set for financial forecasting with neural networks

ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II

The use of NARX neural networks to predict chaotic time series

WSEAS Transactions on Computer Research

Quantified Score

Hi-index	0.00

Visualization

Abstract

We propose a new computational method of input selection for stock market forecasting with neural networks. The method results from synthetically considering the special feature of input variables of neural networks and the special feature of stock market time series. We conduct the experiments to compare the prediction performance of the neural networks based on the different input variables by using the different input selection methods for forecasting S&P 500 and NIKKEI 225. The experiment results show that our method performs best in selecting the appropriate input variables of neural networks.