DocumentCode :
3446278
Title :
Forecasting stock/futures prices by using neural networks with feature selection
Author :
Chih-Ming Hsu
Author_Institution :
Dept. of Bus. Adm., Minghsin Univ. of Sci. & Technol., Hsinchu, Taiwan
Volume :
1
fYear :
2011
fDate :
20-22 Aug. 2011
Firstpage :
1
Lastpage :
7
Abstract :
Stock/futures price forecasting is an important financial topic for individual investors, stock fund managers and financial analysts, and is currently receiving considerable attention from both researchers and practitioners. However, the inherent characteristics of stock/futures prices, namely, high volatility, complexity, and turbulence, make forecasting a challenging endeavor. In the past, various approaches have been proposed to deal with the problems of stock/futures price forecasting, that are difficult to resolve by using only a single soft computing technique. In this study, a systematic procedure based on a backpropagation (BP) neural network and a feature selection technique is proposed to tackle stock/futures price forecasting problems with the use of technical indicators. The feasibility and effectiveness of this procedure are evaluated through a case study on forecasting the closing prices of Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) futures of the spot month. Experimental results show that the proposed forecasting procedure is a feasible and effective tool for forecasting stock/futures prices. Furthermore, the statistical hypothesis testing indicates that the forecasting performance of a BP model with feature selection is better than that obtained through a simple BP model.
Keywords :
backpropagation; forecasting theory; neural nets; share prices; statistical analysis; stock markets; Taiwan Stock Exchange Capitalization Weighted Stock Index; backpropagation neural network; closing price forecasting; feature selection technique; futures price forecasting; single soft computing technique; statistical hypothesis testing; stock forecasting; Backpropagation; Biological neural networks; Data models; Forecasting; Input variables; Predictive models; Training; backpropagation neural network; feature selection; stock/futures price forecasting;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information Technology and Artificial Intelligence Conference (ITAIC), 2011 6th IEEE Joint International
Conference_Location :
Chongqing
Print_ISBN :
978-1-4244-8622-9
Type :
conf
DOI :
10.1109/ITAIC.2011.6030137
Filename :
6030137
Link To Document :
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