Forecasting method of stock price based on polynomial smooth twin support vector regression

Authors:
Shifei Ding;Huajuan Huang;Ru Nie
Affiliations:
School of Computer Science and Techn., China Univ. of Mining and Techn., Xuzhou, China,Key Laboratory of Intelligent Inf. Processing, Institute of Computing Techn., Chinese Academy of Science, Bei ...;School of Computer Science and Techn., China Univ. of Mining and Techn., Xuzhou, China,Key Laboratory of Intelligent Inf. Processing, Institute of Computing Techn., Chinese Academy of Science, Bei ...;School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, China,Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese ...
Venue:
ICIC'13 Proceedings of the 9th international conference on Intelligent Computing Theories
Year:
2013

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Abstract

The stock price prediction has become an important research topic in Economics. However, the traditional forecasting methods only can be used in linear system, whose prediction accuracy is not satisfactory. In this paper, a new forecasting method of stock price based on polynomial smooth twin support vector regression is proposed. In the proposed method, we firstly construct the polynomial smooth twin support vector regression (PSTSVR) model and prove its global convergence. Then PSTSVR is used as the opening price of stock prediction model. The experimental results on the stock data from the great wisdom stock software show that the proposed method can obtain the better regression performance compared with SVR and twin support vector regression (TSVR).