Combining Technical Analysis and Support Vector Machine for Stock Trading

Author

Kantavat, Pittipol ; Kijsirikul, Boonserm

Author_Institution

Dept. of Comput. Eng., Chulalongkorn Univ., Bangkok

fYear

2008

fDate

10-12 Sept. 2008

Firstpage

915

Lastpage

918

Abstract

Support vector machine (SVM) is a very powerful machine learning algorithm that can be applied to many kinds of applications, not only computation sciences but investing tasks also. This paper presents a new algorithm combining SVM with technical analysis for investing in stocks. RReliefF feature selection is used to choose the appropriate training and trading features for SVM. The experimental results show that we can make very appreciating investments from the new investing strategy.

Keywords

investment; learning (artificial intelligence); stock markets; support vector machines; RReliefF feature selection; SVM; machine learning algorithm; stock investing; stock trading; support vector machine; technical analysis; Algorithm design and analysis; Artificial neural networks; Economic forecasting; Economic indicators; Gain measurement; Machine learning algorithms; Power engineering and energy; Power engineering computing; Stock markets; Support vector machines; Stock Trading; Support Vector Machine; Technical Analysis; Trading Indicator; Trading Signal;

fLanguage

English

Publisher

ieee

Conference_Titel

Hybrid Intelligent Systems, 2008. HIS '08. Eighth International Conference on

Conference_Location

Barcelona

Print_ISBN

978-0-7695-3326-1

Electronic_ISBN

978-0-7695-3326-1

Type

conf

DOI

10.1109/HIS.2008.76

Filename

4626749