Title :
Mining Stock Market Tendency by RS-Based Support Vector Machines
Author :
Sai, Ying ; Yuan, Zheng ; Gao, Kanglin
Author_Institution :
Shandong Univ. of Finance, Jinan
Abstract :
In this study, a hybrid data mining methodology, rough set based support vector machine (RS-SVM) model, is proposed to explore stock market tendency. In this approach, rough set is used for feature vectors selection to reduce the computation complexity of SVM and then the SVM is used to identify stock market movement tendency based on the historical data. To evaluate the forecasting ability of RS-SVM, we compare its performance with that of conventional methods and neural network models. The empirical results reveal that RS-SVM outperforms other forecasting models, implying that the proposed approach is a promising model to stock market tendency exploration.
Keywords :
computational complexity; data mining; economic forecasting; rough set theory; stock markets; support vector machines; computational complexity; feature vector selection; forecasting ability; neural network model; rough set-based support vector machine model; stock market movement tendency mining; Artificial neural networks; Economic forecasting; Error correction; Finance; Neural networks; Predictive models; Risk management; Stock markets; Support vector machine classification; Support vector machines;
Conference_Titel :
Granular Computing, 2007. GRC 2007. IEEE International Conference on
Conference_Location :
Fremont, CA
Print_ISBN :
978-0-7695-3032-1
DOI :
10.1109/GrC.2007.104