Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/55675
Title: Application of feature-weighted support vector regression using grey correlation degree to stock price forecasting
Authors: Hu, YX
Liu, JNK
Keywords: Grey correlation degree
Regression
Stock price forecasting
Support vector machines
Issue Date: 2013
Publisher: Springer
Source: Neural computing and applications, 2013, v. 22 , no. SUPPL.1, p. 143-152 How to cite?
Journal: Neural computing and applications 
Abstract: A feature-weighted Support Vector Machine regression algorithm is introduced in this paper. We note that the classical SVM is based on the assumption that all the features of the sample points supply the same contribution to the target output value. However, this assumption is not always true in real problems. In the proposed new algorithm, we give different weight values to different features of the samples in order to improve the performance of SVM. In our algorithm, firstly, a measure named grey correlation degree is applied to evaluate the correlation between each feature and the target problem, and then the values of the grey correlation degree are used as weight values assigned to the features. The proposed method is tested on sample stock data sets selected from China Shenzhen A-share market. The result shows that the new version of SVM can improve the accuracy of the prediction.
URI: http://hdl.handle.net/10397/55675
ISSN: 0941-0643
DOI: 10.1007/s00521-012-0969-3
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