An SVM-based approach for stock market trend prediction

Yuling Lin, Haixiang Guo, Takayuki Furuzuki

Research output: Chapter in Book/Report/Conference proceedingConference contribution

39 Citations (Scopus)

Abstract

In this paper, an SVM-based approach is proposed for stock market trend prediction. The proposed approach consists of two parts: feature selection and prediction model. In the feature selection part, a correlation-based SVM filter is applied to rank and select a good subset of financial indexes. And the stock indicators are evaluated based on the ranking. In the prediction model part, a so called quasi-linear SVM is applied to predict stock market movement direction in term of historical data series by using the selected subset of financial indexes as the weighted inputs. The quasi-linear SVM is an SVM with a composite quasi-linear kernel function, which approximates a nonlinear separating boundary by multi-local linear classifiers with interpolation. Experimental results on Taiwan stock market datasets demonstrate that the proposed SVM-based stock market trend prediction method produces better generalization performance over the conventional methods in terms of the hit ratio. Moreover, the experimental results also show that the proposed SVM-based stock market trend prediction system can find out a good subset and evaluate stock indicators which provide useful information for investors.

Original languageEnglish
Title of host publicationProceedings of the International Joint Conference on Neural Networks
DOIs
Publication statusPublished - 2013
Event2013 International Joint Conference on Neural Networks, IJCNN 2013 - Dallas, TX
Duration: 2013 Aug 42013 Aug 9

Other

Other2013 International Joint Conference on Neural Networks, IJCNN 2013
CityDallas, TX
Period13/8/413/8/9

Fingerprint

Feature extraction
Set theory
Interpolation
Classifiers
Financial markets
Composite materials

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

Cite this

Lin, Y., Guo, H., & Furuzuki, T. (2013). An SVM-based approach for stock market trend prediction. In Proceedings of the International Joint Conference on Neural Networks [6706743] https://doi.org/10.1109/IJCNN.2013.6706743

An SVM-based approach for stock market trend prediction. / Lin, Yuling; Guo, Haixiang; Furuzuki, Takayuki.

Proceedings of the International Joint Conference on Neural Networks. 2013. 6706743.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Lin, Y, Guo, H & Furuzuki, T 2013, An SVM-based approach for stock market trend prediction. in Proceedings of the International Joint Conference on Neural Networks., 6706743, 2013 International Joint Conference on Neural Networks, IJCNN 2013, Dallas, TX, 13/8/4. https://doi.org/10.1109/IJCNN.2013.6706743
Lin Y, Guo H, Furuzuki T. An SVM-based approach for stock market trend prediction. In Proceedings of the International Joint Conference on Neural Networks. 2013. 6706743 https://doi.org/10.1109/IJCNN.2013.6706743
Lin, Yuling ; Guo, Haixiang ; Furuzuki, Takayuki. / An SVM-based approach for stock market trend prediction. Proceedings of the International Joint Conference on Neural Networks. 2013.
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