An SVM-based approach for stock market trend prediction

Yuling Lin, Haixiang Guo, Jinglu Hu

研究成果: Conference contribution

60 被引用数 (Scopus)

抄録

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.

本文言語English
ホスト出版物のタイトル2013 International Joint Conference on Neural Networks, IJCNN 2013
DOI
出版ステータスPublished - 2013 12 1
イベント2013 International Joint Conference on Neural Networks, IJCNN 2013 - Dallas, TX, United States
継続期間: 2013 8 42013 8 9

出版物シリーズ

名前Proceedings of the International Joint Conference on Neural Networks

Conference

Conference2013 International Joint Conference on Neural Networks, IJCNN 2013
CountryUnited States
CityDallas, TX
Period13/8/413/8/9

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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