한국컴퓨터정보학회논문지 (Journal of the Korea Society of Computer and Information)

  • 제22권11호
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  • Pages.111-116
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  • 2017
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  • 1598-849X(pISSN)
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  • 2383-9945(eISSN)
한국컴퓨터정보학회 (Korean Society of Computer Information)
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Predicting stock price direction by using data mining methods : Emphasis on comparing single classifiers and ensemble classifiers

  • Eo, Kyun Sun (Sungkyunkwan University) ;
  • Lee, Kun Chang (SKK Business School/SAIHST (Samsung Advanced Institute of Health Sciences & Technology), Sungkyunkwan University)
  • 투고 : 2017.03.19
  • 심사 : 2017.08.09
  • 발행 : 2017.11.30
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초록

This paper proposes a data mining approach to predicting stock price direction. Stock market fluctuates due to many factors. Therefore, predicting stock price direction has become an important issue in the field of stock market analysis. However, in literature, there are few studies applying data mining approaches to predicting the stock price direction. To contribute to literature, this paper proposes comparing single classifiers and ensemble classifiers. Single classifiers include logistic regression, decision tree, neural network, and support vector machine. Ensemble classifiers we consider are adaboost, random forest, bagging, stacking, and vote. For the sake of experiments, we garnered dataset from Korea Stock Exchange (KRX) ranging from 2008 to 2015. Data mining experiments using WEKA revealed that random forest, one of ensemble classifiers, shows best results in terms of metrics such as AUC (area under the ROC curve) and accuracy.

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