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@article{ART002285808},
author={Kyun Sun Eo and Kun-Chang Lee},
title={Predicting stock price direction by using data mining methods : Emphasis on comparing single classifiers and ensemble classifiers},
journal={Journal of The Korea Society of Computer and Information},
issn={1598-849X},
year={2017},
volume={22},
number={11},
pages={111-116},
doi={10.9708/jksci.2017.22.11.111}

TY - JOUR
AU - Kyun Sun Eo
AU - Kun-Chang Lee
TI - Predicting stock price direction by using data mining methods : Emphasis on comparing single classifiers and ensemble classifiers
JO - Journal of The Korea Society of Computer and Information
PY - 2017
VL - 22
IS - 11
PB - The Korean Society Of Computer And Information
SP - 111
EP - 116
SN - 1598-849X
AB - 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.
KW - Stock price direction prediction;Data Mining;Feature selection;Single classifiers;Ensemble classifiers
DO - 10.9708/jksci.2017.22.11.111
ER -

Kyun Sun Eo and Kun-Chang Lee. (2017). Predicting stock price direction by using data mining methods : Emphasis on comparing single classifiers and ensemble classifiers. Journal of The Korea Society of Computer and Information, 22(11), 111-116.

Kyun Sun Eo and Kun-Chang Lee. 2017, "Predicting stock price direction by using data mining methods : Emphasis on comparing single classifiers and ensemble classifiers", Journal of The Korea Society of Computer and Information, vol.22, no.11 pp.111-116. Available from: doi:10.9708/jksci.2017.22.11.111

Kyun Sun Eo, Kun-Chang Lee "Predicting stock price direction by using data mining methods : Emphasis on comparing single classifiers and ensemble classifiers" Journal of The Korea Society of Computer and Information 22.11 pp.111-116 (2017) : 111.

Kyun Sun Eo, Kun-Chang Lee. Predicting stock price direction by using data mining methods : Emphasis on comparing single classifiers and ensemble classifiers. 2017; 22(11), 111-116. Available from: doi:10.9708/jksci.2017.22.11.111

Kyun Sun Eo and Kun-Chang Lee. "Predicting stock price direction by using data mining methods : Emphasis on comparing single classifiers and ensemble classifiers" Journal of The Korea Society of Computer and Information 22, no.11 (2017) : 111-116.doi: 10.9708/jksci.2017.22.11.111

Kyun Sun Eo; Kun-Chang Lee. Predicting stock price direction by using data mining methods : Emphasis on comparing single classifiers and ensemble classifiers. Journal of The Korea Society of Computer and Information, 22(11), 111-116. doi: 10.9708/jksci.2017.22.11.111

Kyun Sun Eo; Kun-Chang Lee. Predicting stock price direction by using data mining methods : Emphasis on comparing single classifiers and ensemble classifiers. Journal of The Korea Society of Computer and Information. 2017; 22(11) 111-116. doi: 10.9708/jksci.2017.22.11.111

Kyun Sun Eo, Kun-Chang Lee. Predicting stock price direction by using data mining methods : Emphasis on comparing single classifiers and ensemble classifiers. 2017; 22(11), 111-116. Available from: doi:10.9708/jksci.2017.22.11.111

Kyun Sun Eo and Kun-Chang Lee. "Predicting stock price direction by using data mining methods : Emphasis on comparing single classifiers and ensemble classifiers" Journal of The Korea Society of Computer and Information 22, no.11 (2017) : 111-116.doi: 10.9708/jksci.2017.22.11.111