Abstract—This research presents the data-driven modeling method to derive a combined trading model from the analysis of negative correlations among the top-five active stocks from each sector of the Thailand stock market. The negative movements are computed from the closing price direction of major stocks in the eight biggest sectors. The highly negative correlated stocks among market groups are then used to build predictive trading models with three algorithms: regression analysis, generalized linear modeling, and chi-square automatic interaction detection. An ensemble from the combination of the best two models is then created. The experimental results reveal that the proposed method of trading based on negative movement analysis can accurately predict closing price of the active stock with low error rate around 1.86%.

Index Terms—Negative correlation analysis, stock trading, regression, chi-squared automatic interaction detection, CHAID.

Kacha Chansilp is with School of Computer Engineering, Suranaree University of Technology, Nakhon Ratchasima, Thailand (email: kacha@sut.ac.th).
Kittisak Kerdprasop and Nittaya Kerdprasop areSchool of Computer Engineering, Suranaree University of Technology, Nakhon Ratchasima, Thailand. They are also with Data and Knowledge Engineering Research Unit, School of Computer Engineering, Suranaree University of Technology, Thailand.
Paradee Chuaybamroong is with Department of Environmental Science, Thammasat University, Rangsit Campus, Thailand.

Cite: Kacha Chansilp, Kittisak Kerdprasop, Paradee Chuaybamroong, Nittaya Kerdprasop "Stock Movement Modeling Based on the Analysis of Negative Correlation," International Journal of e-Education, e-Business, e-Management and e-Learning vol. 10, no. 2, pp. 125-134, 2020.

Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).