Home > Archive > 2020 > Volume 10 Number 2 (Feb. 2020) >
IJMLC 2020 Vol 10(2): 213-219 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2020.10.2.922

Stock Performance Classification in Stock Exchange of Thailand (SET) by Using Supervised Machine Learning Model

Chayanant Kosol and Punnamee Sachakamol

Abstract—Most investors decide to invest in a stock market in order to win from an inflation. And, Financial Statement is the top tool that Thai investors have been using a financial statement to support their buying/selling decision in the stock market for a long time. Even in a digital era as nowadays, the financial statement is still in use by many investors. Particularly, they manually review the statement and make a decision based on their own judgement. The purpose of this research is to use a proper of technology and financial statement to build classification models to identify a winning stock in the Stock Exchange of Thailand (SET).

Index Terms—Classification algorithms, logistic regression, K-nearest neighbors, support vector machine, supervised machine learning, stock market, financial statement.

Chayanant Kosol is with the Engineering Management, Kasetsart University (KU), Thailand (e-mail: chayanant.ko@ku.th).
Punnamee Sachakamol is with the Department of Industrial Engineering and Engineering Management, Kasetsart University (KU), Thailand (e-mail: fengpmsa@ku.ac.th).


Cite: Chayanant Kosol and Punnamee Sachakamol, "Stock Performance Classification in Stock Exchange of Thailand (SET) by Using Supervised Machine Learning Model," International Journal of Machine Learning and Computing vol. 10, no. 2, pp. 213-219, 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).


General Information

  • E-ISSN: 2972-368X
  • Abbreviated Title: Int. J. Mach. Learn.
  • Frequency: Quaterly
  • DOI: 10.18178/IJML
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals LibraryCNKI.
  • E-mail: ijml@ejournal.net

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