Home > Archive > 2020 > Volume 10 Number 2 (Feb. 2020) >
IJMLC 2020 Vol.10(2): 309-315 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2020.10.2.936

Improving the Criteria of the Investment on Stock Market Using Data Mining Techniques: The Case of S&P500 Index

Carlos Montenegro and Marco Molina

Abstract—The stock market data, as S&P500 Index, is massive, complex, non-linear and noised. Thus, the investment criteria using this information have been a challenge. This study proposes the following short-term step by step strategy: to combine two information sources that the investors can analyse to make a decision. First, the index data constitutes the input for a Deep Learning Neural Network training, for representing and forecasting next day stock value. Second, this research identifies the most representative enterprises, included on Index, which represent the Index behavioural tendency, using Feature Selection Analysis. Finally, the outputs are complemented and corroborated; the process shows promising results to improve the investor's decision. Thus, the academics can revise a new experience in data analysis; for the practitioners, the research contributes to an approach for supporting investment decisions in the stock market.

Index Terms—Deep learning, feature selection, S&P500 index, stock market.

Carlos Montenegro and Marco Molina are with Department of Computer Sciences and Informatics of Escuela Polit├ęcnica Nacional (EPN), Ecuador (e-mail: carlos.montenegro@epn.edu.ec).


Cite: Carlos Montenegro and Marco Molina, "Improving the Criteria of the Investment on Stock Market Using Data Mining Techniques: The Case of S&P500 Index," International Journal of Machine Learning and Computing vol. 10, no. 2, pp. 309-315, 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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