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
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 Politécnica Nacional (EPN), Ecuador (e-mail: firstname.lastname@example.org).
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).