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Using Deep Neural Networks for Stock Market Data Forecasting: An Effectiveness Comparative Study

  • Carlos MontenegroEmail author
  • Marco Molina
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1066)

Abstract

Stock market value forecasting has been a challenge, because data are massive, complex, non-linear and noised. Nevertheless, some deep learning promising techniques can be reviewed in technical literature. Using S&P500 historical data as a case study, this work proposes the following approach: (i) NARX and Back Propagation Neural Networks are selected and trained for representing Index data; (ii) A sliding window technique for Index value forecasting is defined and tested; and, (iii) An effectiveness comparison is performed. The results suggest the best model for representing and forecasting S&P500 Index data. Thus, the academics can revise a new experience in data analysis; and practitioners will have an approach concerning the forecasting calculation in the stock market.

Keywords

Deep learning Neural networks S&P500 index Forecasting Stock market 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Escuela Politécnica NacionalQuitoEcuador

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