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

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Advances in Emerging Trends and Technologies (ICAETT 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((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.

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Authors and Affiliations

  1. Escuela Politécnica Nacional, Quito, Ecuador

    Carlos Montenegro & Marco Molina

Authors
  1. Carlos Montenegro
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  2. Marco Molina
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Corresponding author

Correspondence to Carlos Montenegro .

Editor information

Editors and Affiliations

  1. Eindhoven University of Technology, Eindhoven, Noord-Brabant, The Netherlands

    Miguel Botto-Tobar

  2. Universidad Técnica de Babahoyo, Babahoyo, Ecuador

    Joffre León-Acurio

  3. Universitat de Valencia, Valencia, Valencia, Spain

    Angela Díaz Cadena

  4. Centro de Investigación y Desarrollo Profesional, Babahoyo, Los Rios Province, Ecuador

    Práxedes Montiel Díaz

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Montenegro, C., Molina, M. (2020). Using Deep Neural Networks for Stock Market Data Forecasting: An Effectiveness Comparative Study. In: Botto-Tobar, M., León-Acurio, J., Díaz Cadena, A., Montiel Díaz, P. (eds) Advances in Emerging Trends and Technologies. ICAETT 2019. Advances in Intelligent Systems and Computing, vol 1066. Springer, Cham. https://doi.org/10.1007/978-3-030-32022-5_37

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