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Price Direction Prediction on High Frequency Data Using Deep Belief Networks

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Applied Computer Sciences in Engineering (WEA 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 657))

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Abstract

This paper presents the use of Deep Belief Networks (DBN) for direction forecasting on financial time series, particularly those associated to the High Frequency Domain. The paper introduces some of the key concepts of the DBN, presents the methodology, results and its discussion. DBNs achieves better performance for particular configurations and training times were acceptable, however if they want to be pursued in real applications, windows sizes should be evaluated.

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Notes

  1. 1.

    It means five sell intentions and five buy intentions, recorded every five seconds. If something changed in the LOB within the 5 s interval it is not registered.

  2. 2.

    Candlestick is an ancient representation from rice Japanese traders in the 1700’s. Widely used by technical traders to analyze price formations (Luca, 2000).

  3. 3.

    Traded quantity of Facebook stock for each 15-seconds interval.

  4. 4.

    https://cran.r-project.org/web/packages/darch/index.html.

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Author information

Authors and Affiliations

  1. Universidad Nacional de Colombia, Bogotá, Colombia

    Jaime Humberto Niño-Peña & Germán Jairo Hernández-Pérez

Authors
  1. Jaime Humberto Niño-Peña
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  2. Germán Jairo Hernández-Pérez
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Corresponding author

Correspondence to Jaime Humberto Niño-Peña .

Editor information

Editors and Affiliations

  1. Universidad Distrital Francisco José de Caldas, Bogotá, Colombia

    Juan Carlos Figueroa-García

  2. Universidad Distrital Francisco José de Caldas, Bogotá, Colombia

    Eduyn Ramiro López-Santana

  3. Universidad Distrital Francisco José de Caldas, Bogotá, Colombia

    Roberto Ferro-Escobar

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Niño-Peña, J.H., Hernández-Pérez, G.J. (2016). Price Direction Prediction on High Frequency Data Using Deep Belief Networks. In: Figueroa-García, J., López-Santana, E., Ferro-Escobar, R. (eds) Applied Computer Sciences in Engineering. WEA 2016. Communications in Computer and Information Science, vol 657. Springer, Cham. https://doi.org/10.1007/978-3-319-50880-1_7

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  • DOI: https://doi.org/10.1007/978-3-319-50880-1_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-50879-5

  • Online ISBN: 978-3-319-50880-1

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