Price Direction Prediction on High Frequency Data Using Deep Belief Networks

  • Jaime Humberto Niño-PeñaEmail author
  • Germán Jairo Hernández-Pérez
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 657)


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.


Deep Belief Networks Stocks forecasting High frequency data 


  1. 1.
    Bank for International Settlements, Settlement Systems Technical Committee of the International Organization of Securities Commissions Principles for financial market infrastructures, Basel, Switzerland, March 2011Google Scholar
  2. 2.
    Darskuviene, V.: “Finanical Markets.” Leonardo Da Vinci Transfer of Innovation Program, pp. 1–140 (2010)Google Scholar
  3. 3.
    Sandoval, J.: High Frequency Exchange rate prediction using dynamic bayesian networks over the limit order book information. Universidad Nacional de Colombia (2015)Google Scholar
  4. 4.
    Arévalo, A., Niño, J., Hernández, G., Sandoval, J.: High-frequency trading strategy based on deep neural networks. In: Huang, D., Han, K., Hussain, A. (eds.) ICIC 2016. LNCS, vol. 9773, pp. 424–436. Springer, Heidelberg (2016). doi: 10.1007/978-3-319-42297-8_40 CrossRefGoogle Scholar
  5. 5.
    ESMA, “High-frequency trading activity in EU equity markets” (2014)Google Scholar
  6. 6.
    UK’s Gov Office, “The Future of Computer Trading in Financial Markets An International Perspective” (2012)Google Scholar
  7. 7.
    Sandoval, J., Nino, G., Hernandez, G., Cruz, A.: Detecting informative patterns in financial market trends based on visual analysis. Procedia Comput. Sci. 80, 752–761 (2016)CrossRefGoogle Scholar
  8. 8.
    Ohlsson, S.: Stellan Ohlsson: deep learning: how the mind overrides experience. Sci. Educ. 21, 1381–1392 (2012). Cambridge University Press, New YorkCrossRefGoogle Scholar
  9. 9.
    Tay, F.E.H., Cao, L.: Application of support vector machines in financial time series forecasting. Omega 29, 309–317 (2001)CrossRefGoogle Scholar
  10. 10.
    Ortega, L.F.: A neuro-wavelet method for the forecasting of financial time series. In: Proceedings of the World Congress on Engineering and Computer Science, vol. I (2012)Google Scholar
  11. 11.
    Schmidhuber, J.: Deep Learning in Neural Networks: An Overview, vol. 61, pp. 1–66. arXiv Preprint arXiv1404.7828 (2014)
  12. 12.
    Hinton, G.E., Osindero, S., Teh, Y.-W.: Communicated by Yann Le Cun a fast learning algorithm for deep belief nets. Neural Comput. 18, 1527–1554 (2006)MathSciNetCrossRefzbMATHGoogle Scholar
  13. 13.
    Hinton, G.: A practical guide to training restricted Boltzmann machines. Comput. (Long Beach Calif.) 9, 1 (2010)Google Scholar
  14. 14.
    Zhu, C., Yin, J., Li, Q.: A stock decision support system based on DBNs⋆. J. Comput. Inf. Syst. 2, 883–893 (2014)Google Scholar
  15. 15.
    Längkvist, M., Karlsson, L., Loutfi, A.: A review of unsupervised feature learning and deep learning for time-series modelling. Pattern Recogn. Lett. 42(1), 11–24 (2014)CrossRefGoogle Scholar
  16. 16.
    Kuremoto, T., Kimura, S., Kobayashi, K., Obayashi, M.: Time series forecasting using a deep belief network with restricted Boltzmann machines. Neurocomputing 137, 47–56 (2014)CrossRefGoogle Scholar
  17. 17.
    Dalto, M.: Deep neural networks for time series prediction with applications in ultra-short-term wind forecasting (2015)Google Scholar
  18. 18.
    Lai, A., Li, M.K., Pong, F.W.: Forecasting Trade Direction and Size of Future Contracts Using Deep Belief Network (2012)Google Scholar
  19. 19.
    Salakhutdinov, R., Hinton, G.: Deep Boltzmann machines. In: Artificial Intelligence and Statistics, vol. 5, no. 3, pp. 448–455 (2009)Google Scholar
  20. 20.
    Bengio, Y.: Learning deep architectures for AI. Found. Trends Mach. Learn. 2(1), 1–127 (2009)MathSciNetCrossRefzbMATHGoogle Scholar
  21. 21.
    Fawcett, T.: An introduction to ROC analysis. Pattern Recogn. Lett. 27(8), 861–874 (2006)MathSciNetCrossRefGoogle Scholar
  22. 22.
    Powers, D.: Evaluation: from precision, recall and F-measure to ROC, informedness, markedness & correlation. J. Mach. Learn. Technol. 2(1), 37–63 (2011)MathSciNetGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Jaime Humberto Niño-Peña
    • 1
    Email author
  • Germán Jairo Hernández-Pérez
    • 1
  1. 1.Universidad Nacional de ColombiaBogotáColombia

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