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Can Deep Machine Learning Outsmart The Market? A Comparison Between Econometric Modelling And Long- Short Term Memory

Author

Listed:
  • Eva DEZSI

    (Babes-Bolyai University - Faculty of Business of Cluj)

  • Ioan Alin NISTOR

    (Babes-Bolyai University - Faculty of Business of Cluj)

Abstract

Using long-short term memory (LSTM) recurrent neural network (RNN) architecture, we analyse data from the Romanian stock markets in the attempt to forecast its future trend. Then we try to compare the results using the classical statistical modelling tools, further employing back testing to prove our findings. We believe that the LSTM should be the next tool in balancing portfolios and reducing market risk.

Suggested Citation

  • Eva DEZSI & Ioan Alin NISTOR, 2016. "Can Deep Machine Learning Outsmart The Market? A Comparison Between Econometric Modelling And Long- Short Term Memory," Romanian Economic Business Review, Romanian-American University, vol. 11(4.1), pages 54-73, december.
    • Handle: RePEc:rau:journl:v:11:y:2016:i:4.1:p:54-73
    as

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    References listed on IDEAS

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    Cited by:

    1. Omer Berat Sezer & Mehmet Ugur Gudelek & Ahmet Murat Ozbayoglu, 2019. "Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019," Papers 1911.13288, arXiv.org.

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