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Sentiment and Knowledge Based Algorithmic Trading with Deep Reinforcement Learning

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Database and Expert Systems Applications (DEXA 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13426))

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Abstract

Algorithmic trading, due to its inherent nature, is a difficult problem to tackle; there are too many variables involved in the real-world which makes it almost impossible to have reliable algorithms for automated stock trading. The lack of reliable labelled data that considers physical and physiological factors that dictate the ups and downs of the market, has hindered the supervised learning attempts for dependable predictions. To learn a good policy for trading, we formulate an approach using reinforcement learning which uses traditional time series stock price data and combines it with news headline sentiments, while leveraging knowledge graphs for exploiting news about implicit relationships.

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Notes

  1. 1.

    https://wordnet.princeton.edu/.

  2. 2.

    https://www.nlm.nih.gov/research/umls/.

  3. 3.

    https://wiki.dbpedia.org/.

  4. 4.

    https://finance.yahoo.com/quote/MSFT/history/.

  5. 5.

    https://twitter.com/reuters.

  6. 6.

    https://developers.google.com/knowledge-graph/.

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Acknowledgments

The work is supported in part by the Natural Sciences and Engineering Research Council of Canada (NSERC). Osmar Zaïane is supported by the Amii Fellow Program and the Canada CIFAR AI Chair Program.

Author information

Authors and Affiliations

  1. University of Alberta, Edmonton, Canada

    Abhishek Nan, Anandh Perumal & Osmar R. Zaiane

  2. Alberta Machine Intelligence Institute, Edmonton, Canada

    Abhishek Nan, Anandh Perumal & Osmar R. Zaiane

Authors
  1. Abhishek Nan
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  2. Anandh Perumal
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  3. Osmar R. Zaiane
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Corresponding author

Correspondence to Osmar R. Zaiane .

Editor information

Editors and Affiliations

  1. University of Vienna, Vienna, Austria

    Christine Strauss

  2. University of Calabria, Rende, Italy

    Alfredo Cuzzocrea

  3. Johannes Kepler University of Linz, Linz, Austria

    Gabriele Kotsis

  4. Vienna University of Technology, Vienna, Austria

    A Min Tjoa

  5. Johannes Kepler University of Linz, Linz, Austria

    Ismail Khalil

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Nan, A., Perumal, A., Zaiane, O.R. (2022). Sentiment and Knowledge Based Algorithmic Trading with Deep Reinforcement Learning. In: Strauss, C., Cuzzocrea, A., Kotsis, G., Tjoa, A.M., Khalil, I. (eds) Database and Expert Systems Applications. DEXA 2022. Lecture Notes in Computer Science, vol 13426. Springer, Cham. https://doi.org/10.1007/978-3-031-12423-5_13

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  • DOI: https://doi.org/10.1007/978-3-031-12423-5_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-12422-8

  • Online ISBN: 978-3-031-12423-5

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