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State-of-the-Art in Applying Machine Learning to Electronic Trading

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Enterprise Applications, Markets and Services in the Finance Industry (FinanceCom 2020)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 401))

Abstract

This paper presents a literature survey of how machine learning techniques are being used in the area of electronic financial market trading. It first defines the essential components of an electronic trading system. It then examines some existing research efforts in applying machine learning techniques to the area of electronic trading, examining the target areas, methods used and their purpose. It also identifies the gaps and opportunities for further research in this new expanding field.

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Acknowledgements

We wish to acknowledge Plato Consulting and in particular Mike Bellaro for sponsoring this project. We also would like to thank Professor Carole Comerton-Forde, Professor Peter Gomber, Dr Giuseppe Nuti and Dr Kingsley Jones for their help and advice related to this project.

Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, University of New South Wales, Sydney, Australia

    Fethi A. Rabhi

  2. Alliance Manchester Business School, The University of Manchester, Manchester, UK

    Nikolay Mehandjiev

  3. Researcher and Consultant, 3814974695, Arak, Iran

    Ali Baghdadi

Authors
  1. Fethi A. Rabhi
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  2. Nikolay Mehandjiev
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  3. Ali Baghdadi
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Corresponding author

Correspondence to Fethi A. Rabhi .

Editor information

Editors and Affiliations

  1. Goethe University Frankfurt, Frankfurt, Germany

    Benjamin Clapham

  2. Goethe University Frankfurt, Frankfurt, Germany

    Jascha-Alexander Koch

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Rabhi, F.A., Mehandjiev, N., Baghdadi, A. (2020). State-of-the-Art in Applying Machine Learning to Electronic Trading. In: Clapham, B., Koch, JA. (eds) Enterprise Applications, Markets and Services in the Finance Industry. FinanceCom 2020. Lecture Notes in Business Information Processing, vol 401. Springer, Cham. https://doi.org/10.1007/978-3-030-64466-6_1

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  • DOI: https://doi.org/10.1007/978-3-030-64466-6_1

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