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Discovering the ecosystem of an electronic financial market with a dynamic machine-learning method

Author

Listed:
  • Mankad, Shawn

    (Department of Statistics, University of Michigan, Michigan, USA)

  • Michailidis, George

    (Department of Statistics, University of Michigan, Michigan, USA)

Abstract

Not long ago securities were traded by human traders in face-to-face markets. The ecosystem of an open outcry market was well-known, visible to a human eye, and rigidly prescribed. Now trading is increasingly done in anonymous electronic markets where traders do not have designated functions or mandatory roles. In fact, the traders themselves have been replaced by algorithms (machines) operating with little or no human oversight. While the process of electronic trading is not visible to a human eye, machine-learning methods have been developed to recognize persistent patterns in the data. In this study, we develop a dynamic machine-learning method that designates traders in an anonymous electronic market into five persistent categories: high frequency traders, market makers, opportunistic traders, fundamental traders, and small traders. Our method extends a plaid clustering technique with a smoothing framework that filters out transient patterns. The method is fast, robust, and suitable for a discovering trading ecosystems in a large number of electronic markets

Suggested Citation

  • Mankad, Shawn & Michailidis, George, 2013. "Discovering the ecosystem of an electronic financial market with a dynamic machine-learning method," Algorithmic Finance, IOS Press, vol. 2(2), pages 151-165.
    • Handle: RePEc:ris:iosalg:0021
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    Citations

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    Cited by:

    1. Pankaj Kumar, 2021. "Deep Hawkes Process for High-Frequency Market Making," Papers 2109.15110, arXiv.org.
    2. Adamantios Ntakaris & Martin Magris & Juho Kanniainen & Moncef Gabbouj & Alexandros Iosifidis, 2017. "Benchmark Dataset for Mid-Price Forecasting of Limit Order Book Data with Machine Learning Methods," Papers 1705.03233, arXiv.org, revised Mar 2020.

    More about this item

    Keywords

    trading strategies; high frequency trading; machine learning; clustering;
    All these keywords.

    JEL classification:

    • H00 - Public Economics - - General - - - General

    Statistics

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