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Identification of high-frequency trading: A machine learning approach

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  • Goudarzi, Mostafa
  • Bazzana, Flavio

Abstract

This study aims to develop a probabilistic model using machine learning techniques to identify high-frequency trading (HFT) based on order book data. The model enables precise intraday identifications, addressing the lack of a widely accepted framework for HFT identification and the inconsistencies arising from proxy indicators. Leveraging academic data, the model offers improved consistency and reproducibility for future HFT research. By incorporating fuzzy logic, the probabilistic model allows policymakers greater flexibility in shaping policies. The study utilises data from the BEDOFIH database of the French capital market and develops a robust classification model capable of accurately distinguishing HFT. Additionally, reverse engineering enhances the model’s interpretability by transforming it into an interpretable regression tree without compromising its predictability. This research contributes to advancing HFT research, providing valuable insights, and offering a transferable methodology for identifying HFT in diverse market contexts.

Suggested Citation

  • Goudarzi, Mostafa & Bazzana, Flavio, 2023. "Identification of high-frequency trading: A machine learning approach," Research in International Business and Finance, Elsevier, vol. 66(C).
    • Handle: RePEc:eee:riibaf:v:66:y:2023:i:c:s0275531923002040
    DOI: 10.1016/j.ribaf.2023.102078
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    References listed on IDEAS

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    1. Bernales, Alejandro, 2019. "Make-take decisions under high-frequency trading competition," Journal of Financial Markets, Elsevier, vol. 45(C), pages 1-18.
    2. Bazzana, Flavio & Collini, Andrea, 2020. "How does HFT activity impact market volatility and the bid-ask spread after an exogenous shock? An empirical analysis on S&P 500 ETF," The North American Journal of Economics and Finance, Elsevier, vol. 54(C).
    3. Jonathan Brogaard & Terrence Hendershott & Ryan Riordan, 2014. "High-Frequency Trading and Price Discovery," The Review of Financial Studies, Society for Financial Studies, vol. 27(8), pages 2267-2306.
    4. Ekkehart Boehmer & Dan Li & Gideon Saar, 2018. "The Competitive Landscape of High-Frequency Trading Firms," The Review of Financial Studies, Society for Financial Studies, vol. 31(6), pages 2227-2276.
    5. Benos, Evangelos & Sagade, Satchit, 2016. "Price discovery and the cross-section of high-frequency trading," Journal of Financial Markets, Elsevier, vol. 30(C), pages 54-77.
    6. Oguz Ersan & Cumhur Ekinci, 2016. "Algorithmic and high-frequency trading in Borsa Istanbul," Borsa Istanbul Review, Research and Business Development Department, Borsa Istanbul, vol. 16(4), pages 233-248, December.
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    Cited by:

    1. Carè, Rosella & Cumming, Douglas, 2024. "Technology and automation in financial trading: A bibliometric review," Research in International Business and Finance, Elsevier, vol. 71(C).

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