IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2405.08101.html

Data-driven measures of high-frequency trading

Author

Listed:
  • G. Ibikunle
  • B. Moews
  • D. Muravyev
  • K. Rzayev

Abstract

High-frequency trading (HFT) accounts for almost half of equity trading volume, yet it is not identified in public data. We develop novel data-driven measures of HFT activity that separate strategies that supply and demand liquidity. We train machine learning models to predict HFT activity observed in a proprietary dataset using concurrent public intraday data. Once trained on the dataset, these models generate HFT measures for the entire U.S. stock universe from 2010 to 2023. Our measures outperform conventional proxies, which struggle to capture HFT's time dynamics. We further validate them using shocks to HFT activity, including latency arbitrage, exchange speed bumps, and data feed upgrades. Finally, our measures reveal how HFT affects fundamental information acquisition. Liquidity-supplying HFTs improve price discovery around earnings announcements while liquidity-demanding strategies impede it.

Suggested Citation

  • G. Ibikunle & B. Moews & D. Muravyev & K. Rzayev, 2024. "Data-driven measures of high-frequency trading," Papers 2405.08101, arXiv.org, revised Mar 2025.
    • Handle: RePEc:arx:papers:2405.08101
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2405.08101
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jonathan Brogaard & Björn Hagströmer & Lars Nordén & Ryan Riordan, 2015. "Trading Fast and Slow: Colocation and Liquidity," The Review of Financial Studies, Society for Financial Studies, vol. 28(12), pages 3407-3443.
    2. Brogaard, Jonathan & Carrion, Allen & Moyaert, Thibaut & Riordan, Ryan & Shkilko, Andriy & Sokolov, Konstantin, 2018. "High frequency trading and extreme price movements," Journal of Financial Economics, Elsevier, vol. 128(2), pages 253-265.
    3. Lee, Charles M. C. & Radhakrishna, Balkrishna, 2000. "Inferring investor behavior: Evidence from TORQ data," Journal of Financial Markets, Elsevier, vol. 3(2), pages 83-111, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Adamantios Ntakaris & Gbenga Ibikunle, 2024. "Minimal Batch Adaptive Learning Policy Engine for Real-Time Mid-Price Forecasting in High-Frequency Trading," Papers 2412.19372, arXiv.org, revised Dec 2024.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ana Fiorella Carvajal & Ricardo Bebczuk, 2024. "Liquidity In Corporate Markets," World Bank Publications - Reports 41408, The World Bank Group.
    2. Yang, Haijun & Ge, Hengshun & Luo, Ying, 2020. "The optimal bid-ask price strategies of high-frequency trading and the effect on market liquidity," Research in International Business and Finance, Elsevier, vol. 53(C).
    3. Aït-Sahalia, Yacine & Brunetti, Celso, 2020. "High frequency traders and the price process," Journal of Econometrics, Elsevier, vol. 217(1), pages 20-45.
    4. Sida Li & Xin Wang & Mao Ye, 2019. "Who Provides Liquidity, and When?," NBER Working Papers 25972, National Bureau of Economic Research, Inc.
    5. Khairul Zharif Zaharudin & Martin R. Young & Wei‐Huei Hsu, 2022. "High‐frequency trading: Definition, implications, and controversies," Journal of Economic Surveys, Wiley Blackwell, vol. 36(1), pages 75-107, February.
    6. Kemme, David M. & McInish, Thomas H. & Zhang, Jiang, 2022. "Market fairness and efficiency: Evidence from the Tokyo Stock Exchange," Journal of Banking & Finance, Elsevier, vol. 134(C).
    7. Aquilina, Matteo & Budish, Eric B. & O'Neill, Peter, 2020. "Quantifying the High-Frequency Trading "Arms Race": A Simple New Methodology and Estimates," Working Papers 300, The University of Chicago Booth School of Business, George J. Stigler Center for the Study of the Economy and the State.
    8. Tao Chen, 2022. "Are individuals informed in global markets?," Empirical Economics, Springer, vol. 63(1), pages 243-263, July.
    9. Jurkatis, Simon, 2022. "Inferring trade directions in fast markets," Journal of Financial Markets, Elsevier, vol. 58(C).
    10. Nihad Aliyev & Matteo Aquilina & Khaladdin Rzayev & Sonya Zhu, 2024. "Through stormy seas: how fragile is liquidity across asset classes and time?," BIS Working Papers 1229, Bank for International Settlements.
    11. Andriy Shkilko & Konstantin Sokolov, 2020. "Every Cloud Has a Silver Lining: Fast Trading, Microwave Connectivity, and Trading Costs," Journal of Finance, American Finance Association, vol. 75(6), pages 2899-2927, December.
    12. Sánchez Serrano Antonio, 2020. "High-Frequency Trading and Systemic Risk: A Structured Review of Findings and Policies," Review of Economics, De Gruyter, vol. 71(3), pages 169-195, December.
    13. Michael Goldstein & Amy Kwan & Richard Philip, 2023. "High-Frequency Trading Strategies," Management Science, INFORMS, vol. 69(8), pages 4413-4434, August.
    14. Dodd, Olga & Frijns, Bart & Indriawan, Ivan & Pascual, Roberto, 2023. "US cross-listing and domestic high-frequency trading: Evidence from Canadian stocks," Journal of Empirical Finance, Elsevier, vol. 72(C), pages 301-320.
    15. Alex Frino & Dionigi Gerace & Masud Behnia, 2021. "The impact of algorithmic trading on liquidity in futures markets: New insights into the resiliency of spreads and depth," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 41(8), pages 1301-1314, August.
    16. Bellia, Mario & Christensen, Kim & Kolokolov, Aleksey & Pelizzon, Loriana & Renò, Roberto, 2022. "Do designated market makers provide liquidity during a flash crash?," SAFE Working Paper Series 270, Leibniz Institute for Financial Research SAFE, revised 2022.
    17. O'Hara, Maureen & Alex Zhou, Xing, 2021. "The electronic evolution of corporate bond dealers," Journal of Financial Economics, Elsevier, vol. 140(2), pages 368-390.
    18. Malay K. Dey & B. Radhakrishna (Radha), 2007. "Who Trades Around Earnings Announcements? Evidence from TORQ Data," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 34(1‐2), pages 269-291, January.
    19. Seida, Jim A. & Wempe, William F., 2000. "Do capital gain tax rate increases affect individual investors' trading decisions?," Journal of Accounting and Economics, Elsevier, vol. 30(1), pages 33-57, August.
    20. Saggese, Pietro & Belmonte, Alessandro & Dimitri, Nicola & Facchini, Angelo & Böhme, Rainer, 2023. "Arbitrageurs in the Bitcoin ecosystem: Evidence from user-level trading patterns in the Mt. Gox exchange platform," Journal of Economic Behavior & Organization, Elsevier, vol. 213(C), pages 251-270.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2405.08101. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.