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A new approach for detecting high-frequency trading from order and trade data

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  • Ekinci, Cumhur
  • Ersan, Oguz

Abstract

We suggest a two-step approach in detecting HFT activity from order and trade data. While the first step focuses on multiple actions of an order submitter in low latency, the second searches for the surroundings of these orders to link related orders. On a sample of 2015 data from Borsa Istanbul, we estimate that average HFT involvement is 1.23%. HFT activity is generally higher in large cap stocks (2.88%). Most HFT orders are in the form of very rapidly canceled order submissions. A robustness check reveals a mean accuracy rate of 97% in the linkage of orders.

Suggested Citation

  • Ekinci, Cumhur & Ersan, Oguz, 2018. "A new approach for detecting high-frequency trading from order and trade data," Finance Research Letters, Elsevier, vol. 24(C), pages 313-320.
    • Handle: RePEc:eee:finlet:v:24:y:2018:i:c:p:313-320
    DOI: 10.1016/j.frl.2017.09.020
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    References listed on IDEAS

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    1. Aitken, Michael & Cumming, Douglas & Zhan, Feng, 2015. "High frequency trading and end-of-day price dislocation," Journal of Banking & Finance, Elsevier, vol. 59(C), pages 330-349.
    2. Menkveld, Albert J., 2013. "High frequency trading and the new market makers," Journal of Financial Markets, Elsevier, vol. 16(4), pages 712-740.
    3. Andrei Kirilenko & Albert S. Kyle & Mehrdad Samadi & Tugkan Tuzun, 2017. "The Flash Crash: High-Frequency Trading in an Electronic Market," Journal of Finance, American Finance Association, vol. 72(3), pages 967-998, June.
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    5. Carrion, Allen, 2013. "Very fast money: High-frequency trading on the NASDAQ," Journal of Financial Markets, Elsevier, vol. 16(4), pages 680-711.
    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.
    7. Hasbrouck, Joel & Saar, Gideon, 2013. "Low-latency trading," Journal of Financial Markets, Elsevier, vol. 16(4), pages 646-679.
    8. Conrad, Jennifer & Wahal, Sunil & Xiang, Jin, 2015. "High-frequency quoting, trading, and the efficiency of prices," Journal of Financial Economics, Elsevier, vol. 116(2), pages 271-291.
    9. Hagströmer, Björn & Nordén, Lars, 2013. "The diversity of high-frequency traders," Journal of Financial Markets, Elsevier, vol. 16(4), pages 741-770.
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    Cited by:

    1. Liu, Wei, 2021. "Can HFT profit in Chinese stock market?," Economics Letters, Elsevier, vol. 209(C).
    2. Karkowska, Renata & Palczewski, Andrzej, 2023. "Does high-frequency trading actually improve market liquidity? A comparative study for selected models and measures," Research in International Business and Finance, Elsevier, vol. 64(C).
    3. Ersan, Oguz & Simsir, Serif Aziz & Simsek, Koray D. & Hasan, Afan, 2021. "The speed of stock price adjustment to corporate announcements: Insights from Turkey," Emerging Markets Review, Elsevier, vol. 47(C).
    4. Jurich, Stephen N. & Mishra, Ajay Kumar & Parikh, Bhavik, 2020. "Indecisive algos: Do limit order revisions increase market load?," Journal of Behavioral and Experimental Finance, Elsevier, vol. 28(C).
    5. Ke, Yun & Zhang, Yanan, 2020. "Does high-frequency trading reduce market underreaction to earnings news?," Finance Research Letters, Elsevier, vol. 34(C).
    6. Görkem Ataman & Serpil Kahraman, 2022. "Comparing Decision Trees and Association Rules for Stock Market Expectations in BIST100 and BIST30," Scientific Annals of Economics and Business (continues Analele Stiintifice), Alexandru Ioan Cuza University, Faculty of Economics and Business Administration, vol. 69(3), pages 459-475, September.
    7. Ekinci, Cumhur & Ersan, Oğuz, 2022. "High-frequency trading and market quality: The case of a “slightly exposed” market," International Review of Financial Analysis, Elsevier, vol. 79(C).

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    More about this item

    Keywords

    High-frequency trading (HFT); HFT detection; Low latency trading; Borsa Istanbul;
    All these keywords.

    JEL classification:

    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • G23 - Financial Economics - - Financial Institutions and Services - - - Non-bank Financial Institutions; Financial Instruments; Institutional Investors

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