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High-frequency trading behaviour and its impact on market quality: evidence from the UK equity market

Author

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  • Benos, Evangelos

    (Bank of England)

  • Sagade, Satchit

    (ICMA Centre, Henley Business School, University of Reading)

Abstract

We analyse the intraday behaviour of high-frequency traders (HFTs) and its impact on aspects of market quality such as liquidity, price discovery and excess volatility. For that, we use a unique transactions data set for four UK stocks, over the period of a randomly selected week. Our data identifies the counterparties to each transaction, enabling us to track the trading behaviour of individual HFTs. We first find that HFTs differ significantly from each other in terms of liquidity provision: while some HFTs mostly consume liquidity (ie trade more ‘aggressively’) by primarily executing trades via market orders, others mostly supply liquidity (ie trade more ‘passively’) by primarily executing trades via limit orders. To examine how trading behaviour is related to these patterns of liquidity provision, we split the HFTs in two groups, according to their trade aggressiveness, and examine the behaviour and impact of each group separately. We find that the ‘passive’ HFTs follow a trading strategy consistent with market making and as such their trades have alternating signs and are independent of recent (ten-second) price changes. By contrast, ‘aggressive’ HFTs exhibit persistence in the direction of their trades and trade in line with the recent (ten-second) price trend. We then explore the relationship between HFT activity and market quality. We find that both higher price volatility and lower spreads cause HFT activity to increase. We suggest a number of reasons as to why this might be so. Finally, we use a tick time specification to examine the impact of HFT activity on price discovery (ie information-based volatility) and noise (ie excess volatility). We find that while HFTs have a higher information-to-noise contribution ratio than non-HFTs, there are instances where this is accompanied by a large absolute noise contribution.

Suggested Citation

  • Benos, Evangelos & Sagade, Satchit, 2012. "High-frequency trading behaviour and its impact on market quality: evidence from the UK equity market," Bank of England working papers 469, Bank of England.
  • Handle: RePEc:boe:boeewp:0469
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    References listed on IDEAS

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    Cited by:

    1. Oliver Linton & Soheil Mahmoodzadeh, 2018. "Implications of High-Frequency Trading for Security Markets," Annual Review of Economics, Annual Reviews, vol. 10(1), pages 237-259, August.
    2. Antoaneta Sergueiva, 2013. "Systemic Risk Identification, Modelling, Analysis, and Monitoring: An Integrated Approach," Papers 1310.6486, arXiv.org.
    3. Phiri, Andrew, 2017. "Threshold convergence between the federal fund rate and South African equity returns around the colocation period," Business and Economic Horizons (BEH), Prague Development Center (PRADEC), vol. 13(1).
    4. Viktor Manahov, 2018. "The rise of the machines in commodities markets: new evidence obtained using Strongly Typed Genetic Programming," Annals of Operations Research, Springer, vol. 260(1), pages 321-352, January.
    5. Anderson, Nicola & Webber, Lewis & Noss, Joseph & Beale, Daniel & Crowley-Reidy, Liam, 2015. "Financial Stability Paper 34: The resilience of financial market liquidity," Bank of England Financial Stability Papers 34, Bank of England.
    6. Karvik, Geir-Are & Noss, Joseph & Worlidge, Jack & Beale, Daniel, 2018. "The deeds of speed: an agent-based model of market liquidity and flash episodes," Bank of England working papers 743, Bank of England.
    7. Massa, Massimo & von Beschwitz, Bastian & Keim, Donald B, 2015. "First to ?Read? the News: News Analytics and Institutional Trading," CEPR Discussion Papers 10534, C.E.P.R. Discussion Papers.
    8. Fry, John & Serbera, Jean-Philippe, 2017. "Modelling and mitigation of Flash Crashes," MPRA Paper 82457, University Library of Munich, Germany.
    9. Robert Jarrow & Hao Li, 2013. "Abnormal Profit Opportunities and the Informational Advantage of High Frequency Trading," Quarterly Journal of Finance (QJF), World Scientific Publishing Co. Pte. Ltd., vol. 3(02), pages 1-12.
    10. Elias Strehle, 2016. "Optimal Execution in a Multiplayer Model of Transient Price Impact," Papers 1609.00599, arXiv.org, revised Mar 2019.
    11. Schlepper, Kathi, 2016. "High-frequency trading in the Bund futures market," Discussion Papers 15/2016, Deutsche Bundesbank.
    12. Bholat, David, 2015. "Big data and central banks," Bank of England Quarterly Bulletin, Bank of England, vol. 55(1), pages 86-93.
    13. Takuo Higashide & Katsuyuki Tanaka & Takuji Kinkyo & Shigeyuki Hamori, 2021. "New Dataset for Forecasting Realized Volatility: Is the Tokyo Stock Exchange Co-Location Dataset Helpful for Expansion of the Heterogeneous Autoregressive Model in the Japanese Stock Market?," JRFM, MDPI, vol. 14(5), pages 1-18, May.

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    More about this item

    Keywords

    High-frequency trading; liquidity; price discovery; volatility;
    All these keywords.

    JEL classification:

    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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