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

Author

Listed:
  • 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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    File URL: https://www.bankofengland.co.uk/-/media/boe/files/working-paper/2012/high-frequency-trading-behavious-and-its-impact-on-market-quality-evidence-from-the-uk-equity-market.pdf
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    References listed on IDEAS

    as
    1. Hasbrouck, Joel, 1991. " Measuring the Information Content of Stock Trades," Journal of Finance, American Finance Association, vol. 46(1), pages 179-207, March.
    2. Michael J. Barclay & Terrence Hendershott & D. Timothy McCormick, 2003. "Competition among Trading Venues: Information and Trading on Electronic Communications Networks," Journal of Finance, American Finance Association, vol. 58(6), pages 2637-2666, December.
    3. Terrence Hendershott & Charles M. Jones & Albert J. Menkveld, 2011. "Does Algorithmic Trading Improve Liquidity?," Journal of Finance, American Finance Association, vol. 66(1), pages 1-33, February.
    4. Hasbrouck, Joel, 1991. "The Summary Informativeness of Stock Trades: An Econometric Analysis," Review of Financial Studies, Society for Financial Studies, vol. 4(3), pages 571-595.
    5. Schwert, G William, 1989. " Why Does Stock Market Volatility Change over Time?," Journal of Finance, American Finance Association, vol. 44(5), pages 1115-1153, December.
    6. Alain P. Chaboud & Benjamin Chiquoine & Erik Hjalmarsson & Clara Vega, 2009. "Rise of the machines: algorithmic trading in the foreign exchange market," International Finance Discussion Papers 980, Board of Governors of the Federal Reserve System (U.S.).
    7. Hasbrouck, Joel, 1993. "Assessing the Quality of a Security Market: A New Approach to Transaction-Cost Measurement," Review of Financial Studies, Society for Financial Studies, vol. 6(1), pages 191-212.
    8. Hendershott, Terrence & Moulton, Pamela C., 2011. "Automation, speed, and stock market quality: The NYSE's Hybrid," Journal of Financial Markets, Elsevier, vol. 14(4), pages 568-604, November.
    9. Lee, Charles M C & Ready, Mark J, 1991. " Inferring Trade Direction from Intraday Data," Journal of Finance, American Finance Association, vol. 46(2), pages 733-746, June.
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    Citations

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    Cited by:

    1. Antoaneta Sergueiva, 2013. "Systemic Risk Identification, Modelling, Analysis, and Monitoring: An Integrated Approach," Papers 1310.6486, arXiv.org.
    2. 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.
    3. Andrew Phiri, 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, vol. 13(1), pages 1-9, March.
    4. Keim, Donald B & Massa, Massimo & von Beschwitz, Bastian, 2015. "First to “Read” the News: News Analytics and Institutional Trading," CEPR Discussion Papers 10534, C.E.P.R. Discussion Papers.
    5. Fry, John & Serbera, Jean-Philippe, 2017. "Modelling and mitigation of Flash Crashes," MPRA Paper 82457, University Library of Munich, Germany.
    6. repec:wsi:qjfxxx:v:03:y:2013:i:02:n:s2010139213500122 is not listed on IDEAS
    7. Elias Strehle, 2016. "Are Order Anticipation Strategies Harmful? A Theoretical Approach," Papers 1609.00599, arXiv.org, revised Sep 2017.
    8. Schlepper, Kathi, 2016. "High-frequency trading in the Bund futures market," Discussion Papers 15/2016, Deutsche Bundesbank.
    9. Bholat, David, 2015. "Big data and central banks," Bank of England Quarterly Bulletin, Bank of England, vol. 55(1), pages 86-93.
    10. Linton, O. & Mahmoodzadeh, S., 2018. "Implications of High-Frequency Trading for Security Markets," Cambridge Working Papers in Economics 1802, Faculty of Economics, University of Cambridge.

    More about this item

    Keywords

    High-frequency trading; liquidity; price discovery; volatility;

    JEL classification:

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

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