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High-Frequency Market Making to Large Institutional Trades

Author

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  • Robert A Korajczyk
  • Dermot Murphy

Abstract

We study market-making high-frequency trader (HFT) dynamics around large institutional trades in Canadian equities markets using order-level data with masked trader identification. Following a regulatory change that negatively affected HFT order activity, we find that bid-ask spreads increased and price impact decreased for institutional trades. The decrease in price impact is strongest for informed institutional traders. During institutional trade executions, HFTs submit more same-direction orders and increase their inventory mean reversion rates. Our evidence indicates that high-frequency trading is associated with lower transaction costs for small, uninformed trades and higher transaction costs for large, informed trades.Received May 24, 2016; editorial decision June 21, 2018 by Editor Andrew Karolyi. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.

Suggested Citation

  • Robert A Korajczyk & Dermot Murphy, 2019. "High-Frequency Market Making to Large Institutional Trades," The Review of Financial Studies, Society for Financial Studies, vol. 32(3), pages 1034-1067.
    • Handle: RePEc:oup:rfinst:v:32:y:2019:i:3:p:1034-1067.
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    File URL: http://hdl.handle.net/10.1093/rfs/hhy079
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    Cited by:

    1. Ziyi Xu & Xue Cheng, 2023. "The Effects of High-frequency Anticipatory Trading: Small Informed Trader vs. Round-Tripper," Papers 2304.13985, arXiv.org, revised Feb 2024.
    2. Jun Deng & Hua Zong & Yun Wang, 2022. "Static Replication of Impermanent Loss for Concentrated Liquidity Provision in Decentralised Markets," Papers 2205.12043, arXiv.org, revised Mar 2023.
    3. Eaton, Gregory W. & Green, T. Clifton & Roseman, Brian S. & Wu, Yanbin, 2022. "Retail trader sophistication and stock market quality: Evidence from brokerage outages," Journal of Financial Economics, Elsevier, vol. 146(2), pages 502-528.
    4. Brice Corgnet & Mark DeSantis & Christoph Siemroth, 2023. "Algorithmic Trading, Price Efficiency and Welfare: An Experimental Approach," Working Papers 2313, Groupe d'Analyse et de Théorie Economique Lyon St-Étienne (GATE Lyon St-Étienne), Université de Lyon.
    5. Tripathi, Abhinava & Dixit, Alok & Vipul,, 2021. "Information content of order imbalance in an order-driven market: Indian Evidence," Finance Research Letters, Elsevier, vol. 41(C).
    6. Chen, Marie & Garriott, Corey, 2020. "High-frequency trading and institutional trading costs," Journal of Empirical Finance, Elsevier, vol. 56(C), pages 74-93.
    7. Zheng, Jiayi & Zhu, Yushu, 2023. "Algorithmic trading and block ownership initiation: An information perspective," The British Accounting Review, Elsevier, vol. 55(4).
    8. Breedon, Francis & Chen, Louisa & Ranaldo, Angelo & Vause, Nicholas, 2023. "Judgment day: Algorithmic trading around the Swiss franc cap removal," Journal of International Economics, Elsevier, vol. 140(C).
    9. Karolis Liaudinskas, 2022. "Human vs. Machine: Disposition Effect among Algorithmic and Human Day Traders," Working Paper 2022/6, Norges Bank.
    10. Cox, Justin & Woods, Donovan, 2023. "COVID-19 and market structure dynamics," Journal of Banking & Finance, Elsevier, vol. 147(C).
    11. 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.
    12. Neumeier, Christian & Gozluklu, Arie & Hoffmann, Peter & O’Neill, Peter & Suntheim, Felix, 2023. "Banning dark pools: Venue selection and investor trading costs," Journal of Financial Markets, Elsevier, vol. 65(C).
    13. 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).
    14. Sagade, Satchit & Scharnowski, Stefan & Westheide, Christian, 2022. "Broker colocation and the execution costs of customer and proprietary orders," SAFE Working Paper Series 366, Leibniz Institute for Financial Research SAFE.
    15. Pham, Manh Cuong & Anderson, Heather Margot & Duong, Huu Nhan & Lajbcygier, Paul, 2020. "The effects of trade size and market depth on immediate price impact in a limit order book market," Journal of Economic Dynamics and Control, Elsevier, vol. 120(C).
    16. Nicholas Hirschey, 2021. "Do High-Frequency Traders Anticipate Buying and Selling Pressure?," Management Science, INFORMS, vol. 67(6), pages 3321-3345, June.
    17. Mark Marner-Hausen, 2022. "Developing a Framework for Real-Time Trading in a Laboratory Financial Market," ECONtribute Discussion Papers Series 172, University of Bonn and University of Cologne, Germany.
    18. 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.
    19. Xiao Chen & Jin Hyuk Choi & Kasper Larsen & Duane J. Seppi, 2022. "Learning about latent dynamic trading demand $$^*$$ ∗," Mathematics and Financial Economics, Springer, volume 16, number 1, June.
    20. Gider, Jasmin & Schmickler, Simon & Westheide, Christian, 2019. "High-frequency trading and price informativeness," SAFE Working Paper Series 248, Leibniz Institute for Financial Research SAFE, revised 2019.
    21. Corey Garriot & Ryan Riordan, 2020. "Trading on Long-term Information," Staff Working Papers 20-20, Bank of Canada.
    22. Matteo Aquilina & Eric Budish & Peter O’Neill, 2022. "Quantifying the High-Frequency Trading “Arms Race”," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 137(1), pages 493-564.

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