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High-Frequency Trading and the Execution Costs of Institutional Investors

Author

Listed:
  • Michael Goldstein
  • Jonathan Brogaard
  • Terrence Hendershott
  • Stefan Hunt
  • Carla Ysusi

Abstract

This paper studies whether high-frequency trading (HFT) increases the execution costs of institutional investors. We use technology upgrades that lower the latency of the London Stock Exchange to obtain variation in the level of HFT over time. Following upgrades, the level of HFT increases. Around these shocks to HFT institutional traders’ costs remain unchanged. We find no clear evidence that HFT impacts institutional execution costs.

Suggested Citation

  • Michael Goldstein & Jonathan Brogaard & Terrence Hendershott & Stefan Hunt & Carla Ysusi, 2014. "High-Frequency Trading and the Execution Costs of Institutional Investors," The Financial Review, Eastern Finance Association, vol. 49(2), pages 345-369, May.
    • Handle: RePEc:bla:finrev:v:49:y:2014:i:2:p:345-369
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    Citations

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    Cited by:

    1. 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.
    2. 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.
    3. Benos, Evangelos & Sagade, Satchit, 2016. "Price discovery and the cross-section of high-frequency trading," Journal of Financial Markets, Elsevier, vol. 30(C), pages 54-77.
    4. Manahov, Viktor, 2016. "A note on the relationship between high-frequency trading and latency arbitrage," International Review of Financial Analysis, Elsevier, vol. 47(C), pages 281-296.
    5. Zheng, Jiayi & Zhu, Yushu, 2023. "Algorithmic trading and block ownership initiation: An information perspective," The British Accounting Review, Elsevier, vol. 55(4).
    6. Fabio S. Dias & Gareth W. Peters, 2020. "A Non-parametric Test and Predictive Model for Signed Path Dependence," Computational Economics, Springer;Society for Computational Economics, vol. 56(2), pages 461-498, August.
    7. Guanqing Liu, 2019. "Technical Trading Behaviour: Evidence from Chinese Rebar Futures Market," Computational Economics, Springer;Society for Computational Economics, vol. 54(2), pages 669-704, August.
    8. Frino, Alex & Mollica, Vito & Webb, Robert I. & Zhang, Shunquan, 2017. "The impact of latency sensitive trading on high frequency arbitrage opportunities," Pacific-Basin Finance Journal, Elsevier, vol. 45(C), pages 91-102.
    9. Mestel, Roland & Murg, Michael & Theissen, Erik, 2018. "Algorithmic trading and liquidity: Long term evidence from Austria," Finance Research Letters, Elsevier, vol. 26(C), pages 198-203.
    10. Upson, James & Van Ness, Robert A., 2017. "Multiple markets, algorithmic trading, and market liquidity," Journal of Financial Markets, Elsevier, vol. 32(C), pages 49-68.
    11. Efstathios Panayi & Gareth Peters, 2015. "Stochastic simulation framework for the Limit Order Book using liquidity motivated agents," Papers 1501.02447, arXiv.org, revised Jan 2015.
    12. Efstathios Panayi & Gareth W. Peters, 2015. "Stochastic simulation framework for the limit order book using liquidity-motivated agents," International Journal of Financial Engineering (IJFE), World Scientific Publishing Co. Pte. Ltd., vol. 2(02), pages 1-52.
    13. Sifat, Imtiaz Mohammad & Mohamad, Azhar, 2015. "Order imbalance and selling aggression under a shorting ban: Evidence from the UK," International Review of Financial Analysis, Elsevier, vol. 42(C), pages 368-379.
    14. Rzayev, Khaladdin & Ibikunle, Gbenga & Steffen, Tom, 2023. "The market quality implications of speed in cross-platform trading: evidence from Frankfurt-London microwave," LSE Research Online Documents on Economics 119989, London School of Economics and Political Science, LSE Library.
    15. 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).
    16. Aldrich, Eric M. & Friedman, Daniel, 2017. "Order protection through delayed messaging," Discussion Papers, Research Professorship Market Design: Theory and Pragmatics SP II 2017-502, WZB Berlin Social Science Center.
    17. Hu, Gang & Jo, Koren M. & Wang, Yi Alex & Xie, Jing, 2018. "Institutional trading and Abel Noser data," Journal of Corporate Finance, Elsevier, vol. 52(C), pages 143-167.
    18. Zhou, Hao & Kalev, Petko S., 2019. "Algorithmic and high frequency trading in Asia-Pacific, now and the future," Pacific-Basin Finance Journal, Elsevier, vol. 53(C), pages 186-207.
    19. John Cotter & Niall McGeever, 2018. "Are equity market anomalies disappearing? Evidence from the U.K," Working Papers 201804, Geary Institute, University College Dublin.
    20. Eric M. Aldrich & Daniel Friedman, 2023. "Order Protection Through Delayed Messaging," Management Science, INFORMS, vol. 69(2), pages 774-790, February.
    21. Aït-Sahalia, Yacine & Brunetti, Celso, 2020. "High frequency traders and the price process," Journal of Econometrics, Elsevier, vol. 217(1), pages 20-45.
    22. Ziyi Xu & Xue Cheng, 2022. "Are Large Traders Harmed by Front-running HFTs?," Papers 2211.06046, arXiv.org, revised Jul 2023.
    23. Sağlam, Mehmet & Moallemi, Ciamac C. & Sotiropoulos, Michael G., 2019. "Short-term trading skill: An analysis of investor heterogeneity and execution quality," Journal of Financial Markets, Elsevier, vol. 42(C), pages 1-28.

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