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Empirical Limitations on High Frequency Trading Profitability

Author

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  • Michael Kearns
  • Alex Kulesza
  • Yuriy Nevmyvaka

Abstract

Addressing the ongoing examination of high-frequency trading practices in financial markets, we report the results of an extensive empirical study estimating the maximum possible profitability of the most aggressive such practices, and arrive at figures that are surprisingly modest. By "aggressive" we mean any trading strategy exclusively employing market orders and relatively short holding periods. Our findings highlight the tension between execution costs and trading horizon confronted by high-frequency traders, and provide a controlled and large-scale empirical perspective on the high-frequency debate that has heretofore been absent. Our study employs a number of novel empirical methods, including the simulation of an "omniscient" high-frequency trader who can see the future and act accordingly.

Suggested Citation

  • Michael Kearns & Alex Kulesza & Yuriy Nevmyvaka, 2010. "Empirical Limitations on High Frequency Trading Profitability," Papers 1007.2593, arXiv.org, revised Sep 2010.
    • Handle: RePEc:arx:papers:1007.2593
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    Cited by:

    1. Carlos Lenczewski, 2016. "The Role of High-Frequency Traders in the Foreign Exchange Market Bid-Ask Spreads," EUSP Department of Economics Working Paper Series Ec-01/16, European University at St. Petersburg, Department of Economics.
    2. Marouane Anane & Frédéric Abergel, 2014. "Optimal high frequency strategy in an omniscient order book," Working Papers hal-01006401, HAL.
    3. Murray, Hamish & Pham, Thu Phuong & Singh, Harminder, 2016. "Latency reduction and market quality: The case of the Australian Stock Exchange," International Review of Financial Analysis, Elsevier, vol. 46(C), pages 257-265.
    4. Viktor Manahov & Robert Hudson, 2013. "New Evidence of Technical Trading Profitability," Economics Bulletin, AccessEcon, vol. 33(4), pages 2493-2503.
    5. Serbera, Jean-Philippe & Paumard, Pascal, 2016. "The fall of high-frequency trading: A survey of competition and profits," Research in International Business and Finance, Elsevier, vol. 36(C), pages 271-287.
    6. Bonnie F. Van Ness & Robert A. Van Ness & Serhat Yildiz, 2017. "The role of HFTs in order flow toxicity and stock price variance, and predicting changes in HFTs’ liquidity provisions," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 41(4), pages 739-762, October.
    7. Álvaro Cartea & José Penalva, 2012. "Where is the Value in High Frequency Trading?," Quarterly Journal of Finance (QJF), World Scientific Publishing Co. Pte. Ltd., vol. 2(03), pages 1-46.
    8. J. Doyne Farmer & Spyros Skouras, 2013. "An ecological perspective on the future of computer trading," Quantitative Finance, Taylor & Francis Journals, vol. 13(3), pages 325-346, February.
    9. Alexandru-Ioan Stan, 2018. "Computational speed and high-frequency trading profitability: an ecological perspective," Electronic Markets, Springer;IIM University of St. Gallen, vol. 28(3), pages 381-395, August.
    10. Rahi, Rohit & Zigrand, Jean-Pierre, 2013. "Market quality and contagion in fragmented markets," LSE Research Online Documents on Economics 60971, London School of Economics and Political Science, LSE Library.
    11. Taiga Saito & Akihiko Takahashi, 2018. "Online Supplement for "Stochastic Differential Game in High Frequency Market"," CIRJE F-Series CIRJE-F-1087, CIRJE, Faculty of Economics, University of Tokyo.
    12. Carlos Lenczewski, 2016. "The Role of High-Frequency Traders in the Foreign Exchange Market Bid-Ask Spreads," EUSP Department of Economics Working Paper Series 2016/01, European University at St. Petersburg, Department of Economics.
    13. Manahov, Viktor & Hudson, Robert & Gebka, Bartosz, 2014. "Does high frequency trading affect technical analysis and market efficiency? And if so, how?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 28(C), pages 131-157.
    14. Purba Mukerji & Christine Chung & Timothy Walsh & Bo Xiong, 2019. "The Impact of Algorithmic Trading in a Simulated Asset Market," JRFM, MDPI, vol. 12(2), pages 1-11, April.

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