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The Competitive Landscape of High-Frequency Trading Firms

Author

Listed:
  • Ekkehart Boehmer
  • Dan Li
  • Gideon Saar

Abstract

We examine product differentiation in the high-frequency trading (HFT) industry, where the “products” are secretive proprietary trading strategies. We demonstrate how principal component analysis can be used to detect underlying strategies common to multiple HFT firms and show that there are three product categories with distinct attributes. We study how HFT competition in each product category affects the market environment and present evidence that indicates how it influences the short-horizon volatility of stocks as well as the viability of trading venues. Received October 10, 2016; editorial decision September 30, 2017 by Editor Itay Goldstein. 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

  • Ekkehart Boehmer & Dan Li & Gideon Saar, 2018. "The Competitive Landscape of High-Frequency Trading Firms," The Review of Financial Studies, Society for Financial Studies, vol. 31(6), pages 2227-2276.
    • Handle: RePEc:oup:rfinst:v:31:y:2018:i:6:p:2227-2276.
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    File URL: http://hdl.handle.net/10.1093/rfs/hhx144
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    Cited by:

    1. Watson, Ethan D. & Woods, Donovan, 2022. "Exchange introduction and market competition: The entrance of MEMX and MIAX," Global Finance Journal, Elsevier, vol. 54(C).
    2. Bastian von Beschwitz & Donald B Keim & Massimo Massa, 2020. "First to “Read” the News: News Analytics and Algorithmic Trading," The Review of Asset Pricing Studies, Society for Financial Studies, vol. 10(1), pages 122-178.
    3. Nicholas Hirschey, 2021. "Do High-Frequency Traders Anticipate Buying and Selling Pressure?," Management Science, INFORMS, vol. 67(6), pages 3321-3345, June.
    4. Adrian, Tobias & Capponi, Agostino & Fleming, Michael & Vogt, Erik & Zhang, Hongzhong, 2020. "Intraday market making with overnight inventory costs," Journal of Financial Markets, Elsevier, vol. 50(C).
    5. Breckenfelder, Johannes, 2019. "Competition among high-frequency traders, and market quality," Working Paper Series 2290, European Central Bank.
    6. 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.
    7. Roşu, Ioanid, 2019. "Fast and slow informed trading," Journal of Financial Markets, Elsevier, vol. 43(C), pages 1-30.
    8. Bernales, Alejandro, 2019. "Make-take decisions under high-frequency trading competition," Journal of Financial Markets, Elsevier, vol. 45(C), pages 1-18.
    9. 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.
    10. Nilabhra Bhattacharya & Bidisha Chakrabarty & Xu (Frank) Wang, 2020. "High-frequency traders and price informativeness during earnings announcements," Review of Accounting Studies, Springer, vol. 25(3), pages 1156-1199, September.
    11. Ersan, Oguz & Simsir, Serif Aziz & Simsek, Koray D. & Hasan, Afan, 2021. "The speed of stock price adjustment to corporate announcements: Insights from Turkey," Emerging Markets Review, Elsevier, vol. 47(C).
    12. 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).
    13. Philip, R., 2020. "Estimating permanent price impact via machine learning," Journal of Econometrics, Elsevier, vol. 215(2), pages 414-449.
    14. Park, Seongkyu Gilbert & Ryu, Doojin, 2019. "Speed and trading behavior in an order-driven market," Pacific-Basin Finance Journal, Elsevier, vol. 53(C), pages 145-164.
    15. Bizzozero, Paolo & Flepp, Raphael & Franck, Egon, 2018. "The effect of fast trading on price discovery and efficiency: Evidence from a betting exchange," Journal of Economic Behavior & Organization, Elsevier, vol. 156(C), pages 126-143.
    16. Irtisam, Rasheek & Sokolov, Konstantin, 2023. "Do stock exchanges specialize? Evidence from the New Jersey transaction tax proposal," Journal of Banking & Finance, Elsevier, vol. 154(C).
    17. Cox, Justin & Woods, Donovan, 2023. "COVID-19 and market structure dynamics," Journal of Banking & Finance, Elsevier, vol. 147(C).
    18. 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.
    19. Anagnostidis, Panagiotis & Fontaine, Patrice, 2020. "Liquidity commonality and high frequency trading: Evidence from the French stock market," International Review of Financial Analysis, Elsevier, vol. 69(C).
    20. Zijian Shi & John Cartlidge, 2023. "Neural Stochastic Agent-Based Limit Order Book Simulation: A Hybrid Methodology," Papers 2303.00080, arXiv.org.
    21. Sánchez Serrano Antonio, 2020. "High-Frequency Trading and Systemic Risk: A Structured Review of Findings and Policies," Review of Economics, De Gruyter, vol. 71(3), pages 169-195, December.
    22. Michael Goldstein & Amy Kwan & Richard Philip, 2023. "High-Frequency Trading Strategies," Management Science, INFORMS, vol. 69(8), pages 4413-4434, August.
    23. Choi, Jungjun & Yang, Xiye, 2022. "Asymptotic properties of correlation-based principal component analysis," Journal of Econometrics, Elsevier, vol. 229(1), pages 1-18.

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