IDEAS home Printed from https://ideas.repec.org/a/spr/elcore/v18y2018i2d10.1007_s10660-017-9273-8.html
   My bibliography  Save this article

Do high-frequency fleeting orders exacerbate market illiquidity?

Author

Listed:
  • Kun Li

    (Beijing Normal University)

Abstract

This paper investigates whether fleeting orders account for market illiquidity. By discussing relevant trading strategies, our study suggests that fleeting orders serve for market making and contribute to market liquidity. Moreover, fleeting orders do not distort price accuracy and are not the outcome of illegal manipulation. We then empirically examine fleeting orders using a NASDAQ ITCH dataset. Our results indicate that fleeting orders have very small effects on market illiquidity and account for neither the amplification of price impact nor the decrease of revenues to liquidity providers. In summary, fleeting orders are not the trigger of market illiquidity and thus should not be considered as “spoofing” defined by the Dodd–Frank Act.

Suggested Citation

  • Kun Li, 2018. "Do high-frequency fleeting orders exacerbate market illiquidity?," Electronic Commerce Research, Springer, vol. 18(2), pages 241-255, June.
    • Handle: RePEc:spr:elcore:v:18:y:2018:i:2:d:10.1007_s10660-017-9273-8
    DOI: 10.1007/s10660-017-9273-8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10660-017-9273-8
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10660-017-9273-8?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Glosten, Lawrence R, 1987. "Components of the Bid-Ask Spread and the Statistical Properties of Transaction Prices," Journal of Finance, American Finance Association, vol. 42(5), pages 1293-1307, December.
    2. Hasbrouck, Joel & Saar, Gideon, 2009. "Technology and liquidity provision: The blurring of traditional definitions," Journal of Financial Markets, Elsevier, vol. 12(2), pages 143-172, May.
    3. Bessembinder, Hendrik, 2003. "Issues in assessing trade execution costs," Journal of Financial Markets, Elsevier, vol. 6(3), pages 233-257, May.
    4. 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.
    5. Jonathan Brogaard & Terrence Hendershott & Ryan Riordan, 2014. "High-Frequency Trading and Price Discovery," The Review of Financial Studies, Society for Financial Studies, vol. 27(8), pages 2267-2306.
    6. Gerig, Austin & Michayluk, David, 2017. "Automated liquidity provision," Pacific-Basin Finance Journal, Elsevier, vol. 45(C), pages 1-13.
    7. Menkveld, Albert J., 2013. "High frequency trading and the new market makers," Journal of Financial Markets, Elsevier, vol. 16(4), pages 712-740.
    8. Austin Gerig & David Michayluk, 2010. "Automated Liquidity Provision and the Demise of Traditional Market Making," Papers 1007.2352, arXiv.org.
    9. Albert S. Kyle & S. Viswanathan, 2008. "How to Define Illegal Price Manipulation," American Economic Review, American Economic Association, vol. 98(2), pages 274-279, May.
    10. James Angel & Douglas McCabe, 2013. "Fairness in Financial Markets: The Case of High Frequency Trading," Journal of Business Ethics, Springer, vol. 112(4), pages 585-595, February.
    11. Andrei Kirilenko & Albert S. Kyle & Mehrdad Samadi & Tugkan Tuzun, 2017. "The Flash Crash: High-Frequency Trading in an Electronic Market," Journal of Finance, American Finance Association, vol. 72(3), pages 967-998, June.
    12. 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.
    13. Cooper, Ricky & Davis, Michael & Van Vliet, Ben, 2016. "The Mysterious Ethics of High-Frequency Trading," Business Ethics Quarterly, Cambridge University Press, vol. 26(1), pages 1-22, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Baldauf, Markus & Mollner, Joshua, 2022. "Fast traders make a quick buck: The role of speed in liquidity provision," Journal of Financial Markets, Elsevier, vol. 58(C).
    2. Bellia, Mario & Pelizzon, Loriana & Subrahmanyam, Marti & Uno, Jun & Yuferova, Darya, 2017. "Coming early to the party," SAFE Working Paper Series 182, Leibniz Institute for Financial Research SAFE.
      • Mario Bellia & Loriana Pelizzon & Marti G. Subrahmanyam & Jun Uno & Darya Yuferova, 2020. "Coming early to the party," Working Papers 2020:11, Department of Economics, University of Venice "Ca' Foscari".
    3. Andrea Roncella & Ignacio Ferrero, 2022. "The Ethics of Financial Market Making and Its Implications for High-Frequency Trading," Journal of Business Ethics, Springer, vol. 181(1), pages 139-151, November.
    4. 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.
    5. 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.
    6. Hans Degryse & Rudy de Winne & Carole Gresse & Richard Payne, 2018. "Cross-Venue Liquidity Provision: High Frequency Trading and Ghost Liquidity," Post-Print hal-01947824, HAL.
    7. Hussain, Syed Mujahid & Ahmad, Nisar & Ahmed, Sheraz, 2023. "Applications of high-frequency data in finance: A bibliometric literature review," International Review of Financial Analysis, Elsevier, vol. 89(C).
    8. Zhou, Hao & Elliott, Robert J. & Kalev, Petko S., 2019. "Information or noise: What does algorithmic trading incorporate into the stock prices?," International Review of Financial Analysis, Elsevier, vol. 63(C), pages 27-39.
    9. Ravi Kashyap, 2019. "Imitation in the Imitation Game," Papers 1911.06893, arXiv.org.
    10. Michael Goldstein & Amy Kwan & Richard Philip, 2023. "High-Frequency Trading Strategies," Management Science, INFORMS, vol. 69(8), pages 4413-4434, August.
    11. 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.
    12. Albert J. Menkveld & Marius A. Zoican, 2017. "Need for Speed? Exchange Latency and Liquidity," The Review of Financial Studies, Society for Financial Studies, vol. 30(4), pages 1188-1228.
    13. Ya‐Kai Chang & Robin K. Chou, 2022. "Algorithmic trading and market quality: Evidence from the Taiwan index futures market," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(10), pages 1837-1855, October.
    14. Jagannathan, Ravi & Pelizzon, Loriana & Schaumburg, Ernst & Sherman, Mila Getmansky & Yuferova, Darya, 2022. "Recovery from fast crashes: Role of mutual funds," Journal of Financial Markets, Elsevier, vol. 59(PB).
    15. Hautsch, Nikolaus & Noé, Michael & Zhang, S. Sarah, 2017. "The ambivalent role of high-frequency trading in turbulent market periods," CFS Working Paper Series 580, Center for Financial Studies (CFS).
    16. Schlepper, Kathi, 2016. "High-frequency trading in the Bund futures market," Discussion Papers 15/2016, Deutsche Bundesbank.
    17. Angerer, Martin & Neugebauer, Tibor & Shachat, Jason, 2023. "Arbitrage bots in experimental asset markets," Journal of Economic Behavior & Organization, Elsevier, vol. 206(C), pages 262-278.
    18. 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).
    19. Ben Ammar, Imen & Hellara, Slaheddine & Ghadhab, Imen, 2020. "High-frequency trading and stock liquidity: An intraday analysis," Research in International Business and Finance, Elsevier, vol. 53(C).
    20. Benjamin Clapham & Martin Haferkorn & Kai Zimmermann, 2023. "The Impact of High-Frequency Trading on Modern Securities Markets," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 65(1), pages 7-24, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:elcore:v:18:y:2018:i:2:d:10.1007_s10660-017-9273-8. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.