IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2304.13985.html
   My bibliography  Save this paper

The Effects of High-frequency Anticipatory Trading: Small Informed Trader vs. Round-Tripper

Author

Listed:
  • Ziyi Xu
  • Xue Cheng

Abstract

In an extended Kyle's model, the interactions between a large informed trader and a high-frequency trader (HFT) who can anticipate the former's incoming order are studied. We find that, in equilibrium, HFT may play the role of Small-IT or Round-Tripper: both of them trade in the same direction as IT in advance, but when IT's order arrives, Small-IT continues to take liquidity away, while Round-Tripper supplies liquidity back. So Small-IT always harms IT, while Round-Tripper may benefit her. What's more, with an anticipatory HFT, normal-speed small uninformed traders suffer less and price discovery is accelerated.

Suggested Citation

  • 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.
    • Handle: RePEc:arx:papers:2304.13985
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2304.13985
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Liyan Yang & Haoxiang Zhu, 2020. "Back-Running: Seeking and Hiding Fundamental Information in Order Flows," Review of Finance, European Finance Association, vol. 33(4), pages 1484-1533.
    2. Bessembinder, Hendrik & Carrion, Allen & Tuttle, Laura & Venkataraman, Kumar, 2016. "Liquidity, resiliency and market quality around predictable trades: Theory and evidence," Journal of Financial Economics, Elsevier, vol. 121(1), pages 142-166.
    3. Thierry Foucault & Johan Hombert & Ioanid Roşu, 2016. "News Trading and Speed," Journal of Finance, American Finance Association, vol. 71(1), pages 335-382, February.
    4. Albert J. Menkveld, 2016. "The Economics of High-Frequency Trading: Taking Stock," Annual Review of Financial Economics, Annual Reviews, vol. 8(1), pages 1-24, October.
    5. Dermot P. Murphy & Ramabhadran S. Thirumalai, 2017. "Short†Term Return Predictability And Repetitive Institutional Net Order Activity," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 40(4), pages 455-477, December.
    6. Kyle, Albert S, 1985. "Continuous Auctions and Insider Trading," Econometrica, Econometric Society, vol. 53(6), pages 1315-1335, November.
    7. Bruce Ian Carlin & Miguel Sousa Lobo & S. Viswanathan, 2007. "Episodic Liquidity Crises: Cooperative and Predatory Trading," Journal of Finance, American Finance Association, vol. 62(5), pages 2235-2274, October.
    8. 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.
    9. Yan, Lei & Irwin, Scott H. & Sanders, Dwight R., 2022. "Sunshine vs. predatory trading effects in commodity futures markets: New evidence from index rebalancing," Journal of Commodity Markets, Elsevier, vol. 26(C).
    10. Menkveld, Albert J., 2013. "High frequency trading and the new market makers," Journal of Financial Markets, Elsevier, vol. 16(4), pages 712-740.
    11. Huddart, Steven & Hughes, John S & Levine, Carolyn B, 2001. "Public Disclosure and Dissimulation of Insider Trades," Econometrica, Econometric Society, vol. 69(3), pages 665-681, May.
    12. Markus Baldauf & Joshua Mollner, 2020. "High‐Frequency Trading and Market Performance," Journal of Finance, American Finance Association, vol. 75(3), pages 1495-1526, June.
    13. 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.
    14. Bernhardt, Dan & Taub, Bart, 2008. "Front-running dynamics," Journal of Economic Theory, Elsevier, vol. 138(1), pages 288-296, January.
    15. Liyan Yang & Haoxiang Zhu, 2020. "Back-Running: Seeking and Hiding Fundamental Information in Order Flows," The Review of Financial Studies, Society for Financial Studies, vol. 33(4), pages 1484-1533.
    16. Hoffmann, Peter, 2014. "A dynamic limit order market with fast and slow traders," Journal of Financial Economics, Elsevier, vol. 113(1), pages 156-169.
    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. 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.
    2. Ziyi Xu & Xue Cheng, 2024. "Trading Large Orders in the Presence of Multiple High-Frequency Anticipatory Traders," Papers 2403.08202, arXiv.org.
    3. 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).
    4. Bongaerts, Dion & Achter, Mark Van, 2021. "Competition among liquidity providers with access to high-frequency trading technology," Journal of Financial Economics, Elsevier, vol. 140(1), pages 220-249.
    5. 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.
    6. Ziyi Xu & Xue Cheng, 2022. "Are Large Traders Harmed by Front-running HFTs?," Papers 2211.06046, arXiv.org, revised Jul 2023.
    7. Corey Garriot & Ryan Riordan, 2020. "Trading on Long-term Information," Staff Working Papers 20-20, Bank of Canada.
    8. 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.
    9. 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.
    10. Nicholas Hirschey, 2021. "Do High-Frequency Traders Anticipate Buying and Selling Pressure?," Management Science, INFORMS, vol. 67(6), pages 3321-3345, June.
    11. Xu, Ke, 2023. "High frequency market making during stressed periods," International Review of Economics & Finance, Elsevier, vol. 87(C), pages 379-397.
    12. Roşu, Ioanid, 2019. "Fast and slow informed trading," Journal of Financial Markets, Elsevier, vol. 43(C), pages 1-30.
    13. 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).
    14. 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".
    15. Choi, Jin Hyuk & Larsen, Kasper & Seppi, Duane J., 2019. "Information and trading targets in a dynamic market equilibrium," Journal of Financial Economics, Elsevier, vol. 132(3), pages 22-49.
    16. 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.
    17. 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).
    18. Aliyev, Nihad & Huseynov, Fariz & Rzayev, Khaladdin, 2022. "Algorithmic trading and investment-to-price sensitivity," LSE Research Online Documents on Economics 118844, London School of Economics and Political Science, LSE Library.
    19. 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.
    20. Li, Sida & Ye, Mao & Zheng, Miles, 2023. "Refusing the best price?," Journal of Financial Economics, Elsevier, vol. 147(2), pages 317-337.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    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:arx:papers:2304.13985. 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: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    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.