IDEAS home Printed from https://ideas.repec.org/p/chu/wpaper/20-39.html
   My bibliography  Save this paper

Speed Traps: Algorithmic Trader Performance Under Alternative Market Structures

Author

Listed:
  • Yan Peng

    (School of Economics and Management, Wuhan University)

  • Jason Shachat

    (Durham University Business School; Economics and Management School, Wuhan University; Chapman University)

  • Lijia Wei

    (School of Economics and Management, Wuhan University)

  • S. Sarah Zhang

    (Alliance Manchester Business School, University of Manchester)

Abstract

Using laboratory experiments, we illustrate that trading algorithms that prioritize low latency pose certain pitfalls in a variety of market structures and configurations. In hybrid double auctions markets with human traders and trading agents, we find superior performance of trading agents to human traders in balanced markets with the same number of human and Zero Intelligence Plus (ZIP) buyers and sellers only, thus providing a partial replication of Das et al. (2001). However, in unbalanced markets and extreme market structures, such as monopolies and duopolies, fast ZIP agents fall into a speed trap and both human participants and slow ZIP agents outperform fast ZIP agents. For human traders, faster reaction time significantly improves trading performance, while Theory of Mind can be detrimental for human buyers, but beneficial for human sellers.

Suggested Citation

  • Yan Peng & Jason Shachat & Lijia Wei & S. Sarah Zhang, 2020. "Speed Traps: Algorithmic Trader Performance Under Alternative Market Structures," Working Papers 20-39, Chapman University, Economic Science Institute.
    • Handle: RePEc:chu:wpaper:20-39
    as

    Download full text from publisher

    File URL: https://digitalcommons.chapman.edu/esi_working_papers/334/
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.
    3. Bao, Te, 2022. "Comments on “the role of information in a continuous double auction: An experiment and learning model” by Mikhail Anufriev, Jasmina Arifovic, John Ledyard and Valentyn Panchenko," Journal of Economic Dynamics and Control, Elsevier, vol. 141(C).

    More about this item

    Keywords

    Trading agents; Speed; Algorithmic trading; Laboratory experiment;
    All these keywords.

    JEL classification:

    • C78 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Bargaining Theory; Matching Theory
    • C92 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Group Behavior
    • D40 - Microeconomics - - Market Structure, Pricing, and Design - - - General

    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:chu:wpaper:20-39. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Megan Luetje (email available below). General contact details of provider: https://edirc.repec.org/data/esichus.html .

    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.