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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
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    File URL: https://digitalcommons.chapman.edu/esi_working_papers/334/
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    Citations

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    Cited by:

    1. Corgnet, Brice & DeSantis, Mark & Siemroth, Christoph, 2023. "Algorithmic Trading, Price Efficiency and Welfare: An Experimental Approach," Economics Discussion Papers 36273, University of Essex, Department of Economics.
    2. 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).
    3. 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.
    4. Te Bao & Elizaveta Nekrasova & Tibor Neugebauer & Yohanes E. Riyanto, 2022. "Algorithmic trading in experimental markets with human traders: A literature survey," Chapters, in: Sascha Füllbrunn & Ernan Haruvy (ed.), Handbook of Experimental Finance, chapter 23, pages 302-322, Edward Elgar Publishing.

    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

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