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Latency arbitrage in fragmented markets: A strategic agent-based analysis

Author

Listed:
  • Wah, Elaine

    (IEX Group, Inc.)

  • Wellman, Michael P.

    (Computer Science and Engineering, University of Michigan)

Abstract

We study the effect of latency arbitrage on allocative efficiency and liquidity in fragmented financial markets. We employ a simple model of latency arbitrage in which a single security is traded on two exchanges, with price quotes available to regular traders only after some delay. An infinitely fast arbitrageur reaps profits when the two markets diverge due to this latency in cross-market communication. Using an agent-based approach, we simulate interactions between high-frequency and zero-intelligence trading agents. From simulation data over a large space of strategy combinations, we estimate game models and compute strategic equilibria in a variety of market environments. We then evaluate allocative efficiency and market liquidity in equilibrium, and we find that market fragmentation and the presence of a latency arbitrageur reduces total surplus and negatively impacts liquidity. By replacing continuous-time markets with periodic call markets, we eliminate latency arbitrage opportunities and achieve further efficiency gains through the aggregation of orders over short time periods.

Suggested Citation

  • Wah, Elaine & Wellman, Michael P., 2016. "Latency arbitrage in fragmented markets: A strategic agent-based analysis," Algorithmic Finance, IOS Press, vol. 5(3-4), pages 69-93.
    • Handle: RePEc:ris:iosalg:0051
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    Citations

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    Cited by:

    1. Colin M. Van Oort & Ethan Ratliff-Crain & Brian F. Tivnan & Safwan Wshah, 2023. "Adaptive Agents and Data Quality in Agent-Based Financial Markets," Papers 2311.15974, arXiv.org.
    2. Eibelshäuser, Steffen & Smetak, Fabian, 2022. "Frequent batch auctions and informed trading," SAFE Working Paper Series 344, Leibniz Institute for Financial Research SAFE.
    3. Xintong Wang & Christopher Hoang & Yevgeniy Vorobeychik & Michael P. Wellman, 2021. "Spoofing the Limit Order Book: A Strategic Agent-Based Analysis," Games, MDPI, vol. 12(2), pages 1-43, May.
    4. Zhang, Zeyu & Ibikunle, Gbenga, 2023. "The market quality effects of sub-second frequent batch auctions: Evidence from dark trading restrictions," International Review of Financial Analysis, Elsevier, vol. 89(C).
    5. Zijian Shi & John Cartlidge, 2023. "Neural Stochastic Agent-Based Limit Order Book Simulation: A Hybrid Methodology," Papers 2303.00080, arXiv.org.

    More about this item

    Keywords

    High-frequency trading; latency arbitrage; agent-based simulation;
    All these keywords.

    JEL classification:

    • C00 - Mathematical and Quantitative Methods - - General - - - General

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