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On Detecting Spoofing Strategies in High Frequency Trading

Author

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  • Xuan Tao
  • Andrew Day
  • Lan Ling
  • Samuel Drapeau

Abstract

Spoofing is an illegal act of artificially modifying the supply to drive temporarily prices in a given direction for profit. In practice, detection of such an act is challenging due to the complexity of modern electronic platforms and the high frequency at which orders are channeled. We present a micro-structural study of spoofing in a simple static setting. A multilevel imbalance which influences the resulting price movement is introduced upon which we describe the optimization strategy of a potential spoofer. We provide conditions under which a market is more likely to admit spoofing behavior as a function of the characteristics of the market. We describe the optimal spoofing strategy after optimization which allows us to quantify the resulting impact on the imbalance after spoofing. Based on these results we calibrate the model to real Level 2 datasets from TMX, and provide some monitoring procedures based on the Wasserstein distance to detect spoofing strategies in real time.

Suggested Citation

  • Xuan Tao & Andrew Day & Lan Ling & Samuel Drapeau, 2020. "On Detecting Spoofing Strategies in High Frequency Trading," Papers 2009.14818, arXiv.org, revised Dec 2020.
  • Handle: RePEc:arx:papers:2009.14818
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    File URL: http://arxiv.org/pdf/2009.14818
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    References listed on IDEAS

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    1. Hart, Oliver D & Kreps, David M, 1986. "Price Destabilizing Speculation," Journal of Political Economy, University of Chicago Press, vol. 94(5), pages 927-952, October.
    2. Alexander Lipton & Umberto Pesavento & Michael G Sotiropoulos, 2013. "Trade arrival dynamics and quote imbalance in a limit order book," Papers 1312.0514, arXiv.org.
    3. Rama Cont & Arseniy Kukanov & Sasha Stoikov, 2014. "The Price Impact of Order Book Events," Journal of Financial Econometrics, Oxford University Press, vol. 12(1), pages 47-88.
    4. Álvaro Cartea & Ryan Donnelly & Sebastian Jaimungal, 2018. "Enhancing trading strategies with order book signals," Applied Mathematical Finance, Taylor & Francis Journals, vol. 25(1), pages 1-35, January.
    5. Justin A. Sirignano, 2019. "Deep learning for limit order books," Quantitative Finance, Taylor & Francis Journals, vol. 19(4), pages 549-570, April.
    6. Robert A. Jarrow, 2008. "Market Manipulation, Bubbles, Corners, and Short Squeezes," World Scientific Book Chapters, in: Financial Derivatives Pricing Selected Works of Robert Jarrow, chapter 6, pages 105-130, World Scientific Publishing Co. Pte. Ltd..
    7. Lee, Eun Jung & Eom, Kyong Shik & Park, Kyung Suh, 2013. "Microstructure-based manipulation: Strategic behavior and performance of spoofing traders," Journal of Financial Markets, Elsevier, vol. 16(2), pages 227-252.
    8. Jim Gatheral, 2010. "No-dynamic-arbitrage and market impact," Quantitative Finance, Taylor & Francis Journals, vol. 10(7), pages 749-759.
    9. Allen, Franklin & Gale, Douglas, 1992. "Stock-Price Manipulation," The Review of Financial Studies, Society for Financial Studies, vol. 5(3), pages 503-529.
    10. Martin D. Gould & Julius Bonart, 2015. "Queue Imbalance as a One-Tick-Ahead Price Predictor in a Limit Order Book," Papers 1512.03492, arXiv.org.
    11. Ke Xu & Martin D. Gould & Sam D. Howison, 2019. "Multi-Level Order-Flow Imbalance in a Limit Order Book," Papers 1907.06230, arXiv.org, revised Oct 2019.
    12. Kyle Bechler & Michael Ludkovski, 2017. "Order Flows and Limit Order Book Resiliency on the Meso-Scale," Papers 1708.02715, arXiv.org.
    13. Aurélien Alfonsi & Alexander Schied, 2010. "Optimal trade execution and absence of price manipulations in limit order book models," Post-Print hal-00397652, HAL.
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    Cited by:

    1. 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.
    2. Breckenfelder, Johannes, 2020. "How does competition among high-frequency traders affect market liquidity?," Research Bulletin, European Central Bank, vol. 78.

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