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Spoofing and Price Manipulation in Order-Driven Markets

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  • Álvaro Cartea
  • Sebastian Jaimungal
  • Yixuan Wang

Abstract

We model the trading strategy of an investor who spoofs the limit order book (LOB) to increase the revenue obtained from selling a position in a security. The strategy employs, in addition to sell limit orders (LOs) and sell market orders (MOs), a large number of spoof buy LOs to manipulate the volume imbalance of the LOB. Spoofing is illegal, so the strategy trades off the gains that originate from spoofing against the expected financial losses due to a fine imposed by the financial authorities. As the fine increases, the investor relies less on spoofing, and if the fine is large, the investor does not spoof the LOB. The arrival rate of buy MOs increases because other traders interpret the spoofed buy-heavy LOB as an upward pressure on prices. When the fine is low, spoofing considerably increases the revenues from liquidating a position. Spoofing increases the PnL because (i) the investor employs fewer MOs to draw the inventory to zero and benefits from roundtrip trades, which stem from spoof buy LOs that are ‘inadvertently’ filled and subsequently unwound with sell LOs; and (ii) the midprice trends upward when the book is buy-heavy; therefore the spoofer sells the asset at better prices.

Suggested Citation

  • Álvaro Cartea & Sebastian Jaimungal & Yixuan Wang, 2020. "Spoofing and Price Manipulation in Order-Driven Markets," Applied Mathematical Finance, Taylor & Francis Journals, vol. 27(1-2), pages 67-98, July.
    • Handle: RePEc:taf:apmtfi:v:27:y:2020:i:1-2:p:67-98
    DOI: 10.1080/1350486X.2020.1726783
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    Citations

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    Cited by:

    1. Long, Yunshen & Yan, Jingzhou & Wu, Liang & Long, Xingchen, 2024. "Market price determination: Interpreting quote order imbalance under zero-profit equilibrium," Economic Modelling, Elsevier, vol. 134(C).
    2. Kasim Khorasanee, 2024. "Spoof, Bluff, Go For It: A Defence of Spoofing," Journal of Business Ethics, Springer, vol. 189(1), pages 201-215, January.
    3. Haochen Li & Maria Polukarova & Carmine Ventre, 2023. "Detecting Financial Market Manipulation with Statistical Physics Tools," Papers 2308.08683, arXiv.org.
    4. Alvaro Arroyo & Alvaro Cartea & Fernando Moreno-Pino & Stefan Zohren, 2023. "Deep Attentive Survival Analysis in Limit Order Books: Estimating Fill Probabilities with Convolutional-Transformers," Papers 2306.05479, arXiv.org.
    5. Mohammad Javad Rajaei & Qusay H. Mahmoud, 2023. "A Survey on Pump and Dump Detection in the Cryptocurrency Market Using Machine Learning," Future Internet, MDPI, vol. 15(8), pages 1-17, August.

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