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Spoofing the Limit Order Book: A Strategic Agent-Based Analysis

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John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA
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Computer Science and Engineering, University of Michigan, Ann Arbor, MI 48109, USA
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Computer Science and Engineering, Washington University in St. Louis, St. Louis, MO 63130, USA
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Lynn A. Conway Collegiate, Computer Science and Engineering, University of Michigan, Ann Arbor, MI 48109, USA
*
Author to whom correspondence should be addressed.
This work was completed while Xintong Wang was a research assistant at the University of Michigan.
Academic Editor: Isa Hafalir
Received: 19 February 2021 / Revised: 16 April 2021 / Accepted: 22 April 2021 / Published: 24 May 2021
We present an agent-based model of manipulating prices in financial markets through spoofing: submitting spurious orders to mislead traders who learn from the order book. Our model captures a complex market environment for a single security, whose common value is given by a dynamic fundamental time series. Agents trade through a limit-order book, based on their private values and noisy observations of the fundamental. We consider background agents following two types of trading strategies: the non-spoofable zero intelligence (ZI) that ignores the order book and the manipulable heuristic belief learning (HBL) that exploits the order book to predict price outcomes. We conduct empirical game-theoretic analysis upon simulated agent payoffs across parametrically different environments and measure the effect of spoofing on market performance in approximate strategic equilibria. We demonstrate that HBL traders can benefit price discovery and social welfare, but their existence in equilibrium renders a market vulnerable to manipulation: simple spoofing strategies can effectively mislead traders, distort prices and reduce total surplus. Based on this model, we propose to mitigate spoofing from two aspects: (1) mechanism design to disincentivize manipulation; and (2) trading strategy variations to improve the robustness of learning from market information. We evaluate the proposed approaches, taking into account potential strategic responses of agents, and characterize the conditions under which these approaches may deter manipulation and benefit market welfare. Our model provides a way to quantify the effect of spoofing on trading behavior and market efficiency, and thus it can help to evaluate the effectiveness of various market designs and trading strategies in mitigating an important form of market manipulation. View Full-Text
Keywords: market manipulation; agent-based simulation; trading agents; empirical game-theoretic analysis market manipulation; agent-based simulation; trading agents; empirical game-theoretic analysis
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MDPI and ACS Style

Wang, X.; Hoang, C.; Vorobeychik, Y.; Wellman, M.P. Spoofing the Limit Order Book: A Strategic Agent-Based Analysis. Games 2021, 12, 46. https://0-doi-org.brum.beds.ac.uk/10.3390/g12020046

AMA Style

Wang X, Hoang C, Vorobeychik Y, Wellman MP. Spoofing the Limit Order Book: A Strategic Agent-Based Analysis. Games. 2021; 12(2):46. https://0-doi-org.brum.beds.ac.uk/10.3390/g12020046

Chicago/Turabian Style

Wang, Xintong, Christopher Hoang, Yevgeniy Vorobeychik, and Michael P. Wellman 2021. "Spoofing the Limit Order Book: A Strategic Agent-Based Analysis" Games 12, no. 2: 46. https://0-doi-org.brum.beds.ac.uk/10.3390/g12020046

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