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

Xintong Wang, Christopher Hoang, Yevgeniy Vorobeychik and Michael Wellman
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Xintong Wang: John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA
Christopher Hoang: Computer Science and Engineering, University of Michigan, Ann Arbor, MI 48109, USA
Yevgeniy Vorobeychik: Computer Science and Engineering, Washington University in St. Louis, St. Louis, MO 63130, USA

Games, 2021, vol. 12, issue 2, 1-43

Abstract: 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.

Keywords: market manipulation; agent-based simulation; trading agents; empirical game-theoretic analysis (search for similar items in EconPapers)
JEL-codes: C C7 C70 C71 C72 C73 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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