Spoofing the Limit Order Book: A Strategic Agent-Based Analysis
Spoofing the Limit Order Book: A Strategic Agent-Based Analysis
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DOI：
10.3390/g12020046
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发表时间：
2021-05
期刊：
影响因子：
0.9
通讯作者：
Xintong Wang;Christopher Hoang;Yevgeniy Vorobeychik;Michael P. Wellman
中科院分区：
文献类型：
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作者：
Xintong Wang;Christopher Hoang;Yevgeniy Vorobeychik;Michael P. Wellman
我们提出了一个基于代理人的模型，通过欺骗来操纵金融市场的价格：提交虚假订单来误导从订单簿中学习的交易员。我们的模型捕捉了单一证券的复杂市场环境，其共同价值由动态的基本时间序列给出。经纪人根据他们的私人价值和对基本面的嘈杂观察，通过限价指令进行交易。我们考虑了两种交易策略：忽略订单簿的非欺骗零情报(ZI)和利用订单簿预测价格结果的可操纵启发式信念学习(HBL)。我们对模拟的代理人在不同环境下的支付行为进行了实证博弈论分析，并在近似战略均衡条件下衡量了欺骗行为对市场绩效的影响。我们证明，HBL交易者可以受益于价格发现和社会福利，但他们的均衡存在使市场容易受到操纵：简单的欺骗策略可以有效地误导交易者，扭曲价格，减少总盈余。基于该模型，我们建议从两个方面来减少欺骗：(1)抑制操纵的机制设计；(2)交易策略的变化，以提高从市场信息中学习的稳健性。我们评估了建议的方法，考虑了代理人的潜在战略反应，并描述了这些方法可能阻止操纵和有利于市场福利的条件。我们的模型提供了一种方法来量化欺骗对交易行为和市场效率的影响，从而有助于评估各种市场设计和交易策略在缓解市场操纵这一重要形式方面的有效性。
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.