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

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 captures a complex market environment for single security, whose common value is given by dynamic fundamental time series. Agents trade limit-order book, based on their private values and noisy observations fundamental. consider background agents following two types trading strategies: non-spoofable zero intelligence (ZI) that ignores book manipulable heuristic belief learning (HBL) exploits predict price outcomes. conduct empirical game-theoretic analysis upon simulated agent payoffs across parametrically different environments measure effect spoofing performance approximate strategic equilibria. demonstrate HBL can benefit discovery social welfare, but existence equilibrium renders vulnerable manipulation: simple strategies effectively traders, distort reduce total surplus. Based this model, we propose mitigate aspects: (1) mechanism design disincentivize manipulation; (2) strategy variations improve robustness information. evaluate proposed approaches, taking into account potential responses agents, characterize conditions under which these approaches may deter manipulation welfare. provides way quantify behavior efficiency, thus it help effectiveness various designs mitigating important form manipulation.