Spoofing the Limit Order Book: An Agent-Based Model

Xintong Wang, Michael Paul Wellman

Abstract: We present an agent-based model of manipulating prices in financial markets through spoofing: submitting spurious orders to mislead traders who observe the order book. Built around the standard limit-order mechanism, our model captures a complex market environment with combined private and common values, the latter represented by noisy observations upon a dynamic fundamental time series. We consider background agents following two types of trading strategies: zero intelligence (ZI) that ignores the order book and heuristic belief learning (HBL) that exploits the order book to predict price outcomes. By employing an empirical game-theoretic analysis to derive approximate strategic equilibria, we demonstrate the effectiveness of HBL and the usefulness of order book information in a range of non-spoofing environments. We further show that a market with HBL traders is spoofable, in that a spoofer can qualitatively manipulate prices towards its desired direction. After re-equilibrating games with spoofing, we find spoofing generally hurts market surplus and decreases the proportion of HBL. However, HBL's persistence in most environments with spoofing indicates a consistently spoofable market. Our model provides a way to quantify the effect of spoofing on trading behavior and efficiency, and thus measures the profitability and cost of an important form of market manipulation.