Spoofing the Limit Order Book: An Agent-Based Model

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 gametheoretic 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.