A Gu, Y Wang, C Mascioli, R Savani, T Turocy, M Chakraborty, and MP Wellman
5th ACM International Conference on AI in Finance (ICAIF), pages 239-247, November 2024.
Recipient of the ICAIF24 Best Paper Award.
Abstract
We investigate the relationship between market liquidity and spoofing, a manipulative practice involving the submission of deceptive orders aimed at misleading other traders. Utilizing an agent-based market simulator, we model markets with varying levels of liquidity, adjusting the spread and intervals of a market maker’s orders to control liquidity. Within these simulated markets, we evaluate the effectiveness of two novel spoofing strategies against a benchmark approach. Our experiments show that in high-liquidity markets, spoofing is substantially less profitable and less detrimental to other traders compared to their low-liquidity counterparts. Additionally, we identify two distinct spoofing behavior regimes based on liquidity, each of which employ drastically different profit-making strategies. Finally, building on our quantitative findings, we identify and expound upon the mechanisms through which liquidity mitigates market manipulation.