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Title: A Cloaking Mechanism to Mitigate Market Manipulation

We propose a cloaking mechanism to deter spoofing, a form of manipulation in financial markets. The mechanism works by symmetrically concealing a specified number of price levels from the inside of the order book. To study the effectiveness of cloaking, we simulate markets populated with background traders and an exploiter, who strategically spoofs to profit. The traders follow two representative bidding strategies: the non-spoofable zero intelligence and the manipulable heuristic belief learning. Through empirical game-theoretic analysis across parametrically different environments, we evaluate surplus accrued by traders, and characterize the conditions under which cloaking mitigates manipulation and benefits market welfare. We further design sophisticated spoofing strategies that probe to reveal cloaked information, and find that the effort and risk exceed the gains.

Authors:
; ;
Award ID(s):
1741190
Publication Date:
NSF-PAR ID:
10067116
Journal Name:
Twenty-Seventh International Joint Conference on Artificial Intelligence
Page Range or eLocation-ID:
541-547
Sponsoring Org:
National Science Foundation
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