A cloaking mechanism to mitigate market manipulation

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageAmerican English
Title of host publicationProceedings of the 27th International Joint Conference on Artificial Intelligence, IJCAI 2018
EditorsJerome Lang
PublisherInternational Joint Conferences on Artificial Intelligence
Pages541-547
Number of pages7
ISBN (Electronic)9780999241127
DOIs
StatePublished - 2018
Externally publishedYes
Event27th International Joint Conference on Artificial Intelligence, IJCAI 2018 - Stockholm, Sweden
Duration: Jul 13 2018Jul 19 2018

Publication series

NameIJCAI International Joint Conference on Artificial Intelligence
Volume2018-July

Conference

Conference27th International Joint Conference on Artificial Intelligence, IJCAI 2018
Country/TerritorySweden
CityStockholm
Period7/13/187/19/18

ASJC Scopus subject areas

  • Artificial Intelligence

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