{"type":"ScholarlyArticle","title":"Market Manipulation: An Adversarial Learning Framework for Detection and Evasion","authors":[{"name":"Xintong Wang","slug":"xintong-wang","given":"Xintong","family":"Wang","affiliation":"University of Michigan , Ann Arbor"},{"name":"Michael P. Wellman","slug":"michael-p-wellman","given":"Michael P.","family":"Wellman","affiliation":"University of Michigan , Ann Arbor"}],"abstract":"We propose an adversarial learning framework to capture the evolving game between a regulator who develops tools to detect market manipulation and a manipulator who obfuscates actions to evade detection. The model includes three main parts: (1) a generator that learns to adapt original manipulation order streams to resemble trading patterns of a normal trader while preserving the manipulation intent; (2) a discriminator that differentiates the adversarially adapted manipulation order streams from normal trading activities; and (3) an agent-based simulator that evaluates the manipulation effect of adapted outputs. We conduct experiments on simulated order streams associated with a manipulator and a market-making agent respectively. We show examples of adapted manipulation order streams that mimic a specified market maker's quoting patterns and appear qualitatively different from the original manipulation strategy we implemented in the simulator. These results demonstrate the possibility of automatically generating a diverse set of (unseen) manipulation strategies that can facilitate the training of more robust detection algorithms.","doi":"10.24963/ijcai.2020/638","url":"https://ijcai.org/proceedings/2020/638","pdf":"https://ijcai.org/proceedings/2020/0638.pdf","markdown":"https://ijcai.org/proceedings/2020/638.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/638","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Special Track on AI in FinTech","track":"Special Track on AI in FinTech","number":638,"pages":{"first":4626,"last":4632},"keywords":["AI for regulation: AI for financial crime detection","AI for regulation: AI for financial fraud detection","AI for regulation: AI for financial market regulation, design and policy implication","Foundation for AI in FinTech: Deep learning and representation for FinTech"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/25217/file_25217.m3u8","length_sec":292,"provider":"hls"}],"references":[{"position":1,"raw":"[Cho et al., 2014] Kyunghyun Cho, Bart van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Hol- ger Schwenk, and Yoshua Bengio. 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