X Wang and MP Wellman

29th International Joint Conference on Artificial Intelligence, Special Track on AI in FinTech.

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.

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