X Wang and MP Wellman

29th International Joint Conference on Artificial Intelligence, Special Track on AI in FinTech, pages 4626–4632, 2020.


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.