Learning-Based Trading Strategies in the Face of Market Manipulation

X Wang, C Hoang, and MP Wellman ACM International Conference on AI and Finance, October 2020. Abstract We study learning-based trading strategies in markets where prices can be manipulated through spoofing: the practice of submitting spurious…

Economic reasoning from simulation-based game models

MP Wellman Œconomia, 10(2):257–278, 2020. Abstract Simulation modeling in economics has historically been viewed as an alternative to mainstream analytic technique, and as such has generally and intentionally avoided the focus on rational…

Market manipulation: An adversarial learning framework for detection and evasion

X Wang and MP Wellman 29th International Joint Conference on Artificial Intelligence, Special Track on AI in FinTech, pages 4626–4632, 2020. Abstract We propose an adversarial learning framework to capture the evolving game between a regulator…

Generating realistic stock market order streams

J Li, X Wang, Y Lin, A Sinha, and MP Wellman 34th AAAI Conference on Artificial Intelligence, pages 727-734, Feb 2020. Abstract We propose an approach to generate realistic and high-fidelity stock market data based on generative adversarial…

Structure learning for approximate solution of many-player games

Z Li and MP Wellman 34th AAAI Conference on Artificial Intelligence, pages 2119-2127, Feb 2020. Abstract Games with many players are difficult to solve or even specify without adopting structural assumptions that enable representation in…

Bounding regret in empirical games

S Jecmen, A Sinha, Z Li, L Tran-Thanh 34th AAAI Conference on Artificial Intelligence (AAAI), 2020. Abstract Empirical game-theoretic analysis refers to a set of models and techniques for solving large-scale games. However, there is a lack…