Market Manipulation: An Adversarial Learning Framework for Detection and Evasion
Open accessAbstract
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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A known tactic called spoofing can be adapted to evade progressively refined detectors, but in doing so its spoofing effectiveness is degraded.
“Likewise, Wang & Wellman (2020) found that a known tactic called spoofing can be adapted to evade progressively refined detectors, but in doing so its spoofing effectiveness is degraded.”
From Open Challenges in Multi-Agent Security: Towards Secure Systems of Interacting AI Agents (2025) · cites this paper as Wang & Wellman, 2020
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- Cho et al., 2014] Kyunghyun Cho, Bart van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Hol- ger Schwenk, and Yoshua Bengio. Learning phrase rep- resentations using RNN encoder-decoder for statistical machine translation. In Empirical Methods in Natural Lan- guage Processing, pages 1724-1734, 2014.
- Ian Goodfellow, Jean Pouget- Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sher- jil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial nets. In International Conference on Neural Information Processing Systems, pages 2672-2680, 2014.
- Ian Goodfellow, Jonathon Shlens, and Christian Szegedy. Explaining and harnessing adver- sarial examples. In International Conference on Learning Representations, 2015.
- Ling Huang, Anthony D. Joseph, Blaine Nelson, Benjamin I.P. Rubinstein, and J. D. Tygar. Ad- versarial machine learning. In Proceedings of the 4th ACM Workshop on Security and Artificial Intelligence, page 43-58, 2011.
- Andrei A. Kirilenko, Albert S. Kyle, Mehrdad Samadi, and Tugkan Tuzun. The flash crash: High frequency trading in an electronic market. Journal of Finance, 72:967-998, 2017.
- Tara E. Levens. Too fast, too frequent? High- frequency trading and securities class actions. University of Chicago Law Review, 82:1511-1558, 2015.
- Junyi Li, Xintong Wang, Yaoyang Lin, Arunesh Sinha, and Michael P. Wellman. Generating real- istic stock market order streams. In 34th AAAI Conference on Artificial Intelligence, 2020.
- Tom C. W. Lin. The new market manipulation. Emory Law Journal, 66:1253-1314, 2015.
- Logeswaran et al., 2018] Lajanugen Logeswaran, Honglak Lee, and Samy Bengio. Content preserving text generation with attribute controls. In International Conference on Neural Information Processing Systems, pages 5108-5118, 2018.
- James Rundle. Nasdaq deploys AI to detect stock-market abuse. Wall Street Journal, 2019.
- Ashish Shrivastava, Tomas Pfister, Oncel Tuzel, Josh Susskind, Wenda Wang, and Russell Webb. Learning from simulated and unsupervised images through adversarial training. In IEEE Conference on Com- puter Vision and Pattern Recognition, pages 2242-2251, 2017.
- Sinha et al., 2018] Aman Sinha, Hongseok Namkoong, and John Duchi. Certifiable distributional robustness with prin- cipled adversarial training. In International Conference on Learning Representations, 2018.
- Sutskever et al., 2014] Ilya Sutskever, Oriol Vinyals, and Quoc V. Le. Sequence to sequence learning with neural net- works. In International Conference on Neural Information Processing Systems, pages 3104-3112, 2014.
- E. Tzeng, J. Hoffman, K. Saenko, and T. Darrell. Adversarial discriminative domain adaptation. In IEEE Conference on Computer Vision and Pattern Recog- nition, pages 2962-2971, 2017.
- Riccardo Volpi, Hongseok Namkoong, Ozan Sener, John Duchi, Vittorio Murino, and Silvio Savarese. Generalizing to unseen domains via adversarial data augmentation. In International Conference on Neural Information Processing Systems, pages 5339-5349, 2018.
- Elaine Wah, Mason Wright, and Michael P. Wellman. Welfare effects of market making in continuous double auctions. Journal of Artificial Intelligence Research, 59:613-650, 2017.
- Wang and Wellman, 2017] Xintong Wang and Michael P. Wellman. Spoofing the limit order book: An agent-based model. In International Conference on Autonomous Agents and Multiagent Systems, pages 651-659, 2017.
- Wang et al., 2018] Xintong Wang, Yevgeniy Vorobeychik, and Michael P. Wellman. A cloaking mechanism to mitigate market manipulation. In International Joint Conference on Artificial Intelligence, pages 541-547, 2018.
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Open Challenges in Multi-Agent Security: Towards Secure Systems of Interacting AI Agents
de Witt, C. S., Krawiecka, K., Krawczuk, I., Hagag, B., Anderson, W. L., Belcak, P., Bucknall, B., Cai, X., Chopra, A., Cohen, D., et al. (2025) · arXiv · doi:10.48550/arxiv.2505.02077 · Cited by 2 · see citing works
“Likewise, Wang & Wellman (2020) found that a known tactic called spoofing can be adapted to evade progressively refined detectors, but in doing so its spoofing effectiveness is degraded.”
- Authors
- Wang, X., & Wellman, M. P.
- Affiliations
- University of Michigan (US)
- Venue
- Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence
- Published
- 2020-07
- Pages
- 4626-4632
- OpenAlex
- W3034571736
- Cited by
- 13
- References
- 0
- Citations by year
- 2025: 5 · 2024: 2 · 2023: 4 · 2022: 1 · 2021: 1
- FWCI
- 2.04
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- International Joint Conference on Artificial Intelligence (publishedVersion)
- Funders
- National Science Foundation (1741190)
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Stock Market Forecasting Methods
- Social Sciences › Economics, Econometrics and Finance › Finance › Financial Markets and Investment Strategies
- Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Keywords
- market manipulation, manipulation detection, evasion, adversarial learning, generative adversarial networks, market making, trade patterns, financial fraud detection, agent-based modeling
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