Can Reinforcement Learning Efficiently Discover Price Manipulation?
In this paper, we investigate whether a model-free RL agent can identify and exploit price manipulation opportunities more effectively than a traditional model-based approach that assumes correct specification of the data-generating process but relies on noisy parameter estimates. We consider a single-asset market in which prices evolve according to an Almgren-Chriss framework with non-linear permanent impact and linear temporary impact. We first establish the existence of price-manipulative strategies in discrete time and compute the optimal benchmark strategy using Sequential Least Squares Quadratic Programming under full information. We then compare two finite-sample learning approache...
| Authors | Ioanna-Yvonni Tsaknaki, Andrea Macrì, Fabrizio Lillo |
|---|---|
| Primary category | q-fin.TR |
| Published | 7 July 2026 |
| Last updated at source | 7 July 2026 |
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