arXiv:2607.06121v1 Announce Type: cross Abstract: 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 b
The rapid advancement in reinforcement learning techniques is enabling their application to increasingly complex and historically human-dominated domains like financial market analysis and strategy.
This research suggests AI can not only identify but potentially exploit market inefficiencies, posing significant implications for market stability, regulation, and the future of quantitative finance.
The perceived difficulty and human expertise required for identifying sophisticated price manipulation strategies may be significantly reduced by advanced AI agents.
- · Hedge Funds using advanced AI
- · Quantitative trading firms
- · AI/ML researchers in finance
- · Traditional algorithmic trading firms
- · Regulators needing to adapt to new forms of manipulation
- · Less technologically advanced market participants
The study directly impacts financial modeling and trading strategy development by demonstrating RL's efficacy in price manipulation detection.
Increased adoption of such AI could lead to a more efficient, but potentially less stable, financial market as manipulation and counter-manipulation strategies evolve.
This could accelerate the need for AI-driven regulatory tools to monitor and prevent new forms of market abuse, leading to an 'AI arms race' in financial supervision.
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Read at arXiv cs.AI