arXiv:2605.21707v1 Announce Type: cross Abstract: We describe an adaptive market-making architecture that preserves the analytical structure of the Avellaneda--Stoikov framework while introducing a successor measure-style adaptation mechanism. In our paper we keep Avellaneda--Stoikov fast Hamilton--Jacobi--Bellman structure and make it adaptive to changing market regimes and trading objectives. The central idea is to separate market dynamics from the trading objective. The market state determines a low-dimensional set of Avellaneda--Stoikov parameters, while recent realized rewards determine a
The rapid advancement in AI, particularly in adaptive learning and zero-shot capabilities, is enabling more sophisticated applications in complex financial environments like order book trading.
This development represents a significant step towards more autonomous and efficient AI systems managing financial assets, potentially leading to increased market efficiency and new competitive advantages for firms employing such technology.
Traditional market-making strategies that rely on static models will face increasing pressure from adaptive AI systems that can dynamically respond to changing market conditions with greater agility.
- · Quantitative hedge funds
- · High-frequency trading firms
- · Financial AI developers
- · Large institutional investors
- · Traditional market makers
- · Brokerage firms relying on manual execution
- · Less technologically advanced trading desks
Increased prevalence of AI-driven market-making leading to tighter spreads and potentially more volatile but efficient markets.
Development of regulatory frameworks specifically designed to monitor and manage the systemic risks associated with autonomous AI in financial markets.
Potential for algorithmic arms races where firms invest heavily in AI to gain fleeting market advantages, driving further innovation and increasing complexity.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG