arXiv:2605.19337v1 Announce Type: new Abstract: A growing body of work explores how Large Language Models (LLMs) can be embedded in trading systems as agents that perceive market information, retrieve context, reason about decisions, emit tradable actions, and adapt under market feedback. This paper reframes LLM-based trading agents as expert-system decision pipelines and presents an audit-oriented evidence map of 77 included studies in a protocol-coded snapshot screened through 2026-03-09. A primary empirical subset (n=19) satisfies the minimum boundary of Action Output plus Closed-Loop Evalu
The rapid advancement and integration of Large Language Models (LLMs) are enabling new applications in complex domains like financial markets, pushing the boundaries of autonomous decision-making.
LLM agents in financial markets introduce a new paradigm for automated trading strategies, potentially increasing market efficiency but also raising new questions about systemic risk and ethical AI deployment.
The ability of LLMs to perceive, reason, and act in financial markets transforms traditional quantitative trading into an agentic, adaptive process, shifting investment analysis from human-centric to AI-driven.
- · AI-first hedge funds
- · Quantitative trading firms
- · LLM developers
- · Financial data providers
- · Traditional asset managers
- · Retail brokers (long term)
- · Human fundamental analysts
- · Legacy trading infrastructure
Increased prevalence of AI-driven algorithmic trading in financial markets.
Potential for new forms of market volatility and flash crashes due to complex agent interactions.
Regulatory bodies will need to adapt quickly to monitor and govern autonomous AI agents in financial systems, possibly leading to 'AI-specific' financial regulations.
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.AI