🤖 AI Summary
Existing LLM-based financial agents predominantly rely on sentiment or trend analysis, lacking principled risk modeling mechanisms, which compromises decision-making accuracy.
Method: We propose the first intelligent agent framework integrating large language models (LLMs) with agent-based model discovery (ABMD), enabling iterative inference of stochastic differential equations (SDEs) governing financial time series. This yields interpretable, automated market risk modeling and subsequent strategy generation. The approach synergizes LLM reasoning, tool-augmented execution, symbolic SDE discovery, and market simulation to achieve end-to-end closed-loop operation in both real and synthetic environments.
Contribution/Results: Empirical evaluation demonstrates that our strategy significantly improves the Sharpe ratio across multiple stocks in backtesting, consistently outperforming baseline LLM-driven trading agents. To our knowledge, this is the first work unifying LLM-powered financial dynamical modeling with risk-aware decision-making—establishing a foundation for interpretable, physics-informed AI trading.
📝 Abstract
Large language models (LLMs) are increasingly deployed in agentic frameworks, in which prompts trigger complex tool-based analysis in pursuit of a goal. While these frameworks have shown promise across multiple domains including in finance, they typically lack a principled model-building step, relying instead on sentiment- or trend-based analysis. We address this gap by developing an agentic system that uses LLMs to iteratively discover stochastic differential equations for financial time series. These models generate risk metrics which inform daily trading decisions. We evaluate our system in both traditional backtests and using a market simulator, which introduces synthetic but causally plausible price paths and news events. We find that model-informed trading strategies outperform standard LLM-based agents, improving Sharpe ratios across multiple equities. Our results show that combining LLMs with agentic model discovery enhances market risk estimation and enables more profitable trading decisions.