Paper: arXiv 2507.08584

Authors: Dimitrios Emmanoulopoulos, Ollie Olby, Justin Lyon, Namid R. Stillman

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.

Complexity vs Empirical Score

  • Math Complexity: 7.5/10
  • Empirical Rigor: 6.0/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper involves advanced stochastic calculus (SDEs) and iterative model discovery, indicating high mathematical density. It reports backtesting with real equities and a synthetic market simulator (Simudyne), demonstrating substantial empirical implementation.

Research Flowchart

  flowchart TD
  A["Research Goal: Develop an agentic system that uses LLMs<br>to estimate market risk and inform trading decisions"] --> B["Methodology: Iterative SDE Discovery"]
  B --> C["Data/Inputs: Historical equity price time series<br>News events (for synthetic market simulator)"]
  C --> D["Computational Process: Agentic Framework<br>1. LLM proposes SDE form<br>2. System fits parameters & evaluates<br>3. Iterates until model is validated"]
  D --> E["Outputs: Estimated Risk Metrics<br>from discovered SDE models"]
  E --> F["Final Action: Model-informed trading strategy<br>executes daily decisions"]
  F --> G["Key Findings/Outcomes:<br>- Outperforms standard LLM agents<br>- Improves Sharpe ratios across equities<br>- Combines LLMs with model discovery for better risk estimation"]

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14 of 62 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.