To Trade or Not to Trade: An Agentic Approach to Estimating Market Risk Improves Trading Decisions

• URL: http://arxiv.org/abs/2507.08584v1
• Date: Fri, 11 Jul 2025 13:29:32 GMT
• Title: To Trade or Not to Trade: An Agentic Approach to Estimating Market Risk Improves Trading Decisions
• Authors: Dimitrios Emmanoulopoulos, Ollie Olby, Justin Lyon, Namid R. Stillman,
• Abstract summary: Large language models (LLMs) are increasingly deployed in agentic frameworks.<n>We develop an agentic system that uses LLMs to iteratively discover differential equations for financial time series.<n>We find that model-informed trading strategies outperform standard LLM-based agents.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Your AI, Not Your View: The Bias of LLMs in Investment Analysis [55.328782443604986]
  Large Language Models (LLMs) face frequent knowledge conflicts due to discrepancies between pre-trained parametric knowledge and real-time market data.<n>This paper offers the first quantitative analysis of confirmation bias in LLM-based investment analysis.<n>We observe a consistent preference for large-cap stocks and contrarian strategies across most models.
  arXiv  Detail & Related papers  (2025-07-28T16:09:38Z)
• Cross-Asset Risk Management: Integrating LLMs for Real-Time Monitoring of Equity, Fixed Income, and Currency Markets [30.815524322885754]
  Large language models (LLMs) have emerged as powerful tools in the field of finance.<n>We introduce a Cross-Asset Risk Management framework that utilizes LLMs to facilitate real-time monitoring of equity, fixed income, and currency markets.
  arXiv  Detail & Related papers  (2025-04-05T22:28:35Z)
• ReMA: Learning to Meta-think for LLMs with Multi-Agent Reinforcement Learning [53.817538122688944]
  We introduce Reinforced Meta-thinking Agents (ReMA) to elicit meta-thinking behaviors from Reasoning of Large Language Models (LLMs)<n>ReMA decouples the reasoning process into two hierarchical agents: a high-level meta-thinking agent responsible for generating strategic oversight and plans, and a low-level reasoning agent for detailed executions.<n> Empirical results from single-turn experiments demonstrate that ReMA outperforms single-agent RL baselines on complex reasoning tasks.
  arXiv  Detail & Related papers  (2025-03-12T16:05:31Z)
• Adversarial Reasoning at Jailbreaking Time [49.70772424278124]
  Large language models (LLMs) are becoming more capable and widespread.<n>Recent advances in standardizing, measuring, and scaling test-time compute suggest new methodologies for optimizing models to achieve high performance on hard tasks.<n>In this paper, we apply these advances to the task of model jailbreaking: eliciting harmful responses from aligned LLMs.
  arXiv  Detail & Related papers  (2025-02-03T18:59:01Z)
• TradingAgents: Multi-Agents LLM Financial Trading Framework [4.293484524693143]
  TradingAgents proposes a novel stock trading framework inspired by trading firms.<n>It features LLM-powered agents in specialized roles such as fundamental analysts, sentiment analysts, technical analysts, and traders with varied risk profiles.<n>By simulating a dynamic, collaborative trading environment, this framework aims to improve trading performance.
  arXiv  Detail & Related papers  (2024-12-28T12:54:06Z)
• FinVision: A Multi-Agent Framework for Stock Market Prediction [0.0]
  This research introduces a multi-modal multi-agent system designed specifically for financial trading tasks. A key feature of our approach is the integration of a reflection module, which conducts analyses of historical trading signals and their outcomes.
  arXiv  Detail & Related papers  (2024-10-29T06:02:28Z)
• When AI Meets Finance (StockAgent): Large Language Model-based Stock Trading in Simulated Real-world Environments [55.19252983108372]
  We have developed a multi-agent AI system called StockAgent, driven by LLMs. The StockAgent allows users to evaluate the impact of different external factors on investor trading. It avoids the test set leakage issue present in existing trading simulation systems based on AI Agents.
  arXiv  Detail & Related papers  (2024-07-15T06:49:30Z)
• An Auction-based Marketplace for Model Trading in Federated Learning [54.79736037670377]
  Federated learning (FL) is increasingly recognized for its efficacy in training models using locally distributed data. We frame FL as a marketplace of models, where clients act as both buyers and sellers. We propose an auction-based solution to ensure proper pricing based on performance gain.
  arXiv  Detail & Related papers  (2024-02-02T07:25:53Z)
• Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models [51.3422222472898]
  We document the capability of large language models (LLMs) like ChatGPT to predict stock price movements using news headlines. We develop a theoretical model incorporating information capacity constraints, underreaction, limits-to-arbitrage, and LLMs.
  arXiv  Detail & Related papers  (2023-04-15T19:22:37Z)
• Trader-Company Method: A Metaheuristic for Interpretable Stock Price Prediction [3.9189409002585562]
  There are several challenges in financial markets hindering practical applications of machine learning-based models. We propose the Trader-Company method, a novel evolutionary model that mimics the roles of a financial institute and traders. Our method predicts future stock returns by aggregating suggestions from multiple weak learners called Traders.
  arXiv  Detail & Related papers  (2020-12-18T13:19:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.