# When AI Meets Finance (StockAgent): Large Language Model-based Stock Trading in Simulated Real-world Environments

arXiv: https://arxiv.org/abs/2407.18957 · HTML: https://mlsanity.com/paper/2407.18957/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 53 references that are in the map.
- First posted on arXiv 15 Jul 2024.

## Builds on (most cited first, 13 of 13)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework](https://mlsanity.com/paper/2308.00352/) (2023)
- [Exploring Large Language Models for Communication Games: An Empirical Study on Werewolf](https://mlsanity.com/paper/2309.04658/) (2023)
- [OpenAGI: When LLM Meets Domain Experts](https://mlsanity.com/paper/2304.04370/) (2023)
- [A Multimodal Foundation Agent for Financial Trading: Tool-Augmented, Diversified, and Generalist](https://mlsanity.com/paper/2402.18485/) (2024)
- [Revolutionizing Finance with LLMs: An Overview of Applications and Insights](https://mlsanity.com/paper/2401.11641/) (2024)
- [Put Your Money Where Your Mouth Is: Evaluating Strategic Planning and Execution of LLM Agents in an Auction Arena](https://mlsanity.com/paper/2310.05746/) (2023)
- [ABIDES-Gym: Gym Environments for Multi-Agent Discrete Event Simulation and Application to Financial Markets](https://mlsanity.com/paper/2110.14771/) (2021)
- [Knowledge Graph Large Language Model (KG-LLM) for Link Prediction](https://mlsanity.com/paper/2403.07311/) (2024)
- [V-IRL: Grounding Virtual Intelligence in Real Life](https://mlsanity.com/paper/2402.03310/) (2024)
- [What if LLMs Have Different World Views: Simulating Alien Civilizations with LLM-based Agents](https://mlsanity.com/paper/2402.13184/) (2024)
- [ZipLine: In-Network Compression at Line Speed](https://mlsanity.com/paper/2101.05323/) (2021)

## Built on by (most cited first, 26 of 26)

- [OmniRouter: Budget and Performance Controllable Multi-LLM Routing](https://mlsanity.com/paper/2502.20576/) (2025)
- [Enhancing Investment Analysis: Optimizing AI-Agent Collaboration in Financial Research](https://mlsanity.com/paper/2411.04788/) (2024)
- [LiveTradeBench: Seeking Real-World Alpha with Large Language Models](https://mlsanity.com/paper/2511.03628/) (2025)
- [Agentic AI Systems Applied to tasks in Financial Services: Modeling and model risk management crews](https://mlsanity.com/paper/2502.05439/) (2025)
- [How Personality Traits Shape LLM Risk-Taking Behaviour](https://mlsanity.com/paper/2503.04735/) (2025)
- [MASS: Muli-agent simulation scaling for portfolio construction](https://mlsanity.com/paper/2505.10278/) (2025)
- [A Survey of Financial AI: Architectures, Advances and Open Challenges](https://mlsanity.com/paper/2411.12747/) (2024)
- [From Feedback Loops to Policy Updates: Reinforcement Fine-Tuning for LLM-Based Alpha Factor Discovery](https://mlsanity.com/paper/2605.15412/) (2026)
- [Position: Stop Acting Like Language Model Agents Are Normal Agents](https://mlsanity.com/paper/2502.10420/) (2025)
- [Guideline-Grounded Evidence Accumulation for High-Stakes Agent Verification](https://mlsanity.com/paper/2603.02798/) (2026)
- [Integrated Design and Governance of Agentic AI Systems through Adaptive Information Modulation](https://mlsanity.com/paper/2409.10372/) (2024)
- [Bridging Language Models and Financial Analysis](https://mlsanity.com/paper/2503.22693/) (2025)
- [Integrating Large Language Models in Financial Investments and Market Analysis: A Survey](https://mlsanity.com/paper/2507.01990/) (2025)
- [Advancing Financial Engineering with Foundation Models: Progress, Applications, and Challenges](https://mlsanity.com/paper/2507.18577/) (2025)
- [Trade in Minutes! Rationality-Driven Agentic System for Quantitative Financial Trading](https://mlsanity.com/paper/2510.04787/) (2025)
- [MNAFT: modality neuron-aware fine-tuning of multimodal large language models for image translation](https://mlsanity.com/paper/2604.16943/) (2026)
- [Building crypto portfolios with agentic AI](https://mlsanity.com/paper/2507.20468/) (2025)
- [ContiguousKV: Accelerating LLM Prefill with Granularity-Aligned KV Cache Management](https://mlsanity.com/paper/2601.13631/) (2026)
- [Learning to Trade Like an Expert: Cognitive Fine-Tuning for Stable Financial Reasoning in Language Models](https://mlsanity.com/paper/2604.16862/) (2026)
- [AlphaCrafter: Harnessing Multi-Agent Workflows for Cross-Sectional Quantitative Trading](https://mlsanity.com/paper/2605.05580/) (2026)
- [Shachi: A Modular, Controllable Framework for LLM-Based Agent-Based Modeling of Emergent Collective Behavior](https://mlsanity.com/paper/2509.21862/) (2025)
- [AgentBnB: A Browser-Based Cybersecurity Tabletop Exercise with Large Language Model Support and Retrieval-Aligned Scaffolding](https://mlsanity.com/paper/2511.00265/) (2025)
- [FinBoardBench: Benchmarking Dynamic Wealth Management and Strategic Financial Reasoning of LLMs via Board Game Simulations](https://mlsanity.com/paper/2605.27896/) (2026)
- [Beyond Agent Architecture: Execution Assumptions and Reproducibility in LLM-Based Trading Systems](https://mlsanity.com/paper/2606.08285/) (2026)
- [SoK: Trading Agents or Market Crashers? Dissecting Robustness and Security Failures in Academic Financial LLM Trading Schemes](https://mlsanity.com/paper/2609.19705/) (2026)
- [PsyBer-Agent: A Psychology-Driven User Agent for Recommender Systems](https://mlsanity.com/paper/2610.13989/) (2026)

## Abstract (arXiv)

Can AI Agents simulate real-world trading environments to investigate the impact of external factors on stock trading activities (e.g., macroeconomics, policy changes, company fundamentals, and global events)? These factors, which frequently influence trading behaviors, are critical elements in the quest for maximizing investors' profits. Our work attempts to solve this problem through large language model based agents. We have developed a multi-agent AI system called StockAgent, driven by LLMs, designed to simulate investors' trading behaviors in response to the real stock market. The StockAgent allows users to evaluate the impact of different external factors on investor trading and to analyze trading behavior and profitability effects. Additionally, StockAgent avoids the test set leakage issue present in existing trading simulation systems based on AI Agents. Specifically, it prevents the model from leveraging prior knowledge it may have acquired related to the test data. We evaluate different LLMs under the framework of StockAgent in a stock trading environment that closely resembles real-world conditions. The experimental results demonstrate the impact of key external factors on stock market trading, including trading behavior and stock price fluctuation rules. This research explores the study of agents' free trading gaps in the context of no prior knowledge related to market data. The patterns identified through StockAgent simulations provide valuable insights for LLM-based investment advice and stock recommendation. The code is available at https://github.com/MingyuJ666/Stockagent.