[2510.02209] StockBench: Can LLM Agents Trade Stocks Profitably In Real-world Markets?

Computer Science > Machine Learning arXiv:2510.02209 (cs) [Submitted on 2 Oct 2025 (v1), last revised 2 Mar 2026 (this version, v2)] Title:StockBench: Can LLM Agents Trade Stocks Profitably In Real-world Markets? Authors:Yanxu Chen, Zijun Yao, Yantao Liu, Amy Xin, Jin Ye, Jianing Yu, Lei Hou, Juanzi Li View a PDF of the paper titled StockBench: Can LLM Agents Trade Stocks Profitably In Real-world Markets?, by Yanxu Chen and 7 other authors View PDF HTML (experimental) Abstract:Large language models (LLMs) demonstrate strong potential as autonomous agents, with promising capabilities in reasoning, tool use, and sequential decision-making. While prior benchmarks have evaluated LLM agents in various domains, the financial domain remains underexplored, despite its significant economic value and complex reasoning requirements. Most existing financial benchmarks focus on static question-answering, failing to capture the dynamics of real-market trading. To address this gap, we introduce STOCKBENCH, a contamination-free benchmark designed to evaluate LLM agents in realistic, multi-month stock trading environments. Agents receive daily market signals -- including prices, fundamentals, and news -- and make sequential buy, sell, or hold decisions. Performance is measured using financial metrics such as cumulative return, maximum drawdown, and the Sortino ratio, capturing both profitability and risk management. We evaluate a wide range of state-of-the-art proprietary and open-source L...