LLM交易智能体在长期公平测试中表现不佳

LLM trading agents mostly fail when stock-market tests become long, broad, and fair.

The authors built FINSABER, a stricter testing setup that checks LLM trading over about 20 years, across more stocks, and with better protection against cherry-picked results.

They tested LLM systems such as FinMem and FinAgent against simple baselines like Buy and Hold, rule-based trading, forecasting models, and reinforcement learning methods.

The main result is that LLM strategies can look good in narrow tests, but they usually fail to beat simple market strategies once the test becomes longer and fairer.

The paper also finds that these LLMs behave badly across market conditions because they are too cautious when stocks are rising and too risky when stocks are falling.

So current LLMs may understand financial text, but that does not mean they can reliably time the stock market.

Link – arxiv. org/abs/2505.07078v5

Title: "Can LLM-based Financial Investing Strategies Outperform the Market in Long Run?"