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智能评分 60
Can LLM-based Financial Investing Strategies Outperform the Market in Long Run?
AI 推荐理由
LLM在长期公平测试中未能超越基线，表明其金融文本理解不等于可靠的市场时机把握核心解读
作者构建了FINSABER，对约20年跨多只股票的LLM交易进行更严格测试，重点防止选取偏差。作者比较了FinMem、FinAgent等LLM系统与买入持有、规则交易、预测模型和强化学习等基线，结果显示在长期公平测试中LLM策略未能超越这些基线。
全文
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.
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Link – arxiv. org/abs/2505.07078v5
Title: "Can LLM-based Financial Investing Strategies Outperform the Market in Long Run?"
