Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models
Alejandro Lopez-Lira and Yuehua Tang
我们记录了像ChatGPT这样的大型语言模型(LLM)在没有直接财务培训的情况下预测新闻标题的股市反应的能力。 使用知识后切断标题,GPT-4捕获了最初的市场反应,为不可交易的初始反应实现了约90%的投资组合日命中率。 GPT-4得分也显着预测了随后的漂移,特别是对于小股和负面消息。 预测能力通常随着模型大小而增加,这表明财务推理是复杂LLM的新兴能力。 随着LLM采用率的上升,战略回报下降,与提高价格效率一致。 为了理顺这些发现,我们开发了一个理论模型,该模型结合了LLM技术,信息处理能力限制,反应不足和套利限制。
We document the capability of large language models (LLMs) like ChatGPT to predict stock market reactions from news headlines without direct financial training. Using post-knowledge-cutoff headlines, GPT-4 captures initial market responses, achieving approximately 90% portfolio-day hit rates for the non-tradable initial reaction. GPT-4 scores also significantly predict the subsequent drift, especially for small stocks and negative news. Forecasting ability generally increases with model size, su...