Sentiment trading with large language models
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AI 根据所提供的论文材料归类：摘要明确以金融新闻情绪预测后续股票收益，并评估相关交易组合。
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利用大语言模型进行情绪交易
- 研究问题
- 大语言模型提取的美国金融新闻情绪，是否与后续股票收益相关，并在情绪分类、收益预测和组合表现上提供有效信息？
- 方法与思路
- 作者分析约九十六万篇金融新闻，比较 BERT、OPT、FINBERT 与词典模型，结合情绪评估、收益回归及多空组合分析。
- 材料陈述的贡献
- 摘要报告模型情绪分数与后续日收益显著相关，且所考察样本中 OPT 的情绪准确率和多空组合夏普比率较高。
- 阅读关注点
- 应核对新闻时间戳、训练及测试划分、情绪标签来源、模型训练期与信息泄漏，以及交易成本和组合风险调整。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实；摘要未说明样本外设计及交易成本，相关性不能直接解释为因果效应。
原文摘要
We investigate the efficacy of large language models (LLMs) in sentiment analysis of U.S. financial news and their potential in predicting stock market returns. We analyze a dataset comprising 965,375 news articles that span from January 1, 2010, to June 30, 2023; we focus on the performance of various LLMs, including BERT, OPT, FINBERT, and the traditional Loughran-McDonald dictionary model, which has been a dominant methodology in the finance literature. The study documents a significant association between LLM scores and subsequent daily stock returns. Specifically, OPT, which is a GPT-3 based LLM, shows the highest accuracy in sentiment prediction with an accuracy of 74.4%, slightly ahead of BERT (72.5%) and FINBERT (72.2%). In contrast, the Loughran-McDonald dictionary model demonstrates considerably lower effectiveness with only 50.1% accuracy. Regression analyses highlight a robust positive impact of OPT model scores on next-day stock returns, with coefficients of 0.274 and 0.254 in different model specifications. BERT and FINBERT also exhibit predictive relevance, though to a lesser extent. Notably, we do not observe a significant relationship between the Loughran-McDonald dictionary model scores and stock returns, challenging the efficacy of this traditional method in the current financial context. In portfolio performance, the long-short OPT strategy excels with a Sharpe ratio of 3.05, compared to 2.11 for BERT and 2.07 for FINBERT long-short strategies. Strategies based on the Loughran-McDonald dictionary yield the lowest Sharpe ratio of 1.23. Our findings emphasize the superior performance of advanced LLMs, especially OPT, in financial market prediction and portfolio management, marking a significant shift in the landscape of financial analysis tools with implications to financial regulation and policy analysis.