用大模型分析财经新闻情绪，预测股市收益更准。
Sentiment trading with large language models
- 用OPT等大模型提取新闻情绪，比传统词典法更精准。
- OPT模型情绪得分可预测次日股价，系数达0.274，显著正相关。
- 基于OPT的情绪策略年化夏普比率3.05，优于其他方法。
我们研究大型语言模型（LLMs）在美股市金融新闻情感分析中的有效性及其对股票收益率的预测潜力。分析涵盖2010年1月1日至2023年6月30日共965,375篇新闻文章，比较了BERT、OPT、FINBERT及传统Loughran-McDonald词典模型的表现。结果表明，大模型情绪得分与后续日度股价回报存在显著关联。其中，基于GPT-3的OPT模型在情感预测中准确率最高，达74.4%，略高于BERT（72.5%）和FINBERT（72.2%），而词典模型仅50.1%。回归分析显示，OPT情绪得分对次日股价有强正向影响，系数分别为0.274和0.254；BERT与FINBERT也有一定预测能力，但较弱。值得注意的是，词典模型情绪得分与股价无显著关联。在投资组合表现上，基于OPT的情绪多空策略夏普比率达3.05，优于BERT（2.11）和FINBERT（2.07），词典模型策略最低，仅为1.23。研究证明先进大模型在金融市场预测与投资管理中具有显著优势。
原文摘要 · Abstract (English)
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.
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