Sentiment trading with large language models

We analyze the performance of large language models (LLMs) including OPT, BERT, and FINBERT, alongside the traditional Loughran-McDonald dictionary, in sentiment analysis of 965,375 U.S. financial news articles from 2010 to 2023. Our findings reveal that OPT significantly outperforms others, accurately predicting stock market returns with a sentiment prediction accuracy of 74.4%. A long-short strategy based on OPT with 10 bps transactions costs yields a superior Sharpe ratio of 3.05. From August 2021 to July 2023, this strategy produces an impressive 355% gain, outperforming other strategies and traditional market portfolios. This underscores the potential of LLMs to transform financial market prediction and portfolio management, and the necessity of employing sophisticated language models to develop effective investment strategies based on news sentiment.