Sentiment Trading with Large Language Models
Finance Research Letters, 62, 105227. doi: 10.1016/j.frl.2024.105227
14 Pages Posted: 25 Jan 2024 Last revised: 26 Mar 2024
Date Written: March 21, 2024
Abstract
We analyse the performance of the large language models (LLMs) OPT, BERT, and FINBERT, alongside the traditional Loughran-McDonald dictionary, in the sentiment analysis of 965,375 U.S. financial news articles from 2010 to 2023. Our findings reveal that the GPT-3-based OPT model significantly outperforms the others, predicting stock market returns with an accuracy of 74.4%. A long-short strategy based on OPT, accounting for 10 basis points (bps) in transaction costs, yields an exceptional Sharpe ratio of 3.05. From August 2021 to July 2023, this strategy produces an impressive 355% gain, outperforming both other strategies and traditional market portfolios. This underscores the transformative potential of LLMs in financial market prediction and portfolio management and the necessity of employing sophisticated language models to develop effective investment strategies based on news sentiment.
Keywords: Natural language processing (NLP), Large language model (LLM), Generative pretrained transformer (GPT), Machine learning in stock return prediction, Artificial intelligence investment strategies
JEL Classification: C53, G10, G11, G12, G14, G17
Suggested Citation: Suggested Citation