Sentiment trading with large language models

Kemal Kirtac and Guido Germano

Finance Research Letters, 2024, vol. 62, issue PB

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 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 models; Generative pre-trained transformer (GPT); Machine learning in stock return prediction; Artificial intelligence investment strategies (search for similar items in EconPapers)
JEL-codes: C53 G10 G11 G12 G14 G17 (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (5)

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Working Paper: Sentiment trading with large language models (2024)
Working Paper: Sentiment trading with large language models (2024)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:finlet:v:62:y:2024:i:pb:s1544612324002575

DOI: 10.1016/j.frl.2024.105227

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