TY  - JOUR
KW  - Natural language processing (NLP)
KW  -  Large language models
KW  -  Generative pre-trained transformer (GPT)
KW  -  Machine learning in stock return prediction
KW  -  Artificial intelligence investment strategies
PB  - Elsevier BV
A1  - Kirtac, Kemal
A1  - Germano, Guido
JF  - Finance Research Letters
N1  - This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions.
VL  - 62
UR  - http://dx.doi.org/10.1016/j.frl.2024.105227
ID  - discovery10190583
N2  - 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.
AV  - restricted
TI  - Sentiment trading with large language models
Y1  - 2024/04//
ER  -