eprintid: 10190583
rev_number: 7
eprint_status: archive
userid: 699
dir: disk0/10/19/05/83
datestamp: 2024-04-10 16:30:31
lastmod: 2024-04-10 16:30:31
status_changed: 2024-04-10 16:30:31
type: article
metadata_visibility: show
sword_depositor: 699
creators_name: Kirtac, Kemal
creators_name: Germano, Guido
title: Sentiment trading with large language models
ispublished: pub
divisions: UCL
divisions: B04
divisions: C05
divisions: F48
keywords: Natural language processing (NLP), Large language models, Generative pre-trained transformer (GPT), Machine learning in stock return prediction, Artificial intelligence investment strategies
note: This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions.
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.
date: 2024-04
date_type: published
publisher: Elsevier BV
official_url: http://dx.doi.org/10.1016/j.frl.2024.105227
full_text_type: other
language: eng
verified: verified_manual
elements_id: 2266344
doi: 10.1016/j.frl.2024.105227
lyricists_name: Germano, Guido
lyricists_id: GGERM33
actors_name: Kalinowski, Damian
actors_id: DKALI47
actors_role: owner
full_text_status: restricted
publication: Finance Research Letters
volume: 62
article_number: 105227
citation:        Kirtac, Kemal;    Germano, Guido;      (2024)    Sentiment trading with large language models.                   Finance Research Letters , 62     , Article 105227.  10.1016/j.frl.2024.105227 <https://doi.org/10.1016/j.frl.2024.105227>.      
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10190583/1/Germano_Sentiment%20trading%20with%20large%20language%20models_AAM.pdf