Cited by 117Stock Market Forecasting MethodsFinancial Markets and Investment StrategiesSentiment Analysis and Opinion Mining

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Sentiment trading with large language models

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Kirtac, Kemal, Germano, Guido·2024·Finance Research Letters

DOI 10.1016/j.frl.2024.105227

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Abstract

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.

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Authors: Kirtac, K., & Germano, G.
Affiliations: University College London (GB)
Systemic Risk Centre (GB)
Corresponding author: Kirtac, Kemal
ORCID: Kirtac, Kemal
Venue: Finance Research Letters
Publisher: Elsevier BV
Published: 2024-04
Volume: 62
Pages: 105227
ISSN: 1544-6123 (ISSN-L)
DOI: 10.1016/j.frl.2024.105227
OpenAlex: W4392859372
Cited by: 115
References: 30
Citations by year: 2026: 58 · 2025: 45 · 2024: 12
FWCI: 34.98
Type: journal article
Language: en
Open access: hybrid
doi.org
Licence: cc-by
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
creativecommons.org/licenses/by/4.0/ (vor, 11 d)
Locations: Finance research letters (publishedVersion, cc-by)
SSRN Electronic Journal (acceptedVersion)
London School of Economics and Political Science Research Online (London School of Economics and Political Science) (acceptedVersion)
RePEc: Research Papers in Economics (submittedVersion)
arXiv (Cornell University) (submittedVersion, public-domain)
UCL Discovery (University College London) (submittedVersion)
London School of Economics and Political Science Theses Online (London School of Economics and Political Science) (submittedVersion)
arXiv (Cornell University) (public-domain)
Funders: Economic and Social Research Council (ES/K002309/1, ES/R009724/1, ES/Y010612/1)
Topics: Social Sciences › Decision Sciences › Management Science and Operations Research › Stock Market Forecasting Methods
Social Sciences › Economics, Econometrics and Finance › Finance › Financial Markets and Investment Strategies
Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
Keywords: large language models, OPT, BERT, FinBERT, financial news sentiment, stock return prediction, long-short strategies, Sharpe ratio

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