The DOI matches its Crossref record; 32 of the 33 references it cites were found in a registry or an index, and 1 are not checked yet.
Sentiment trading with large language models
Open accessAbstract
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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“Additionally, [18, 56] researched the performance of LLMs(FinGPT, OPT, etc.) that specifically fine-tuned with financial related dataset and demonstrated further improvement by aligning LLMs with domain-specific knowledge.”
From Large Language Model Agent in Financial Trading: A Survey (2026) · cites this paper as [18]
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“A similar approach is adopted in [18, 29], where the long-short strategy has shown to outperform both long-only and short-only strategies in backtesting.”
From Large Language Model Agent in Financial Trading: A Survey (2026) · cites this paper as [18]
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“In both[18] and [29], portfolios weighted by market capitalization have shown slightly higher returns than those that are equally weighted.”
From Large Language Model Agent in Financial Trading: A Survey (2026) · cites this paper as [18]
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“In [18] and [60], the F1 score and accuracy are used to measure the model's prediction accuracy of news sentiment.”
From Large Language Model Agent in Financial Trading: A Survey (2026) · cites this paper as [18]
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Citations
Cites
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Price-Derived Headline Market-Impact Labels for Bitcoin Forecasting with Multivariate Transformers
Mažeika, P., Paulavičius, R., & Filatovas, E. (2026) · Big Data and Cognitive Computing · doi:10.3390/bdcc10090309
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Metadata
- 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)
- 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
- 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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