@inproceedings{kirtac-germano-2024-enhanced,
    title = "Enhanced Financial Sentiment Analysis and Trading Strategy Development Using Large Language Models",
    author = "Kirtac, Kemal  and
      Germano, Guido",
    editor = "De Clercq, Orph{\'e}e  and
      Barriere, Valentin  and
      Barnes, Jeremy  and
      Klinger, Roman  and
      Sedoc, Jo{\~a}o  and
      Tafreshi, Shabnam",
    booktitle = "Proceedings of the 14th Workshop on Computational Approaches to Subjectivity, Sentiment, {\&} Social Media Analysis",
    month = aug,
    year = "2024",
    address = "Bangkok, Thailand",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.wassa-1.1",
    pages = "1--10",
    abstract = "This study examines a novel methodology for enhanced financial sentiment analysis and trading strategy development using large language models (LLMs) such as OPT, BERT, FinBERT, LLAMA 3, and RoBERTa. Utilizing a dataset of 965,375 U.S. financial news articles from 2010 to 2023, our research demonstrates that the GPT-3-based OPT significantly outperforms other models, achieving a prediction accuracy of 74.4{\%} for stock market returns. Our findings reveal that the advanced capabilities of LLMs, particularly OPT, surpass traditional sentiment analysis methods such as the Loughran-McDonald dictionary model in predicting and explaining stock returns. For instance, a self-financing strategy based on OPT scores achieves a Sharpe ratio of 3.05 over our sample period, compared to a Sharpe ratio of 1.23 for the strategy based on the dictionary model. This study highlights the superior performance of LLMs in financial sentiment analysis, encouraging further research into integrating artificial intelligence and LLMs in financial markets.",
}