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Does sentiment help in asset pricing? A novel approach using large language models and market-based labels

Author

Listed:
  • Jule Schuettler

    (University of St.Gallen)

  • Francesco Audrino

    (University of St. Gallen; Swiss Finance Institute)

  • Fabio Sigrist

    (Lucerne University of Applied Sciences and Arts)

Abstract

We present a novel approach to sentiment analysis in financial markets by using a state-of-the-art large language model, a market data-driven labeling approach, and a large dataset consisting of diverse financial text sources including earnings call transcripts, newspapers, and social media tweets. Based on our approach, we define a predictive high-low sentiment asset pricing factor which is significant in explaining cross-sectional asset pricing for U.S. stocks. Further, we find that a long/short equal-weighted portfolio yields an average annualized return of 35.56% and an annualized Sharpe ratio of 2.21, remaining substantially profitable even when transaction costs are considered. A comparison with an alternative financial sentiment analysis tool (FinBERT) underscores the superiority of our data-driven labeling approach over traditional human-annotated labeling.

Suggested Citation

  • Jule Schuettler & Francesco Audrino & Fabio Sigrist, 2024. "Does sentiment help in asset pricing? A novel approach using large language models and market-based labels," Swiss Finance Institute Research Paper Series 24-69, Swiss Finance Institute.
    • Handle: RePEc:chf:rpseri:rp2469
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    Keywords

    natural language processing; large language models; DeBERTa; asset pricing;
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