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

Jule Schuettler, Francesco Audrino and Fabio Sigrist
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Jule Schuettler: University of St.Gallen
Francesco Audrino: University of St. Gallen; Swiss Finance Institute
Fabio Sigrist: Lucerne University of Applied Sciences and Arts

No 24-69, Swiss Finance Institute Research Paper Series from Swiss Finance Institute

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.

Keywords: natural language processing; large language models; DeBERTa; asset pricing (search for similar items in EconPapers)
Pages: 43 pages
Date: 2024-08
New Economics Papers: this item is included in nep-ain, nep-big and nep-cmp
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Persistent link: https://EconPapers.repec.org/RePEc:chf:rpseri:rp2469

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