Does sentiment help in asset pricing? A novel approach using large language models and market-based labels
Type
conference paper
Date Issued
2024-08-28
Author(s)
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.
Language
English
Event Title
COMPSTAT 2024
Event Location
Giessen
Event Date
27. - 30. August 2024
Subject(s)
Division(s)
Contact Email Address
francesco.audrino@unisg.ch