Francesco Audrino
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Title
Prof. Ph.D.
Last Name
Audrino
First name
Francesco
Email
francesco.audrino@unisg.ch
Phone
+41 71 224 2431
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SSRN
https://papers.ssrn.com/sol3/cf_dev/AbsByAuth.cfm?per_id=333418

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  • Publication
    Does sentiment help in asset pricing? A novel approach using large language models and market-based labels
    ( 2024-08-28)
    Francesco Audrino
    ;
    Jule Schüttler
    ;
    Fabio Sigrist
    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.
    Type: conference paper
    URI: https://www.alexandria.unisg.ch/handle/20.500.14171/120801
  • Publication
    Yield Curve Trading Strategies Exploiting Sentiment Data
    ( 2022-12)
    Audrino, Francesco
    ;
    Serwart, Jan
    This paper builds upon previous research findings that show macro sentiment data-augmented models are better at predicting the yield curve. We extend the dynamic Nelson-Siegel model with macro sentiment data from either Twitter or RavenPack. Vector autogressive (VAR) models and Markov-switching VAR models are used to predict changes in the shape of the yield curve. We build bond butterfly trading strategies that exploit our yield curve shape change predictions. Although the economic returns from our trading strategies based upon models exploiting macro sentiment data do not statistically significantly differ from those which do not rely on it, we find some evidence that models exploiting inflation sentiment are economically useful when trading the curvature of the yield curve.
    Type: conference paper
    URI: https://www.alexandria.unisg.ch/handle/20.500.14171/108013