Abstract. We propose a novel methodology to identify managerial beliefs from earnings call transcripts, using lexicon-based and FinBERT sentiment analysis alongside machine-learning guided topic modeling. We provide a dual contribution to the literature. First, we find that managerial sentiment significantly predicts analyst forecast revisions, with presentation sentiment showing stronger associations than question and answer (Q&A) interactions. Second, we show that these sentiment-driven revisions lead to systematic forecast errors, suggesting that narrative content shapes analyst expectations beyond fundamental information. Our analysis offers a scalable alternative to traditional survey-based approaches for measuring economic beliefs, providing high-frequency and near-universal coverage across firms and time.
Coauthors. Yuting Chen (Dublin City University), Pablo Pastor y Camarasa (Ghent University), and Valerio Potì (University College Dublin).
Manuscript. You can find the latest draft here.
Presentations. The Annual Irish Academy of Finance Conference.
Publication. Journal of Economic Psychology, forthcoming.