The News in Earnings Announcement Disclosures: Capturing Word Context Using LLM Methods

Abstract

This study examines the information content of textual disclosures in firms' earnings announcements. Using a large language model (LLM) to capture information in both words and word context, I show that the news in earnings press releases (i) explains three times more variation in short-window stock returns than a host of textual measures based on dictionary and non-LLM machine learning methods; (ii) doubles the R2 of an array of financial statement surprises, modeled with conventional regression or machine learning approaches; and (iii) accounts for a large fraction of immediate price revisions within just 5 minutes of release. LLM-modeled conference calls further enhance R2 by one-fourth compared to press releases and financial surprises. Textual disclosures are more informative when earnings are less persistent and during periods of aggregate uncertainty. Most news arises from text describing numbers, at the beginning of the disclosure, and including novel contents. These findings highlight the role of firms' textual disclosures in moving stock prices and advance our understanding of how investors utilize corporate disclosures.

Keywords: Disclosure, Earnings Announcements, Textual Analysis, Large Language Models (LLMs), Machine Learning

JEL Classification: G12, G14, G32, M21, M41