Publication Type

Journal Article

Version

publishedVersion

Publication Date

10-2024

Abstract

The study provides large-scale descriptive evidence on the timing and nature of corporate financial tweeting. Using an unsupervised machine learning approach to analyze 24 million tweets posted by S&P 1500 firms from 2012 to 2020, we find that firms are more likely to tweet financial information around significantly negative or positive news events, such as earnings announcements and the filing of financial statements. This convex U-shaped relation between the likelihood of posting financial tweets and the materiality of accounting events becomes stronger over time. Whereas research based on early samples concludes that firms are less likely to disseminate financial information on Twitter when the news is bad and material, the symmetric dissemination behavior we find suggests that these conclusions should be revised. We also show that a machine learning algorithm (Twitter-Latent Dirichlet Allocation) is superior to a dictionary approach in classifying short messages like tweets.

Keywords

disclosures, discretionary dissemination, social media, Twitter

Discipline

Accounting | Corporate Finance | Social Media

Publication

Contemporary Accounting Research

First Page

1

Last Page

34

ISSN

0823-9150

Identifier

10.1111/1911-3846.12986

Publisher

Wiley

Copyright Owner and License

Publisher-CC-NC-ND

Additional URL

https://doi.org/10.1111/1911-3846.12986

Download

Share

COinS