#Fail: Social Media, Firm Distress, and Going Concern Opinions

50 Pages Posted: 2 Sep 2020 Last revised: 7 Dec 2023

See all articles by Eric Condie

Eric Condie

Georgia Institute of Technology

James Moon

Georgia Institute of Technology - Scheller College of Business

Date Written: December 2023

Abstract

Audit firms and regulators have both commented extensively on the potential for new sources of data to transform the audit process. Focusing on auditors’ going concern opinions, we use deep learning to measure the “bearishness” of posts on social media and find it strongly predicts the likelihood of firm failure. This association is incremental to other market-based signals, such as the default likelihood or short interest. Interestingly, this signal appears largely orthogonal to an auditor’s going concern opinion, implying that social media predicts future events that precipitate failure not fully considered by auditors. Despite this, we find that social media bearishness enhances going concern opinions’ predictive ability. We consider potential channels for these results and find that bearishness predicts investor reluctance to provide capital to distressed firms. Our evidence should be informative to regulators and audit firms, both of whom are currently evaluating the usefulness of “new” data to auditors.

Keywords: Stocktwits, Twitter, Social Media, Sentiment, Auditing, Going-Concern Opinions, Firm Failure

JEL Classification: M40, M41, M42, M49, G33

Suggested Citation

Condie, Eric and Moon, James, #Fail: Social Media, Firm Distress, and Going Concern Opinions (December 2023). Available at SSRN: https://ssrn.com/abstract=3659762 or http://dx.doi.org/10.2139/ssrn.3659762

Eric Condie (Contact Author)

Georgia Institute of Technology ( email )

800 West Peachtree St.
Atlanta, GA 30308
United States

James Moon

Georgia Institute of Technology - Scheller College of Business ( email )

800 West Peachtree St.
Atlanta, GA 30308
United States

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