#Fail: Social Media, Firm Distress, and Going Concern Opinions
50 Pages Posted: 2 Sep 2020 Last revised: 7 Dec 2023
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: Suggested Citation