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Identifying Unintentional Error in Restatement Disclosures Using Automated Content Analysis

Fri, January 18, 10:15 to 11:45am, TBA

Abstract

Research attention is frequently focused on fraud and earnings management. However, unintentional errors in audited financial statements of public companies occur frequently. I identify post-SOX restatements that correct unintentional errors by using automated content analysis to search for language asserting or implying lack of intent in restatement disclosures. I validate the identification showing, in comparison with restatements that correct intentional misstatements, restatements that correct unintentional error have a lower proportion of revenue recognition issues, correct proportionately fewer net income overstatements, and are associated with less operating income smoothing. Further, I find that “other” restatements (those without indications of fraud, irregularity or misrepresentation and without indications of lack of intent), have greater positive performance matched discretionary accruals and greater income smoothing than unintentional error restatements. This new proxy for unintentional error has the potential to contribute to future research seeking to disentangle the effects due to flaws in audit detection methods from the effects of reporting incentives in the observed associations between auditor characteristics (e.g., size, industry specialization and local engagement office size) and financial reporting quality. Restatements that correct unintentional errors reflect more on auditors’ audit detection ability and less on their negotiation stance than restatements that correct intentional misstatements.

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