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Research on restatements has grown significantly in recent years. Many of these studies test hypotheses about the causes and consequences of intentional managerial misreporting but rely on restatement data (such as the GAO database) that contains both irregularities (intentional misstatements) and errors (unintentional misstatements). We argue that researchers can significantly enhance the power of tests related to restatements by distinguishing between errors and irregularities, particularly in recent periods when the relative frequency of error-related restatements is increasing. Based on prior research, the reading of numerous restatement announcements, and the guidance that boards receive from lawyers, auditors, and the SEC on how to respond to suspicions of deliberate misreporting, we propose a straightforward procedure for classifying restatements as either errors or irregularities. We show that most of the restatements we classify as irregularities are followed by fraud-related class action lawsuits as compared to only one lawsuit in the group of restatements classified as errors. As further validation of our proxy, we report that the market reaction to the restatement announcement for our irregularities sample (−14 percent) is also significantly more negative than it is for our errors sample (−2 percent). Finally, we demonstrate the importance of distinguishing errors from irregularities by showing the impact it has on inferences about the relation between restatements and CEO/CFO turnover over time.

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This study evaluates alternative measures of the tone of financial narrative. We present evidence that word-frequency tone measures based on domain-specific wordlists—compared to general wordlists—better predict the market reaction to earnings announcements, have greater statistical power in short-window event studies, and exhibit more economically consistent post-announcement drift. Further, inverse document frequency weighting, advocated in Loughran and McDonald (2011), provides little improvement to the alternative approach of equal weighting. We also provide evidence that word-frequency tone measures are as powerful as the Naïve Bayesian machine-learning tone measure from Li (2010) in a regression of future earnings on MD&A tone. Overall, although more complex techniques are potentially advantageous in certain contexts, equal-weighted, domain-specific, word-frequency tone measures are generally just as powerful in the context of financial disclosure and capital markets. Such measures are also more intuitive, easier to implement, and, importantly, far more amenable to replication.

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