SEC-mandated, machine-readable structured filings are an alternative source to Compustat for companies' accounting data. Discrepancies between as-filed and Compustat data, potentially a result of Compustat's standardizations, are more pronounced for firms with complex financial reporting. We show that these data discrepancies affect inferences in four research settings: (i) properties of accrual accounting, including accruals-cash flow relationships and abnormal accruals; (ii) real earnings management; (iii) the existence and magnitude of six of 21 accounting-based anomalies examined, including the accruals anomaly; and (iv) disclosure quality assessments based on the hierarchical structure of financial statement items. FactSet data also exhibit significant and often larger discrepancies from as-filed data. Our findings demonstrate the importance of these data discrepancies for the interpretation of empirical tests.

We propose and validate a new measure of earnings quality based on a hidden Markov model. This measure, termed earnings fidelity, captures how faithful earnings signals are in revealing the true economic state of the firm. We estimate the measure using a Markov chain Monte Carlo procedure in a Bayesian hierarchical framework that accommodates cross-sectional heterogeneity. Earnings fidelity is positively associated with the forward earnings response coefficient. It significantly outperforms existing measures of quality in predicting two external indicators of low-quality accounting: restatements and Securities and Exchange Commission comment letters.