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Abstract
JEL classification
۱٫ Introduction
۲٫ The model and estimation method
۳٫ Data, variables, and estimation results
۴٫ Empirical analysis
۵٫ Concluding remarks
Appendix.
References
Abstract
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.
Introduction
Earnings reports are affected by both a firm’s fundamental performance and the measurement process as governed by reporting standards, auditing technology, and managerial discretion (e.g., Dechow et al., 1998; Nikolaev, 2017). Prior research has used the statistical properties of earnings (e.g., smoothness, kinks in earnings, and target beating) and regression-based abnormal accrual models to measure the quality of earnings (for a review, see Dechow et al., 2010). In regression-based models, researchers separate the “abnormal” portion of accruals from the “normal” portion related to fundamental performance and define accruals quality based on abnormal accruals (e.g., Dechow and Dichev, 2002). Yet recent studies point out that an important issue with this approach is that proxies for unobservable “true” earnings may confound performance shocks with reporting discretion (e.g., Guay et al., 1996; Ball, 2013; Dichev et al., 2013; Owens et al., 2017). Our approach uses a structural model to separate accounting quality from the process of true earnings, thereby significantly alleviating the concern of having to rely on the noisy proxies. We assume that each firm transitions among states according to a Markov process. The fundamental performance of the firm is its state, which is either Low (L) or High (H). The firm’s state is unobservable; however, in each period, the firm issues an earnings signal, which is either low (l) or high (h). The probability that the firm issues a particular signal depends on the unobservable state, so inferences about the firm’s state can be made from its earnings signals.