Using a Hidden Markov Model to Measure Earnings Quality
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.
|Using a Hidden Markov Model to Measure Earnings Quality
|CC BY-NC-ND 4.0 (Attribution-NonCommercial-NoDerivatives)
|Publisher Identifier (DOI)
|July 07, 2020
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