Using a Hidden Markov Model to Measure Earnings Quality
Journal of Accounting and Economics, Forthcoming
57 Pages Posted: 8 May 2018 Last revised: 22 Dec 2019
Date Written: October 1, 2019
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.
Keywords: Hidden Markov model; Bayesian hierarchical framework; MCMC methods; Earnings quality; Earnings fidelity
JEL Classification: C11, C13, M41, M43
Suggested Citation: Suggested Citation