Using a hidden Markov model to measure earnings quality

Kai Du, Steven Huddart, Lingzhou Xue, Yifan Zhang

Research output: Contribution to journalArticle


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.

Original languageEnglish (US)
Article number101281
JournalJournal of Accounting and Economics
Publication statusAccepted/In press - Jan 1 2019


All Science Journal Classification (ASJC) codes

  • Accounting
  • Finance
  • Economics and Econometrics

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