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Using a hidden Markov model to measure earnings quality

Author

Listed:
  • Du, Kai
  • Huddart, Steven
  • Xue, Lingzhou
  • Zhang, Yifan

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.

Suggested Citation

  • Du, Kai & Huddart, Steven & Xue, Lingzhou & Zhang, Yifan, 2020. "Using a hidden Markov model to measure earnings quality," Journal of Accounting and Economics, Elsevier, vol. 69(2).
    • Handle: RePEc:eee:jaecon:v:69:y:2020:i:2:s016541011930076x
    DOI: 10.1016/j.jacceco.2019.101281
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    References listed on IDEAS

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    More about this item

    Keywords

    Hidden Markov model; Bayesian hierarchical framework; MCMC methods; Earnings quality; Earnings fidelity;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • M41 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Accounting - - - Accounting

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