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Using a hidden Markov model to measure earnings quality

Kai Du, Steven Huddart (), Lingzhou Xue and Yifan Zhang

Journal of Accounting and Economics, 2020, vol. 69, issue 2

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.

Keywords: Hidden Markov model; Bayesian hierarchical framework; MCMC methods; Earnings quality; Earnings fidelity (search for similar items in EconPapers)
JEL-codes: C11 C13 M41 (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:eee:jaecon:v:69:y:2020:i:2:s016541011930076x

DOI: 10.1016/j.jacceco.2019.101281

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Journal of Accounting and Economics is currently edited by J. L. Zimmerman, S. P. Kothari, T. Z. Lys and R. L. Watts

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