TY - JOUR
T1 - Using a hidden Markov model to measure earnings quality
AU - Du, Kai
AU - Huddart, Steven
AU - Xue, Lingzhou
AU - Zhang, Yifan
N1 - Funding Information:
We received many helpful comments from Wayne Guay (editor), Frank Zhou (referee), and Dan Givoly. We thank Orie Barron, Sudipta Basu, Jeremy Bertomeu, Dirk Black, Lei Dong, Guojin Gong, Daniel Jiang, Chen Li (discussant), Jia Li, Pierre Liang, Yi Liang, John Liechty, Henock Louis, Jed Neilson, Lyndon Orton, Hong Qu, Jack Stecher, Shuyang Wang, Biqin Xie, Luo Zuo, and workshop participants at Penn State University, Michigan State University, Carnegie Mellon University, and the 2018 American Accounting Association Annual Meeting for comments and suggestions. Kai Du and Steven Huddart acknowledge financial support from the Penn State Smeal College of Business. Lingzhou Xue acknowledges the financial support in part from the National Science Foundation. The Securities and Exchange Commission (SEC) disclaims responsibility for any private publication or statement of any SEC employee or Commissioner. This paper expresses the authors' views and does not necessarily reflect those of the Commission, the Commissioners, or members of the staff. An earlier version of this paper was titled “Using a hidden Markov model to measure reporting systems.” Data on earnings fidelity are publicly available at https://sites.psu.edu/fidelity.
Publisher Copyright:
© 2019
PY - 2020/4/1
Y1 - 2020/4/1
N2 - 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.
AB - 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.
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U2 - 10.1016/j.jacceco.2019.101281
DO - 10.1016/j.jacceco.2019.101281
M3 - Article
AN - SCOPUS:85076860594
SN - 0165-4101
VL - 69
JO - Journal of Accounting and Economics
JF - Journal of Accounting and Economics
IS - 2-3
M1 - 101281
ER -