Using a hidden Markov model to measure earnings quality

被引：14

作者：

Du, Kai ^{[1
,2
]}

Huddart, Steven ^{[1
]}

Xue, Lingzhou ^{[3
]}

Zhang, Yifan ^{[1
]}

机构：

[1] Penn State Univ, Smeal Coll Business, University Pk, PA 16802 USA

[2] US Secur & Exchange Commiss, 100 F St NE, Washington, DC 20549 USA

[3] Penn State Univ, Eberly Coll Sci, University Pk, PA 16802 USA

来源：

JOURNAL OF ACCOUNTING & ECONOMICS | 2020年 / 69卷 / 2-3期

基金：

美国国家科学基金会;

关键词：

Hidden Markov model; Bayesian hierarchical framework; MCMC methods; Earnings quality; Earnings fidelity; TIME-SERIES; MANAGEMENT; DISTRIBUTIONS; DETERMINANTS; DISCLOSURE; ACCRUALS; RETURNS; PROXIES; FUTURE;

D O I：

10.1016/j.jacceco.2019.101281

中图分类号：

F8 [财政、金融];

学科分类号：

0202 ;

摘要：

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. Published by Elsevier B.V.

引用

页数：27

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