Using a hidden Markov model to measure earnings quality

• 成果类型: Article
• 署名作者: Du, Kai; Huddart, Steven; Xue, Lingzhou; Zhang, Yifan
• 署名单位: Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; U.S. Securities & Exchange Commission (SEC); Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park
• 刊物名称: JOURNAL OF ACCOUNTING & ECONOMICS
• ISSN/ISSBN: 0165-4101
• DOI: 10.1016/j.jacceco.2019.101281
• 发表日期: 2020
• 关键词: time-series; management; distributions; determinants; disclosure; accruals; returns; proxies; FUTURE

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.