This site provides information on a new measure of earnings quality based on a hidden Markov model. This measure, proposed and validated by Du, Huddart, Xue, and Zhang (2019), is termed earnings fidelity. 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.
On this site, you will find information on our theoretical model, estimation method, data, and programs for the MCMC algorithm that you can adopt to estimate other constructs.
Kai Du, Steven J. Huddart, Lingzhou Xue, and Yifan Zhang: “Using a Hidden Markov Model to Measure Earnings Quality.” Journal of Accounting and Economics, Feb 2020. Working paper version available at SSRN.