Debiasing Earnings Persistence Estimates

37 Pages Posted: 26 Jun 2017 Last revised: 27 Dec 2017

See all articles by Brian Rountree

Brian Rountree

Rice University - Jesse H. Jones Graduate School of Business

Shiva Sivaramakrishnan

Rice University

Yanyan Wang

Xiamen University

Lisheng Yu

Xiamen University

Date Written: December 26, 2017

Abstract

We offer a theoretical framework to help isolate persistence estimates of fundamental earnings innovations from the effects of accounting measurements. We show that a downward bias results when persistence of earnings innovations is estimated using reported earnings. We show that the greater the accrual estimation errors, the greater this downward bias, thus explaining the empirically observed positive association between accrual quality and estimated earnings persistence. However, when we debias reported earnings persistence as guided by our theoretical framework, we fail to detect any such association. We further show that market returns around earnings announcements are associated with the debiased measure of earnings persistence incremental to reported earnings persistence indicating that the market is able to undo the bias in a manner consistent with our theoretical model. Overall, our results help bring clarity to the literature by pointing out that accrual quality per se does not bear any relation to persistence of earnings innovations; rather, improving accrual quality merely improves the precision in estimating the persistence of these innovations.

Keywords: Accrual quality, persistence, reporting quality

JEL Classification: D21, D22, M41

Suggested Citation

Rountree, Brian Robert and Sivaramakrishnan, Shiva and Wang, Yanyan and Yu, Lisheng, Debiasing Earnings Persistence Estimates (December 26, 2017). Available at SSRN: https://ssrn.com/abstract=2991742 or http://dx.doi.org/10.2139/ssrn.2991742

Brian Robert Rountree

Rice University - Jesse H. Jones Graduate School of Business ( email )

6100 South Main Street
P.O. Box 1892
Houston, TX 77005-1892
United States

Shiva Sivaramakrishnan (Contact Author)

Rice University ( email )

6100 South Main Street
Houston, TX 77005-1892
United States

Yanyan Wang

Xiamen University ( email )

Xiamen, Fujian 361005
China

Lisheng Yu

Xiamen University ( email )

Xiamen, Fujian 361005
China

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