Identifying Accounting Quality

Valeri V. Nikolaev

University of Chicago Booth School of Business

December 31, 2016

Chicago Booth Research Paper No. 14-28

I develop a new approach to understanding accounting accruals. Unlike prior studies, I explicitly address the economic role of accruals in performance measurement. I characterize accounting quality in terms of a new construct, namely, the degree to which accruals facilitate performance measurement. Further, I develop a flexible empirical strategy for identifying accounting quality. The core identifying assumptions derive from institutional properties of both earnings and cash flows: that both are noisy measures of the same economic performance and they converge as the time horizon extends. These assumptions characterize moments of earnings, cash flows, and accruals solved to recover the variance of performance and accounting error in accruals. I implement several model specifications and consider a number of generalizations. My analysis suggests that the variance of the performance component exceeds accounting error and explains a high fraction of accruals’ variance. I conclude that accruals generate a better measure of firm performance than operating cash flows do, thus meeting their primary objective.

Number of Pages in PDF File: 56

Keywords: Accounting quality, Earnings quality, Accruals, Identification, Estimation error

JEL Classification: M41

Open PDF in Browser Download This Paper

Date posted: August 23, 2014 ; Last revised: January 6, 2017

Suggested Citation

Nikolaev , Valeri V., Identifying Accounting Quality (December 31, 2016). Chicago Booth Research Paper No. 14-28. Available at SSRN: https://ssrn.com/abstract=2484958 or http://dx.doi.org/10.2139/ssrn.2484958

Contact Information

Valeri V. Nikolaev (Contact Author)
University of Chicago Booth School of Business ( email )
5807 South Woodlawn Avenue
Chicago, IL 60637
United States
HOME PAGE: http://faculty.chicagobooth.edu/valeri.nikolaev/index.html

Chicago Booth School of Business Logo

Feedback to SSRN

Paper statistics
Abstract Views: 6,237
Downloads: 1,721
Download Rank: 6,768