An Improved Test for Earnings Management Using Kernel Density Estimation

49 Pages Posted: 15 Apr 2010 Last revised: 18 May 2015

See all articles by Henry Lahr

Henry Lahr

The Open University - Business School; University of Cambridge - Centre for Business Research (CBR)

Date Written: August 19, 2013

Abstract

This paper improves methods developed by Burgstahler and Dichev (1997) and Bollen and Pool (2009) to test for earnings management by identifying discontinuities in distributions of scaled earnings or earnings forecast errors. While existing methods use preselected bandwidths for kernel density estimation and histogram construction, the proposed test procedure addresses the key problem of bandwidth selection by endogenizing the selection step using a bootstrap test. The main advantage offered by the bootstrap test over prior methods is that it provides a reference distribution that cannot be globally distinguished from the empirical distribution instead of assuming a correct reference distribution. This procedure limits the researcher's degrees of freedom and offers a simple procedure to find and test a local discontinuity. I apply the bootstrap density estimation to earnings, earnings changes, and earnings forecast errors in U.S. firms over the period 1976–2010. Significance levels found in earlier studies are greatly reduced, often to insignificant values. Discontinuities cannot be detected in analysts' forecast errors, while such findings of discontinuities in earlier research can be explained by a simple rounding mechanism. Earnings data show a large drop in loss aversion after 2003 that cannot be detected in changes of earnings.

Keywords: Earnings management, Loss aversion, Earnings forecasts, Distribution of reported earnings, Discontinuities in distributions

JEL Classification: M41, C14, G14, G30

Suggested Citation

Lahr, Henry, An Improved Test for Earnings Management Using Kernel Density Estimation (August 19, 2013). European Accounting Review 23(4), 559-591. Available at SSRN: https://ssrn.com/abstract=1587969 or http://dx.doi.org/10.2139/ssrn.1587969

Henry Lahr (Contact Author)

The Open University - Business School ( email )

Milton Keynes, MK6 7AA
United Kingdom

University of Cambridge - Centre for Business Research (CBR) ( email )

Top Floor, Judge Business School Building
Trumpington Street
Cambridge, CB2 1AG
United Kingdom

HOME PAGE: http://www.cbr.cam.ac.uk

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