Title: A new approach to evaluating earnings management models

Authors: Giseok Nam; June Woo Park

Addresses: College of Business, Hankuk University of Foreign Studies, 107 Imun-ro, Dongdaemun-gu, 02450 Seoul, Korea ' Schulich School of Business, York University, 4700 Keele St, Toronto, ON M3J 1P3, Canada

Abstract: This paper proposes a new full-scale simulation approach for correctly measuring the detection ability of the modified Jones model (MJM). For ideal evaluation accuracy, we define perfect prior information on whether a firm under investigation manipulates its earnings with the allowance for doubtful accounts by differentiating firms that engage in no earnings manipulation from those that do. The MJM shows a detection rate of only 49.1%, which is lower than 50% for a fair coin toss, and thus it is not reliable. This low detection rate is due to the oversensitivity of the MJM, which causes it to misinterpret long-term increases in sales revenue from normal business activity as earnings management. Therefore, the MJM has a tendency to incorrectly detect non-discretionary accruals (NDACs) as discretionary accruals (DACs) and thus misjudge a clean firm as a dirty one, because it shows a low (moderately high) detection rate for clean (dirty) firms.

Keywords: detection rate; discretionary accruals; earnings management models; hypothetical firms; modified Jones model; model oversensitivity; perfect prior information; simulation; earnings manipulation; model evaluation; sales revenue.

DOI: 10.1504/IJMFA.2016.081855

International Journal of Managerial and Financial Accounting, 2016 Vol.8 No.3/4, pp.247 - 269

Received: 15 Dec 2015
Accepted: 27 Jun 2016
Published online: 29 Jan 2017 *

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