The Detection of Earnings Manipulation

TLDR

The study profiles earnings manipulators and proposes a model to detect manipulation. The model employs variables that capture financial statement distortions or preconditions that may lead to manipulation. The model identifies about half of manipulators before public discovery, revealing a systematic link between manipulation probability and financial statement variables, and suggesting its utility for investors, though distinguishing manipulation from other distortions remains needed.

Abstract

Presented are a profile of a sample of earnings manipulators, their distinguishing characteristics, and a suggested model for detecting manipulation. The model's variables are designed to capture either the financial statement distortions that can result from manipulation or preconditions that might prompt companies to engage in such activity. The results suggest a systematic relationship between the probability of manipulation and some financial statement variables. This evidence is consistent with the usefulness of accounting data in detecting manipulation and assessing the reliability of reported earnings. The model identifies approximately half of the companies involved in earnings manipulation prior to public discovery. Because companies that are discovered manipulating earnings see their stocks plummet in value, the model can be a useful screening device for investment professionals. The screening results, however, require determination of whether the distortions in the financial statement numbers result from earnings manipulation or have another structural root.