(corrected July 2003)
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 identifiesapproximately 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.
The extent to which earnings are manipulated has long been of interest to analysts, regulators, researchers, and other investment professionals. The U.S. SEC’s recent commitment to vigorous investigation of earnings manipulation (see Levitt 1998) has sparked renewed interest in the area, but the academic and professional literature contains little discussion of the detection of earnings manipulation. This article presents a model to distinguish manipulated from nonmanipulated reporting.1 Earnings manipulation is defined as an instance in which a company’s managers violate generally accepted accounting principles (GAAP) to favorably represent the company’s financial performance. To develop the model, I used financial statement data to construct variables that would capture the effects of manipulation and preconditions that might prompt companies to engage in such activity.
Conclusion
Some accounting variables can be used to identify companies that are manipulating their reported earnings. I found that, because manipulation typically consists of an artificial inflation of revenues or deflation of expenses, variables that take into account simultaneous bloating in asset accounts have predictive content. I also found that sales growth has discriminatory power: The primary characteristic of sample manipulators was that they had high growth prior to periods during which manipulation was in force. The evidence presented here was based on a sample of companies whose manipulation of earnings was publicly discovered. Such companies probably represent the upper tail of the distribution of companies that seek to influence their reported earnings—successful and undiscovered manipulators undoubtedly exist—so the evidence should be interpreted in that light. Given this caution, evidence has been presented here of a systematic association between earnings manipulation and financial statement data that is of interest to accounting researchers and investment professionals. The evidence suggests that accounting data not only meet the test of providing useful information, but they also enable an assessment of the reliability of the reporting. The explicit classification model described here requires only two years of data (one annual report) to evaluate the likelihood of manipulation and can be inexpensively applied by the SEC, auditors, and investors to screen a large number of companies and identify potential manipulators for further investigation. Although the model is cost-effective relative to a strategy of treating all companies as nonmanipulators, its large rate of classification errors makes further investigation of the screening results important. The model’s variables exploit distortions in financial statement data that might or might not result from manipulation. For example, the distortions could be the result of a material acquisition during the period examined, a material shift in the company’s value-maximizing strategy, or a significant change in the company’s economic environment.
One limitation of the model was that it is estimated using financial information for publicly traded companies. Therefore, it cannot be reliably used to study privately held companies. Another limitation is that the earnings manipulation in the sample involved earnings overstatement rather than understatement; therefore, the model cannot be reliably used to study companies operating in circumstances that are conducive to decreasing earnings.
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