Predicting Material Accounting Misstatements
PATRICIA M. DECHOW,
University of California, Berkeley
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University of Washington
CHAD R. LARSON,
Washington University in St. Louis
RICHARD G. SLOAN,
University of California, Berkeley
Predicting Material Accounting Misstatements – Introduction
What causes managers to misstate their financial statements? How best can investors, auditors, financial analysts, and regulators detect misstatements? Addressing these questions is of critical importance to the efficient functioning of capital markets. For an investor it can lead to improved returns, for an auditor it can mean avoiding costly litigation, for an analyst it can mean avoiding a damaged reputation, and for a regulator it can lead to enhanced investor protection and fewer investment debacles. Our research has two objectives. First, we develop a comprehensive database of financial misstatements. Our objective is to describe this database and make it broadly available to other researchers to promote research on earnings misstatements.1 Second, we analyze the financial characteristics of misstating firms and develop a model to predict misstatements. The output of this analysis is a scaled probability (F-score) that can be used as a red flag or signal of the likelihood of earnings management or misstatement.
We compile our database through a detailed examination of firms that have been subject to enforcement actions by the U.S. Securities and Exchange Commission (SEC) for allegedly misstating their financial statements. Since 1982, the SEC has issued Accounting and Auditing Enforcement Releases (AAERs) during or at the conclusion of an investigation against a company, an auditor, or an officer for alleged accounting and ? or auditing misconduct. These releases provide varying degrees of detail on the nature of the misconduct, the individuals and entities involved, and the effect on the financial statements. We examine the 2,190 AAERs released between 1982 and 2005. Our examination identifies 676 unique firms that have misstated at least one of their quarterly or annual financial statements.
Using AAERs as a source to investigate characteristics of firms that manipulate financial statements has both advantages and disadvantages. The SEC has a limited budget, so it selects firms for enforcement action where there is strong evidence of manipulation. Firms selected often have already admitted a ‘‘mistake’’ by restating earnings or having large write-offs (e.g., Enron or Xerox); other firms have already been identified by the press or analysts as having misstated earnings (see Miller 2006); in addition, insider whistleblowers often reveal problems directly to the SEC. Therefore, one advantage of the AAER sample is that researchers can have a high level of confidence that the SEC has identified manipulating firms (the Type I error rate is low). However, one disadvantage is that many firms that manipulate earnings are likely to go unidentified, and a second disadvantage is that there could be selection biases in cases pursued by the SEC. For example, the SEC may be more likely to pursue cases where stock performance declines rapidly after the manipulation is revealed, because the identifiable losses to investors are greater. Selection biases may limit the generalizability of our results to other settings. It is worth noting, however, that problems with selection bias exist for other samples of manipulators identified by an external source — for example, shareholder litigation firms, Sarbanes-Oxley Act (SOX) internal control violation firms, or restatement firms.3 Bias concerns also exist for discretionary accrual measures (Dechow, Sloan, and Sweeney 1995). Thus selection bias is a general concern when analyzing the determinants of earnings manipulation and is not unique to AAER firms.
In our tests we focus on variables that can be easily measured from the financial statements because we want our analysis to be applicable in most settings facing investors, regulators, or auditors. Our tests focus only on AAER firm-years that have overstated earnings. We examine (i) accrual quality, (ii) financial performance, (iii) nonfinancial measures, (iv) off-balance-sheet activities, and (v) market-based measures for identifying misstatements.
We investigate several measures of accrual quality. We examine working capital accruals and the broader measure of accruals that incorporates long-term net operating assets (Richardson, Sloan, Soliman, and Tuna 2005). We provide an analysis of two specific accruals, changes in receivables and inventory. These accounts have direct links to revenue recognition and cost of goods sold, both of which impact gross profit, a key performance metric. We measure the percentage of ‘‘soft’’ assets on the balance sheet (defined as the percentage of assets that are neither cash nor property, plant, and equipment (PP&E). We predict that the more assets on the balance sheet that are subject to changes in assumptions and forecasts, the greater the manager’s flexibility to manage short-term earnings (e.g., Barton and Simko 2002; Richardson et al. 2005). We find that all measures of accrual quality are unusually high in misstating years relative to the broad population of firms. We also find that the percentage of soft assets is high, which suggests that manipulating firms have more ability to change and adjust assumptions to influence short-term earnings.
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