A couple of recent articles discuss new tools and methods the SEC and others are using to detect suspect accounting.
Some ways to detect false accounting have been known for a long time. Regulators' antennae tend to go up if a company is moving items off the balance sheet, reports earnings that move in the opposite direction of cash flows or shows a sudden jump in accruals. Acquisitions are also often used as a device for earnings management. According to a recent study, however, financial distortion may be much more prevalent than regulators' enforcement statistics would indicate. A study of nearly 400 CFOs concluded that the CFOs "believe that in a given period, one-fifth of companies are ‘distorting' earnings — that, is following the letter of generally accepted accounting principles but not necessarily the spirit. On average, the earnings distortion is as large as 10 cents on every $1 of earnings, said the CFOs in the study." According to the study, "influencing the stock price" was the most common motivation for manipulating earnings within GAAP, but other reasons may include the pressure to hit benchmarks (presumably, for comp purposes), fears that poor performance will affect careers, and the need to avoid violating debt covenants. "But close to 60% of the CFOs said something else — something disturbing: that they and their peers may be motivated to use accounting techniques to paint a rosy picture of earnings ‘because it will likely go undetected.' Amplifying the answer, one finance chief told the researchers that, depending on the industry, a firm could misrepresent earnings for two to three years without getting caught by an analyst. Indeed, the CFO said, it may take up to five years for anyone to uncover the sleight of hand."
Companies that are managing earnings can look "extremely vibrant," according to another study, with cash margins shrinking but cash sales rising, expansion of capital bases and operations, and financing and related off-balance-sheet activities. "In addition, price-earnings ratios and market-to-book ratios were often unusually high, and ‘misstating firms [had] unusually strong stock-return performance in the years prior to misstatement,' according to the study."
But advanced computer analysis is now making it easier for regulators to spot earnings management. One of the older analytical models used is the "M-Score," a composite of eight ratios designed to find earnings manipulation or predispositions to engage in the same. The "ratios are designed to capture unusual accumulation in receivables; unusual expense capitalization and declines in depreciation; and the extent to which accounting profits are supported by cash profits." The M-Score was validated "by correctly identifying 76% of firms subject to SEC accounting enforcement actions during a 10-year period." The F-Score, developed in 2011, "focuses not just on accrual quality, but also financial performance, nonfinancial measures (in particular, abnormal reductions in the number of employees), off-balance-sheet activities, and market-based measures for identifying misstatements." The F-Score, however, does not address manipulation of sales through techniques such as "channel-stuffing at quarter's end, or ‘encouraging sales to customers with return provisions that violate the definition of a sale.…'" Another measure used by forensic investigators and auditors is "the law of first digits, which refers to the frequency distribution of digits in real-life sources of data…..[F]for example, in a given distribution the number 1 occurs as the leading digit about 30% of the time." This benefit of this statistically driven measure is that it is not "based only on those that have been caught by the SEC[, which] is inherently skewed."
The SEC's Financial Reporting and Audit Task Force has announced it will be using "the Accounting Quality Model, also known as RoboCop, to detect earnings management. The AQM is a quantitative analytic model — econometric-based — that will spot earnings management by, among other things, determining whether a registrant's financial statements stick out from other filers' in its industry. The AQM will look at discretionary accounting choices by, for instance, examining total accruals and then estimating discretionary accruals…." These accruals are then analyzed as indicators of risk or incentives to take risk. One "risk indicator could be an accounting policy in which a high proportion of transactions are structured off–balance sheet, says Craig Lewis, [former] chief economist at the SEC, and a risk inducer could be a company losing market share to competitors. The AQM produces a score for each filing and compares it with the filer's industry peer group, assessing the likelihood that fraud is occurring. The AQM could vastly increase the number of comment letters companies receive from the SEC, although Lewis says the list could be adjusted to accommodate ‘evolving staff experiences and priorities.'" Because AQM uses XBRL data, problems with XBRL exhibits could result in a company's being flagged for further investigation, even if the underlying financials are not really problematic.
One problem that appears to affect all the analytical models is the potential for "'a high frequency of false positives.' That is, ‘many ﬁrms that do not have enforcement actions against them are predicted to have misstated their earnings.' Moreover, analytical models can miss such things as the quality of the estimates and assumptions underlying the present-value calculations of assets, which normally appear in the footnotes to financial statements." Likewise, analytical models cannot make subjective judgments about matters such as the credibility of management. According to the Lewis, however, "if the commission can keep false positives to a manageable level, the AQM could be a powerful means of improving accounting, not just for identifying fraud but also for helping staff assist corporate filers with disclosing financials in compliance with GAAP."
As this article from the Washington Post illustrates, the SEC is also using new cutting-edge technology introduced by forensic accountants (and former FBI staffers) to detect fraud. Forensic accountants at the SEC have been used to collect and interpret electronic data "to help build cases involving insider trading, market manipulation, accounting fraud and securities violations…." They have also been developing "models and algorithms to identify trends and patterns in financial statements to proactively approach companies if there's something wrong." For example, one forensic accountant recommended the purchase of software that could discover links between electronic phone connections, allowing "SEC investigators to determine if callers from different phone numbers called the same phone line. ‘We use phone record analysis in almost every single insider trading case. Before use of the software, it would take months to find connections we now can make much more quickly'…." The SEC is also employing "new software to crunch massive amounts of data. The expectation is that it will help the SEC detect illegal activity faster and more easily by linking trading records and personal contact information from paid databases with the agency's own tips, complaints and referrals."