2.1 prior research
Academic research suggests that auditors’ analytical procedures are ineffective at detecting fraud for at least three reasons. First, auditors may not recognize unusual trends and ratios within the financial statements because they lack a sufficient understanding of their client's business (Erickson, Mayhew, and Felix ). Second, auditors tend to rely on management's explanations without adequately testing their validity (Anderson and Koonce , Hirst and Koonce , Bierstaker, Bedard, and Biggs ). Third, traditional analytical procedures using financial statement data lead to high rates of misclassification and, therefore, yield limited success in identifying fraud (Beneish , Kaminski and Wetzel , Hogan et al. ). If NFMs can be used to detect fraud, requiring auditors to use them could help address these challenges. For example, NFMs could be used to help auditors understand a client's business by pointing them to the drivers of economic performance (Ittner and Larcker ). Similarly, if NFMs exist that are easily verified and are not being manipulated by management (Bell, Peecher, and Solomon ), then using them will provide an avenue for auditors to both generate reliable expectations for their analytical procedures and test the validity of management's explanations to their inquiries.
The ability to use NFMs to validate financial performance implies that a correlation exists between NFMs and underlying firm performance. The use of NFMs in evaluating underlying firm performance has garnered much attention since Kaplan and Norton  published The Balanced Scorecard. NFM proponents claim that NFMs are not subject to the limitations of traditional financial measures (i.e., short-term focus, emphasis on narrow groups of stakeholders, and limited guidance for future actions; see Langfield-Smith ). In auditing, SAS no. 56 (AICPA ) suggests that auditors may want to consider NFMs when determining the reasonableness of their clients’ financial statements.
Prior research investigates the relations between NFMs and financial performance measures. Amir and Lev  and Riley, Preason, and Trompeter  study the cell-phone and airline industries, respectively, and conclude that investors overwhelmingly value nonfinancial information over traditional, financial statement variables. The former study also stresses the importance of significantly expanding the use of nonfinancial information in both practice and research. Ittner and Larcker  find one form of NFM, customer satisfaction, is significantly related to future accounting performance and is partially reflected in current accounting book values.
Two additional studies investigate associations between NFMs and financial statement data in the airline industry. Liedtka  uses factor analyses to show that 19 NFMs disclosed by the airline industry represent 7 constructs not measured by 18 common financial measures. Behn and Riley  find that airline industry NFMs are useful for predicting quarterly revenue, expense, and net income numbers. Last, in a study of the retail industry, Lundholm and McVay  illustrate that growth in retail outlets and same-store sales data can be modeled to provide sales forecasts that rival IBES analysts’ forecasts. Consistent with this research, audit guidance suggests that NFMs, such as production capacity, should be correlated with revenue reported on the income statement (AICPA ).
In addition to this research, anecdotal evidence suggests that considering NFMs in conjunction with financial results may, in some cases, help auditors identify fraudulent financial statements. For example, Delphi Corporation appears to have boosted net income through sham sales during a period when Delphi and its competitors were laying off workers and experiencing production cuts (Lundegaard ). Similar to the HealthSouth prosecutor's comments noted previously, it appears that Delphi's auditors might have detected this fraud if they had noted the inconsistency between Delphi's reported financial performance and its NFMs. In addition, both short-sellers and fraud examiners appear to consider NFMs when evaluating the reasonableness of sales growth that exceeds expectations (WSJ [2005a]).
Interestingly, internal and external stakeholders are pressuring businesses to report more NFMs (Ballou, Heitger, and Landes , Holder-Webb et al. [2008, 2009]). As businesses respond to this pressure, it may become more difficult to conceal inconsistencies between financial performance and NFMs. We explore whether fraud firms’ financial results are inconsistent with publicly available NFMs such that the financial results suggest significantly stronger performance than the NFMs. For example, a retailer that is closing outlets is not likely to achieve substantial revenue growth. Such an inconsistency suggests a higher likelihood of fraud.
The PCAOB recognizes the potential for NFMs to be a powerful, independent benchmark for evaluating the validity of financial statement data, and recently endorsed their usage to improve fraud detection (PCAOB ). In addition, Bell, Peecher, and Solomon  claim that NFMs are less vulnerable to manipulation and are often more easily verified than financial data.3 However, cases do exist where fraud firms have manipulated NFMs. For example, The New York Times  reports that WorldCom inflated its Internet traffic growth while committing fraud. The article explains that when investors discovered that WorldCom's Internet data strategy was not profitable, they were shocked because they “had come to believe the boasts of (WorldCom) executives that Internet traffic was doubling about every three months.” Regarding WorldCom's NFM fraud, Scott Cleland, CEO of Precursor Group (a Washington DC research firm), stated: “The $4 billion accounting fraud is baby stuff compared to the fraud of data traffic growth, which allowed WorldCom's stock to appreciate tenfold” (NYT ).
Despite the cases where NFMs have been misstated, several factors suggest that many NFMs are difficult to manipulate, or at least that such manipulations may be difficult to conceal. First, while financial controls can be overridden by management and while financial statements are produced internally, some NFMs are produced and reported by independent sources (e.g., customer satisfaction ratings produced by J.D. Power and Associates). Second, many NFMs are not difficult for auditors to verify (e.g., number of acquisitions, production facilities, or employees), whereas many financial results are difficult to verify (e.g., the estimation of the allowance for doubtful accounts). Third, if management attempts to manipulate its NFMs to conceal a fraud, it will need to expand the perpetrator pool in order to conceal the misstated NFM (e.g., involve human resource employees to manipulate head counts). Thus, a fraud involving both misstated financial data and NFMs will require a greater degree of collusion to conceal. Finally, the manipulation of NFMs involves another set of data that management will need to falsify, which adds complexity to the act of fraud. To summarize, NFM manipulations may not be commonplace for fraud firms. We do not test whether NFMs are more difficult to manipulate than financial data. We believe such a test would be difficult, if not impossible, to perform. However, if fraud firms have manipulated NFMs to be consistent with their fraudulent financial statements, our empirical tests will likely not detect differences between fraud and nonfraud firms with respect to inconsistencies between financial data and NFMs.
Our goal is to explore whether NFMs can be used to detect fraud. Importantly, our study does not provide auditors or other interested parties with a specific model or variable for detecting fraud. We use publicly available empirical data to test the validity of claims by regulators (AICPA , PCAOB ) and educators (Messier, Glover, and Prawitt ) that NFMs provide valuable incremental information for assessing fraud risk. We assume that because auditors have access to a larger pool of firm-specific data than what is publicly available, empirical tests using publicly available NFM data will be no more (and probably less) likely to detect fraud than the NFM data available to auditors. Thus, tests using publicly available data that suggest NFMs can detect fraud will provide strong evidence that auditors can effectively use NFMs as part of their forensic procedures.4 Our findings can be used by policymakers to determine whether benefits to the audit profession would accrue if auditors were required to use NFM data when assessing fraud risk. We offer two anecdotal examples suggesting that such benefits would accrue.
The following examples illustrate how NFMs may be used to detect fraud. Del Global Technologies makes electronic components, assemblies, and systems for medical, industrial, and defense uses. The Securities and Exchange Commission (SEC) alleges that in fiscal years 1997–2000, Del Global Technologies Corp. (Del) engaged in improper revenue recognition when it held open quarters, prematurely shipped products to third-party warehouses, and recorded sales on products that Del had not yet manufactured (SEC [2004a]). Del overstated pretax income in 1997 by at least $3.7 million, or 110%. Del's revenue increased 25% from $43.7 million in 1996 to $54.7 million in 1997. However, Del reported a decrease in the total number of employees over the same period. Employees decreased from 440 in 1996 to 412 in 1997. We believe that while a company could increase profits by cutting payroll, it is improbable that the company would double in profitability while laying off employees, and it is even less probable that employee layoffs would correspond with a significant increase in revenue. In addition, Del's total number of distributors also decreased from 400 to 250 from 1996 to 1997. A decrease in distributors would also seem unlikely to correspond with a significant increase in revenue. This case illustrates how an unusual relationship between NFMs (i.e., total number of employees and of distribution dealers) and financial data (i.e., revenue) could help an auditor assess fraud risk. In contrast, one of Del Global's competitors, Fischer Imaging Corp., realized a 27% decrease in revenue over the same period, accompanied by a 20% decrease in employees and a 7% decrease in distributors.
Anicom Inc. represents another case of unusual trends among NFMs and financial data. Prior to filing for bankruptcy in 2001, the company was a leading distributor of industrial and multimedia wire, cable, and fiber-optic products. The SEC alleges that from January 1, 1998, through March 30, 2000, Anicom's management perpetrated a massive fraud in which it falsely reported millions of dollars of nonexistent sales and used other fraudulent techniques to inflate net income by more than $20 million (SEC [2004b]). During the first year of the fraud, 1998, Anicom reported a substantial increase in employees (46%), in the number of facilities (55%), and in square feet of operations (29%). However, the company's revenue growth was 93% over the same period. Anicom's revenue increased from $244 million in 1997 to $470 million in 1998. Anicom's growth in NFMs (i.e., employees and facilities), while robust, did not keep pace with its enormous revenue growth. In contrast, one of Anicom's closest competitors, Graybar Electric Company Inc., reported more modest sales growth (11%) from 1997 to 1998. Graybar's growth in NFMs was consistent with its revenue growth: Total employees increased 10%, total number of facilities increased 3%, and square feet of operations increased 6%. While we recognize that factors other than fraud can cause unusual relationships between NFMs and financial data, we test whether firms that are committing fraud are more likely to exhibit these relationships.
Levitt and Dubner  posit that one reason academics know very little about the practicalities of fraud is the paucity of good data. Ideally, a study of NFMs would focus on common, industry-specific NFMs. Compiling a reasonable database of fraud firms in one industry is problematic because publicized fraud cases are rare. To overcome this limitation, we construct a measure that is consistent across firms in different industries with different NFMs. We do so by using NFMs with an expected positive correlation with revenue and determine whether inconsistencies between revenue growth and NFM growth discriminate between fraud and nonfraud firms.5 For example, we select the number of retail outlet stores as an NFM for a firm in the retail industry. Then, we examine the difference between an identified fraud firm's percentage change in revenue and the percentage change in retail outlets from the year prior to the fraud to the year of the fraud. We then compare this difference with that of an industry competitor with the expectation that the difference between revenue growth and NFM growth will be larger for fraud firms than for their nonfraud competitors. Thus, we test the following hypothesis:
When performing analytical procedures, auditors commonly rely on trends in prior-year financial data to develop expectations for the current-year's financial performance (Anderson and Koonce , Hirst and Koonce , Bierstaker, Bedard, and Biggs , POB ). As mentioned previously, audit guidance suggests that auditors should incorporate the results of analytical procedures into their fraud risk assessments. SAS no. 99 (AICPA , ¶28) specifically states:
In performing analytical procedures … the auditor develops expectations about plausible relationships that are reasonably expected to exist, based on the auditor's understanding of the entity and its environment. When comparison of those expectations with recorded amounts yields unusual or unexpected relationships, the auditor should consider those results in identifying the risk of material misstatement due to fraud.
The PCAOB  contends that comparing financial data to NFMs is more likely to help auditors detect fraud than performing analytical procedures based solely on financial data that has also been subject to manipulation or fraud. To test this claim, we explore whether the consistency between financial measures and NFMs is associated with fraudulent financial reporting when controlling for other financial variables (e.g., leverage) known to discriminate fraud from nonfraud firms. We also control for nonoperational/nonfinancial data (e.g., corporate governance variables, auditor type, age of the firm, etc.) that have been linked to fraud. In a fraudulent financial reporting model, the explanatory power of these nonoperational/nonfinancial factors should be complemented by including NFMs that serve as a reliable benchmark for financial reporting accuracy.
Prior research and audit guidance identifies three factors—collectively known as the fraud triangle—that lead to fraud: incentive, opportunity, and attitude (Loebbecke, Eining, and Willingham , Albrecht, Wernz, and Williams , AICPA ). Incentive factors include inducement from capital markets and compensation schemes that result in a perceived benefit from committing fraud. Opportunity factors include weak corporate governance and other working conditions that result in circumstances that allow management to commit fraud. Attitude factors are items that reveal management's propensity to rationalize fraudulent behavior. Archival research shows that factors related to both incentive and opportunity are related to fraud (e.g., Beasley ). However, we are not aware of prior archival research that measures and controls for management's attitude, a finding confirmed by Hogan et al.  in their review of the fraud literature.6
Prior archival studies and educators identify variables related to suspicious accounting (e.g., special items) that are useful in detecting fraud or earnings management (Albrecht et al. , Marquardt and Wiedman , McVay ). Thus, three categories of factors found in prior archival research to be associated with fraud are: incentive, opportunity, and suspicious accounting. To determine if inconsistencies between financial measures and NFMs discriminate fraud firms from nonfraud firms, we incorporate our variable of interest into a model containing financial and nonoperational proxies for incentive, opportunity, and suspicious accounting and measure its effects. Our expectation is formalized as follows: