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Understanding investor perceptions of financial statement fraud and their use of red flags: evidence from the field

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We surveyed 194 experienced, nonprofessional investors to examine the relations between their perceptions of the frequency of financial reporting fraud, their use of financial statement information, the importance they place on conducting their own fraud risk assessments, and their use of fraud red flags. We find that investors’ perceptions of the frequency of fraud and their use of financial statement information positively influence the importance they place on conducting their own fraud risk assessments. Investors who place importance on assessing fraud risk make greater use of fraud red flags to avoid fraudulent investments. Red flags commonly relied upon include SEC investigations, pending litigation, violations of debt covenants, and high management turnover. Investors rely less on company size and age, the need for external financing, and the use of a non-Big 4 auditor. We also find evidence of positive associations between the use of specific red flags and investors’ portfolio returns.

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  1. Our use of the term, “fraud,” refers only to fraudulent financial reporting. We use the terms “fraud” and “fraudulent financial reporting” interchangeably. Similarly, our use of the term “investors” refers explicitly to nonprofessional investors. Future research can determine whether the findings of our study generalize to professional investors.

  2. Table 3, Panel A in Ljungqvist and Qian (2014) provides a breakdown of the information provided by these short sellers, which, in some cases, could indicate fraudulent financial reporting. Our results suggest that nonprofessional investors are unlikely to look to the information provided by short sellers as informative about potential fraud red flags. Consequently, this could also prove detrimental to short sellers who make public their private information to reduce arbitrage risk, as the market’s failure to trade in a timely manner on this information could increase short sellers’ risk exposure.

  3. The accumulation and disclosure of early- and late-stage red flag data by a regulator resembles actions that are underway at the Public Company Accounting Oversight Board (PCAOB) in relation to measuring audit quality. The PCAOB notes that the “visibility of audit quality to investors is limited.” As such, it is developing a set of audit quality indicators that will include early-stage (e.g., partner workload) and late-stage (e.g., financial statement restatement) measures of audit quality for a given company’s year-end audit. The intent is to collect data in relation to these indicators and make them available to a variety of capital market participants (,

  4. For example, during the 7-year fraud at HealthSouth, the company employed five different CFOs ( Management turnover, particularly CFO turnover, could also be related to the incentives of the CEO to pressure subordinates to manage earnings. Indeed, Feng et al. (2011) present findings that suggest that CFOs involved in material accounting manipulations are often succumbing to pressure from CEOs.

  5. We will use the term “reliance on financial statement information” to describe the relative reliance on financial statement information versus other information sources and data. In Sect. 3, we describe how, similar to Elliott et al. (2008), we develop a measure of investors’ reliance on financial statement information relative to other information.

  6. The Toluna Group provides online research and survey technology solutions to market researchers, the media, corporations, and academicians. At the time of our study, Toluna maintained a global panel of 3.7 million active consumers, investors, and professionals. Toluna has been providing polling and survey data since 2000 and is the second largest company in its industry in terms of revenues. Its main competitors are Research Now, uSamp, and Survey Sampling International. For any particular data collection, a cross-section of the panel can be used or specific subgroups can be targeted. Participants in our survey were incentivized by Toluna’s points reward system (the points awarded for completing a survey are determined in advance based on the length of the survey). Points are not awarded based on the reasonableness or accuracy of participants’ responses. The survey was advertised to participants via our ambiguous title, “Survey on Investor Beliefs.” The average Toluna participant completes eight surveys per year.

  7. For example, the response rate for Elliott et al. (2008) was approximately 3 %. Because not all individuals responded to our survey, we examined the potential for nonresponse bias. Previous research (Filion 1975; Wallace and Mellor 1988; Oppenheim 1992) finds that nonrespondents behave like late respondents. Thus Wallace and Mellor (1988) and Oppenheim (1992) recommend comparing data from late respondents to that of early respondents to assess this bias. Accordingly, we compared the responses from the first quartile of respondents to those in the last quartile. Results indicate no statistically significant differences for any of our hypothesized variables in Table 1 (including all underlying questions). This suggests that the responses for early and late respondents are similar and that nonresponse bias is not likely a concern (Wallace and Mellor 1988).

  8. Descriptive statistics (control variables) for our sample are presented in Table 2 and discussed in Sect. 4.

  9. Data from the ICI indicate that 54 % of households with income between $50,000 and $74,999 and 69 % of households with income between $75,000 and $99,999 held individual securities. In addition, for individuals who held securities, 67 % had completed four years of college. Seventy-five percent of individuals who had completed some graduate school or obtained a graduate degree held individual securities (ICI 2008).

  10. We also compare the findings of Barber and Odean (2001) to certain participant-reported information gathered in our study regarding trading behavior and returns. Barber and Odean (2001) find that males trade more than females but realize lower returns. Results (not tabulated) from our survey participants are consistent. Males in our study report more trades per year than females (p = 0.02); however, the average return of these males is lower than that of the females (p = 0.04).

  11. See the version of the paper available at for Appendices A and B, which contain the exact questions and response scales posed to investor participants to obtain our hypothesized and control variables.

  12. While the rate at which fraud is perpetrated is unknown, it is likely that our participants’ perception of this rate is, on average, higher than the actual rate. Still, investors’ perceptions are their reality and likely affect their subsequent actions (e.g., assessment of fraud risk, use of red flags). Our framework tests these relations.

  13. To be clear, we do not believe that either of these percentages (PERCEPTION OF FRAUD or OWNED THE STOCK OF A FRAUD COMPANY) represents an estimate of the actual rate of fraud across publicly traded companies. Rather, they likely represent that a substantial portion of investors have held the stock of at least one fraud firm. Given the size of the market capitalizations of companies that have committed fraud (e.g., Enron, WorldCom, HealthSouth, Waste Management, Xerox, etc.), investors who had a portfolio of any reasonable size likely held some shares of one of those companies. As expected, OWNED THE STOCK OF A FRAUD COMPANY and PERCEPTION OF FRAUD are positively correlated (p < 0.01) for our sample.

  14. Investors may obtain certain red flags (e.g., debt covenant violations) from the popular press, investor websites, etc. (versus company disclosures). For two examples see:,-has-entered-negotiations-with-its-lenders and

  15. Participants in our study took the survey from August 21 to 25, 2008. We asked them to approximate their return on their investment portfolio for the last 12 months on a scale of 1 (less than −20 %) to 11 (more than 20 %). The average response was 6.12 (approximately a 0 % return). While the average return for the NYSE was negative during that time frame, Table 2, Panel D, shows that the most popular industries of investment for our participants were energy/utilities, high tech/communications, and manufacturing. The energy/utilities industry survived the 2008 recession less scathed than others. Several firms in the industry even reported positive returns during the period we examined (e.g., Duke Energy, NextEra Energy, New Jersey Resources, NorthEast Utilities, and Pacific Gas and Electric). Biotech companies and manufacturers of consumer staples also fared well during this time period (e.g., Biogen, Bristol Myers Squibb, Colgate Palmolive, Diamond Foods, General Mills, Heinz, Hormel Foods, Hershey Foods, and Kellogg). See Therefore, given the industries invested in by our survey participants and the lack of diversification of their portfolios (see Table 2, Panel C), the average reported return being above the average return for the NYSE is not surprising.

  16. See Doyle et al. (2007) for a similar examination with respect to the association between firm size and internal control weaknesses. They note firm complexity could be a confounding variable. As such, they do not observe a significant, bivariate correlation between size and internal control weaknesses but find a significant relation between these two variables when they control for confounding variables, such as firm complexity.

  17. While USE OF RED FLAGS is the average of survey participants’ reported use of all individual red flags, in Sect. 2.1 we describe how individual red flags can be categorized based on the fraud triangle (i.e., incentive, opportunity, or attitude) as well as comparisons between financial and nonfinancial information. As such, we assigned red flags to these categories and re-performed our test of H2. In untabulated analyses, we observe that the relation between FRA and each category is positive and highly significant (p’s < 0.01). Thus it does not appear that investors who perceive fraud risk as important favor the use of red flags in any one of these categories.

  18. The three equations include all control variables that were significant in Table 4. For ease of interpretation, the coefficient variable terms (e.g., a, b, c, c’) and other terminology are the same as those used by Zhao et al. (2010). Prior research had relied on the Sobel (1982) test (e.g., Baron and Kenny 1986) to test mediation; however, Zhao et al. (2010, p. 22) explain why the Preacher-Hayes bootstrap test is superior to the Sobel (1982) test. A macro to run the Preacher-Hayes test in SAS and SPSS can be found at

  19. Complex analytical skills developed at higher levels of education may be necessary to collect and analyze red flags (e.g., accrual levels). Thus some forms of investor financial expertise, education, experience, or a combination of these may be associated with the use of the most effective red flags (e.g., the accrual red flag examined in this section and early-stage fraud red flags studied in Sect. 4.7.2). Interestingly, controlling for the variables provided in Table 2, we do not observe positive relations between either INVESTING EXPERIENCE, EDUCATION, or other measures of financial expertise and use of these particular red flags. In particular, the lack of a relation between our measures of financial expertise and the use of the most effective red flags is concerning and may reflect a lack of coverage of financial statement red flags in many business-related educational programs.

  20. We control for the variables used in Elliot et al. (2008) because they specifically examine the returns of nonprofessional investors. However, we do not include the variable “training” from Elliott et al. (2008), as it was specific to training provided by the investment club from which their sample was derived. Additionally, it was not statistically significant in their analysis.

  21. In Table 1, investors report using five fraud red flags relatively more often than other red flags: SEC investigations, pending litigation, violations of debt covenants, high management turnover, and insider trading. Although high management turnover and insider trading may occur at any point, the other three red flags can be considered late-stage fraud indicators.

  22. For example, to aid investors in evaluating the validity of financial information, disclosures in firms’ 10-Ks could present a more concise and centralized picture of changes in nonfinancial measures. See pages 39 and 40 of the Tenet Healthcare 2013 10-K for an example of such a transparent disclosure:


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This study has benefited from comments provided by Chris Agoglia, Jagadison Aier, Ben Ayers, Linda Bamber, Michael Bamber, Mark Beasley, Paul Beswick, Frank Buckless, Tina Carpenter, Brian Croteau, Brooke Elliott, Blake Hetrick, Frank Hodge, Susan Krische, James Kroeker, Kathleen Linn, Molly Mercer, Jason Smith, Steve Smith, Hun-Tong Tan and input received from presentations to the Office of the Chief Accountant of the Securities and Exchange Commission, the Financial Industry Regulatory Authority (FINRA), the 2011 Conference of the Research Center on the Prevention of Financial Fraud, the 2012 Mid-Atlantic Region Conference for the Institute of Internal Auditors, and the 2012 Meeting of the Association of Certified Fraud Examiners—Central Carolina Chapter. This research was supported by a grant from the FINRA Investor Education Foundation. All results, interpretations, and conclusions expressed are those of the authors alone and do not necessarily represent the views of the FINRA Investor Education Foundation or any of its affiliated companies.

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Brazel, J.F., Jones, K.L., Thayer, J. et al. Understanding investor perceptions of financial statement fraud and their use of red flags: evidence from the field. Rev Account Stud 20, 1373–1406 (2015).

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