Do Nonprofessional Investors React to Fraud Red Flags?
Joseph F. Brazel*
Department of Accounting
College of Management
North Carolina State University
Campus Box 8113
Nelson Hall
Raleigh, NC 27695
919-513-1772
joe_brazel@ncsu.edu
Keith L. Jones
Department of Accounting
George Mason University
Enterprise Hall, MSN 5F4
Fairfax, VA 22030-4444
703-993-4819
kjonesm@gmu.edu
Rick C. Warne
Department of Accounting
George Mason University
Enterprise Hall, MSN 5F4
Fairfax, VA 22030-4444
703-993-1763
rwarne@gmu.edu
August 2010
We appreciate the helpful comments from Chris Agoglia, Ted Christenson, Brooke Elliott, Karen
Kitching, David Wood, and Mark Zimbelman. We thank workshop participants at Brigham
Young University, North Carolina State University, and George Mason University. We are
grateful for the research assistance provided by Meredith Fincher. This study was funded by a
research grant from the Financial Industry Regulatory Authority Investor Education Foundation.
* Corresponding Author
Do Nonprofessional Investors React to Fraud Red Flags?
ABSTRACT
We conducted an experiment to determine if and how nonprofessional investors react to red flags
related to fraudulent financial reporting. We also examined if making red flag data more
transparent affects investor judgments. Investors reviewed information related to a hypothetical
company and decided whether to increase or decrease their investment. We manipulated the
presence and non-presence of two specific red flags between investors: (1) a large difference in
sales growth and growth in related nonfinancial measures (e.g., number of employees, size of
production space), and (2) a large difference between net income and cash flow from operations
(i.e., high accruals). In general, we do not find that investors react uniformly to red flags. We do
not observe investors decreasing investment levels when the accrual red flag is present, even
when it is made more transparent. Conversely, we find that making a large difference in sales
growth and growth in corresponding nonfinancial measures (NFMs) transparent leads to lower
investment levels. Analyses reveal that investors perceive the NFM red flag to be more intuitive
and this difference likely drives our conflicting results. Interestingly, if the NFM red flag is not
made transparent to investors, investors appear to interpret the abnormal inconsistency as a
positive signal regarding operational efficiency and, in turn, increase their investment levels. We
also present evidence that a complementary effect occurs between red flags (i.e., the presence of
high accruals is needed to increase the salience of the transparent NFM red flag). Finally, we
illustrate that making it transparent that the NFM red flag is not present (i.e., sales growth is
consistent with NFM growth) can serve as a “green flag” for investors and spawn increased
investment.
Keywords: accruals; fraud; investor; nonfinancial measures; red flag
Data availability: Contact the authors
I. INTRODUCTION
With the growth of the financial markets, nonprofessional investors remain a significant
component of the overall equity market (Elliott, Hodge, and Jackson 2008), owning
approximately 34% of shares outstanding (Bogle 2005). Thus, nonprofessional investors are
susceptible to significant losses from financial statement fraud (e.g., NASAA 2006). Indeed,
Brazel, Jones, and Warne (2010) find that 25% of their sample of nonprofessional investors had
previously held shares of a company that committed fraudulent financial reporting. The primary
objective of this study is to examine nonprofessional investors’ reactions to red flags related to
fraudulent financial reporting (hereafter, fraud). We evaluate if greater transparency of red flag
data can improve the investment decisions of nonprofessional investors (hereafter, investors). In
addition, we study if more intuitive red flags illicit stronger investor reactions. Finally, we
consider if making positive information (i.e., lack of red flags or “green flags”) transparent also
improves the investment decisions of nonprofessionals.
Firms that commit financial statement fraud often exhibit basic “red flags” that investors
either do not notice or simply chose to ignore (Schilit 2002). During the course of the fraud at
HealthSouth, the company periodically reported an increase in sales and assets while
simultaneously reporting a decrease in the total number of facilities (WSJ 2005a). During the
second year of the Xerox fraud, the company’s 10-Ks reported net income ($395 million) that
differed substantially from cash flow from operations (-$1,165 million). In hindsight, these red
flags appear to be glaring indicators that the financial results at these companies were suspect.
However, as one would expect, these red flags are rarely transparent to investors.
Transparency and accessibility of financial information to investors is a major concern
for policymakers. In a 2009 speech about the current financial crisis, SEC Commissioner Luis A.
1
Aguilar stated, “We must act now to re-establish transparency and accountability to investors by
looking to Congress and the SEC for immediate action.”1 President Obama renewed this call to
protect investors in his 2010 State of the Union Address: “We need to make sure consumers and
middle-class families have the information they need to make financial decisions.”2
We examine investors’ reactions to two empirically validated red flags that are disclosed
in 10-K filings, but are not typically transparent: (1) a large difference between sales growth and
related nonfinancial measures (Brazel, Jones, and Zimbelman 2009); and (2) a large
inconsistency between net income and cash flow from operations (a high level of accruals as
described by Lee, Ingram, and Howard 1999). Consistent with Elliott, Krische, and Peecher
(2010), we consider red flags to be transparent when the relevant information is presented in
close proximity and in a layout that allows users to more easily identify and process the red flags
(e.g., sales growth and growth in nonfinancial measures are calculated and compared side by
side).3 As currently presented in the financial statements, sales data resides on the income
statement while nonfinancial measures (NFMs) are dispersed throughout the Management
Discussion and Analysis (Brazel, Jones, and Zimbelman 2009). Information related to accruals is
contained within the financial statements; however, net income and cash flow from operations
are not compared side by side.
Despite calls from policymakers for greater transparency, little research exists on
investors’ use of fraud red flags and whether greater transparency of red flags will actually
provide greater investor protection with respect to fraud. For transparency to significantly affect
1
http://www.sec.gov/news/speech/2009/spch020609laa.htm
http://abcnews.go.com/Politics/State_of_the_Union/state-of-the-union-2010-president-obama-speechtranscript/story?id=9678572&page=2
3
Elliott, Krische, and Peecher (2010) argue that greater transparency occurs when information is more accessible,
comprehensible, or understandable. Other studies use similar terms such as “opacity” (Bhattacharya, Daouk, and
Welker 2003), “clarity” (Hirst and Hopkins 1998), “salience” (Elliott 2006), and “prominence” (Hunton, Libby, and
Mazza 2006) to describe similar constructs. While we use the term “transparency”, other terms could also be used to
describe the same theoretical construct.
2
2
investors’ reactions to a red flag, investors must: (1) ignore the red flag in its current, nontransparent state; but still (2) analyze fraud risk when making investment decisions. If investors
already identify and react to a red flag, then making it more transparent will not affect its use.
Conversely, if investors do not ordinarily consider the possibility of fraud when making
investment decisions, then investors are unlikely to rely on fraud red flags in general. For
example, nonprofessional investors may not feel qualified to assess fraud risk and rely on others
who are more qualified (e.g., auditors and regulators) to detect fraud. Finally, for transparency to
affect behavior, investors must understand the intuition behind the red flag. For example, if
investors do not understand why net income may be different from cash flow from operations,
then making large accruals (e.g., Xerox) more transparent would not affect investor decision
making.
We therefore address two important and compelling empirical questions: (1) Do investors
react to non-transparent red flags? (2) Is greater transparency of red flags needed to protect
investors? Despite the possibilities that investors may not specifically screen for fraud (e.g., they
rely on auditors or the SEC) or that investors do not understand the intuition behind many red
flags, we expect that transparency will affect investor decision making for two reasons. First, we
study two red flags that investors with basic accounting and/or business backgrounds should find
intuitive.4 Second, concurrent survey research finds that nonprofessional investors report relying
4
For example, fictitious sales will have no related increase in nonfinancial measures (e.g., number of retail outlets or
employees) or corresponding cash flow. However, we chose red flags that require different types of intuition.
Understanding how large accruals can serve as a red flag requires a basic knowledge of accounting. Understanding
how a large difference between sales and NFMs could indicate fraud requires more of a general knowledge of
business (e.g., fewer employees and products would not likely lead to greater sales). In follow up questions, we
assess whether the intuitiveness of the red flag (to investors) can explain why investors might not react to
transparent red flags in a uniform manner.
3
on these red flags when investing (Brazel, Jones, and Warne 2010).5 Thus, investors should react
to these red flags. However, prior research finds that nonprofessional investors do not typically
follow any systematic data evaluation method, misinterpret financial information, often fail to
recognize relations among various data that are not proximate to each other, and sometimes
become overwhelmed by the complexities associated with investment-related data (Bouwman
1982; Maines and McDaniel 2000; Hodder, Hopkins, and Wood 2008). Given the dispersed state
of the accrual and NFM red flag data in the financial statements and elsewhere, we expect that
investors’ reactions to these red flags will be more pronounced when they are made transparent.
In addition, we investigate positive signals (i.e., “green flags”) vis-à-vis fraud. In the
absence of red flags, consistency exists between (1) sales and NFMs and/or (2) net income and
cash flow. For example, low accruals can signal greater earnings persistence and consequently
higher earnings quality (Sloan 1996). Making green flags transparent should boost investor
confidence and illicit higher investment levels. We therefore posit that investors will not react
significantly to non-transparent red flags, but that transparent red (green) flags will lead to lower
(higher) investment levels. Finally, we predict that making a red flag transparent will have a
greater effect if another red flag is present.
We experimentally tested our hypotheses by asking 237 investors to assume that they
owned stock in a hypothetical company. Our sample consisted of a diverse set of experienced
investors who actively trade individual shares of stock (similar to our experimental setting). We
provided them with the information that investors reportedly use when investing (c.f., Elliott,
Hodge, and Jackson 2008) and asked whether they would increase, maintain, or decrease their
investment in the company. We conducted a 2X2X2 experimental design by manipulating
5
The questionnaire in Brazel, Jones, and Warne (2010) asked investors to rate the importance of each red flag when
making investment decisions (on a scale where 1 = “very unimportant” and 7 = “very important”). The average
response was approximately 5 for each measure (accrual and NFM red flags).
4
accruals (high vs. low), the difference between sales growth and NFM growth (high vs. low), and
the transparency of the red flags (transparent vs. not transparent). For example, when accruals
are high, the accrual red flag is present. When accruals are low, the accrual red flag is not present
(i.e., a green flag is present).
Overall, our results suggest that the effect of transparency depends on the intuitiveness of
the red flag. When the NFM red flag is transparent, we observe lower investment levels.
Conversely, when the accrual red flag is transparent, we do not observe lower investment levels.
Post-experimental questions provide evidence that investors possess a greater understanding of
the intuition behind the NFM red flag (versus the accrual red flag). However, the NFM red flag
must be transparent to reduce investment levels. When NFM red flag is not transparent, investors
appear to misinterpret the abnormal inconsistency between sales and NFM growth as a positive
signal regarding operational efficiency. Alarmingly, a non-transparent NFM red flag leads
investors to increase investment levels. We therefore find compelling evidence that the
transparency of the NFM red flag, and potentially other intuitive red flags, can substantially
affect investor behavior and improve investor protection.
We also observe that the aforementioned effect of the transparent NFM red flag is
dependent on the presence of another red flag. When the accrual red flag is not present,
providing a transparent NFM red flag does not have a significant effect on investment levels.
This result is important and relevant as fraud firms typically exhibit several red flags
concurrently (e.g., Brazel, Jones, and Zimbelman 2009; Dechow et al. 2010). Finally, we provide
limited evidence that making a “green” flag (i.e., sales growth is consistent with NFM growth)
transparent can boost investor confidence and generate increased investment.
5
Our results make a significant contribution to both the academic literature and public
policy. First, while investors often fall prey to fraud and extensive research has examined the
usefulness of red flags to identify fraudulent financial reporting (see Hogan et al. 2008), we
provide the first empirical evidence of how investors react to red flags. We also illustrate that
investor decision-making related to fraud is ill-defined and substantial future research is required
to enhance our understanding of these processes. As standard-setters continue to consider
investor behavior when forming public policy (Zweig 2009), this line of research should
ultimately lead to greater investor protection. Second, our results provide practical guidance to
regulators in their efforts to promote investor protection and improve the transparency of
information used by investors. For example, regulators could require companies to report and
tabulate NFM data (along with relevant financial data) in a footnote to the financial statements.
This regulatory change would be relatively costless because companies already report NFM data
throughout the 10-K.6 Importantly, our evidence suggests that public policy actions towards
greater transparency are critical. Even the most intuitive red flags are misinterpreted or often go
unnoticed if not presented explicitly to investors.
The remainder of the paper is organized as follows. Section II presents the background
and hypotheses development. Sections III and IV contain the method and results of the study,
respectively. Section V concludes the paper and provides guidance for future research.
6
See Appendix A for an example of how NFMs are commonly reported and how Tenet Healthcare Corporation
provides financial data and related NFMs in a very transparent manner. If NFM data are disclosed in a footnote to
the financial statements, the NFM data would then fall subject to the external audit and could be tagged for XBRL
purposes. Auditing (Tagging) NFM data would improve their reliability (allow for an easy analysis of the NFM red
flag as described in this study). Currently, the majority of NFM data disclosed by companies resides in
Management’s Discussion and Analysis (MD&A) section of their 10-K filings. At this time, the SEC has indicated
that the use of interactive tagged data is not appropriate for the MD&A because current tagging sets provided under
XBRL are considered inadequate (SEC 2008; Rummell 2008; Laux 2009; Schneider and So 2009).
6
II. BACKGROUND AND DEVELOPMENT OF HYPOTHESES
Statement of Financial Accounting Concept No.1 asserts that “financial reporting should
provide information that can be used by all – nonprofessionals as well as professionals – who are
willing to learn how to use it properly. Efforts may be needed to increase the understandability of
financial information” (FASB 2008, 11). The Financial Accounting Standards Board (FASB)
concept statement specifically notes that nonprofessionals are the baseline for assessing whether
financial reporting is useful (FASB 2008). Congress (Public Law [107-204] 2002) and the
Securities and Exchange Commission (Cox 2005) have explicitly stated their intent to protect
nonprofessional investors. This paper ties prior archival research in fraud detection to research in
psychology to help regulators and researchers identify methods to improve investor decisionmaking with respect to fraud.
Researchers have identified red flags that indicate an elevated risk of fraudulent financial
reporting. For example, Brazel, Jones, and Zimbelman (2009) examine a sample of fraud firms
and non-fraud firms and report that the difference between growth in sales and growth in related
NFMs is higher for fraud firms. Similarly, Dechow, Sloan, and Sweeney (1996) and Lee,
Ingram, and Howard (1999) find that a large difference between net income and cash flow from
operations is also a red flag with respect to fraud. Hogan et al. (2008) provide a review of the red
flag literature, highlighting that fraud firms tend to exhibit these and other red flags (e.g., less
independent boards). Thus, while investors fall victim to fraud and extensive research has
examined the usefulness of red flags to identify fraudulent financial reporting, our study
advances the literature by taking an important next step: studying investors’ reactions to red
flags.
7
Brazel, Jones, and Warne (2010) survey nonprofessional investors about their use of
fraud red flags. They find that investors who report greater reliance on financial statement
information to make investment decisions perceive fraud risk assessment to be a more important
investment activity. This relation is stronger when investors’ perceptions of the current rate of
fraudulent financial reporting are higher. Investors who place more importance on fraud risk
assessment are, in turn, more likely to use fraud red flags as part of their investment decisions.
While Brazel, Jones, and Warne (2010) survey investors about their use of red flags, we extend
this new stream of literature by experimentally examining if investors recognize red flags when
they are not transparent, what types of red flags are more likely to influence investor decisionmaking, and to what extent transparency can improve investor decisions.
We investigate the aforementioned red flags related to NFMs and accruals for several
reasons. First, these measures both differ significantly between fraud and non-fraud firms as
documented in the fraud literature (e.g., Brazel, Jones, and Zimbelman 2009; Dechow et al.
2010). Second, as described previously, both measures are fairly intuitive regarding their
indication of elevated fraud risk. Investors are more likely to incorporate intuitively appealing
information into their judgments (Hodder, Hopkins, and Wood 2008). However, these red flags
represent two separate types of intuition. Investors must have a basic level of accounting
knowledge to understand why net income can differ from cash flow from operations. In contrast,
investors need some general business knowledge to understand that a large difference between
sales growth and growth in underlying NFMs (e.g., retail outlets) can be a red flag. This
knowledge differential could lead to non-uniform reactions to the red flags. Third, these
measures can be represented on a continuum of egregiousness, whereas other red flags are binary
in nature (e.g., the presence of an SEC investigation or pending litigation). The presence of
8
“obvious” binary red flags may present demand effects in an experiment.7 Fourth, Brazel, Jones,
and Warne (2010) report that investors are inclined to use our two red flags when assessing fraud
risk. The red flags investors report using most often are typically manifest later in the fraud
discovery process and usually revealed to investors ex post (i.e., after the fraud has been
detected) (Brazel, Jones, and Warne 2010).8 Consequently, investors’ attention and reaction to
such ex post red flags would not likely reduce their losses due to fraud. Conversely, the two red
flags we investigate are ex ante (i.e., prior to the fraud detection) indicators of fraud. Ex ante
fraud indicators can help investors detect fraud early and avoid financial losses.
Information Processing of Fraud Red Flags
The Elaboration Likelihood Model (ELM) explains that individuals process information
via one of two possible routes: the central route or the peripheral route (Petty and Cacioppo
1986).9 The central route involves deliberate, conscious consideration of the information. In
contrast, the peripheral route involves limited cognitive effort to process information. Prior
research suggests that professional analysts process information via valuation models and
directly search financial statements for data necessary to perform their analyses (Bouwman,
Frishkoff, and Frishkoff 1987; Hunton and McEwen 1997). This structured method of gathering
data would allow analysts to process red flag information via the central route and cause them to
attend to red flag data regardless of how the data is presented in the financial statements or
elsewhere.
7
Furthermore, Levitt and Dubner (2009) describe how red flags used to identify terrorist subjects are more
effective/accurate if they are measured on a continuous (vs. binary) scale.
8
Brazel, Jones, and Warne (2010) find that investors report to use four fraud red flags relatively more often than
other red flags: SEC investigations, pending litigation, violations of debt covenants, and high management turnover.
Though high management turnover may occur before or after the market discovers the fraud, the other three red
flags can be considered ex post fraud indicators.
9
The Heuristic Systematic Model (Chen and Chaiken 1999) also describes decision-makers’ behaviors using two
“routes,” a simple route (heuristic processing) and a route that involves the comprehensive consideration of the
information (systematic processing). These two dual-processing theories yield similar descriptions of individuals’
judgments and are sometimes referenced together in accounting research (e.g., Alexander 2003).
9
On the other hand, nonprofessional investors utilize non-structured data-gathering
techniques and process information in sequential order (Bouwman 1982; Hunton and McEwen
1997; Maines and McDaniel 2000). Since financial statements and related disclosures contain a
significant amount of information, and red flag data is typically dispersed; nonprofessional
investors likely process red flag data using the peripheral route. In fact, the FASB has
acknowledged that some Board members “believe that at some point the sheer volume of all
required disclosures may overwhelm users’ ability to assimilate information and focus on the
more important matters” (FASB 1990, p. 101). We expect that investors will process nontransparent red flags via the peripheral route. Thus, we do not predict a significant investor
reaction to non-transparent red flags.
We posit that greater transparency (i.e., placing red flag data in close proximity and
providing red flag analyses) will have a moderating effect on investor attention to red flags. We
expect that nonprofessional investors will attend to accrual and NFM red flags and process this
red flag information via the central processing route if the information is made transparent.
Investors who observe a transparent red flag will choose to maintain lower investment levels
relative to investors who are presented with a non-transparent red flag.
Conversely, we expect that investors who receive a transparent green flag (i.e., the red
flag is not present) will increase their investment levels relative to those who receive a nontransparent green flags. For example, we expect that investors who deliberately consider the
implications of having net income that is consistent with cash flow from operations will more
fully appreciate the consistency and its future implications on stock price (e.g., Sloan 1996). We
therefore predict a disordinal interaction. As shown in Figure 1, transparency will lead investors
10
who observe a red flag to decrease investment levels and investors who observe a green flag to
increase investment levels.
Insert Figure 1 here
We formally state our first hypothesis as follows:
H1: A transparent red (green) flag leads to lower (higher) investment levels than a nontransparent red (green) flag.
We separately test this prediction for two specific red flags: the NFM red flag and the
accrual red flag. As previously stated, the intuition behind the two red flags could influence the
effect of making a red flag transparent. For example, prior literature suggests that investors may
not fully appreciate earnings that are fully supported by cash flow (e.g. Sloan 1996; Hewitt
2009). Thus, the possibility exists that transparency will not uniformly affect both red flags. In
follow up questions, we post-experimentally assess whether the intuitiveness of the red flag (to
investors) can explain why investors might not react to even a transparent red flag.
Transparency and Multiple Red Flags
Though a single red flag may indicate fraud, companies that commit financial statement
fraud typically exhibit multiple red flags throughout their 10-K filings. For example, Enron’s
financial statements from 1995 include multiple red flags related to the accuracy of its statements
(Hubbard 2002). Prior academic research has produced similar findings. For example, Brazel,
Jones, and Zimbelman (2009) and Dechow et al. (2010) both find that fraud firms have
significantly higher levels of accruals (vs. non-fraud firms). Additionally, both studies also
document that fraud firms have greater differences between their financial measures and NFMs.
However, the joint effect of multiple red flags on investor decision making has yet to be studied.
We investigate the potential for joint effects between multiple red flags.
11
The Lens Model describes human judgment in a variety of contexts (Brunswick 1952,
Bonner 2008; see Karelaia and Hogarth 2008 and Kaufmann and Athanasou 2009 for reviews
and meta-analyses). Based on established criteria, individuals use external cues to predict an
unknown event or outcome (e.g., the likelihood of fraud). Each cue either provides support for or
against a given outcome. Individuals weigh each cue according to its relative importance in
predicting an outcome. Additional relevant cues that point to an outcome increase the likelihood
an individual will make a correct judgment (Karelaia and Hogarth 2008). Thus, if an investor
observes a transparent red flag (e.g., a large difference between sales and NFMs) in the presence
of no other red flags (e.g., green flags or low accruals), that investor may consider the transparent
red flag to be an anomaly and assign very little weight to its relevancy. However, if that same
investor observes an additional red flag (e.g., high accruals), the investor would likely sense a
trend and assign a greater weight to the transparent red flag. Thus, we predict that the effect of
making a red flag transparent will be greater in the presence of another red flag. We formally
state Hypothesis 2 as follows:
H2: Making a red flag transparent will have a more negative effect on investment levels
when another red flag is present (vs. not present).
III. METHOD
Sample
Two hundred and thirty-seven nonprofessional investors completed online experimental
instruments for this study. Given (1) the objective of this study is to examine causal effects or
investor reactions to red flags, and (2) data related to nonprofessional investor decisions are not
publicly available, the experimental method is most appropriate for this study. Greenfield Online
(http://www.greenfield.com) distributed the instruments. For the purposes of our study,
Greenfield screened its database for participants who actively traded individual shares of stock
12
(vs. simply investing in mutual funds). We further screened participants by requiring that they
answer “yes” to the following question in order to complete the instrument: “Have you bought or
sold individual shares of company stock (i.e., not mutual funds) in the last six months?” Further,
to ensure exclusion of professional investors, participants were required to answer “no” to the
following question: “As part of your full-time job responsibilities, do you analyze or trade stocks
or other securities?” Greenfield distributed the survey to 649 participants. Thus, our response
rate is 36.5%, which is comparatively high given the response rates of previous studies that have
attempted to access active investors (e.g., the response rate for Elliott, Hodge, and Jackson 2008
was approximately 3%). Participants completed the survey from August 25 – 29, 2009.10
Participants resided in 41 different states and Washington D.C., were 48.5% male and
well educated (67% had a bachelor’s degree or higher). Mean responses to demographic
questions reveal that our participants were between 40-49 years old, had an annual household
income of $60,000-$90,000, and had 6-10 years of investing experience. Given that researchers
commonly use MBA students to proxy for nonprofessional investors, our sample of investors
appears relatively diverse and experienced. We provide demographic data in Table 1.
Insert Table 1 here
We collected data related to participants’ red flag usage, their fraud
experiences/perceptions, and their general investment experiences, activities, and returns. Of
particular note is that, consistent with Brazel, Jones, and Warne (2010), participants reported
10
The CBOE Volatility Index® (VIX®) is a key measure of market expectations of near-term volatility conveyed
by S&P 500 stock index option prices. Since its introduction in 1993, the VIX has been considered by many to be
the world's premier barometer of investor sentiment and market volatility (see
http://www.cboe.com/micro/vix/vixwhite.pdf). In short, higher indices are indicative of greater market fear. During
the period our data was collected the highest measure of the index was 25.13, whereas in mid-September 2008, the
index rose above 30 and did not fall below 30 until June 1, 2009 (see Lauricella 2009 and
http://www.cboe.com/micro/vix/historical.aspx). As of July 2, 2010, the VIX was 22.59 and the 52-week range for
the index was 15.23 – 48.20 (http://finance.yahoo.com/q/bc?s=%5EVIX&t=2y).
13
moderate use of (1) the specific red flags used in this study, and (2) red flags in general
(Variables 5, 6, and 8 in Table 1). Approximately 22% of participants (Variable 7) also selfreported using other sources for red flags (e.g., data collected from the Internet, broker-provided
assessment tools, and financial statement footnotes). Participants felt that managers fraudulently
misstate financial statements at an alarming rate (34.9%, Variable 9). This high rate is likely due
to the fact that 16% of the sample had previously invested in a company that committed fraud
(Variable 10). For example, ten and five of our participants were invested in Enron and
WorldCom, respectively. Brazel, Jones, and Warne (2010) find similar rates in their sample of
nonprofessional investors and provide additional discussion of these relatively high rates.11
Experimental Task, Independent Variables, and the Dependent Variable
We provided participants with a case which placed them in the role of an individual
investor who currently held 1,000 shares of stock in Madison Sporting Goods Co., a
manufacturer of athletic equipment. We instructed them that their main task was to decide
whether to increase or decrease their investment in Madison, and by how many shares, based on
the information provided in the case. Participants were instructed to “feel free to use a calculator
or any other tool that you would use to make a real investment decision.” We then supplied all
investors with an overview of Madison, industry data, audited financial statements, financial
ratios, a financial analyst’s research report, and NFM data related to Madison’s operations.12 All
participants were informed, and the financial statements illustrated, that both Madison and its
industry were experiencing steady, modest growth (e.g., sales growth of between 4-6%). The
11
With the exception of the use of other red flags (Variable 7 in Table 1), all the variables presented in Table 1 are
not significantly different between conditions (all p’s > 0.40). The use of other red flags was significant at p = 0.052.
As such, we include the use of other red flags as a covariate in our analysis of H1 and H2 (see Table 2).
12
Nonprofessional investors surveyed by Brazel, Jones, and Warne (2010) report that they use these six information
sources when making investment decisions. Others studies report similar findings (e.g., Elliott, Hodge, and Jackson
2008).
14
analyst report also highlighted stable growth for Madison. We adopted both the financial
statements and the analyst report from Elliott et al. (2007). Participants took, on average, 32.51
minutes to complete the case. Time to complete the case was not significant between
experimental conditions (p = 0.45).
We manipulated three independent variables at two levels between participants in our
study (2X2X2 design). Participants received a case where a large difference between sales
growth and NFM growth (NFM RED FLAG) was either present or not present in the current
year. For all participants, current year Madison sales growth was 6%. We relied on the
descriptive data of Brazel, Jones, and Zimbelman (2009) to manipulate the consistency of the
sales/NFM relation in a realistic manner. For non-fraud firms, sales growth exceeds NFM growth
by approximately 8% (NFM RED FLAG not present), whereas sales growth exceeds NFM
growth by approximately 25% for fraud firms (NFM RED FLAG present). Brazel, Jones, and
Zimbelman (2009) describe the types of capacity-related NFMs that publicly-traded companies
typically disclose in their 10-Ks (i.e., NFMs available to investors). We provided participants
with the following prior year and current year NFMs specifically related to Madison sales:
number of patents, number of customer accounts, number of new products introduced to the
market, square footage of production space, number of employees, and number of product
lines.13 Participants receiving the NFM RED FLAG not present (green flag) manipulation were
provided with current year NFM growth of, on average, 0% (6 percentage points different from
sales growth). For participants in the NFM RED FLAG present treatment group, current year
NFM growth was, on average, -19% (25 percentage points different from sales growth).
13
Brazel, Jones, and Zimbelman (2009) find that, for non-fraud firms, these NFMs are typically highly correlated
with sales (i.e., diagnostic). For example, when regressing change in sales on change in number of employees, they
report a highly significant, positive relation and an R2 of 0.28.
15
Similarly, we manipulated the ACCRUAL RED FLAG based upon the findings of Lee,
Ingram, and Howard (1999).14 For the ACCRUAL RED FLAG present (not present) group, the
current year difference between net income and cash flow from operations represented 11% (1%)
of total assets.15 Financial statement data on the income statement and balance sheet were kept
constant between all participants, while current year cash flow from operations was changed to
achieve the accrual manipulation. For all participants, the NFM and ACCRUAL RED FLAGS
were not present (e.g., green flags) in the prior year.16
After receiving the two red flag manipulations, we either provided or did not provide
participants with TRANSPARENT information related to the aforementioned red flags.
Participants in the TRANSPARENT condition received a chart which provided the data that
constituted the red flag manipulations (i.e., changes in sales, changes in NFMs, net income
levels, cash flow from operations) and the red flag calculations/percentages (noted above) related
to their experimental condition. For example, a participant in the TRANSPARENT, NFM RED
FLAG present, ACCRUAL RED FLAG not present condition received a table that explicitly
documented that the current year sales growth exceeded NFM growth by 25% and the difference
between current year net income and cash flow from operations represented 1% of total assets
(and the information used to calculate these percentages). Participants in the nonTRANSPARENT condition were not provided with this information. See Appendix B for the red
14
Because the measurements of the ACCRUAL RED FLAG for fraud (15% of total assets) and non-fraud firms
(1%) provided by Lee, Ingram, and Howard (1999) may be currently dated, we average their findings with the
ACCRUAL RED FLAG measure of Brazel, Jones, and Zimbelman (2009). Brazel, Jones, and Zimbelman observe
the ACCRUAL RED FLAG for fraud (non-fraud) firms to be 7% (0% (rounded)) of total assets.
15
Alternatively, we could have manipulated a measure of discretionary accruals or working capital accruals (e.g.,
Jones 1991, Dechow and Dichev 2002); however, Jones, Krishnan, and Melendrez (2008) find a simple measure of
total accruals is equally as likely to detect fraudulent financial reporting. In addition, we felt it was extremely
unlikely that nonprofessional investors would have the training/experience to run models of discretionary or
working capital accruals.
16
Manipulation checks for our red flag manipulations (requesting participants to recall the size of the NFM and
ACCRUAL RED FLAGs) provided differences in means in the expected direction and p’s < 0.05.
16
flag-related information provided to participants in the TRANSPARENT, NFM RED FLAG
present, ACCRUAL RED FLAG present condition.17
We then asked participants two questions: (1) “Would you increase or decrease your
investment in Madison (SELL)?” and (2) “By how many shares would you increase or decrease
your investment (INVESTMENT LEVEL)?” (ranging from -1,000 to more than 1,000 shares).
Question (2) is our dependent variable of interest in this study (i.e., do red flags affect investment
levels?). As noted previously, we instructed participants that they held 1,000 shares of Madison.
We also perform additional analyses related to question (1) to determine if red flags cause
investors to sell/avoid questionable investments. Participants then answered a series of caserelated and demographic questions.
IV. RESULTS
Hypothesis One Testing
H1 predicts that a transparent red flag leads to lower investment levels (vs. a nontransparent red flag); however, a transparent green flag leads to higher investment levels (vs. a
non-transparent green flag). To test H1 (and H2), we perform a 2X2X2 ANCOVA and analyze
differences in means to determine the nature of any significant terms. We include USE OF
OTHER RED FLAGS as a covariate due to its significance between experimental conditions (see
footnote 11).18 Table 2 Panel A provides the results of H1 testing. Importantly and consistent
with our theory, we do not observe significant main effects for either ACCRUAL RED FLAG (A)
or NFM RED FLAG (B) on INVESTMENT LEVEL. Investors do not appear to react to the mere
17
Our TRANSPARENT manipulation is fairly similar to the one used by Elliott, Krische and Peecher (2010). They
placed the effects of the firm’s available-for-sale securities transactions in a separate performance statement
immediately following the income statement for their transparent condition and in the statement of changes in
shareholders’ equity for their non-transparent condition.
18
USE OF OTHER RED FLAGS is not significant in Table 2 Panel A (and all other analyses) and results related to
our variables of interest are not qualitatively affected by the inclusion or exclusion of USE OF OTHER RED
FLAGS. For purposes of parsimony, we exclude this covariate from all other tabulated results.
17
presence of either of these red flags (e.g., high accruals do not lead to lower levels of
investment). Also, we do not observe a significant interaction between ACCRUAL RED FLAG
and NFM RED FLAG (A X B). Thus, without considering the effects of making red flags
TRANSPARENT, we do not see a complimentary effect between red flags (e.g., the effect of the
NFM RED FLAG is not stronger when the ACCRUAL RED FLAG is present).
Insert Table 2 here
Contrary to expectations, we do not find that TRANSPARENT moderates the effect of
ACCRUAL RED FLAG on INVESTMENT LEVEL (p = 0.38 for ACCRUAL RED FLAG X
TRANSPARENT [A X C]). However, and supporting H1, we find a marginally significant
interaction between NFM RED FLAG and TRANSPARENT (B X C) on INVESTMENT LEVEL (p
= 0.06). One might therefore conclude that investors perceive the TRANSPARENT, NFM RED
FLAG as more diagnostic or intuitive than the TRANSPARENT, ACCRUAL RED FLAG. In other
words, investors better understand the ramifications of a large difference between reported
financial results and operational results vs. the opacity involved with accrual information.
Indeed, Sloan (1996) finds investors fixate on earnings and have difficulty distinguishing
between earnings derived from cash flows and earnings derived from accruals. In an
experimental setting, Hewitt (2009) reports similar findings when examining the earnings
forecasts of professional and nonprofessional investors. Thus, nonprofessional and professional
investors likely fail to understand the ramifications of the ACCRUAL RED FLAG.
However, data presented in Table 1 suggests that investors in our sample perceive the
NFM and ACCRUAL RED FLAGs as equally intuitive and report to use them equally (see
Variables 3, 4, 5, and 6 in Table 1). However, Ball (2008, p. 427) suggests that “people do not
always do what they say they do, or even what they think they do.” Indeed, declared preferences
18
are often not supported when revealed preferences are exposed (Levitt and Dubner 2009). Our
H1 results reveal that, despite investors reporting to use both red flags equally, they appear to
prefer/use the NFM RED FLAG over the ACCRUAL RED FLAG. To provide evidence related to
why they might prefer NFM RED FLAG, we post-experimentally asked our participants to
explain what might cause the presence of a NFM RED FLAG and an ACCRUAL RED FLAG.
Responses were coded as plausible or implausible by one of the authors and a research assistant
who was unaware of the study’s objectives (based on the discussions of the red flags by Brazel,
Jones, and Zimbelman 2009 and Lee, Ingram, and Howard 1999). The Cohen’s (1960) kappa
measure of agreement between coders was significant at p < 0.001. Investors were significantly
more likely to articulate plausible explanations for the NFM RED FLAG (50% of investors) than
the ACCRUAL RED FLAG (21% of investors) (p < 0.01). Consequently, investors appear to
better understand the ramifications of a TRANSPARENT NFM RED FLAG despite investors
reporting to find both red flags equally intuitive. This knowledge differential potentially explains
our mixed H1 result.
We analyze and illustrate the nature of the significant NFM RED FLAG X
TRANSPARENT (B X C) interaction in Table 2 Panel B. When NFM RED FLAG is present, we
observe a significantly lower INVESTMENT LEVEL when the red flag is made TRANSPARENT
to investors. Mean INVESTMENT LEVEL is 338.60 shares when the NFM RED FLAG is present
and not TRANSPARENT. Making the NFM RED FLAG transparent to investors led to a
significantly lower mean INVESTMENT LEVEL of 171.19 shares (p = 0.04, see footnote d in
Panel B).19 On the other hand, when NFM RED FLAG is not present (a green flag), the
19
As described in the Method section, our experimental materials (with the exception of red flag-related data)
presented a generally positive view of Madison. As such, it is not surprising that our cell means indicate that
participants were, on average, buying additional vs. selling their existing shares in Madison. Our experimental
materials were largely drawn from Elliott et al. (2007). Similar to our participants, the participants in Elliott et al.
19
difference in means between making the positive information/green flag transparent is in the
expected direction (TRANSPARENT leads to higher investment levels), but not significant (p =
0.31, see footnote c in Panel B). These differences in means support an ordinal interaction (see
the graph in Table 2 Panel B). Making it TRANSPARENT that an NFM RED FLAG is present
leads to lower investment levels. However, H1 posits a disordinal interaction (see Figure 1).
Thus, we provide only limited evidence, related to the NFM RED FLAG, in support of H1 (i.e.,
the NFM RED FLAG X TRANSPARENT interaction is significant, but ordinal in nature).
However, this result for H1 should be considered in light of our results for H2.
In addition to finding mixed results in relation to H1, we find that INVESTMENT
LEVELs in the non-TRANSPARENT conditions actually increase as we move from NFM RED
FLAG not present to NFM RED FLAG present (see the upward sloping line in the Table 2 Panel
B graph). Note that we predicted a flat line as we posited minimal investor reactions to nonTRANSPARENT red flags (see Figure 1). We randomly assigned investors to experimental
conditions, and this random assignment appears successful (see footnote 11). Thus, we provide
evidence that these two groups do not differ significantly on many traits that would likely
explain investor reactions to red flags or the propensity to purchase stock (other than the
presence of an NFM RED FLAG). Future research may investigate why we observe the highest
INVESTMENT LEVEL in the NFM RED FLAG present/non-TRANSPARENT condition (mean =
338.60 shares). However, one explanation is that investors consider an NFM RED FLAG, when
not TRANSPARENT, to be a positive signal. In other words, investors feel that sales growth
substantially outpacing NFM growth signals that the company is obtaining better results (sales)
with fewer resources (NFMs), cutting slack, and operating more efficiently. Indeed, when we
(2007), on average, viewed the company as a good investment. In additional analyses, we examine if our findings
with respect to selling Madison stock (SELL) are consistent with those related to INVESTMENT LEVEL.
20
asked participants in the NFM RED FLAG present/non-TRANSPARENT condition to explain
why an abnormal difference between sales and NFM growth might exist (i.e., an NFM RED
FLAG), we find that 27 of the 57 participants (47.4%) in that condition perceived the difference
as a positive signal.20 This is an interesting/alarming finding and further supports the need for
investors to have access to the TRANSPARENT, NFM RED FLAG. The large NFM RED FLAG
used in this study was drawn from the NFM RED FLAG observed by Brazel, Jones, and
Zimbelman (2009) for a sample of fraud firms and thus, at minimum, should not cause more
investment. Our data suggest that, only when the large NFM RED FLAG is made
TRANSPARENT, do investors question the large difference between sales and NFM growth and
reduce investment.
Hypothesis Two Testing
H2 posits a three-way interaction between ACCRUAL RED FLAG, NFM RED FLAG, and
TRANSPARENT on INVESTMENT LEVEL. Specifically, we expect that the effect of making a
red flag transparent will be greater in the presence of another red flag. Table 2 Panel A provides
the results of H2 testing. The three-way interaction of ACCRUAL RED FLAG X NFM RED
FLAG X TRANSPARENT (A X B X C) is significant (p = 0.04).
In Table 3, we analyze and illustrate the nature of the significant ACCRUAL RED FLAG
X NFM RED FLAG X TRANSPARENT interaction. Because only the NFM RED FLAG X
TRANSPARENT interaction was significant in Table 2 Panel A, we will examine if, consistent
with H2, the NFM RED FLAG X TRANSPARENT interaction is stronger when the ACCRUAL
20
Examples of participant responses include: It could mean they are being more productive with less people and
equipment - being more efficient, downsizing to make the company more lean/profitable, selling better per
operational unit, better sales from reduced sales personnel, and it suggests that the existing stores and other
resources are becoming more effective at generating sales.
21
RED FLAG is present (vs. not present).21 To perform this analysis, we (1) split our sample
between participants that received either the present or not present ACCRUAL RED FLAG
conditions, and (2) performed two 2X2 ANOVAs with NFM RED FLAG and TRANSPARENT as
independent variables and INVESTMENT LEVEL as the dependent variable. As noted in Table 3
Panel A, NFM RED FLAG X TRANSPARENT is not significant when the ACCRUAL RED
FLAG is not present (p = 0.45). Thus, making a NFM RED FLAG TRANSPARENT does not
significantly affect investment levels when the ACCRUAL RED FLAG is not present. One
potential explanation for this result is that the proximity of net income to cash flow from
operations (ACCRUAL RED FLAG not present) calmed investors enough to not react
substantially to the TRANSPARENT, NFM RED FLAG signal.
Insert Table 3 here
Consistent with H2, we find that, when the ACCRUAL RED FLAG is present, the NFM
RED FLAG X TRANSPARENT interaction is significant (p = 0.01, see Table 3 Panel B). We
analyze and illustrate the nature of this significant NFM RED FLAG X TRANSPARENT
interaction in Table 3 Panel C. Similar to results documented in Table 2 Panel B, when
ACCRUAL RED FLAG is high, we observe a significantly lower INVESTMENT LEVEL when
the presence of an NFM RED FLAG is made TRANSPARENT vs. not TRANSPARENT (mean
INVESTMENT LEVELs = 130.00 shares vs. 386.67 shares, respectively (p = 0.04, see footnote
d)). Consequently, making a NFM RED FLAG TRANSPARENT has a more negative effect on
investment levels when the ACCRUAL RED FLAG is present (vs. not present). We find
empirical support for H2.
As one would expect, given ACCRUAL RED FLAG’s insignificant results in relation to H1, in non-tabulated
analyses we do not find support for the following application of H2: making an ACCRUAL RED FLAG
TRANSPARENT has a more negative effect on investment levels when NFM RED FLAG is present (vs. not present).
21
22
While not explicitly posited by H2, we see the opposite effect when the NFM RED FLAG
is not present (i.e., when an NFM green flag is observed). Given the presence of an ACCRUAL
RED FLAG, as described in our H1 development, we see that making a NFM RED FLAG not
present (green flag) TRANSPARENT leads to a higher INVESTMENT LEVEL (p = 0.09, see
footnote c in Panel C).
Our results related to H2 should be considered in light of the archival findings of Brazel,
Jones, and Zimbelman (2009) and Dechow et al. (2010). Both studies find that fraud firms
generally exhibit both higher NFM RED FLAGs and ACCRUAL RED FLAGs than non-fraud
firms. Therefore, in a fraud setting, investors would more likely experience the right sides of the
table and graph of Table 3 Panel C (vs. the left sides where accruals are high, the NFM RED
FLAG is not present, and we observe the the aforementioned green flag effect). As such, we
stress that caution should be taken in interpreting the generalizeability of our green flag finding.
However, our finding of H2 support should spur policymakers (e.g., SEC) to develop
disclosure requirements or tools that make red flag analysis easier for investors. In short, in the
typical fraud setting (both red flags present), investors would likely utilize and react
appropriately to a TRANSPARENT NFM RED FLAG. Whether this finding holds for other
intuitive and ex ante fraud red flags (e.g., management turnover) is a question for future research.
Additional Analyses
While the aforementioned analyses examine how transparent red flags can affect
INVESTMENT LEVELs (i.e., the number of shares bought or sold by investors), it does not
consider if our independent variables can impact whether the investor sells (vs. buys) the stock of
a company with high fraud risk (i.e., a company exhibiting one or more red flags). As noted in
the Method section, before collecting data on the INVESTMENT LEVEL, we asked participants,
23
“Would you increase or decrease your investment in Madison (SELL)?” Participants were
informed that they held 1,000 shares of Madison. Because SELL is a binary dependent variable
(1= sell, 0 = buy), similar to Kadous (2001) and Jamal and Tan (2009), we examine the effects of
ACCRUAL RED FLAG, NFM RED FLAG, and TRANSPARENT on SELL via a 2X2X2
categorical ANOVA (see Table 4). Results are qualitatively similar (and in fact stronger) to those
presented in Table 2. One exception is that we observe a marginally significant main effect for
ACCRUAL RED FLAG (p = 0.08). A non-tabulated analysis of means suggests that the presence
of an ACCRUAL RED FLAG (regardless of being TRANSPARENT or non-TRANSPARENT)
increases the likelihood that the investor SELLs the stock they hold in a company. Thus, using
SELL as the dependent variable, we provide limited evidence that investors react to the
ACCRUAL RED FLAG. Other non-tabulated tests of means (for the NFM RED FLAG X
TRANSPARENT and three-way interactions) provide qualitatively similar results to those noted
above for H1 and H2 (substituting SELL as the dependent variable and noting that a lower
INVESTMENT LEVEL should equate to a higher likelihood to SELL).
Insert Table 4 here
V. CONCLUSION
This study examines nonprofessional investors’ reactions to fraud red flags when making
investment decisions. We conducted an experiment to investigate whether investors react to
fraud red flags in their natural, dispersed state and whether making red flags more transparent
affects investor behavior. In addition, we consider whether the saliency of one transparent red
flag increases if another red flag is also present.
While investors are victims of fraud and extensive research has examined the efficacy of
various fraud red flags, this is the first study to examine investor reactions to red flags. Overall,
24
we provide the first empirical evidence that: (1) investors do not react uniformly to red flags; (2)
investors should benefit from mechanisms which make intuitive red flags more transparent; (3)
there are complementary effects between red flags; and (4) a transparent green flag can boost
investor confidence and increase investment levels.
While Table 1 suggests that investors are willing to use these and other red flags to avoid
investing in fraudulent companies, investors currently have few tools that make red flags
transparent. In fact, only one participant in our study reported using an automated tool to gather
information and assess fraud risk (vs. performing his/her own analysis of fraud red flags).
Investors could have access to this information at minimal expense. For example, web scraping
uses computer algorithms to extract and compile publicly-available information in a meaningful
way. Researchers have documented the usefulness of web scraping in a variety of contexts from
determining optimal inventory prices (Dewan, Freimer, and Jiang 2007) to ascertaining investor
sentiment (Das and Chen 2007). Web scraping techniques could likely extract the appropriate
information necessary to produce the transparent red flag information proposed in this study.
Brokerage companies, investor protection groups, and/or regulators could house such a red flag
tool on their websites. In a more direct fashion, policymakers could require firms to more
explicitly disclose red-flag related information to investors in financial statement footnote
disclosures (e.g., changes in NFMs and relevant financial measures). These data would then be
subject to an external audit and tagged for XBRL purposes, giving management the opportunity
to explain any abnormal inconsistencies that exist (e.g., how sales growth is positive, while retail
outlets and employee headcount have decreased). As previously mentioned, in Exhibit B of
Appendix A we provide an example of one firm that provides NFM and financial data in a very
transparent format. Our finding that investors have the propensity to consider NFMs as a
25
benchmark for financial data supports calls by internal and external stakeholders for companies
to report more NFMs (Ballou et al. 2006; Holder-Webb et al. 2009a and 2009b).
Our findings shine light on investors’ use of red flags and should help researchers and
regulators understand how to best protect investors from fraudulent financial reporting. If
investors are provided with tools that explicitly identify and explain red flags, then investors
should sell potentially fraudulent investments more quickly, reduce the extent to which they
suffer losses and, in turn, reduce the extent and length of frauds perpetrated by companies.
26
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Accounting Organizations and Society 15:199–220.
Schilit, H. 2002. Financial Shenanigans: How to Detect Accounting Gimmicks & Fraud in
Financial Reports. New York, NY: McGraw-Hill.
Schneider, B., and W. So. 2009. XBRL: An Interview with Amy Pawlicki of the AICPA (Part 1).
Hitachi Data Interactive.com (July 24, http://hitachidatainteractive.com/2009/07/24/xbrlan-interview-with-amy-pawlicki-of-the-aicpa-part-1/).
Sloan, R. G. 1996. Do stock prices fully reflect information in accruals and cash flows about
future earnings? The Accounting Review 71 (July): 289-315.
Wall Street Journal (WSJ). 2005a. Defense expert: Healthsouth fraud too complex for
detection. May 6.
Wall Street Journal (WSJ). 2005b. Delphi discloses accounting problems. March 7.
Zweig, J. 2009. About time: Regulation based on human nature. The Wall Street Journal. (June
20-21).
31
FIGURE 1
Graph depicting the disordinal interaction between Red Flag Presence (not present vs.
present) and Transparent/Not Transparent on the Level of Investment (Hypothesis 1).
HIGH
Investment Level
LOW
NOT
PRESENT
(GREEN FLAG)
Red Flag Presence
PRESENT
(RED FLAG)
Notes:
indicates the red flag information is transparent.
indicates the red flag information is not transparent.
The above disordinal interaction is predicted for both of the specific red flags examined in this
study: the NFM red flag and the accrual red flag.
32
TABLE 1
Demographic Data
Response
[n = 237]
Mean (Std. Dev.)
Variables
Screening Questions a
1.
% that bought or sold individual company stock in the last
the last six months
100.00
2.
100.00
% that were not a professional investor
Fraud Red Flag-related Measures b
3.
Intuitiveness of accrual red flag
4.10 (1.59)
4.
Intuitiveness of NFM red flag
4.24 (1.58)
5.
Use of accrual red flag when assessing fraud risk
3.93 (1.86)
6.
Use of NFM red flag when assessing fraud risk
3.83 (1.91)
7.
% that used fraud red flags other than accruals or NFM
21.50
8.
Use of fraud red flags
3.81 (1.67)
Fraud-related Measures b
9.
Perception of the rate (%) of fraudulent financial reporting
34.90
10.
% that owned the stock of a fraud company
16.00
11.
% that had received fraud training
7.20
12.
Importance of fraud risk assessment
4.28 (1.61)
Investing Experience, Activity, and Return b
13.
Investing experience
3.34 (1.59)
14.
Trading activity
2.09 (1.34)
15.
Value of portfolio
3.80 (2.26)
16.
Return on investments
5.84 (2.94)
33
Other Demographic Data b
17. Education
3.16 (1.22)
18.
% with at least an undergraduate degree
61.20
19.
% Male
48.50
20.
Age
4.46 (1.43)
21.
Household income
3.12 (1.46)
a
Participants were asked to respond to the following two questions (screening questions): (1)
Have you bought or sold individual shares of company stock (i.e., not mutual funds) in the last
six months? (2) As part of your full-time job responsibilities, do you analyze or trade stocks or
other securities? Participants could respond “yes” or “no.” Question (1) is coded 1 for “yes”
and 0 for “no”. Question (2) is coded 1 for “no” and “0” for “yes.”
b
Intuitiveness of accrual red flag = One possible way to assess whether a company’s financial
statements are fraudulent is to compare net income to cash flows from operations (i.e., accrual
levels). For you, how intuitive (intuitive = easy to understand) is this fraud risk assessment tool
or red flag? Measured 1 = “not intuitive” and 7 = “very intuitive.”
Intuitiveness of NFM red flag = One possible way to assess whether a company’s financial
statements are fraudulent is to compare growth in sales to growth in operational measures (e.g.,
number of employees, square footage of operating space)? For you, how intuitive (intuitive =
easy to understand) is this fraud risk assessment tool or red flag? Measured 1 = “not intuitive”
and 7 = “very intuitive.”
Use of accrual red flag when assessing fraud risk = How often do you compare net income to
cash flows from operations (i.e., accrual levels) when assessing the risk of financial statement
fraud for a company, measured 1 = “never” and 7 = “always.”
Use of NFM red flag when assessing fraud risk = How often do you compare sales to growth in
operational measures (e.g., number of employees, square footage of operating space) when
assessing the risk of financial statement fraud for a company, measured 1 = “never” and 7 =
“always.”
% that used fraud red flags other than accruals or NFM = As an investor, do you use another tool
or red flag to assess the risk of fraudulent financial reporting at a company? “Yes” responses
coded 1 and “no” responses coded “0.”
Use of fraud red flags = To what extent do you use red flags to try to assess the risk of financial
statement fraud for companies that you currently hold in your personal investment portfolio?
Measured on a scale where 1 = “never” and 7 = “always.”
Perception of the rate (%) of fraudulent financial reporting = In your opinion, how often do
managers of publicly-traded companies commit financial statement fraud, measured on a scale
from “0% of the time” to “100% of the time.”
% that owned the stock of a fraud company = Have you ever owned the stock of an individual
company when it was found to have been committing financial statement fraud, measured 1 =
“yes” and 0 = “no.”
% that had received fraud training = Have you ever received training in relation to assessing
fraud risk or detecting financial statement fraud? Responses coded 1 for “yes” and 0 for “no.”
34
Importance of fraud risk assessment = How important is your assessment of the risk of financial
statement fraud, relative to other factors, when making buy/sell decisions for stocks that you
currently hold in your portfolio? Measured on a scale where 1 = “not at all important” and 7 =
“extremely important.”
Trading activity = Approximately, how many times, on average, do you buy or sell stocks of
individual companies in a one-year period, measured on a scale where 1 = “1-5 times” and 5 =
“more than 20 times.”
Value of portfolio = What is the approximate value of your personal investment portfolio,
measured on a scale where 1 = “less than $10,000” and 8 = “more than $1,000,000.”
Return on investments = Over the last twelve months, what was the approximate return on your
personal investment portfolio, measured on a scale where 1 = “less than -20 percent” and 11 =
“more than 20%.”
Education = Please indicate the highest level of education you have completed, measured on a
scale where 1 = “high school” and 5 = “post-graduate degree.”
% with at least an undergraduate degree = coded 1 if participant obtained an undergraduate
degree or higher, 0 otherwise.
% Male = Coded 1 if male, 0 otherwise.
Age = Measured on a scale where 1 = “under 20” and 8 = “80 or above.”
Household income = What is your total annual household income, measured on a scale where 1
= “$0 - $30,000” and 7 = “more than $200,000.”
35
TABLE 2
Hypotheses One and Two: INVESTMENT LEVEL a
PANEL A: 2X2X2 ANCOVA
Independent Variables b
USE OF OTHER RED FLAGS
ACCRUAL RED FLAG (A)
NFM RED FLAG (B)
TRANSPARENT (C)
AXB
A X C (H1)
B X C (H1)
A X B X C (H2)
Error
df
1
1
1
1
1
1
1
1
228
Mean
square
126,743
67,179
158,798
194,620
46,436
25,133
630,541
749,714
264,039
F
.480
.254
.601
.737
.176
.095
2.385
2.830
p
.489
.307
.220
.391
.675
.379
.062
.047
PANEL B: Cell Means (Cell Sizes) for INVESTMENT LEVEL
NOT TRANSPARENT
TRANSPARENT
NFM RED FLAG NOT PRESENT
(“green flag”)
175.41 c (n = 61)
221.67 c (n = 60)
NFM RED FLAG PRESENT
(“red flag”)
338.60 d (n = 57)
171.19 d (n = 59)
Means for INVESTMENT LEVEL
400
NOT TRANSPARENT
TRANSPARENT
300
200
100
0
Not Present Present
(green flag) (red flag)
(red
NFM RED FLAG
a
INVESTMENT LEVEL = Participants were informed that they held 1,000 shares in Madison.
After receiving the experimental manipulations, participants were asked: “by how many shares
would you increase or decrease your investment?” Responses were measured in increments of
100 shares on a scale ranging from -1,000 to more than 1,000 shares. Three participants
responded “more than one hundred shares” and their responses were coded 1,100. Removing
these three participants from our analyses does not qualitatively change our results.
b
USE OF OTHER RED FLAGS = As an investor, do you use another tool or red flag to assess
the risk of fraudulent financial reporting at a company? “Yes” responses coded 1 and “no”
responses coded “0.”
36
ACCRUAL RED FLAG and NFM RED FLAG = Manipulated between participants as not
present and present. See the Method section for additional information.
TRANSPARENT = Red flag data manipulated between participants as transparent and not
transparent. See the Method section for additional information.
c
Means not significantly different (p = . 313).
d
Means significantly different (p = .043).
37
TABLE 3
Additional Analyses Related to Hypothesis Two: INVESTMENT LEVEL a
PANEL A: 2X2 ANOVA under
ACCRUAL RED FLAG not present
Independent Variables b
NFM RED FLAG
TRANSPARENT
NFM RED FLAG X TRANSPARENT
Error
Df
1
1
1
112
Mean
square
10,994
196,361
3,483
227,177
F
.048
.864
.015
p
.413
.355
.451
Df
1
1
1
117
Mean
square
242,931
41,500
1,458,801
298,152
F
.815
.139
4.893
p
.185
.710
.015
PANEL B: 2X2 ANOVA under
ACCRUAL RED FLAG present
Independent Variables b
NFM RED FLAG
TRANSPARENT
NFM RED FLAG X TRANSPARENT
Error
PANEL C: Cell Means for INVESTMENT LEVEL (Cell Sizes) under ACCRUAL RED FLAG
present
NOT TRANSPARENT
TRANSPARENT
NFM RED FLAG NOT PRESENT
(“green flag”)
77.42 c (n = 31)
260.00 c (n = 30)
Means for INVESTMENT LEVEL
400
NOT TRANSPARENT
TRANSPARENT
300
200
100
0
Not Present Present
(green flag) (red flag)
NFM RED FLAG
38
NFM RED FLAG PRESENT
(“red flag”)
386.67 d (n = 30)
130.00 d (n = 30)
a
INVESTMENT LEVEL = Participants were informed that they held 1,000 shares in Madison.
After receiving the experimental manipulations, participants were asked: “by how many shares
would you increase or decrease your investment?” Responses were measured in increments of
100 shares on a scale ranging from -1,000 to more than 1,000 shares. Three participants
responded “more than one hundred shares” and their responses were coded 1,100. Removing
these three participants from our analyses does not qualitatively change our results.
b
ACCRUAL RED FLAG and NFM RED FLAG = Manipulated between participants as not
present and present. See the Method section for additional information.
TRANSPARENT = Red flag data manipulated between participants as transparent and not
transparent. See the Method section for additional information.
c
Means significantly different (p = .098).
d
Means significantly different (p = .037).
39
TABLE 4
Additional Analyses: SELL a
2X2X2 Categorical ANOVA
Independent Variables b
ACCRUAL RED FLAG (A)
NFM RED FLAG (B)
TRANSPARENT (C)
AXB
AXC
BXC
AXBXC
df
1
1
1
1
1
1
1
a
Chi-square
1.93
.58
.41
1.00
.26
3.14
6.31
p
.082
.224
.521
.317
.305
.038
.006
SELL = Participants were informed that they held 1,000 shares in Madison, Inc. After receiving
the experimental manipulations, participants were asked: “would you increase or decrease your
investment in Madison?” Responses were coded as follows: decrease (sell) =1 and increase
(buy) = 0.
b
ACCRUAL RED FLAG and NFM RED FLAG = Manipulated between participants as not
present and present. See the Method section for additional information.
TRANSPARENT = Red flag data manipulated between participants as transparent and not
transparent. See the Method section for additional information.
40
APPENDIX A
Exhibit A – The information below was extracted from the Health Net, Inc. 2008 10-K. It
illustrates how NFMs are commonly reported within a 10-K (i.e., dispersed). It is important
to note that only one year’s worth of data is typically reported (see
http://www.sec.gov/Archives/edgar/data/916085/000119312509039486/d10k.htm).
Page 10: Physician Relationships
The following table sets forth the number of primary care and specialist physicians contracted either directly
with our HMOs or through our contracted participating physician groups (“PPGs”) as of December 31, 2008:
Primary Care Physicians (includes both HMO and PPO physicians)
Specialist Physicians (includes both HMO and PPO physicians)
Total
70,265
238,371
308,636
Page 18: Employees
As of December 31, 2008, Health Net, Inc. and its subsidiaries employed 9,396 persons on a full-time basis
and 250 persons on a part-time or temporary basis. These employees perform a variety of functions, including,
among other things, provision of administrative services for employers, providers and members; negotiation of
agreements with physician groups, hospitals, pharmacies and other health care providers; handling of claims for
payment of hospital and other services; and provision of data processing services. Our employees are not unionized
and we have not experienced any work stoppages since our inception. We consider our relations with our employees
to be very good.
Page 39: Properties.
We lease office space for our principal executive offices in Woodland Hills, California. Our executive offices,
comprising approximately 176,490 square feet, are occupied under two separate leases, one of which expired on
December 31, 2008 (with respect to 51,175 square feet of space) and the other will expire on December 31, 2014
(with respect to 125,315 square feet of space). We have vacated the office space that was covered by the lease that
expired on December 31, 2008. A significant portion of our California HMO operations are also housed in
Woodland Hills, in a separate 333,954 square foot leased facility. The lease for this two-building facility expires
December 31, 2011. Combined rent and rent-related obligations for our Woodland Hills facilities were
approximately $16.2 million in 2008.
We also lease an aggregate of approximately 548,807 square feet of office space in Rancho Cordova,
California for certain Health Plan Services and Government Contract operations. Our aggregate rent and rent-related
obligations under these leases were approximately $11.1 million in 2008. These leases expire at various dates
ranging from 2009 to 2013. We also lease a total of approximately 59,750 square feet of office space in San Rafael
California for certain specialty services operations.
On March 29, 2007 we sold our 68-acre commercial campus in Shelton, Connecticut (the Shelton Property) to
The Dacourt Group, Inc. (Dacourt), dba HN Property Owner, LLC, and leased it back from the Buyer under an
operating lease agreement for an initial term of ten years with an option to extend for two additional terms of ten
years each. We received net cash proceeds of $83.9 million and recorded a deferred gain of $60.9 million, which is
amortized into income as contra-G&A expense over the lease term. Under the Shelton Property lease agreement and
other lease agreements, we lease an aggregate of approximately 492,673 square feet of office space in Shelton,
Connecticut for certain Health Plan Services for our Northeast Division. Our aggregate rent and rent-related
obligations under these leases was approximately $9.0 million in 2008. These leases expire at various dates ranging
from 2016 to 2017.
41
In addition to the office space referenced above, we lease approximately 76 sites in 24 states, totaling
approximately 811,426 square feet of space. We also own a data center facility in Rancho Cordova, California
comprising approximately 82,000 square feet of space.
We believe that our ownership and rental costs are consistent with those associated with similar space in the
applicable local areas. Our properties are well maintained, adequately meet our needs and are being utilized for their
intended purposes.
Exhibit B - The following is a table extracted from the Tenet Healthcare 2008 10-K. It
illustrates that one company does provide financial data and NFMs from two different
years with a corresponding percent change in a very transparent manner (see
http://www.sec.gov/Archives/edgar/data/70318/000119312509035651/d10k.htm).
Page 48:
42
APPENDIX B
The following red flag-related information was provided to participants in the
TRANSPARENT, NFM RED FLAG present, ACCRUAL RED FLAG present condition.
Information about Madison Sporting Goods Co. Accruals (information obtained from the previous financial
statements for Madison)
Madison net income ($ millions):
2009
$61
2008
$57
2007
$43
Madison net cash provided by (used in) operating activities ($ millions):
2009
$(42)
2008
$48
2007
$35
Difference between Madison Net Income and Madison net cash provided by (used in) operating activities ($
millions):
2009
$103
2008
$9
2007
$9
Difference between Madison Net Income and Madison net cash provided by (used in) operating activities as a
percentage of total assets (Accruals):
2009
11%
2008
1%
2007
1%
Information about Madison Sporting Goods Co. Financial versus Operating Performance (information
obtained from the previous financial statements and operational information for Madison)
Madison sales ($ millions):
2009
2008
$771
$728
2007
$699
Madison sales growth
2009
2008
6%
4%
Madison growth (decline) in operational measures (e.g., number of employees, patents, square feet of production
space)
2009
-19%
2008
-6%
Difference between Madison sales growth and operational measures growth (decline)
2009
25%
2008
10%
43