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Management, Auditor, and Audit Committee Influence on MD&A: Evidence from Critical
Accounting Estimate Quantitative Sensitivity Disclosures
by
Matt Glendening
Trulaske College of Business
University of Missouri—Columbia
Elaine Mauldin
Trulaske College of Business
University of Missouri—Columbia
Kenneth W. Shaw
Trulaske College of Business
University of Missouri—Columbia
September, 2014
We thank Adrienne Rhodes and workshop participants at University of Missouri and the 2014 AAA
Annual Meeting for their helpful comments.
Management, Auditor, and Audit Committee Influence on MD&A: Evidence from Critical
Accounting Estimate Quantitative Sensitivity Disclosures
Abstract: Securities and Exchange Commission (SEC) guidance calls on firms to provide
disclosures quantifying the impact on earnings of reasonably likely changes in firms’ critical
accounting estimates (CAE) in Management’s Discussion and Analysis (MD&A). The SEC
intends CAE disclosures to aid users in understanding the extent of noise and discretion within
the accrual estimation process. Consistent with equity risk incentives providing managers the
incentive to misreport, and thus the disincentive to disclose measurement leeway in accrual
estimates, we find the likelihood and number of CAE disclosures are decreasing in the sensitivity
of managers’ wealth to changes in equity risk incentives (portfolio vega). We also find the
likelihood and number of CAE disclosures are decreasing in the external auditor’s expressed
opposition to the SEC’s CAE disclosure requirement, in spite of limited required auditor
involvement in MD&A. Finally, we find the likelihood and number of CAE disclosures are
increasing in audit committee accounting expertise, consistent with more knowledgeable audit
committees constraining managers’ and auditors’ negative disclosure incentives. Overall, our
evidence suggests the discretion allowed in determining required disclosures introduces strategic
CAE disclosures involving management, auditors, and audit committees, the three primary
parties responsible for financial reporting.
Key Words: Critical accounting estimates; quantitative disclosure; management
incentives; auditor incentives; audit committee accounting expertise
1
I. Introduction
We study the determinants of firms’ decisions to provide quantitative sensitivity disclosures
about critical accounting estimates (CAE), “material” and “highly uncertain” accounts requiring
judgment (SEC 2002; SEC 2003).1 Accounting estimates comprise a large and growing
component of financial statements, making the dividing line between fact and conjecture largely
unknown to investors (Lev, Li, and Sougiannis 2010). Since capital market inefficiencies can
result if investors are led by estimates-based accounting information to misallocate resources, the
SEC mandates that firms provide quantitative CAE information when “quantitative information
is reasonably available and will provide material information for investors” (Lev et al. 2010,
SEC 2003, V.). Consistent with CAE disclosures informing investors about the reliability of
accounting estimates, CAE disclosures reduce the value relevance of reported accounting
numbers (Glendening 2014). Many firms do not provide quantitative CAE disclosures (Levine
and Smith 2011; Bauman and Shaw 2014), and the SEC remains concerned about the lack of
quantitative disclosure, frequently requesting enhancement to CAE disclosures (Cassell, Dreher,
and Myers 2013; Ernst and Young 2011).2
We suggest the discretion allowed in SEC guidance, combined with the subjective nature of
CAEs, provides firms considerable leeway in deciding whether to provide CAE disclosures.
Since discretion in mandatory reporting reduces the likelihood of voluntary disclosure and
introduces strategic disclosures, we extend prior research by demonstrating that CAE disclosure
decisions reflect strategic preferences of those responsible for financial reporting (Einhorn 2005).
1 Common accounting estimates seen in CAE disclosures include defined-benefit pension plans, sales returns,
inventory obsolescence, warranty reserves, and uncollectible accounts receivable. As an example of a CAE
disclosure, Nordstrom Inc. reports “a 10% change in our allowance for doubtful accounts would have affected net
earnings by $12 (million) for the fiscal year ended January 30, 2010” (NORDSTROM INC, 10-K, March 22, 2010). 2 Cassell et al. (2013) report critical accounting estimates appear in over 25 percent of SEC comment letters and
Ernst and Young (2011) list critical accounting estimates first in their analysis of current reporting issues.
2
The SEC addressed their initial alert concerning CAE to management, external auditors, and the
audit committee, consistent with the three-pronged corporate governance “mosaic” described in
prior literature (SEC 2001; Cohen, Krishnamoorthy, and Wright 2004). Though management has
ultimate responsibility for financial reporting, the auditor and the audit committee have
important oversight responsibilities (Cohen et al. 2004). Recognizing this shared oversight role,
the New York Stock Exchange (NYSE) and the Public Company Accounting Oversight Board
(PCAOB) require audit committee/auditor discussion of MD&A and CAE (PCAOB 2012;
Cohen, Gaynor, Krishnamoorthy, and Wright 2007). Thus, we examine how management,
external auditors, and the audit committee impact CAE disclosure decisions, after controlling for
other firm financial, CAE, and governance characteristics.
For management, we consider managers’ equity risk incentives because the sensitivity of
managers’ wealth to changes in risk (portfolio vega) unambiguously increases managers’
incentive to misreport (Armstrong, Larcker, Ormazabal, and Taylor 2013). Given that chief
financial officers (CFOs) report that accounting estimates provide the most common vehicle for
misreporting (Dichev, Graham, Harvey, and Rajgopal 2013), management may anticipate that
CAE disclosures reveal sources of accrual discretion. Consistent with this idea, managers in an
experimental market report less aggressive point estimates in a regime with mandated disclosure
of estimate ranges (Majors 2014). Extending this idea to the more discretionary CAE disclosure
regime, we first hypothesize a negative association between portfolio vega and the likelihood and
number of CAE disclosures.
For external auditors, we consider the level of opposition they expressed in comment letters
to the SEC regarding CAE disclosures. Even though auditors are only required to review the
MD&A for inconsistencies with the financial statements, CAE disclosures received substantial
3
pushback from the Big 4 audit firms. While the amount of opposition in the comment letters
varied by firm, some of the major concerns related to increased auditor involvement, higher
preparation costs, and the questionable usefulness of the CAE disclosures for investors. If
auditors influence managers’ disclosure decisions, our second hypothesis predicts a negative
association between the likelihood and number of CAE disclosures and the degree of auditor
opposition.3
For audit committees, we consider accounting expertise because CAEs are often complex
and require considerable accounting knowledge and expertise to understand and advocate for
disclosure. PCAOB inspections reveal even knowledgeable auditors have difficulty with
accounting estimates (PCAOB 2008). Since a lack of accounting expertise could constrain the
ability of the audit committee to question either the auditor or management, we hypothesize a
positive association between the likelihood and number of CAE disclosures and audit committee
accounting expertise.
Our sample includes 2,298 firm-years (317 distinct S&P 500 firms) spanning 2003-2010.4
We model the existence and number of CAE disclosures as a function of top-five management
portfolio vega, auditor opposition to CAE, and audit committee accounting expertise. To rule out
alternative explanations for CAE disclosures, we control for other equity incentives, other board
and audit committee attributes, other innate firm financial attributes, other attributes that capture
the extent of uncertainty in the accrual measurement process, litigation risk, analyst following,
and institutional ownership. We hand-collect CAE disclosure data from 10-K reports, with about
50 percent of firm-years providing CAE disclosure.
3 We use a measure from Li (2008), which captures the negative versus positive tone of the auditor’s comment letter
to the SEC on the issue of CAE disclosures. 4 Firms did not provide CAE disclosures before 2002. We sample large firms because FR-72 resulted from a
targeted review of large firms in 2001 (SEC 2003). If these firms responded by including quantitative sensitivity
analyses, it biases against our finding results.
4
We find that the likelihood of CAE disclosure is negatively associated with top-five
management portfolio vega and auditor opposition to CAE, and positively associated with audit
committee accounting expertise.5 These findings support our three hypotheses and they are also
economically significant, ranging from about a 7 to 14 percent change in the probability of CAE
disclosure as portfolio vega and auditor opposition change from the first to the third quartile or
the audit committee changes from having no accounting expertise to having accounting
expertise. We find similar results for the number of CAE disclosures. In addition, many of our
control variables are also statistically significant and in the predicted direction. Our results are
also robust to tests surrounding actual incidences of misreporting, the time trend of portfolio
vega, and deleting the year of CAE initiation.
The study makes several important contributions to the literature. First, we provide empirical
evidence on firms’ decisions to provide quantitative CAE disclosures. The SEC’s push for
increased CAE disclosure was meant to mitigate the “illusion of precision” in accounting reports,
with the idea that CAE disclosure would aid in the use and interpretation of estimates in financial
reports (SEC 2001; Glassman 2006). However, our evidence suggests the allowed discretion
under SEC guidance limits the effectiveness of MD&A regulation over CAE disclosure.
Second, we provide evidence about the influence of each of the important parties involved in
the financial reporting and disclosure processes - management, external auditors, and audit
committees - consistent with calls for further research into how these parties contribute to
MD&A quality (Cohen et al. 2007). Our findings suggest each of the financial reporting parties
5 In additional analyses, we find reductions in top-five management portfolio vega subsequent to a CAE disclosure,
consistent with firms reducing managers’ misreporting incentives and supporting our conjecture that management
could anticipate potential negative consequences of quantitative sensitivity disclosures and engage in strategic
disclosure.
5
provide input to the disclosure decision and suggest that auditors and audit committees may not
always collaboratively provide greater monitoring.
Third, we add to the existing literature on the association between management equity risk
incentives and financial statement misreporting. CAE disclosures provide an interesting and
powerful setting to test the association between managers’ equity risk incentives and disclosure
(via the incentive to misreport). Our findings suggest managers’ equity risk incentives influence
disclosure choices, especially when the disclosure decision is aligned with misreporting
incentives (Armstrong et al. 2013).
Finally, we provide evidence on the auditor’s role in MD&A disclosures. Auditors are
traditionally viewed as taking a limited role in MD&A disclosure that is confined to reviewing
the MD&A for material inconsistencies with the financial statements. However, the SEC’s call
for increased CAE disclosures increased the potential for auditor involvement. Auditors voiced
concerns about the preparation and interpretation of CAE, and our findings indicate that firms’
CAE decisions were partially conditioned on those concerns.
The remainder of this study is organized as follows. Section II describes the institutional
background pertaining to CAE disclosures and develops hypotheses. Section III describes the
sample selection process and the research design, Section IV presents empirical results and
Section V concludes the study.
II. Institutional Background and Hypotheses Development
Institutional Background
The SEC has long stressed the importance of MD&A for providing informative and
transparent disclosures that help readers understand companies (SEC 2003). Since the early
2000s the SEC has pursued increased disclosure about CAEs. The SEC defines CAE as
6
“material” and “highly uncertain” accounts requiring judgment, such as estimates of anticipated
sales returns, inventory obsolescence, warranty reserves, doubtful accounts, and pensions (SEC
2002; SEC 2003). Prior research finds that greater accrual estimation is associated with lower
accruals quality and persistence, suggesting the SEC’s desire for greater CAE transparency could
indeed help readers better interpret accruals (Chen and Li 2013).6
In 2001, the SEC issued cautionary advice FR-60 to remind registrants that, under existing
MD&A disclosure rules, MD&A should explain the firm’s critical accounting policies (CAP) in
plain English as a supplement to, and not merely duplicating, required footnote disclosure. FR-60
noted the primary purpose of CAP disclosures was to provide transparency surrounding “the
judgments and uncertainties affecting the application of those policies, and the likelihood that
materially different amounts would be reported under different conditions or using different
assumptions” (SEC 2001). While CAP disclosures moved in the direction of increasing
transparency, CAP disclosures are less precise than CAE disclosures because they do not
quantify the degree of uncertainty in the estimates.
Disclosures provided by Target and Walmart in their 2009 10-Ks, shown in Appendix A,
illustrate the difference in precision between CAP and CAE disclosures. Both firms provide a
CAP disclosure relating to their self-insurance liability, suggesting the estimate involves material
and highly uncertain judgments at each firm. However, Walmart, but not Target, also provides a
CAE disclosure quantifying the uncertainty more precisely. Here, Walmart’s CAE disclosure
stipulates that a reasonably likely change (defined as one percent increase or decrease) would
change its self-insurance accrued liability (and accrual earnings) by $26 million.
6 Accrual estimation is measured by the number of sentences in the notes to the financial statements and the CAP
section of the MD&A that portray the use of estimates, such as “we estimated receivables and purchased inventory”
(Chen and Li 2013).
7
Perhaps because of the lack of precision in CAP disclosures, the SEC remained concerned
about inadequate CAE disclosure and introduced Proposed Rule 33-8098 in May 2002. The
proposed rule specifically calls on firms to provide a quantitative sensitivity analysis, CAE
disclosure (SEC 2002). Though Proposed Rule 33-098 was never adopted, the SEC issued
interpretive guidance FR-72 in December 2003, again emphasizing CAE disclosures:
“Since critical accounting estimates and assumptions are based on matters that are
highly uncertain, a company should analyze their specific sensitivity to change, based
on other outcomes that are reasonably likely to occur and would have a material
effect. Companies should provide quantitative as well as qualitative disclosure when
quantitative information is reasonably available and will provide material information
for investors.” (SEC 2003, V.)
Even though FR-72 is mandatory, firms still have considerable discretion in implementation,
and the SEC remains concerned about a lack of quantitative disclosures, as indicated by the
common issuance of comment letters on the subject (Holtzman 2007; Cassell et al. 2013).
Consistent with SEC concerns, prior research finds considerable lack of quantitative disclosure.
In a review of 5,984 10-K filings before February 2005, Levine and Smith (2011) note that only
14 percent of the sample even mention sensitivity analysis and very few of those actually
quantify the uncertainty.7 In a small sample of firms with material defined-benefit pension plans,
Bauman and Shaw (2014) find 40 percent of their sample firms do not disclose pension-related
CAEs. We extend prior research by examining the determinants of firms’ CAE disclosure
decisions, focusing on the roles of management, auditors and audit committees in CAE
disclosure.
Hypotheses Development
Management Equity-Risk Incentives and CAE Disclosures
7 In contrast, Levine and Smith (2011) find that 80 percent of their sample firms provide a qualitative discussion, of,
on average, six to seven CAPs.
8
We begin by considering manager equity-risk incentives to strategically make CAE
disclosures. Management holds the primary responsibility for preparing and disseminating
financial information. Since CAE disclosures provide greater transparency about the inherent
uncertainty of firms’ accrual accounting estimates, the disclosures not only can aid users in
interpreting accruals, but also can increase users’ insights into the extent of managers’ discretion
to change earnings during the accrual estimation process. Given that estimates provide the most
common vehicle for manipulating earnings, managers have incentives to not reveal the extent of
their discretion through CAE disclosures (Dichev et al. 2013).
Though prior literature uses a number of different proxies for equity incentives, including
equity compensation, in-the-money options, or the sensitivity of wealth to stock price changes
(portfolio delta), results are mixed.8 Armstrong et al. (2013) suggest the use of portfolio vega
instead of the other proxies because the other proxies provide more ambiguous incentives which
may drive the mixed results. For example, Armstrong et al. (2013) argue that while larger
portfolio delta increases the incentive to misreport because misreporting inflates equity values,
portfolio delta also decreases the incentive to misreport because misreporting increases stock
price risk. The opposing forces on the incentive to misreport makes the theoretical effect of delta
on the incentive to misreport ambiguous. Armstrong et al. (2013) argue that, unlike portfolio
delta, portfolio vega, measuring the increase in the value of managers’ portfolio for an increase
in firm risk, unambiguously increases the incentive to misreport. Managers with greater portfolio
vega are incentivized to inflate equity values through misreporting, but also benefit from the
higher equity risk that results from misreporting. Armstrong et al. (2013) provide robust
8 Studies documenting a positive relation include Cheng and Warfield (2005), Bergstresser and Philippon (2006),
Burns and Kedia (2006), Efendi, Srivastava, and Swanson (2007), Cheng and Farber (2008), Cornett, Marcus, and
Tehranian (2008). Studies finding no relation include Erickson, Hanlon, Maydew (2006), Larcker, Richardson, and
Tuna (2007), Hribar and Nichols (2007), Armstrong, Jagolinzer, and Larcker (2010).
9
evidence of a positive relation between portfolio vega and financial misreporting (using
discretionary accruals, restatements, and SEC enforcement actions). Furthermore, portfolio vega
subsumes the effect of delta on misreporting.9 Given that portfolio vega unambiguously
increases managers’ incentive to misreport, we predict a negative association between the
likelihood and number of CAE disclosures and portfolio vega, formally stated as follows:
H1: The likelihood and number of CAE disclosures are negatively associated with
portfolio vega.
Auditor Opposition and CAE Disclosures
Auditors’ responsibility for MD&A disclosures is arguably limited to reviewing disclosures
for consistency with the financial statements and for material misstatements or omissions that
could render MD&A misleading (AICPA 1975, Statement on Auditing Standards [SAS] No. 8).
In spite of this limited role, the SEC advises auditors to bring particular focus to CAE, including
disclosure (SEC 2001). Further, the PCAOB cautions auditors about consistent problems with
estimates in the audit found during the inspection process (PCAOB 2008).
Auditors’ comment letters to the SEC about increasing CAE disclosure reveal a variety of
negative concerns. The major negative concerns voiced by auditors involved their own legal
exposure and preparation costs, whether they would be forced to become more involved in
MD&A disclosures, the practicality of the disclosures for registrants, and the usefulness of CAE
disclosures to investors. Psychology theory and research suggest affective responses, positive or
negative feelings toward a stimulus, occur automatically and impact decision-making (Slovic,
Finucane, Peters, and MacGregor 2007). Specific to auditors, research finds both negative mood
in general and negative interpersonal affect towards the client impacts auditors’ judgments about
9 Since equity compensation and options provide both delta and vega neither provide unambiguous incentives to
take risk, and hence, vega continues to be significant even after controlling for these alternative measures of equity
incentives (Armstrong et al. 2013).
10
inventory obsolescence estimates (Bhattacharjee, Moreno, and Riley 2012; Chung, Cohen, and
Monroe 2008). In a similar manner, we suggest auditor negative affect towards CAE disclosure
regulation could negatively influence client’s CAE disclosure decisions.
Auditors work in close proximity to management throughout the year and audit
methodologies rely extensively on auditor/client interactions (Knechel 2007; Hellman 2011).
Thus, auditors have many opportunities to make their views known to management. Further,
audit partners report a desire to be in the role of expert advisor, and seek to provide advice to
management (McCracken, Salterio, and Gibbons 2008). Therefore, we expect that auditors who
expressed more opposition towards CAE disclosures are less likely to consider lack of CAE
disclosure as materially misleading to investors and are less likely to advise clients to provide
CAE disclosure, formally stated as follows:
H2: The likelihood and number of CAE disclosures are negatively associated with
auditor opposition to CAE disclosures.
Audit Committee Accounting Expertise and CAE Disclosures
The SEC cautions audit committees to carefully review and proactively discuss CAE and
CAE disclosures with both management and auditors (SEC 2001). The NYSE requires the audit
committee to discuss MD&A with the auditor (Cohen et al. 2007). In addition, the PCAOB
requires the auditor to communicate information about CAEs with the audit committee (PCAOB
2012, AS NO. 16). Thus, CAE disclosure decisions should appear on audit committee agendas.
We expect audit committees advocate for complying with the spirit of CAE disclosures required
by FR-72 because of committee members’ incentives to avoid legal liability, protect shareholder
interests, and protect their own reputation capital (Fama 1980; Gilson 1990; Sahlman 1990).
However, as indicated in PCAOB inspection reports, CAEs are often complex, and by
definition highly uncertain. Though audit committee incentives provide support for greater CAE
11
disclosure, committee members must have accounting-specific expertise to actually achieve
greater CAE disclosure. Grasping the nature of estimation uncertainty that underlies accrual-
based accounting, and proactively advocating for CAE disclosure, requires solid understanding
of the GAAP-based accounting principles and firm-based accounting policies that drive
accounting estimates. Prior research highlights the importance of audit committee accounting
expertise for monitoring accounting estimates, as evidenced by the association between
discretionary accruals and accounting expertise (Cohen, Hoitash, Krishnamoorthy, and Wright
2014; Dhaliwal, Naiker, and Navissi 2010). Thus, we predict audit committees with accounting
expertise more likely advocate for CAE disclosures as they discuss the disclosures with
management and the auditor, formally stated as follows:
H3: The likelihood and number of CAE disclosure are positively associated with audit
committee accounting expertise.
III. Sample Selection and Research Design
Sample
Table 1, Panel A describes our sample selection. We begin with the 460 firms in the S&P
500 that appear in ExecuComp in 2004. We use 2004 because it was the first year after the
SEC’s guidance on CAE disclosures became effective on December 29, 2003. Concentrating on
the S&P 500 makes the CAE data hand-collection process more manageable while also
providing for a comprehensive set of the largest firms of interest to the SEC. We exclude firms in
the financial services industry (four-digit SIC code: 6000-6999), utilities industry (four-digit SIC
code: 4900-4949), and non-classifiable firms (four-digit SIC code: 9900-9999) to allow for more
commonality in both firms’ accrual estimates and the information contained in firms’ CAE
12
disclosures. We exclude acquired firms to provide a stable sample for data collection purposes.10
We obtain audit committee and other governance data from Morningstar’s Executive
Compensation Database provided by Audit Analytics and exclude observations missing data. We
also exclude firms with a non-Big 4 auditor. Finally, we remove observations without necessary
Execucomp, Compustat, and I/B/E/S data to calculate the additional test and control variables
used in our analyses. The final sample consists of 2,298 firm-years (317 distinct firms) from
2003 to 2010.
We hand-collect CAE disclosure data for these firms. We first examine the 10-Ks for each
sample firm in 2004 (i.e. the year following the SEC’s guidance) and 2010 (i.e. the final year in
our data collection sample period) to identify which firms provide a CAE disclosure in either
year. If neither the 2004 10-K nor the 2010 10-K have a CAE disclosure, we assume the firm did
not provide a CAE disclosure in any year of our sample period.11
If a firm provides a CAE
disclosure in either 2004 or 2010, then we examine the firm’s 10-Ks from 2003 to 2010 and
identify for each year (1) the presence of a CAE disclosure and (2) the number of CAEs
disclosed.12
Panel B of Table 1 reports the average CAE disclosure rate for our sample during
2003-2010 is 50.96 percent; that is, about half of the firm-year observations disclose CAE and
half do not. Panel B also reports the annual CAE disclosure frequency. CAE disclosure rates
monotonically increase from 32.19 percent in 2003 to 58.14 percent in 2010. The largest annual
increases in disclosure rates occur between 2003, after the SEC issued its proposed rule, and
2004, after the SEC issued FR-72. After 2004, CAE disclosure rates continue to increase, but at a
10
When collecting CAE data, we first check for disclosures in the boundary years of the sample, thus we require
firms to exist during the entire sample period. 11
136 sample firms do not provide a CAE disclosure in either 2004 or 2010. To assess the validity of the assumption
that the 952 unexamined firm-years have no CAE disclosure, we randomly select 20 firm-years out of the 952 firm-
years. After examining the 10-Ks for these 20 firm-years, we find none of the 20 firm-years have a CAE disclosure. 12
Appendix B provides examples of CAE disclosures.
13
slower rate. Panel C of Table 1 reports information about the number of CAEs disclosed by
disclosing firms during 2003-2010. On average, CAE-disclosing firms disclose between two and
three critical accounting estimates, and the number of CAEs disclosed remains steady throughout
the sample period.
Research Design
Empirical Model and Test Variables
To test our hypotheses, we estimate the following logistic regression:
Pr(CAEit) = α0 + α1VEGAit + α2AUDITOROPPOSITIONit + α3ACCT_EXPERTit +
α4DELTAit + α5EQUITYPAYit + α6LEGAL_EXPERTit + α7AC_SIZEit +
α8AC_TENUREit + α9AC_MEETit + α10BD_SIZEit + α11BD_INDit +
α12DUALit + α13ROAit + α14BTMit + α15SIZEit + α16LEVERAGEit +
α17ACCRUALSit + α18ICWit + α19SALESVOLit + α20OPERVOLit +
α21LOSSit + α22COMPLEXITYit + α23LITIGATIONit + α24COVERAGEit
+ α25INSTOWNit + α26PENSIONit + εit
(1)
The dependent variable, CAE, is an indicator variable equal to 1 for firm-years providing a CAE
disclosure, and zero otherwise.13
H1 predicts that the likelihood of a CAE disclosure is
negatively associated with managers’ portfolio vega (1 < 0). To test H1, we include VEGA,
which equals natural logarithm of one plus the average dollar change (in $000s) in the top-five
executives' wealth associated with a 1% change in the standard deviation of the firm’s returns
(see Core and Guay [2002] and Coles, Daniel, and Naveen [2006[).14
To test H2, we include AUDITOROPPOSITION, which equals the negative versus positive
tone of the auditor's comment letter to the SEC on the issue of CAE disclosures. We follow Li
(2008) when measuring negative versus positive tone using Linguistic Inquiry and Word Count
13
Appendix C provides definitions of all variables used in this study. 14
We obtain data on VEGA and DELTA from the following website: http://astro.temple.edu/~lnaveen/data.html. We
thank Jeffrey Coles, Naveen Daniel, and Lalitha Naveen for making their data available.
14
(LIWC). Appendix D provides a detailed explanation of our auditor opposition measure.
Consistent with H2, we expect a negative coefficient on AUDITOROPPOSITION (2 < 0).
H3 predicts that the likelihood of a CAE disclosure is positively associated with audit
committee accounting expertise. To test H3, we include ACCT_EXPERT, an indicator variable
equal to 1 for firm-years where the audit committee includes at least one accounting expert.15
Consistent with H3, we expect a positive coefficient on ACCT_EXPERT (3 > 0). Although the
SEC requirements for audit committee financial expertise includes supervisory or finance
expertise in addition to accounting expertise, we consider only accounting expertise due to the
complex nature of accounting estimates and prior research support for accounting expertise over
the SEC’s broader definition (e.g., Krishnan and Visvanathan 2008). ACCT_EXPERT includes
audit committee members with experience as public accountants, chief financial officers,
comptrollers, or other principal accounting officers.
Control Variables
When estimating Equation (1), we first include controls for other equity-based incentives that
are potentially correlated with VEGA and also may influence the incentive to misreport, and thus
the propensity to disclose CAEs. Specifically, we control for DELTA and EQUITYPAY. DELTA
is the natural logarithm of one plus the average dollar change (in $000s) in top-five executives'
wealth associated with a 1% change in the firm’s stock price. EQUITYPAY is the natural
logarithm of one plus the average dollar value (in $000s) of annual equity-based compensation to
the top-five executives, where equity-based compensation equals the fair value of option grants
plus the fair value of restricted stock awards. DELTA and EQUITYPAY are expected to decrease
the likelihood of a CAE disclosure if DELTA and EQUITYPAY increase the incentive to
15
In untabulated sensitivity tests, we define ACCT_EXPERT as the proportion of accounting experts on the audit
committee and find qualitatively similar results to those reported in Tables 4 and 6.
15
misreport. However, if managers with higher values of DELTA and EQUITYPAY are more
averse to the equity risk resulting from misreporting (Armstrong et al. 2013), then the likelihood
of a CAE disclosure is expected to be positively related to DELTA and EQUITYPAY. Hence, we
do not predict the sign of the coefficients on DELTA and EQUITYPAY.
Next, we include controls for other governance characteristics because prior research
indicates higher quality disclosures result from better corporate governance mechanisms (Eng
and Mak 2003; Laksmana 2008; Beyer, Cohen, Lys, and Walther 2010). Though we expect audit
committee accounting expertise is the most important governance characteristic related to CAE
disclosure decisions, we also control for other board and audit committee characteristics that
might support more transparent disclosure practices. We include BD_SIZE (AC_SIZE) to control
for the resources, or size, of the board (audit committee), where BD_SIZE (AC_SIZE) equals the
number of board of director (audit committee) members. We include LEGAL_EXPERT to
control for the legal competency of the audit committee, where LEGAL_EXPERT equals one for
firm-years where the audit committee includes at least one legal expert, zero otherwise. To
control for the experience and effort of the audit committee, we include variables for the number
of years the audit committee members have served as directors (AC_TENURE) and the number
of meetings held by the audit committee (AC_MEET). We control for the independence of the
board by including BD_IND, which equals the percent of outside board members. We control for
the CEO’s power over the board by including DUAL, which is an indicator variable equal to 1
for firm-years with a CEO that also serves as the chairperson of the board of directors, zero
otherwise. If stronger corporate governance leads to more transparent disclosure practices, we
predict positive coefficients on LEGAL_EXPERT, AC_SIZE, AC_TENURE, AC_MEET,
BD_SIZE, and BD_IND and a negative coefficient on DUAL.
16
Equation (1) also includes controls for innate firm attributes that might influence the CAE
disclosure decision. We control for return on assets (ROA), the book-to-market ratio (BTM), the
natural logarithm of total assets (SIZE), and the ratio of long-term debt to total assets
(LEVERAGE). Based on Lang and Lundholm (1993), we predict positive coefficients on ROA
and SIZE. Following Guay (2008), we predict a positive coefficient on LEVERAGE. We do not
form a prediction on BTM.
In addition to the above firm traits, we include controls to capture the extent of uncertainty in
the accrual measurement process. Since CAE disclosures capture uncertainty in accrual
estimates, and uncertain accrual estimates are more likely in firms with greater levels of accruals,
we include ACCRUALS as a control variable. ACCRUALS is measured as earnings before
extraordinary items less operating cash flows taken directly from the statement of cash flows,
scaled by total assets. We include ICW to control for the possibility that CAE disclosures are
related to internal control weaknesses, but we do not make prediction on ICW. ICW equals 1 for
firm-years with a material weakness under SOX sections 302 or 404 and zero otherwise. Because
we expect firms operating in volatile business environments to experience more uncertainty
when making accrual estimates, we control for SALESVOL and OPERVOL. SALESVOL is the
standard deviation of sales over the previous three years. OPERVOL is the standard deviation of
operating cash flows over the previous three years. We include LOSS to control for the relation
between CAE disclosures and negative earnings, without a predicted sign. CAE disclosures may
be more likely during loss years if losses are recognized with less precision or CAE disclosures
may be less likely during loss years if firms with losses are more financially weak and less
willing to disclose a CAE. We control for operating complexity (COMPLEXITY) because
managers of complex businesses likely face more uncertainty when making accrual estimates.
17
COMPLEXITY is defined as the natural logarithm of a firm's total geographic and business
segments.
Previous studies investigating the link between ex ante litigation risk and voluntary
disclosure provide evidence that managers are less likely to make voluntary disclosures when the
level of ex ante litigation risk is high (Johnson, Kasznik, and Nelson 2001; Baginski, Hassell,
and Kimbrough 2002; Rogers and Van Buskirk 2009). Other research finds that ex ante litigation
risk increases the likelihood of providing CAP disclosures (Levine and Smith 2011). Since CAE
disclosures are stated as reasonably likely changes within the current financial report, they may
not qualify for the same safe harbor protections granted by the 1995 Private Securities Litigation
Reform Act (PSLRA) to voluntary disclosures of forward-looking statements, such as earnings
guidance. Considering that investors might interpret the disclosures as managers’
acknowledgment of inaccuracies in currently reported accounting numbers, managers may
believe that CAE disclosures increase the risk of litigation. Insofar as managers are able to
exercise their discretion when providing CAE disclosures, managers of high litigation risk firms
may exhibit greater aversion to disclosing a quantitative sensitivity analysis of accounting
estimates. Following, we include LITIGATION, which equals the probability of litigation
estimated using the coefficients from the litigation risk model from Kim and Skinner (2012).16
We also control for other potential channels of information in the market, including analyst
following and institutional ownership (Einhorn 2005). COVERAGE is defined as the natural
logarithm of the number of analysts issuing a forecast for the firm prior to the fourth quarter
earnings announcement for each firm-year.17
INSTOWN equals percentage ownership by
16
See Table 7, Model (2) of Kim and Skinner (2012). 17
Analyst data are obtained from the IBES Summary database.
18
institutional investors at the fourth quarter of each firm-year.18
On one hand, we expect a positive
association between the likelihood of a CAE disclosure and analyst following and institutional
ownership. This is consistent with previous studies that document a positive relation between
disclosure levels and analyst following (Lang and Lundholm 1996) and institutional ownership
(Healy, Hutton, and Palepu 1999). However, COVERAGE and INSTOWN may negatively impact
the likelihood of a CAE disclosure. The importance of meeting analysts’ earnings expectations
increases with analyst coverage (Graham, Harvey, and Rajgopal 2005), thus the incentive to
misreport and the incentive not to disclose CAEs may also increase with analyst coverage. Also,
institutional investors are considered to be sophisticated users of financial statements (e.g., Hand
1990; Walther 1997; Collins, Gong, and Hribar 2003), suggesting the demand for information
conveyed in CAE disclosures is lower in firms with greater institutional holdings. Thus, we do
not make predictions for COVERAGE and INSTOWN.
Since the SEC explicitly mentioned that critical accounting estimates potentially include
pension-related estimates, we expect firms with defined-benefit pension plans to exhibit a higher
CAE disclosure rate. Thus, we include PENSION, an indicator variable equal to one for firm-
years with a defined-benefit pension liability (projected benefit obligation), and zero otherwise.
Finally, because CAE disclosure rates are expected to vary by year and industry the model
includes (untabulated) year and industry (at the 2-digit industry level) fixed effects.
IV. Results
18
Institutional ownership data are from Thomson Financial’s CDA/Spectrum database, which contains institutions’
quarterly shareholding data based on their 13-F filings to the SEC.
19
Descriptive Statistics
Panel A of Table 2 reports the descriptive statistics for the variables used in estimating
Equation 1. The mean (median) of VEGA is 4.403 (4.597). AUDITOROPPOSITION has a mean
of -0.256, indicating auditors’ comment letters on CAE disclosures were more positive than
negative, on average. Panel A also reports that 65.1 percent of firm-years have an audit
committee with an accounting expert.
For the equity incentive control variables, Panel A reports the mean (median) of DELTA and
EQUITYPAY is 5.591 (5.543) and 6.107 (6.903), respectively. For the controls for other board
attributes, 47.2 percent of firm-years had a CEO that also chaired the board of directors and the
percent of independent members on the board averaged 88 percent. Sample firms’ boards (audit
committees) include about ten (four) members. Panel A also reports descriptive for other firm
characteristics. Sample firms are profitable on average and exhibit a mean leverage ratio of 21.4
percent. About 68 percent of sample firms have a defined-benefit pension plan.
Panel B of Table 2 reports the differences in the mean and median values of variables
between disclosing firm-years (1,171 observations) and non-disclosing firm-years (1,127
observations). Inconsistent with H1, the mean (median) value of VEGA for disclosing firm-years
is insignificantly (significantly) greater compared to non-disclosing firm-years. In support of H2,
the CAE disclosing sub-sample has statistically lower mean and median values for
AUDITOROPPOSTION. Consistent with H3, the mean and median values for ACCT_EXPERT
are statistically greater for firm-years providing a CAE disclosure. Panel B also reveals that
firms that do and do not disclose CAE differ significantly on many of the dimensions controlled
for in our multivariate analyses.
20
Table 3 reports Pearson (below the diagonal) and Spearman (above the diagonal)
correlations. Similar to the results in Panel B of Table 2, we find that CAE exhibits a significant
positive Spearman correlation with VEGA (p-value = 0.046), but the Pearson correlation between
CAE and VEGA is statistically insignificant (p-value = 0.487). Table 3 reports significantly
negative Spearman and Pearson correlations between CAE and AUDITOROPPOSTION (p-
value<0.0001). We also find CAE exhibits significant positive correlations with ACCT_EXPERT
(p-value<0.001). While Panel B of Table 2 and Table 3 provide some univariate support for H2
and H3, we rely on our multivariate analysis for making inferences.
Determinants of CAE Disclosures
Table 4 reports the results from estimating Equation (1). Consistent with H1, the coefficient
on VEGA is negative and significant (coefficient = -0.198, p-value = 0.045), suggesting that CAE
disclosures are less likely when the incentive to misreport is high, perhaps due to the increased
transparency CAE disclosures provide about the amount of discretion in accounting estimates.
We also find a significantly negative coefficient on AUDITOROPPOSITION (coefficient = -
1.160, p-value = 0.015), consistent with H2 and suggesting that auditors’ opposition to CAE
disclosures reduces the likelihood their clients disclose CAE in the MD&A, an area of financial
reporting typically viewed as having a limited auditor role.
In support of H3, Table 4 reports a statistically positive coefficient on ACCT_EXPERT
(coefficient = 0.553, p-value = 0.012), suggesting audit committee accounting expertise increases
the likelihood of a CAE disclosure, consistent with more knowledgeable audit committees
providing constraint on managers’ and auditors’ negative disclosure incentives. We also find
audit committee size, number of audit committee meetings, and board size provide significant
incremental support for CAE disclosure.
21
For our other control variables, we find several firm attributes influence the propensity to
disclose a CAE. Table 4 reports the likelihood of a CAE disclosure is increasing in firm size,
leverage, and whether the firm has a defined-benefit pension plan and decreasing in the book-to-
market ratio and analyst coverage.
Overall, the results in Table 4 are consistent with our three hypotheses, and suggest
management equity risk incentives, auditor opposition to CAE disclosures, and audit committee
accounting expertise play determinative roles in whether a firm provides a CAE disclosure. To
get a sense of the economic effects of our results, Table 5 presents the change in probability of a
CAE disclosure for selected changes in our independent variables. For each variable, Table 5
reports the change in the probability of CAE disclosure resulting from moving non-dichotomous
variables from the first to the third quartile and dichotomous variables from zero to one, holding
other variables at their mean values.19
Moving from the first to the third quartile of VEGA and
AUDITOROPPOSITION decreases the probability of a CAE disclosure by 6.77% and 13.84%,
respectively. Moving from zero to one for ACCT_EXPERT increases the probability of a CAE
disclosure by 13.73%. Considering that the mean CAE disclosure rate is about 51%, these effects
are economically significant.
Number of CAE Disclosures
While the above analysis examines the likelihood of a CAE disclosure, we also examine the
determinants of the number of CAEs disclosed. We estimate the following ordered logistic
regression to test our predictions regarding the number of CAEs disclosed:
19
We calculate the change in the probability using the following expression: eβ’X
/ (1 + eβ’X
), where β refers to the
vector of coefficients from the model in Table 4 and X refers to the vector of independent variables.
22
Pr(CAE#it) = α0 + α1VEGAit + α2AUDITOROPPOSITIONit + α3ACCT_EXPERTit +
α4DELTAit + α5EQUITYPAYit + α6LEGAL_EXPERTit + α7AC_SIZEit +
α8AC_TENUREit + α9AC_MEETit + α10BD_SIZEit + α11BD_INDit +
α12DUALit + α13ROAit + α14BTMit + α15SIZEit + α16LEVERAGEit +
α17ACCRUALSit + α18ICWit + α19SALESVOLit + α20OPERVOLit +
α21LOSSit + α22COMPLEXITYit + α23LITIGATIONit + α24COVERAGEit
+ α25INSTOWNit + α26PENSIONit + εit
(2)
In Equation (2), we replace the binary dependent variable in Equation 1 (CAE) with an ordinal
variable (CAE#) that equals the number of CAE disclosures provided. CAE# ranges from 0 to 10.
Our purpose in estimating Equation (2) is to examine whether the hypothesized determinants of
the existence of a CAE disclosure are also significant determinants of the breadth of CAE
disclosures.
Table 6 reports the results from estimating Equation 2, providing additional support for our
hypotheses. Consistent with H1, H2, and H3, we find a significantly negative coefficient on
VEGA (coefficient = -0.160, p-value = 0.034), a significantly negative coefficient on
AUDITOROPPOSITION (coefficient = -0.815, p-value = 0.037), and a significantly positive
coefficient on ACCT_EXPERT (coefficient = 0.528, p-value = 0.006), respectively. These results
suggest the number of CAEs disclosed is negatively related to executives’ equity risk incentives
and auditor opposition and positively related to audit committee accounting expertise.
With a few exceptions, the results for our control variables are similar to the results reported
in Table 4. One of the differences in the results for our control variables is that the coefficient on
COVERAGE is no longer significant when examining the number of CAEs disclosed. We also
find that the number of CAEs disclosed is negatively associated with litigation risk, whereas this
variable is not a significant determinant in Table 4.
Overall, the findings reported in Tables 4-6 indicate that management equity-risk incentives,
auditor incentives, and audit committee accounting expertise influence both the likelihood of
23
disclosing a CAE and the number of CAEs disclosed. Thus, our evidence is consistent with
discretion in CAE disclosures introducing strategic incentives among the parties involved in the
three-pronged governance “mosaic,” management, auditors, and audit committees. The results
are robust to a large number of controls for other aspects of corporate governance and firm-
characteristics.
Firm Responses to CAE Disclosures
Our tests of H1 indicate that equity risk incentives, by increasing the incentive to misreport,
discourage managers from disclosing CAEs. Managers with greater misreporting incentives
appear to be less willing to reveal information about the extent of discretion in the accrual
estimation process, and hence, their ability to misreport. However, some managers do indeed
disclose CAEs, and it remains unclear as to how internal monitors (i.e., boards) react to the more
transparent information conveyed in CAE disclosures. Given that CAE disclosures potentially
provide new information about the ability to misreport, it is plausible that boards take actions to
reduce managers’ misreporting incentives.
Though the nature of a CAE does not change with its disclosure, new quantitative disclosures
may require boards to think more carefully about the extent to which accounting reports rely on
estimates that are inherently uncertain. The potential for CAE disclosures to provide new
information is evidenced in the 2011 business roundtable discussion hosted by the SEC that
focused on measurement uncertainty in financial reporting. When speaking about the topic of
CAE disclosures, one director stated (SEC 2011):
“And all of a sudden the most important numbers on the balance sheet were
estimates. Except for the cash and investments that we had, the most numbers [sic]
that were on the balance sheet were estimates. And all of a sudden I started paying
attention to those estimates in a very different way than I did before.”
24
The quote illustrates that CAE disclosures potentially aid boards in understanding the scope
and magnitude of accrual measurement uncertainty. Moreover, since misreporting commonly
occurs through accounting estimates, the disclosures may assist in assessing the extent to which
managers are able to use discretion in accounting estimates as an earnings management device.
To the extent CAE disclosures inform boards about managers’ ability to misreport, the expected
cost of managers’ equity risk incentives (i.e., portfolio vega) increases after the disclosure of a
CAE because managers’ equity risk incentives increase managers’ incentive to misreport
(Armstrong et al. 2013). Following, we expect firms to respond to the disclosure of a CAE by
decreasing managers’ portfolio vega, thereby reducing the incentive to misreport. To test how
portfolio vega responds to the disclosure of a CAE, we estimate the following model:
VEGAit = β0 + β1CAEit-1 + β2DELTAit + β3CASHCOMPit + β4SALEit + β5BTMit +
β6LEVERAGEit + β7R&Dit + β8CAPEXit + β9RETURNVOLit +
β10POSTSOXit + β11POST123(R)it + βFirm + εit
(3)
The dependent variable, VEGA, remains as previously defined. To assess whether disclosing
firms reduce managers’ portfolio vega after the disclosure of a CAE, Equation 3 includes the
lagged version of our disclosure indicator variable, CAE. We predict a negative coefficient on
CAE. To examine how CAE disclosures influence within-firm variation of portfolio vega, we
include firm fixed-effects. By using a firm fixed-effects approach, the coefficient on CAE
conveys the average change in managers’ portfolio vega between disclosing firms’ pre-CAE and
post-CAE periods. Because we are interested in the VEGA responses of disclosing firms, the
sample used to estimate Equation 3 includes only those firms that provide at least one CAE
disclosure. The sample includes years 1995-2010 for these firms to allow for an adequate
comparison of vega practices from before to after the disclosure of a CAE. This process results
in a sample of 2,838 firm-years when estimating Equation 3.
25
Equation 3 includes controls for other known determinants of vega documented in previous
research (e.g., Guay 1999; Cohen, Hall, and Viceira 2000; Rajgopal and Shevlin 2002; Coles et
al. 2006).20
DELTA, BTM, and LEVERAGE remain as previously defined. CASHCOMP is the
natural logarithm of one plus the average cash compensation, salary plus bonus, (in $000s) for
the top-five executives. SALE is the natural logarithm of annual sales revenue. R&D is the ratio
of research and development expenditures to total assets, where firm-years with missing values
for research and development expenditures have R&D set to zero. CAPEX is the ratio of capital
expenditures to total assets. RETURNVOL equals the standard deviation of monthly returns over
the fiscal year. We also include POSTSOX and POST123(R) to control for the influence of the
Sarbanes-Oxley Act (SOX) of 2002 and SFAS No. 123(R) on equity risk incentives.21
We
measure the POSTSOX period as those firm-years ending in 2002 and afterward and the
POST123(R) period as those firm-years ending after June 15, 2006.
Table 7 reports the results from estimating Equation 3. Consistent with our prediction, the
coefficient on CAE in the first and second model specifications equals -0.147 (p-value = 0.026)
and -0.175 (p-value = 0.003), respectively. This result suggests firms reduced vega after the
disclosure of a CAE. In terms of an economic effect, we estimate that firms reduced equity risk
incentives by approximately 4 percent relative to the mean value of VEGA for the sample used in
Table 7. The evidence reported in Table 7 is consistent with the disclosure of a CAE reducing the
appeal of equity risk incentives as a form of compensation. As CAE disclosures potentially
20
The control variables used in Equation 3 are based on the vega model in Table 3 of Coles et al. (2006). 21
Because SOX may have increased the skepticism of financial reporting (Cohen, Dey, and Lys 2008; Zhang 2012),
it is possible that vega decreased after SOX to mitigate the incentive to misreport. However, SOX also constrained
risk-taking behavior (Bargeron, Lehn, and Zutter 2010), so vega may increase after SOX in order to encourage
managers to undertake new investment projects in the post-SOX period. As a result, we do not make a prediction for
the coefficient on POSTSOX. Hayes, Lemmon, and Qiu (2012) show firms reduced vega after the adoption of SFAS
No. 123(R) due to the increased accounting costs associated with stock option compensation. Following, we predict
a negative coefficient on POST123(R).
26
inform boards about managers’ ability to misreport, compensation practices respond to the
disclosure of a CAE in a manner that reduces the incentive to misreport.
For our control variables, as shown in Table 7, we find mangers’ portfolio vega is positively
associated with managers’ portfolio delta, firm size, book-to-market, leverage, R&D, and return
volatility. We note that the positive coefficient on BTM in Table 7 is inconsistent with our
prediction and potentially the result of our sample selection.22
We also find SOX (SFAS No.
123(R)) exhibits a positive (negative) influence on vega.
Additional Analyses
Incidence of Alleged GAAP Violations
CAE disclosures provide information about measurement discretion in accrual estimates.
Since managers can use this estimate flexibility to engage in aggressive financial reporting, H1
predicts that managers will have the incentive to not make CAE disclosures and, thus, reveal
their discretion, when the incentive to misreport is high. Consistent with this prediction, we find
CAE disclosures are decreasing with managers’ portfolio vega. The discretion highlighted by
CAE disclosures could be used to engage in all types of aggressive reporting (i.e., within-GAAP,
gray-GAAP, or non-GAAP). Vega captures only the incentive to engage in aggressive financial
reporting, not the actual incidence of misreporting. However, to ensure that vega does not merely
capture the incidence of non-GAAP earnings management, we re-estimate Equation (1) after
including an indicator variable, AAER, which captures whether the firm-year is associated with
22
The positive association between VEGA and BTM could also reflect the effect of growth expectations on
managers’ misreporting incentives. Managers have a reduced incentive to misreport as the book-to-market ratio
increases because there is less pressure to satisfy capital market expectations and sustain high valuations (Skinner
and Sloan 2002; Dechow, Ge, Larson, Sloan 2011). Since vega unambiguously increases the incentive to misreport
(Armstrong et al. 2013), firms may be more willing to increase managers’ portfolio vega when other misreporting
incentives (e.g. investor optimism) are low.
27
an alleged GAAP violation that resulted in an Accounting and Auditing Enforcement Release
(AAER).23
We find a significantly negative coefficient on AAER (coefficient = -1.371, p-value = 0.067),
suggesting managers are less likely to provide a CAE disclosure during the years in which they
engage in egregious misreporting. More importantly, we continue to find support for our
hypotheses. The coefficients on VEGA, AUDITOROPPOSITION, and ACCT_EXPERT are
significant and in the predicted direction. The results from this test indicate the role of
misreporting incentives provided by vega in the CAE disclosure decision are not subsumed by
the existence of egregious, non-GAAP accounting choices.
Time Trends of CAE Disclosures and Portfolio Vega
Portfolio vega tends to decrease throughout the 2000s, the time frame spanning our sample
period. We also report that CAE disclosure frequencies steadily increase throughout our sample
period. A potential issue with the opposing time trend patterns of portfolio vega and CAE
disclosures is that it could lead to spurious inferences when interpreting our results for H1. While
this issue is alleviated by our inclusion of year fixed effects in Equation (1), in an additional test
we more directly control for the passage of time. Specifically, we re-estimate Equation (1)
without year fixed effects and include TREND, which equals the current year minus 2002, the
year in which the SEC proposed its rule on CAE disclosure. Consistent with CAE disclosures
increasing throughout our sample period, we find a significantly positive coefficient on TREND
(coefficient = 0.174, p-value < 0.0001). We also continue to find support for our hypotheses. The
coefficients on our test variables remain significant and in the predicted direction.
Non-Initiation Years for Disclosure
23
The SEC issues AAERs against a company, an auditor, or an officer for alleged accounting and/or auditing
misconduct (Dechow et al. 2011). In general, AAERs capture the incidence of egregious misreporting. We obtained
AAER data from the Center for Financial Reporting and Management (CFRM).
28
Our final robustness check examines whether our hypothesized test variables are significant
determinants of CAE disclosures in the years subsequent to disclosure initiation. Because CAE
disclosure are sticky, a potential concern is that our results are driven by the year in which the
CAE disclosure was initiated. To mitigate this concern, we re-estimate Equation (1) after
excluding from our sample firm-years associated with a first-time CAE disclosure. This process
decreases our sample to 2,176 observations. The results from re-estimating Equation (1) after
excluding first-time disclosing observations show significantly negative coefficients on VEGA
and AUDITOROPPOSITION and a significantly positive coefficient on ACCT_EXPERT. These
results suggest management, auditor, and audit committee incentives continue to play a role in
CAE disclosure decisions after the disclosure practice is initiated.
V. Conclusion
In the early 2000’s firms began providing quantitative sensitivity disclosures for their critical
accounting estimates. The new disclosures outline the earnings effects of reasonably likely
changes in highly uncertain accounting estimates. While the SEC mandated the new disclosures,
MD&A reporting is largely subjective, and it is likely that considerable discretion exists in the
determination of what is material and highly uncertain as defined by the SEC guidance. Because
of the discretionary aspect of this mandatory disclosure practice, we develop and test hypotheses
on the CAE disclosure preferences of management, auditors, and audit committees.
We find the likelihood and number of CAE disclosures are negatively related to managers’
equity risk incentives (portfolio vega) and auditor opposition to the SEC’s sensitivity disclosure
requirement, and positively related to audit committee accounting expertise. Our findings
suggest an aversion to CAE disclosures induced by the preferences of managers and auditors.
However, our findings also indicate that more knowledgeable audit committees play an
29
important role in mitigating strategic CAE disclosure practices. These results shed light on how
incentives influence reporting practices within the three-pronged governance “mosaic” of
management, auditors, and audit committees.
Other findings indicate management equity risk incentives decreased after the disclosure of a
CAE, suggesting firms respond to the increased transparency about accounting estimation
uncertainty. Consistent with CAE disclosures revealing the degree of discretion in accounting
estimates, firms adjust compensation practices to decrease misreporting incentives. This result
corroborates our conjecture that managers’ CAE disclosure strategies are partially based on their
misreporting incentives.
Our results provide several contributions for academics, regulators, and firms. First, we
provide robust empirical evidence on the determinants of firms’ decisions to provide CAE
disclosures. Second, we provide additional evidence on the role of management equity-risk
incentives in the disclosure process. Third, we document a role for auditors in CAE disclosures,
even though auditors are traditionally viewed as having a very limited role in MD&A disclosure.
Our study informs the SEC on how allowed discretion in mandatory disclosures can limit the
efficacy of such disclosures. Our study also informs firms on how managers, auditors, and the
audit committee influence disclosure quality. However, the study is not without limitations. Our
sample includes only non-financial and non-utilities companies in the S&P 500, and the results
might not generalize to smaller firms or to more regulated firms.
30
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36
Appendix A
CAP Disclosures Versus CAE Disclosures
Source: TARGET CORP, 10-K, March 12, 2010 (CAP Disclosure)
Insurance/self-insurance
We retain a substantial portion of the risk related to certain general liability, workers' compensation, property loss and
team member medical and dental claims. However, we maintain stop-loss coverage to limit the exposure related to
certain risks. Liabilities associated with these losses include estimates of both claims filed and losses incurred but not
yet reported. We estimate our ultimate cost based on an analysis of historical data and actuarial estimates. General
liability and workers' compensation liabilities are recorded at our estimate of their net present value; other liabilities
referred to above are not discounted. We believe that the amounts accrued are adequate, although actual losses may
differ from the amounts provided. Refer to Item 7A for further disclosure of the market risks associated with these
exposures.
Source: WAL MART STORES INC, 10-K, March 30, 2010 (CAE Disclosure)
Self-Insurance
We use a combination of insurance, self-insured retention and self-insurance for a number of risks, including, but not
limited to, workers’ compensation, general liability, vehicle liability, and the company’s obligation for employee-related
health care benefits. Liabilities associated with the risks that we retain are estimated by considering historical claims
experience, including frequency, severity, demographic factors and other actuarial assumptions. In calculating our
liability, we analyze our historical trends, including loss development, and apply appropriate loss development factors to
the incurred costs associated with the claims made against our self-insured program. The estimated accruals for these
liabilities could be significantly affected if future occurrences or loss development differ from these assumptions. For
example, for our workers’ compensation and general liability accrual, a 1% increase or decrease to the
assumptions for claims costs or loss development factors would increase or decrease our self-insurance
accrual by $26 million (emphasis added).
37
Appendix B
Examples of CAE Disclosures
In respect to the wireless assets, a hypothetical 10% increase or decrease in the current cost factors would have
changed the impairment charge by $17 million. Also relative to the wireless assets, a hypothetical 100 basis point
change in the discount factors related to physical deterioration, functional obsolescence and economic obsolescence
would have changed the impairment charge by $10 million (Source: QWEST COMMUNICATIONS
INTERNATIONAL INC, 10-K, February 18, 2005).
An impairment charge of $1,420 million was recorded in 2004. Had we used a discount rate of 12%, the impairment
charge would have been approximately $90 million lower. Had we used a discount rate of 13%, the impairment charge
would have been approximately $80 million higher (Source: CORNING INC /NY, 10-K, February 22, 2005).
To the extent that Microelectronics' actual useful lives differ from management’s estimates by 10 percent,
consolidated net income in 2005 would have been an estimated $48 million higher if the actual lives were longer than
the estimates and an estimated $59 million lower if the actual lives were shorter than the estimates (based upon 2005
results) (Source: INTERNATIONAL BUSINESS MACHINES CORP, 10-K, February 28, 2006).
If the estimated useful lives of all depreciable assets were increased by one year, annual depreciation expense would
decrease by approximately $43 million. If the estimated useful lives of all depreciable assets were decreased by one
year, annual depreciation expense would increase by approximately $45 million (Source: UNION PACIFIC CORP, 10-
K, February 23, 2007).
As a measure of sensitivity, for every 1% of additional inventory valuation allowance at December 31, 2009 we would
have recorded an additional cost of sales of approximately $23 million (Source: AMAZON COM INC, 10-K, January
29, 2010).
For fiscal 2009, a 100 basis point change in total vendor funds earned, including advertising allowances, with no
offsetting changes to the base price on the products purchased, would impact gross profit by 10 basis points...As of
February 28, 2009, each 25 basis point change in the estimated inventory shortages would impact the allowances for
inventory shortages by approximately $13 (Source: SUPERVALU INC, 10-K, April 28, 2009).
A five percent change in the allowance for doubtful accounts would have had a pre–tax impact of approximately $2.6
million in 2005 (Source: BAKER HUGHES INC, 10-K, March 01, 2006).
A significant estimate in the McGraw-Hill Education segment, and particularly within the Higher Education,
Professional and International Group (“HPI”), is the allowance for sales returns, which is based on the historical rate
of return and current market conditions. Should the estimate for the HPI Group vary by one percentage point, it would
have an approximate $11.3 million impact on operating profit (Source: MCGRAW-HILL COMPANIES INC, 10-K,
February 29, 2008).
A change of 5% in the estimated sell-through levels by our wholesaler customers and in the estimated wholesaler
inventory levels would have an effect on our reserve balance of approximately $11 million (Source: MYLAN INC., 10-
K, February 24, 2011).
The effect of a change in the valuation allowance is reported in the current period tax expense. A 1% point increase
(decrease) in the Company’s effective tax rate would have decreased (increased) net income by approximately $15
(Source: AIR PRODUCTS & CHEMICALS INC /DE/, 10-K, November 26, 2008).
We believe that our estimates for the uncertain tax positions and valuation allowances against the deferred tax assets
are appropriate based on current facts and circumstances. A 5 percent change in the amount of the uncertain tax
positions and the valuation allowance would result in a change in net income of approximately $78 million and $26
million, respectively (Source: LILLY ELI & CO, 10-K, February 29, 2008).
38
Appendix B continued
Examples of CAE Disclosures
A 10% change in the sales return reserve would have had a $4 impact on our net earnings for the year ended January
31, 2009 (Source: NORDSTROM INC, 10-K, March 23, 2009).
a 10% variance in the workers’ compensation and general liability reserves at year-end 2008 would have affected net
income by approximately $14 million (Source: J C PENNEY CO INC, 10-K, March 31, 2009).
To the extent that our actual systems warranty costs differed from our estimates by 5 percent, consolidated pre-tax
income would have increased/decreased by approximately $10.0 in 2006 (Source: EMC CORP, 10-K, February 27,
2007).
A one-percentage point increase in the percentage of rebates to related gross sales would decrease net sales and
operating earnings by approximately $109 million in 2005 (Source: ABBOTT LABORATORIES, 10-K, February 22,
2006).
If the environmental reserve balance were to either increase or decrease based on the factors mentioned above, the
amount of the increase or decrease would be immediately recognized in earnings. For example, if the reserve balance
were to decrease by 10 percent, Occidental would record a pre-tax gain of $42 million. If the reserve balance were to
increase by 10 percent, Occidental would record an additional remediation expense of $42 million (Source:
OCCIDENTAL PETROLEUM CORP /DE/, 10-K, March 01, 2006).
A 10% change in our closed property reserves at September 28, 2008, would have affected net income by
approximately $4.0 million for fiscal year 2008 (Source: WHOLE FOODS MARKET INC, 10-K, November 26,
2008).
In addition, if future evidence indicates that the costs of performing services under these contracts are incurred on
other than a straight-line basis, the timing of revenue recognition under these contracts could change. A 10% change
in the amount of revenue recognized in 2009 under these contracts would have affected net earnings by approximately
$9 million (Source: LOWES COMPANIES INC, 10-K, March 30, 2010).
Our pension expense is sensitive to changes in our estimate of discount rate. Holding other assumptions constant, for a
100 basis point reduction in the discount rate, annual pension expense would increase by approximately $19.4 million
before taxes. Holding other assumptions constant, for a 100 basis point increase in the discount rate, annual pension
expense would decrease by approximately $19.2 million before taxes...Our pension expense is sensitive to changes in
our estimate of expected rate of return on plan assets. Holding other assumptions constant, an increase or decrease of
100 basis points in the expected rate of return on plan assets would increase or decrease annual pension expense by
approximately $7.7 million before taxes (Source: FMC TECHNOLOGIES INC, 10-K, March 01, 2007).
39
CAE Disclosure Variables :
CAE = 1 for firm-years with a CAE disclosure, zero otherwise
CAE# = number of CAE disclosures provided
Test Variables :
VEGA = natural logarithm of one plus the average dollar change (in $000s) in top-five
executives' wealth associated with a 1% change in the standard deviation of the
firm’s returns
AUDITOROPPOSITION = negative versus positive tone of the auditor's comment letter to the SEC on the
issue of CAE disclosures (see Appendix D)
ACCT_EXPERT = 1 for firm-years where the audit committee includes at least one accounting
expert, zero otherwise
Controls for Equity Incentives :
DELTA = natural logarithm of one plus the average dollar change (in $000s) in top-five
executives' wealth associated with a 1% change in the firm’s stock price
EQUITYPAY = natural logarithm of one plus the average dollar value (in $000s) of annual equity-
based compensation to the top-five executives, where equity-based compensation
equals the fair value of option grants plus the fair value of restricted stock awards
Controls for Governance Qualities :
LEGAL_EXPERT = 1 for firm-years where the audit committee includes at least one legal expert, zero
otherwise
AC_SIZE = number of audit committee members
AC_TENURE = number of years the audit committee members have served as directors
AC_MEET = number of audit committee meetings held
BD_SIZE = number of board of director members
BD_IND = percent of independent board of director members
DUAL = 1 for firm-years with a CEO that also serves as the chairperson of the board of
directors, zero otherwise.
Controls for Firm Characteristics :
ROA = the ratio of earnings before extraordinary items to total assets at year-end
BTM = ratio of book value of equity to market value of equity at year-end
SIZE = natural logarithm of year-end total assets
LEVERAGE = ratio of long-term debt to total assets at year-end
ACCRUALS = total accruals measured as earnings before extraordinary items less operating
cash flows taken directly from the statement of cash flows (adjusted for the cash
portion of discontinued operations and extraordinary items), scaled by total assets
ICW = 1 for firm-years with a material weakness under SOX Section 302 or SOX
Section 404, zero otherwise
SALESVOL = standard deviation of SALE t, SALE t-1, SALE t-2, where SALE is sales revenue
scaled by total assets
OPERVOL standard deviation of CFO t, CFO t-1, CFO t -2, where CFO is operating cash
flows scaled by total assets
LOSS = 1 for firm-years with negative ROA , zero otherwise
COMPLEXITY = operating complexity, defined as the natural logarithm of a firm's total geographic
and business segments
LITIGATION = probability of litigation estimated using the coefficients from the litigation risk
model in Table 7, Model (2) of Kim and Skinner (2012)
COVERAGE = natural logarithm of the number of analysts issuing a forecast for the firm
INSTOWN = percentage ownership by institutional investors
PENSION = 1 for firm-years with a positive projected benefit obligation, zero otherwise
Appendix C
Variable Definitions
40
Appendix D
Defining Auditor Opposition from Comment Letters
Following Li (2008), the Linguistic Inquiry and Word Count (LIWC) is used to measure the
negative versus positive emotion (NvsP) of each auditor’s comment letter. LIWC is a text
analysis software program developed by James W. Pennebaker, Roger J. Booth, and Martha E.
Francis. The software categorizes dictionary words into particular domains (e.g. positive emotion
words or negative emotion words). The LIWC dictionary contains nearly 4,500 words
categorizes 406 words as positive emotion words (e.g. “love,” “nice,” and “sweet”) and 499
words as negative emotion words (e.g. “hurt,” “ugly,” and “nasty”). See
http://www.liwc.net/tryonline.php.
For the portion of the each auditor's comment letter that discusses CAE,
AUDITOROPPOSITION is calculated as follows:
AUDITOROPPOSITION ( = NvsP) = ln((1 + Negemo)/(1 + Posemo))
where Negemo is the percentage of negative emotion words and Posemo is the percentage of
positive emotion words. The following table reports the AUDITOROPPOSITION score for each
auditor:
LIWC dimension Auditor 1 Auditor 2 Auditor 3 Auditor 4
Positive emotions 1.57 1.96 3.65 2.17
Negative emotions 0.70 1.61 1.42 2.39
Word Count 572.00 1739.00 493.00 461.00
AUDITOROPPOSITION = NvsP -0.413 -0.126 -0.653 0.067
41
Panel A: Sample
Firms Firm-years
460 4,140
(72) (648)
(40) (360)
(3) (27)
(6) (54)
Less: Firm-years without necessary governance data from Morningstar (2) (470)
Less: Firm-years without an auditor comment letter (3) (26)
Less: Firm-years without necessary Execucomp, Compustat, or I/B/E/S data (17) (257)
Sample used in determinants of CAE disclosures analysis 317 2,298
Panel B: CAE Disclosure Frequency by Year
Panel C: Descriptive Statistics for the Number of CAEs Disclosed
Year Disclosing Firms Mean Q1 Median Q3
2003 75 2.40 2.00 2.00 3.00
2004 128 2.36 2.00 2.00 3.00
2005 144 2.32 2.00 2.00 3.00
2006 139 2.26 2.00 2.00 3.00
2007 163 2.29 2.00 2.00 2.00
2008 172 2.33 2.00 2.00 3.00
2009 175 2.30 2.00 2.00 3.00
2010 175 2.48 2.00 2.00 3.00
1,171 2.34 2.00 2.00 3.00
Panel A outlines the sample. The initial sample of firms consists of firms on the S&P 500 for 2004 and includes years 2003-2010
for these firms. The initial sample of firms was identified from Compustat ExecuComp as firms with an SPCODE equal to 'SP' for
2004. Panel B reports the CAE disclosure rate by year. Panel C reports descriptive statistics for the number of CAEs disclosed.
Initial sample of S&P 500 firms identified from Execucomp in 2004
Table 1
Sample Selection and CAE Disclosure Data
Less: Firms acquired during 2002-2010
Less: Non-classifiable firms (four digit SIC code: 9900-9999)
Less: Utilities firms (four-digit SIC code: 4900-4949)
Less: Financial services firms (four-digit SIC code: 6000-6999)
2010
2009
2008
2007
2006
2005
2004
2003
Year
2,298
301
300
310
305
277
290
282
233
Firms Disclosure Rate
Number of CAEs Disclosed
50.18%
49.66%
45.39%
32.19%
50.96%
58.14%
58.33%
55.48%
53.44%
42
Panel A: Descriptive Statistics
Variable N Mean Std Dev Q1 Median Q3
CAE Disclosure Variables :
CAE 2,298 0.510 0.500 0.000 1.000 1.000
CAE# 2,298 1.192 1.485 0.000 1.000 2.000
Test Variables :
VEGA 2,298 4.403 1.305 3.859 4.597 5.234
AUDITOROPPOSITION 2,298 -0.256 0.265 -0.413 -0.413 0.067
ACCT_EXPERT 2,298 0.651 0.477 0.000 1.000 1.000
Controls for Equity Incentives :
DELTA 2,298 5.591 1.193 4.875 5.543 6.271
EQUITYPAY 2,298 6.107 2.608 6.095 6.930 7.620
Controls for Board Characteristics :
LEGAL_EXPERT 2,298 0.211 0.408 0.000 0.000 0.000
AC_SIZE 2,298 4.197 1.085 3.000 4.000 5.000
AC_TENURE 2,298 30.674 16.076 19.000 28.000 39.000
AC_MEET 2,298 9.054 3.118 7.000 9.000 11.000
BD_SIZE 2,298 10.447 2.137 9.000 10.000 12.000
BD_IND 2,298 0.880 0.071 0.857 0.900 0.917
DUAL 2,298 0.472 0.499 0.000 0.000 1.000
Controls for Other Firm Attributes :
ROA 2,298 0.074 0.077 0.043 0.075 0.112
BTM 2,298 0.366 0.253 0.208 0.316 0.482
SIZE 2,298 8.925 1.263 8.029 8.809 9.717
LEVERAGE 2,298 0.214 0.157 0.101 0.200 0.301
ACCRUALS 2,298 -0.057 0.066 -0.078 -0.049 -0.026
ICW 2,298 0.022 0.147 0.000 0.000 0.000
SALESVOL 2,298 0.096 0.108 0.034 0.066 0.117
OPERVOL 2,298 0.029 0.025 0.012 0.022 0.037
LOSS 2,298 0.073 0.260 0.000 0.000 0.000
COMPLEXITY 2,298 2.725 0.730 2.303 2.833 3.219
LITIGATION 2,298 0.576 0.365 0.197 0.611 0.979
COVERAGE 2,298 2.671 0.511 2.398 2.708 3.045
INSTOWN 2,298 0.769 0.166 0.689 0.785 0.863
PENSION 2,298 0.681 0.466 0.000 1.000 1.000
Table 2
Descriptive Statistics
43
Panel B: Differences in Variable Means and Medians
Variable Mean Median Mean Median Mean Median
Test Variables :
VEGA 4.422 4.647 4.384 4.542 0.487 0.046
AUDITOROPPOSITION -0.290 -0.413 -0.219 -0.126 <.0001 <.0001
ACCT_EXPERT 0.687 1.000 0.615 1.000 0.000 <.001
Controls for Equity Incentives :
DELTA 5.486 5.454 5.701 5.646 <.0001 <.0001
EQUITYPAY 6.129 6.915 6.085 6.951 0.685 0.384
Controls for Board Characteristics :
LEGAL_EXPERT 0.239 0.000 0.183 0.000 0.001 0.001
AC_SIZE 4.494 4.000 3.887 4.000 <.0001 <.0001
AC_TENURE 31.620 30.000 29.692 27.000 0.004 <.0001
AC_MEET 9.045 9.000 9.062 9.000 0.897 0.398
BD_SIZE 11.014 11.000 9.858 10.000 <.0001 <.0001
BD_IND 0.895 0.900 0.864 0.889 <.0001 <.0001
DUAL 0.518 1.000 0.424 0.000 <.0001 <.0001
Controls for Other Firm Attributes :
ROA 0.069 0.070 0.078 0.082 0.006 <.0001
BTM 0.371 0.339 0.361 0.303 0.364 0.034
SIZE 9.275 9.165 8.560 8.518 <.0001 <.0001
LEVERAGE 0.249 0.233 0.179 0.153 <.0001 <.0001
ACCRUALS -0.052 -0.046 -0.062 -0.051 0.000 <.0001
ICW 0.022 0.000 0.022 0.000 0.997 0.997
SALESVOL 0.099 0.066 0.094 0.066 0.243 0.700
OPERVOL 0.026 0.020 0.032 0.024 <.0001 <.0001
LOSS 0.064 0.000 0.082 0.000 0.105 0.105
COMPLEXITY 2.779 2.944 2.668 2.773 0.000 <.0001
LITIGATION 0.573 0.600 0.579 0.614 0.695 0.597
COVERAGE 2.578 2.639 2.767 2.833 <.0001 <.0001
INSTOWN 0.772 0.782 0.766 0.786 0.327 0.738
PENSION 0.899 1.000 0.455 0.000 <.0001 <.0001
Table 2 Continued
Descriptive Statistics
Panel A reports the descriptive statistics for variables used in the determinants of CAE disclosures analysis. Panel B reports p-
values for tests of differences in means and medians between disclosing and non-disclosing firm-years. A t-test is used to test
differences in means and a Wilcoxon rank-sum test is used to test differences in medians. All variables are defined in Appendix
C.
P-values for Differences(n = 1,171) (n = 1,127)
CAE = 0CAE = 1
44
CA
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CA
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CAE 1.000 0.934 0.042 -0.134 0.075 -0.090 -0.018 0.069 0.301 0.081 0.018 0.273 0.244 0.094 -0.117 0.044 0.273 0.270 0.086 0.000 0.008 -0.116 -0.034 0.113 -0.011 -0.194 -0.007 0.476
CAE# 0.788 1.000 0.034 -0.151 0.089 -0.103 -0.010 0.042 0.314 0.093 0.019 0.267 0.237 0.117 -0.130 0.051 0.280 0.270 0.066 0.000 0.003 -0.088 -0.027 0.074 0.001 -0.167 0.007 0.464
VEGA 0.015 0.027 1.000 -0.025 -0.057 0.596 0.616 -0.034 0.065 0.043 0.041 0.255 0.171 0.008 0.163 -0.137 0.351 -0.086 0.016 0.006 -0.101 -0.153 -0.139 0.051 0.262 0.347 -0.190 0.069
AUDITOROPPOSITION -0.134 -0.144 -0.019 1.000 -0.026 0.058 -0.014 -0.029 -0.045 0.059 0.081 0.075 -0.001 -0.017 0.027 0.008 -0.032 -0.015 -0.030 -0.040 -0.007 0.043 0.000 -0.032 0.031 0.089 -0.041 -0.085
ACCT_EXPERT 0.075 0.072 -0.038 -0.027 1.000 -0.122 -0.116 0.070 0.007 -0.109 0.074 0.001 0.048 -0.017 -0.027 0.068 -0.096 -0.008 -0.026 -0.020 0.089 0.027 0.046 0.012 0.052 -0.071 0.122 -0.085
DELTA -0.090 -0.090 0.508 0.056 -0.088 1.000 0.440 0.023 -0.068 0.016 0.000 0.099 -0.019 0.014 0.243 -0.226 0.227 -0.181 0.011 -0.016 -0.079 -0.096 -0.175 -0.036 0.105 0.357 -0.217 -0.127
EQUITYPAY 0.008 0.025 0.539 0.009 -0.097 0.246 1.000 -0.032 0.024 -0.052 0.038 0.123 0.093 -0.010 0.108 -0.118 0.214 -0.093 0.024 0.043 -0.046 -0.067 -0.063 0.036 0.148 0.276 -0.099 0.029
LEGAL_EXPERT 0.069 0.003 -0.055 -0.031 0.070 0.027 -0.057 1.000 0.158 0.139 -0.028 -0.006 -0.018 0.019 -0.081 0.062 0.011 0.012 -0.029 0.016 -0.006 0.021 0.011 -0.015 0.026 0.001 -0.001 0.016
AC_SIZE 0.280 0.252 0.052 -0.046 0.016 -0.067 0.040 0.175 1.000 0.452 -0.083 0.399 0.241 0.124 -0.095 0.072 0.364 0.194 0.017 -0.007 -0.010 -0.050 0.015 0.111 0.107 -0.051 -0.099 0.317
AC_TENURE 0.060 0.045 0.047 0.079 -0.125 0.037 -0.051 0.139 0.445 1.000 -0.205 0.136 0.019 0.079 0.086 -0.009 0.136 0.006 0.029 -0.034 -0.024 -0.019 -0.051 0.081 0.025 0.009 -0.101 0.102
AC_MEET -0.003 0.010 0.046 0.078 0.071 0.050 0.035 -0.025 -0.092 -0.184 1.000 0.014 0.051 -0.107 -0.055 0.046 -0.003 -0.022 -0.048 0.063 -0.005 0.082 0.057 0.035 0.154 0.101 -0.012 -0.087
BD_SIZE 0.270 0.226 0.220 0.068 0.001 0.097 0.069 -0.001 0.351 0.119 0.006 1.000 0.376 0.000 -0.065 0.060 0.449 0.147 0.069 -0.017 -0.051 -0.164 -0.057 0.079 0.160 -0.009 -0.282 0.302
BD_IND 0.220 0.182 0.104 -0.040 0.065 -0.116 0.085 0.001 0.161 -0.035 0.059 0.195 1.000 -0.090 -0.075 0.034 0.256 0.213 0.064 0.020 -0.106 -0.096 -0.024 0.070 0.056 -0.029 -0.095 0.282
DUAL 0.094 0.110 -0.019 -0.018 -0.017 0.012 -0.013 0.019 0.106 0.071 -0.103 -0.005 -0.093 1.000 0.014 0.033 0.120 0.069 -0.006 -0.042 0.031 -0.013 -0.080 -0.056 0.011 -0.021 0.052 0.096
ROA -0.058 -0.065 0.146 -0.014 -0.026 0.243 0.053 -0.044 -0.025 0.094 -0.071 -0.058 -0.069 0.045 1.000 -0.529 -0.071 -0.339 0.206 -0.099 -0.052 0.064 -0.450 -0.020 -0.093 0.206 -0.082 -0.148
BTM 0.019 -0.001 -0.122 0.009 0.071 -0.200 -0.116 0.053 0.055 -0.031 0.028 0.036 0.027 0.041 -0.406 1.000 0.142 0.034 -0.015 -0.003 0.070 -0.072 0.153 0.065 0.143 -0.112 0.094 0.101
SIZE 0.283 0.284 0.299 -0.032 -0.087 0.224 0.068 0.012 0.315 0.125 0.019 0.453 0.189 0.112 -0.040 0.117 1.000 0.106 0.100 -0.034 0.071 -0.187 -0.073 0.075 0.482 0.243 -0.322 0.264
LEVERAGE 0.223 0.214 -0.078 -0.007 -0.017 -0.170 -0.016 -0.005 0.131 -0.012 -0.043 0.105 0.162 0.050 -0.228 -0.050 0.045 1.000 0.016 -0.031 -0.056 -0.104 0.143 -0.102 -0.100 -0.292 -0.017 0.265
ACCRUALS 0.074 0.044 0.045 -0.030 -0.024 0.077 0.040 -0.029 0.020 0.032 -0.045 0.068 0.047 0.016 0.576 -0.112 0.078 -0.028 1.000 -0.034 -0.085 -0.100 -0.303 0.094 -0.150 -0.136 -0.064 0.158
ICW 0.000 -0.006 0.022 -0.040 -0.020 -0.011 0.045 0.016 -0.014 -0.038 0.082 -0.014 0.034 -0.042 -0.070 0.000 -0.031 -0.033 -0.012 1.000 -0.044 0.042 0.083 0.008 -0.027 -0.041 0.048 -0.005
SALESVOL 0.024 0.019 -0.102 0.015 0.076 -0.066 -0.064 -0.005 0.011 -0.025 0.014 -0.005 -0.075 0.002 -0.075 0.123 0.095 -0.012 -0.050 -0.014 1.000 0.384 0.102 -0.077 0.098 -0.023 0.094 -0.032
OPERVOL -0.123 -0.078 -0.142 0.052 0.038 -0.090 -0.078 0.035 -0.017 -0.001 0.051 -0.155 -0.078 -0.046 0.023 -0.052 -0.191 0.008 -0.114 0.015 0.308 1.000 0.105 -0.043 0.087 0.020 0.119 -0.129
LOSS -0.034 -0.014 -0.124 -0.002 0.046 -0.197 -0.047 0.011 0.008 -0.052 0.062 -0.045 -0.001 -0.080 -0.607 0.176 -0.076 0.150 -0.460 0.083 0.086 0.109 1.000 0.010 0.175 -0.073 0.063 -0.010
COMPLEXITY 0.076 0.041 0.017 -0.056 0.029 -0.040 0.011 -0.023 0.095 0.036 0.032 0.059 0.047 -0.047 -0.013 0.045 0.030 -0.108 0.053 0.016 -0.057 -0.019 0.004 1.000 0.055 -0.016 -0.069 0.251
LITIGATION -0.008 0.035 0.194 0.025 0.047 0.080 0.014 0.029 0.114 0.050 0.134 0.145 0.009 0.009 -0.129 0.151 0.454 -0.068 -0.156 -0.028 0.061 0.130 0.175 0.015 1.000 0.468 -0.166 -0.129
COVERAGE -0.186 -0.092 0.316 0.108 -0.072 0.332 0.139 -0.013 -0.024 0.031 0.109 -0.006 -0.049 -0.013 0.162 -0.086 0.262 -0.236 -0.065 -0.041 -0.031 0.012 -0.077 -0.036 0.410 1.000 -0.149 -0.265
INSTOWN 0.020 0.030 -0.105 -0.062 0.105 -0.208 -0.022 0.002 -0.057 -0.139 -0.016 -0.286 -0.028 0.049 -0.040 0.069 -0.259 0.000 -0.063 0.042 0.050 0.088 0.053 -0.025 -0.103 -0.129 1.000 -0.031
PENSION 0.476 0.369 0.067 -0.084 -0.085 -0.151 0.065 0.016 0.285 0.088 -0.098 0.293 0.258 0.096 -0.085 0.049 0.254 0.215 0.120 -0.005 -0.028 -0.141 -0.010 0.208 -0.122 -0.240 0.031 1.000
This table provides the Pearson (below diagonal) and Spearman (above diagonal) correlation coefficients for variables used in the determinants of CAE disclosures analysis. Correlations in bold are significant at a level of 10%.
Table 3
Correlations
45
Prediction Estimate p-value
Intercept ? -8.268 0.004
Test Variables :
VEGA - -0.198 0.045
AUDITOROPPOSITION - -1.160 0.015
ACCT_EXPERT + 0.553 0.012
Controls for Equity Incentives :
DELTA ? 0.040 0.714
EQUITYPAY ? 0.043 0.292
Controls for Board Characteristics :
LEGAL_EXPERT + 0.279 0.192
AC_SIZE + 0.192 0.058
AC_TENURE + 0.000 0.494
AC_MEET + 0.064 0.031
BD_SIZE + 0.091 0.072
BD_IND + 1.840 0.140
DUAL - 0.121 0.300
Controls for Other Firm Attributes :
ROA + -0.578 0.388
BTM ? -0.894 0.066
SIZE + 0.418 0.009
LEVERAGE + 1.747 0.025
ACCRUALS + -0.398 0.415
ICW + 0.344 0.265
SALESVOL + 0.891 0.168
OPERVOL + -3.697 0.183
LOSS ? -0.512 0.112
COMPLEXITY + 0.214 0.137
LITIGATION - -0.476 0.163
COVERAGE ? -0.551 0.039
INSTOWN ? 0.234 0.790
PENSION + 1.908 <.0001
N
Likelihood Ratio, χ2
p-value
Table 4
Determinants of CAE Disclosures
2,298
1127.41
<.0001
This table reports the coefficients and p-values from a logistic regression where the dependent variable
is CAE . Year and industry (at the 2-digit SIC level) fixed effects are included but not tabulated. P-values
are based on one-tailed tests where a prediction is made (two-tailed otherwise) using Huber/White
robust standard errors with within-firm clustering.
46
Prediction Change in probability
Test Variables :
VEGA - -6.77%
AUDITOROPPOSITION - -13.84%
ACCT_EXPERT + 13.73%
Controls for Equity Incentives :
DELTA ? N/A
EQUITYPAY ? N/A
Controls for Board Characteristics :
LEGAL_EXPERT + N/A
AC_SIZE + 9.57%
AC_TENURE + N/A
AC_MEET + 6.37%
BD_SIZE + 6.82%
BD_IND + N/A
DUAL - N/A
Controls for Other Firm Attributes :
ROA + N/A
BTM ? -6.08%
SIZE + 17.43%
LEVERAGE + 8.68%
ACCRUALS + N/A
ICW + N/A
SALESVOL + N/A
OPERVOL + N/A
LOSS ? N/A
COMPLEXITY + N/A
LITIGATION - N/A
COVERAGE ? -8.88%
INSTOWN ? N/A
PENSION + 43.83%
Table 5
Using Table 4 results to assess changes in the probability of a CAE disclosure for
selected changes in independent variables
This table reports the change in probability of a CAE disclosure as a result of selected changes in the
value of the variable of interest holding other independent variables at their mean values. Non-
dichotomous variables are changed from the first to the third quartile. Dichotomous variables are
changed from zero to 1. We calculate the change in the probability using the following expression: eβ’X
/ (1 + eβ’X), where β refers to the vector of coefficients from Table 4 and X refers to the vector of
independent variables.
47
Prediction Estimate p-value
Test Variables :
VEGA - -0.160 0.034
AUDITOROPPOSITION - -0.815 0.037
ACCT_EXPERT + 0.528 0.006
Controls for Equity Incentives :
DELTA ? -0.015 0.871
EQUITYPAY ? 0.031 0.387
Controls for Board Characteristics :
LEGAL_EXPERT + 0.077 0.371
AC_SIZE + 0.171 0.023
AC_TENURE + 0.001 0.465
AC_MEET + 0.052 0.028
BD_SIZE + 0.070 0.076
BD_IND + 1.319 0.168
DUAL - 0.209 0.119
Controls for Other Firm Attributes :
ROA + -0.966 0.293
BTM ? -0.686 0.048
SIZE + 0.402 0.003
LEVERAGE + 2.097 0.004
ACCRUALS + 0.390 0.401
ICW + 0.177 0.329
SALESVOL + 0.477 0.239
OPERVOL + 0.009 0.499
LOSS ? -0.168 0.512
COMPLEXITY + 0.092 0.253
LITIGATION - -0.656 0.042
COVERAGE ? -0.226 0.292
INSTOWN ? 0.790 0.296
PENSION + 2.000 <.0001
N
Likelihood Ratio, χ2
p-value <.0001
This table reports the coefficients and p-values from an ordered logistic regression where the dependent
variable is CAE#. The intercepts are not tabulated. Year and industry (at the 2-digit SIC level) fixed
effects are included but not tabulated. P-values are based on one-tailed tests where a prediction is made
(two-tailed otherwise) using Huber/White robust standard errors with within-firm clustering.
Table 6
Determinants of the Number of CAEs Disclosed
2,298
1246.27
48
Prediction Estimate p-value Estimate p-value
Intercept ? -1.287 0.210 -3.620 <.0001
CAE - -0.147 0.026 -0.175 0.003
DELTA + 0.389 <.0001 0.428 <.0001
CASHCOMP ? 0.075 0.360 0.100 0.225
SALE + 0.245 0.006 0.378 <.0001
BTM - 0.459 0.008 0.530 0.005
LEVERAGE + 0.586 0.041 0.893 0.003
R&D + 2.339 0.013 2.605 0.004
CAPEX - -0.273 0.372 -1.489 0.061
RETURNVOL + -0.755 0.109 0.942 0.010
POSTSOX ? 0.573 <.0001
POST123(R) - -0.276 <.0001
Year Fixed-Effects
Firm Fixed-Effects
N
Adjusted R2
Table 7
Effect of CAE Disclosures on Vega
Included Not Included
Included Included
2,838 2,838
70.88% 68.64%
This table reports the coefficients and p-values from a firm fixed-effect regression of VEGA on the
lagged CAE disclosure variable during the pre- and post-disclosure period (1995-2010). For this
expanded sample period, the model is estimated for a sample of only those firms that provide a CAE
disclosure at some point during the post-disclosure period. P-values are based on one-tailed tests
where a prediction is made (two-tailed otherwise) using Huber/White robust standard errors with
within-firm clustering.
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