Review of Accounting Studies
https://doi.org/10.1007/s11142-021-09650-4
Why are expanded audit reports not informative
to investors? Evidence from the United Kingdom
Clive S. Lennox1 · Jaime J. Schmidt2
· Anne M. Thompson3
Accepted: 13 September 2021
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature
2021
Abstract
Standard-setters worldwide have passed new audit reporting requirements aimed
at making audit reports more informative to investors. In the UK, the new standard expands the audit reporting model by requiring auditors to disclose the risks of
material misstatement (RMMs) that had the greatest effect on the financial statement
audit. Using short window tests, prior research indicates that these disclosures are
not incrementally informative to investors (Gutierrez et al. in Review of Accounting Studies 23:1543–1587, 2018). In this study, we investigate three potential explanations for why investors do not find the additional auditor risk disclosures to be
informative. First, using long-window tests, we find no evidence that the insignificant short-window market reactions are due to a delayed investor reaction to RMMs.
Second, using value relevance tests, we show that the insignificant market reactions are not due to auditors disclosing irrelevant information. Finally, we provide
evidence suggesting that RMMs lack information content because investors were
already informed about the financial reporting risks before auditors began disclosing
them in expanded audit reports.
Keywords Audit reporting model · Information content · Value relevance
JEL classification M41 · M42 · M48
* Jaime J. Schmidt
jaime.schmidt@mccombs.utexas.edu
1
University of Southern California, Los Angeles, CA, USA
2
McCombs School of Business, The University of Texas at Austin, Austin, TX 78712, USA
3
University of Illinois at Urbana-Champaign, Champaign, IL, USA
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C. S. Lennox et al.
1 Introduction
The audit report communicates to investors the auditor’s opinion as to whether a
company’s financial statements are fairly presented. Historically, the audit report
has been criticized for being uninformative due to its standardized wording and
pass/fail nature (i.e., the auditor issues either an unqualified [pass] or qualified
[fail] opinion). To address this criticism, standard-setters and regulators worldwide have implemented an expanded model of audit reporting (European Commission 2010; Financial Reporting Council 2013; IAASB 2015; PCAOB 2017).
The new audit reporting framework requires auditors to disclose the matters (e.g.,
financial statement areas or risks of material misstatement) that had the greatest
effect on the overall audit strategy or that involved especially challenging auditor judgments (Financial Reporting Council 2013, PCAOB 2017). The expanded
audit reports were intended to provide investors with more information about the
audit and accompanying financial statements.
The primary objective of the new risk disclosures was to convey useful information to investors. However, prior research finds the disclosures are not incrementally informative (Gutierrez et al. 2018, 2021; Files and Gencer 2020; Liao
et al. 2019; Burke et al. 2020). In this study, we investigate why investors do not
find the disclosures to be informative. One possible explanation is that the risk
disclosures contain useful information but investors do not immediately appreciate and respond to it. A second possibility is that the disclosures fail to reflect
value relevant information. For example, any significant risks of material misstatement should be addressed and mitigated during the audit through substantive audit procedures. Therefore, the risk disclosures may not be relevant to the
audited financial statements even if they were relevant when the auditor was planning the audit. A third possibility is that auditors are disclosing risks that were
already known to investors. In other words, investors could have already had an
adequate understanding of the financial reporting risks from earlier disclosures
such as annual reports, conference calls, or earnings announcements. We explore
each possibility and find no evidence that investors were slow to respond to the
risk disclosures or that the risk disclosures lacked value relevance. Instead, we
find that RMMs lack incremental information because investors already knew
about most of the risks before auditors started disclosing them in the expanded
audit reports.
In the United Kingdom (UK), the expanded model of audit reporting applies
to companies with a premium listing on the London Stock Exchange and became
effective for fiscal years ending on or after September 30, 2013. The new audit
reporting standard requires auditors to disclose the risks of material misstatement
(RMMs) that had the greatest effect on the overall audit strategy, allocation of
resources during the audit, and efforts of the audit engagement team (FRC 2013).
We begin by expanding upon tests in prior research that conclude that investors
did not find RMMs to be incrementally informative (Gutierrez et al. 2018). Specifically, we examine whether the number of RMMs is associated with a company’s signed or unsigned 3-day abnormal market return, abnormal 3-day trading
13
Why are expanded audit reports not informative to investors?…
volume, and/or abnormal 3-day stock return volatility.We find no evidence of an
investor reaction using any of these measures. We continue to find insignificant
investor reactions when we partition RMMs into those that are expected to be
more rather than less informative; i.e., (1) entity versus account-level risks, (2)
unexpected versus expected risks, (3) company-specific versus industry-common
risks, (4) companies in weaker versus stronger information environments, and (5)
risks expressed in more negative versus less negative language. We also examine whether clients view RMMs as informative by examining whether RMM
disclosures affected companies’ auditor appointment decisions. Prior research
documents that companies are more likely to change auditors after receiving an
unfavorable audit opinion (Lennox 2000; Chen et al. 2016; Newton et al. 2016).
Therefore, we would interpret a positive association between RMMs and auditor
turnover as indicating that companies view RMMs as conveying negative news.
We find no association between auditor turnover and the number of RMMs disclosed by the auditor, which is consistent with the previous tests suggesting that
RMMs are not incrementally informative.
Next, we investigate three possible explanations for why investors find the RMM
disclosures to be uninformative. First, we investigate whether the insignificant short
window (i.e., 3-day) market reactions are due to investors failing to respond immediately to the information in the RMMs. We examine long-window abnormal returns
over event horizons of 1 month, 3 months, 6 months, and 12 months. Across all
specifications, we find that RMMs are not associated with long-window abnormal
returns. Thus, we do not find evidence of a delayed reaction by investors.
Second, we examine whether the new disclosures are uninformative because
RMMs do not reflect value relevant information. Auditors identify financial reporting risks at the start of the audit during the audit planning phase and then mitigate
these risks with substantive procedures during the course of the audit. At the end of
the audit, the financial statements should be free from material misstatement and the
risks that were identified during the planning phase of the audit should be mitigated.
Consequently, the risks that auditors report as RMMs at the conclusion of the audit
may not be value relevant at the time of the audited financial statements even if they
were relevant to the pre-audit financial statements.
In contrast, there are several reasons why RMMs could be value relevant even
if they are not incrementally informative. One possibility is that RMM disclosures
reflect the unreliability of the accounting information. For example, a less reliable
earnings number will receive a lower weight in valuation if it reflects uncertainty in
the firm’s ability to generate future cash flows (Holthausen and Verrecchia 1988).
In addition, the expanded audit disclosures may directly correlate with future cash
flows (e.g., an RMM pertaining to potential goodwill impairments may signal lower
future cash flows). Moreover, the RMM disclosures may reflect risks that negatively
impact the firm’s stock price through a higher discount rate. Thus, if RMM disclosures are value relevant for any of the above reasons, we would expect investors to
attach lower values to the audited values of earnings and net assets when auditors
disclose more risks of material misstatement.
We estimate value relevance models (Barth 1991; Barth et al. 2001) and find the
coefficients on earnings per share and net assets per share are significantly smaller
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C. S. Lennox et al.
when auditors disclose more RMMs. These results suggest that RMM disclosures
are indeed value relevant. Further, we document that company performance (as
measured by ROA and operating cash flows) is less persistent among companies
with more RMMs. Together, these findings suggest that the insignificant investor reactions are not attributable to auditors reporting RMMs that are irrelevant to
investors.
Finally, we investigate whether the RMMs lack incremental information content
because investors already knew about the risks prior to auditors disclosing them.
Similar to Dodd et al. (1984), we argue that investors could have become informed
about such risks from earlier disclosures such as prior annual reports, conference
calls, or earnings announcements. We perform two analyses to determine whether
investors were already informed. First, we estimate value relevance models using
data from the year prior to the expanded audit reports (i.e., year t-1) to see if the
information had already been priced by investors in the year prior to disclosure. We
find the valuation coefficients on earnings per share and net assets per share in year
t-1 are significantly smaller for the companies whose auditors disclose more risks
of material misstatement in year t. These results suggest that investors already knew
which companies were more (or less) risky before auditors started disclosing RMMs
in the expanded audit reports.
Second, we search for direct evidence that the specific risks mentioned by auditors had already been disclosed. For the RMMs disclosed in the first year of adoption, we search the year t-1 annual reports, prior earnings announcements, and prior
conference calls to determine whether the risks disclosed by auditors in year t had
previously been disclosed by companies. We undertake this analysis for half the
sample companies due to the high costs of manually searching these multiple information channels. We find that approximately 65% of the risks disclosed by auditors
in the first year of adoption had already been disclosed by the company in the prior
year. In the second year of adoption, 73% of the RMMs were reported by the auditor
in the prior year audit report. These findings suggest that most RMMs had already
been disclosed to investors.
Next, we re-perform our market reaction tests after separating auditors’ risk disclosures into “already disclosed” risks and “new” risks (i.e., risks for which we
could not find evidence of a prior disclosure). As expected, we find insignificant
market reactions to the “already disclosed” RMM disclosures.Perhaps surprisingly,
we also find insignificant market reactions to the “new” RMM disclosures. The lack
of significance could reflect that investors already knew about these risks through
other channels, such as the financial press or analyst research reports, that were not
included in the sources we examined. It is also possible that investors find it difficult to extract new information because the majority of RMMs for each company
were “already disclosed.” Another possibility is that the already disclosed risks were
more important to investors than the new risks. In other words, companies could
have disclosed the most important risks, leaving only the less important risks undisclosed until the audit report date.
Our results contribute to a growing literature on the effects of expanded audit
reporting. Consistent with prior research (e.g., Gutierrez et al. 2018, 2021; Liao
et al. 2019; PCAOB 2020), our study shows that investors do not regard auditors’
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Why are expanded audit reports not informative to investors?…
risk disclosures as incrementally informative. More importantly, our study examines
alternative explanations for why the expanded reports are not incrementally informative. We rule out the potential explanation that RMM disclosures contain useful
information that is not immediately appreciated by investors. We also rule out the
potential explanation that RMM disclosures lack information content because they
are not value relevant. Instead, our findings suggest that RMMs lack incremental
information because investors already knew about the vast majority of risks before
auditors started disclosing them in the expanded audit reports. Thus, our study complements prior research documenting that investors may learn about and react to
company risks in advance of auditors disclosing them in audit reports (e.g. Chow
and Rice 1982; Elliott 1982; Dodd et al. 1984; Czerney et al. 2018).
Our findings should assist the Financial Reporting Council and other standard
setters with their post-implementation review of the new audit reporting standard.
While there may be other benefits to the new reporting standard, such as improved
management disclosures (Burke et al. 2020; Andreicovici et al. 2020), we find no
evidence that the expanded audit disclosures are incrementally informative for investors. However, we acknowledge that the existing auditor reporting requirements may
only be a first step toward enhanced auditor reporting (Minutti-Meza 2020) and that
the full benefit of enhanced auditor communications may take more time to materialize (PCAOB 2020).
2 The new audit reporting standard and prior literature
2.1 The new audit reporting standard
The audit report conveys the auditor’s opinion about whether the financial statements present fairly, in all material respects, the financial position of the company.
An audit report with an unqualified opinion provides investors with reasonable
assurance that the financial statements are free from material misstatement, whereas
an audit report with an “except for” or “adverse” opinion states that one or more
material misstatements exist. Prior research shows that the vast majority of public companies receive unqualified opinions rather than qualified opinions or opinion disclaimers. For example, Lennox (2005) examines a sample of 28,292 audit
opinions issued between 1995 and 1998 and finds that 99.8% are unqualified, 0.1%
are “except for” qualifications, 0.1% are opinion disclaimers, and none are adverse.
Because nearly all audit reports are unqualified with standardized wording (Lennox
2005; Gray et al. 2011; Church et al. 2008), concerns have been raised that the traditional model of audit reporting is uninformative. Moreover, even the small number
of audit reports with qualified (i.e., “except for” or disclaimer) opinions have not
been shown to generate significant market reactions (Dodd et al. 1984).
Auditors have access to a wealth of private information about their clients. Therefore, it is understandable that investors would want auditors to disclose more information about areas of high financial reporting and audit risks (PCAOB 2017). In
response to investor requests to make the audit report more informative, regulators
and standard setters worldwide, including the PCAOB, IAASB, FRC, and European
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C. S. Lennox et al.
Commission, have changed the audit reporting standards to require auditors to disclose more risk-related information. In the UK, the FRC’s revisions to the International Standard on Auditing (ISA) 700 became effective in September 2013 (IAASB
2013). Under the new standard, auditors must discuss the risks of material misstatement (RMMs) that had the greatest effect on the overall audit strategy, allocation
of resources during the audit, and efforts of the audit engagement team, including
how the scope of the audit was altered to mitigate the identified risks (FRC 2013).1
The purpose of such risk disclosures is to provide “potentially valuable information
that investors … have not had access to in the past” (PCAOB 2013, p. 6) in order to
“assist intended users … in understanding the entity and areas of significant management judgment in the audited financial statements” (IAASB 2015, p. 46).
2.2 Prior literature on the impact of the new audit reporting standards
Several experimental and textual analysis studies examine how expanded audit
reports might affect investors and the information available to them. Using audit
report wording that predates the PCAOB’s proposal, Christensen et al. (2014) find
that experimental participants are less willing to invest in a company when an audit
report discloses a critical audit matter as compared with the traditional pass/fail
audit report. Similarly, Kachelmeier et al. (2020) find that experimental participants
have less confidence in accounts that are identified in the audit report as being critical audit matters. In contrast, Kohler et al. (2020) find that investment professionals
are better able to assess the economic situation of a company when presented with a
key audit matter disclosure. Sirois et al. (2020) use eye tracking software to examine
how auditors’ risk disclosures affect users’ navigation of the annual report. They
find that participants devote more attention to risks that are highlighted in the audit
report. However, Boolaky and Quick (2016) find that the disclosure of key audit
matters has no effect on bank directors’ perceptions of financial reporting quality or
their credit approval decisions.
Unlike archival research, a limitation of experimental studies is that they are unable
to test whether auditors’ disclosures provide investors with new incremental information
above and beyond the information contained in other sources (e.g., annual reports, press
releases, conference calls). Gutierrez et al. (2018) use archival data from the UK to determine whether the new audit reporting standard has improved the information environment.
Their study collects data for the rule adopters (i.e., premium-listed UK companies) and
a control group of non-adopters (i.e., companies listed on the UK Alternative Investment
Market) in the 2 years before and after the new rules were adopted in September 2013
(2011–2015). They then implement a difference-in-differences research design to examine
whether the new reporting standard has a different effect on adopters relative to non-adopters. They measure market reactions using cumulative signed abnormal returns, cumulative
absolute abnormal returns, abnormal trading volume, and abnormal return volatility. They
1
The Financial Reporting Council also requires auditors to disclose materiality thresholds. Our tests
focus on the risk disclosures rather than materiality disclosures because the risk disclosures are common
to the proposed or effective revisions to audit reporting standards issued by all the relevant standardsetters and regulators (i.e., the PCAOB, IAASB, and European Commission).
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Why are expanded audit reports not informative to investors?…
find no evidence that the new audit reporting standard has enhanced the information environment for the treatment sample of adopters relative to the control sample of non-adopters
moving from the pre-adoption period to the post-adoption period. In an additional crosssectional analysis, Gutierrez et al. (2018) examine the following cross-sectional characteristics of expanded audit reports among the subsample of adopter companies: the length
of the expanded audit report, the number of disclosed risks, and auditor disclosures about
materiality. In these cross-sectional analyses, the authors continue to find little evidence of
any information content within the expanded audit reports. Gutierrez et al. (2021) examine the extension of expanded audit reports to companies listed on the UK’s Alternative
Investment Market in 2017. Despite AIM companies being smaller and more opaque than
premium-listed companies, Gutierrez et al. (2021) find that the expanded audit disclosures
contain little or no incremental information.
3 Methodology and results
We begin our analysis by expanding upon the cross-sectional tests of Gutierrez et al.
(2018) for the sample of UK adopters. We partition RMMs into several separate
categories in an attempt to isolate potentially informative RMMs from uninformative ones. First, we identify RMM disclosures that discuss risks at the entity level
versus account level because we expect entity-level risks to be more pervasive than
account-level risks (Doyle et al. 2007). Second, we identify risks that are companyspecific versus industry-common because the industry-common RMMs may be
generic and already known by investors, which might explain the lack of a market
response. Third, we use a prediction model to estimate the number of expected and
unexpected RMMs. We use the residuals from the prediction model to measure the
number of unexpected RMMs and then test whether investors react to the unexpected RMMs. Fourth, we isolate the RMM disclosures with more negative versus
less negative language. Finally, we calculate the change in the number of RMMs
between the first and second years of adoption. In all these analyses, we measure
investor responses using 3-day cumulative abnormal returns, unsigned cumulative
abnormal returns, abnormal trading volume, and abnormal stock return volatility.
3.1 Sample selection
The UK’s expanded model of audit reporting applies to companies with a premium
listing on the London Stock Exchange and with fiscal years ending on or after September 30, 2013.2 We examine the first 2 years of the new auditor reporting standard
for the premium-listed companies. We obtain their annual reports from Companies
2
The FRC announced the new audit reporting standard in June 2013, less than 4 months before the
expanded audit reports were required. The late announcement provided little time for companies or auditors to pre-empt the new standard with additional risk disclosures in the prior fiscal year. Moreover, audit
firms and companies were opposed to the expanded model of audit reporting (New York Times 2014),
which reduces the likelihood of voluntary early adoption. There is only one observation in our sample
where an auditor provides risk disclosures prior to the required implementation date; our results are
robust to dropping this company.
13
C. S. Lennox et al.
House and corporate websites and gather other accounting and market data from
FAME, Compustat Global, and Datastream.3 After dropping companies with missing data, we obtain a sample of 478 companies for the first year of adoption (year 1)
and 454 companies for the second year (year 2), giving a total of 932 company-year
observations.For each expanded audit report, we manually code each auditor risk
disclosure, the name of the auditor, and the date that the annual report (and audit
report) was released to investors.4 Table 10 in Appendix A provides extracts from
three audit reports to illustrate the various types of RMMs that auditors disclose.
3.2 Descriptive statistics
Table 1 provides descriptive statistics for the 478 companies in year 1 and the 454
companies in year 2. Panel A shows that the mean number of RMMs in year 1 (3.87)
is not significantly different from the mean number of RMMs in year 2 (3.77). Panel
B presents descriptive statistics for the dependent variables, the RMM variables, and
the control variables for the combined sample (years 1 and 2). (Variable definitions
are provided in Appendix B.) The 25th percentile value of RMM is 3.00 while the
75th percentile is 5.00, signifying cross-sectional variation in the number of RMMs
disclosed by auditors.
3.3 Do RMMs convey new information?
We begin in Eq. (1) by examining whether investors find RMMs to be incrementally
informative.
Market reaction = 𝛼 + 𝛽1 RMM + Controls + 𝜀
(1)
The market reaction dependent variables in Eq. (1) are (1) the 3-day cumulative
abnormal returns centered on company i’s annual report release date (CAR​), (2) the
absolute magnitude of the 3-day cumulative abnormal return (|CAR​|), (3) the abnormal 3-day trading volume (VOLUME), and (4) the abnormal 3-day stock return volatility (VOLATILITY). The variable of interest, RMM, equals the number of risks disclosed by the auditor in the expanded audit report.
Table 2 presents the results for the short window tests. The coefficient for the
RMM variable in Panel A is insignificant using all four dependent variables
3
Companies House is a central depository where every company incorporated in the UK is required
to file its accounts. Premium-listed companies incorporated in Jersey, Guernsey, The Isle of Man, and
Ireland are not required to file their accounts at Companies House, so we obtain their annual reports from
their websites.
4
UK companies typically release their annual reports after announcing their earnings. We obtain the
annual report release dates from the London Stock Exchange website (www.​londo​nstoc​kexch​ange.​com/​
prices-​and-​marke​ts/​marke​ts/​prices.​htm), Morningstar (www.​morni​ngstar.​co.​uk/​uk/​NSM), ADVFN.
com, and companies’ websites. In a few cases, we cannot identify the annual report release date. In these
cases, we use the announcement of the Annual General Meeting (AGM) or the date of the actual AGM,
because many companies release their annual reports at the same time that they announce or hold their
AGMs. We perform sensitivity tests to ensure that our results are robust to excluding the companies with
only AGM dates.
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Why are expanded audit reports not informative to investors?…
Table 1 Descriptive statistics
Panel A: Number of RMMs by industry and year
Year 1
Obs
Agriculture, forestry, fishing, and mining
34
Year 2
Mean RMMs
Obs
Mean RMMs
4.29
39
4.36
Manufacturing
89
3.85
92
3.66
Utilities and construction
47
3.68
46
3.50
Wholesale and retail trade
54
4.13
56
3.92
Transportation and accommodation
31
3.87
32
3.53
Information and communication
40
4.15
38
4.23
Finance, insurance, and real estate
73
3.57
72
3.44
Professional, scientific, and technical services
62
3.83
38
3.58
Other services
Total
48
3.83
41
3.92
478
3.87
454
3.77
Panel B: Descriptive statistics for the variables used in multivariate analysis
Obs
Mean
Std. Dev 25th Pctl
50th Pctl 75th Pctl
Dependent variables
P
CAR​
932
6.893
9.056
1.433
3.507
8.280
932
0.000
0.033
− 0.016
− 0.001
0.014
|CAR|
932
0.023
0.024
0.007
0.015
0.029
VOLUME
919
− 0.163
0.766
− 0.498
− 0.099
0.228
VOLATILITY
932
− 0.808
1.592
− 1.676
− 0.660
0.229
Test variables
RMM
932
3.824
1.447
3.000
4.000
5.000
RMM_ACCT​
932
3.310
1.346
2.000
3.000
4.000
RMM_ENTITY
932
0.525
0.636
0.000
0.000
1.000
RMM_COMPANY
932
3.200
1.470
2.000
3.000
4.000
RMM_INDUSTRY​
932
0.624
0.627
0.000
1.000
1.000
RMM_PRED
932
3.824
0.830
2.237
3.733
4.302
RMM_UE
932
− 0.0001
1.196
− 0.878
− 0.068
0.746
RMM_TONE
923
− 0.016
0.009
− 0.022
− 0.016
− 0.010
RMM_CHANGE
453
− 0.099
1.125
− 1.000
0.000
0.000
Control variables
E
932
0.338
0.606
0.040
0.175
0.475
BV
932
2.717
3.471
0.667
1.557
3.268
14.859
SIZE
932
13.531
2.035
12.348
13.672
BTM
932
0.644
0.903
0.224
0.415
0.799
ANALYSTS
932
11.106
8.627
4.000
9.000
17.000
SEC_REG
932
0.053
0.223
0.000
0.000
0.000
PROBLEM
932
0.033
0.179
0.000
0.000
0.000
DISTRESS
932
0.181
0.385
0.000
0.000
0.000
LN_SUBS
932
3.660
1.509
2.639
3.611
4.559
GC
932
0.087
0.282
0.000
0.000
0.000
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C. S. Lennox et al.
Table 1 (continued)
INVENTORY
932
0.079
0.117
0.000
0.021
0.115
RECEIVABLES
932
0.117
0.116
0.021
0.088
0.182
GOODWILL
932
0.130
0.158
0.000
0.065
0.216
FIXED ASSETS
932
0.346
0.362
0.034
0.231
0.568
ACQ
932
0.011
0.028
0.000
0.000
0.005
LAG_ACQ
932
0.010
0.027
0.000
0.000
0.004
SALES_GROWTH
932
0.106
0.416
− 0.033
0.037
0.127
DEF_TAX
932
0.017
0.027
0.000
0.003
0.026
EXTR_ITEMS
932
0.000
0.002
0.000
0.000
0.000
BUSY
924
0.512
0.500
0.000
1.000
1.000
AC_SIZE
924
3.539
0.930
3.000
3.000
4.000
TENURE
924
2.395
0.900
1.945
2.565
2.995
Table 1 presents descriptive statistics for our sample observations. Variable definitions are in Appendix
B. In Panel A, the mean number of RMMs in year 1 is not significantly different from the mean number
of RMMs in year 2 (t stat = 1.18)
(CAR​, |CAR​|, VOLUME, and VOLATILITY).5 Next, we perform a battery of additional tests to explore whether there is any evidence of information content within
subsets of RMM disclosures. Panel B classifies RMMs as entity-level or accountlevel risks (RMM_ACCT​ and RMM_ENTITY); Panel C classifies RMMs as idiosyncratic to the company or common to the industry (RMM_COMPANY and
RMM_INDUSTRY​); Panel D uses the number of expected and unexpected RMMs
(RMM_PRED and RMM_UE), which are estimated using an RMM prediction model
(see Table 11 in Appendix C); Panel E measures the tone of the language in the
RMMs (the RMM_TONE variable is defined in Appendix B); and Panel F measures
the change in the number of RMMs from year 1 to year 2 (RMM_CHANGE). Combining these alternative RMM measures with our four dependent variables yields a
total of 32 coefficient estimates for the RMM variables.
Table 2 shows that 30 of the 32 coefficients are statistically insignificant. The only
significant results are for RMM_PRED in the trading volume model (VOLUME) and
for the RMM_TONE variable in the abnormal volatility model (VOLATILITY). The
negative coefficient for RMM_PRED when trading volume is the dependent variable suggests that the number of predicted RMMs is associated with significantly
less trading. The positive coefficient for RMM_TONE suggests that firms with more
5
To alleviate concerns that the insignificant results may be due to a lack of statistical power, we perform
a power analysis to determine how small a market reaction we could detect with our sample. We find we
have adequate power to detect a statistically significant coefficient for RMM (when CAR​ is the dependent variable) that is greater than or equal to 0.00187, implying a market reaction (CAR​) to an additional
RMM that is greater than or equal to 0.187%. The corresponding statistic when |CAR| is the dependent
variable is an effect size that is greater than or equal to 0.136% (both using one-tailed tests and power
equal to 0.80). Because the economic magnitude of these effect sizes is so small, we believe we have
adequate power to detect an economically meaningful market reaction to RMMs, if one exists.
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Why are expanded audit reports not informative to investors?…
Table 2 Short window tests of the incremental information content of auditors’ RMM disclosures
CAR​
Panel A: All RMMs
RMM
|CAR|
Volume
Volatility
− 0.001
0.000
− 0.001
0.052
(− 1.159)
(0.875)
(− 0.048)
(1.233)
Controls
Included
Included
Included
Included
R-square
0.037
0.068
0.168
0.061
Panel B: Account-level vs entity-level RMMs
RMM_ACCT​
RMM_ENTITY
− 0.001
0.001
− 0.002
0.054
(− 0.870)
(1.391)
(− 0.099)
(1.235)
− 0.001
− 0.001
0.012
0.086
(− 0.432)
(− 0.632)
(0.313)
(0.915)
Controls
Included
Included
Included
Included
R-square
0.036
0.069
0.167
0.061
Panel C: Company-specific vs industry-level RMMs
RMM_COMPANY
RMM_INDUSTRY​
− 0.001
0.000
0.000
0.051
(− 1.165)
(0.832)
(0.009)
(1.202)
− 0.001
0.001
− 0.020
0.062
(− 0.339)
(0.553)
(− 0.472)
(0.593)
Controls
Included
Included
Included
Included
R-square
0.037
0.068
0.169
0.061
Panel D: Predicted vs unexpected RMMs
RMM_PRED
RMM_UE
− 0.005
− 0.004
− 0.444**
0.226
(− 0.650)
(− 0.896)
(− 2.389)
(0.498)
− 0.001
0.000
− 0.003
0.052
(− 1.173)
(0.844)
(− 0.182)
(1.241)
Controls
Included
Included
Included
Included
R-square
0.037
0.069
0.173
0.061
Panel E: RMM tonal language
RMM_TONE
− 0.113
0.054
2.901
12.080**
(− 1.150)
(0.966)
(1.196)
(2.021)
Controls
Included
Included
Included
Included
R-square
0.035
0.070
0.167
0.067
Panel F: Change in RMMs from year 1 to year 2
RMM_CHANGE
0.001
− 0.000
0.002
− 0.029
(0.590)
(− 0.563)
(0.075)
(− 0.439)
Controls
Included
Included
Included
Included
R-square
0.046
0.105
0.195
0.080
13
C. S. Lennox et al.
Table 2 (continued)
Table 2 presents short-window (i.e., 3-day) tests of the incremental information content of the auditors’
RMM disclosures. The 3-day window is centered on the annual report date (i.e., the date the audit report
would be disclosed to the public). Column 1 presents cumulative abnormal returns. Column 2 presents
unsigned cumulative abnormal returns. Column 3 presents abnormal trading volume. Column 4 presents
abnormal trading volatility. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels,
respectively (two-tailed). T-statistics are reported in parentheses below each coefficient. The estimated
models use robust regression to reduce the influence of outlier observations. Variable definitions are provided in Appendix B. The control variables are SIZE, BTM, ANALYSTS, SEC_REG, PROBLEM, DISTRESS, LN_SUBS, GC, INVENTORY, RECEIVABLES, GOODWILL, FIXED_ASSETS, ACQ, LAG_ACQ,
SALES_GROWTH, EXTR_ITEMS, and DEF_TAX, plus year fixed effects, auditor fixed effects, and industry fixed effects. Following prior literature, |CAR| is included as a control variable when trading volume is
the dependent variable. The sample size when CAR​ and |CAR| are the dependent variables is 932 except
when the independent variable of interest is RMM_TONE (N = 923) and RMM_CHANGE (N = 452). The
sample size when VOLUME is the dependent variable is 919 except when the independent variable is
RMM_TONE (N = 910) and RMM_CHANGE (N = 444). The sample size when Volatility is the dependent
variable is 931 except when RMM_TONE (N = 922) and RMM_CHANGE (N = 452) are the variables of
interest
negative RMM disclosures experience higher volatility. We do not draw strong
inferences from these two coefficients due to a lack of consistent results across the
four dependent variables, coupled with the fact that random chance will result in the
occasional spurious result.
Next, we perform cross-sectional tests after partitioning companies based on
the strength of the information environment. The FRC argued that the benefits of
expanded audit reports may be “particularly important for audited entities where
there are fewer sources of other information, including smaller companies” (FRC
2016, p. 4). Thus, RMMs might be informative for companies in relatively poor
information environments. To examine this possibility, we partition the sample
into (1) large versus small companies, (2) companies with high versus low analyst
following, (3) companies with large versus small shareholders, and (4) companies whose RMM disclosures contain an above (below) median number of words
expressing uncertainty. Table 3 presents these partitioned results using all four market reaction variables (CAR​ in Panel A, |CAR​| in Panel B, VOLUME in Panel C,
and VOLATILITY in Panel D). We find that only one of the 32 RMM coefficients
in Table 3 is statistically significant at the 95% confidence level. Specifically, the
signed CAR​ is significantly more negative for companies receiving a larger number of RMM disclosures within the subsample of companies that receive an aboveaverage number of uncertainty words (Panel A). Again, we do not put much weight
on this one significant coefficient because at least one of the 32 coefficients would
likely attain statistical significance at the 95% confidence level due to chance.
Finally, we consider whether RMM disclosures might inform companies’ auditor
appointment decisions. For instance, prior studies find that the auditor turnover rate
is significantly higher after auditors disclose going-concern problems or material
internal control weaknesses at their clients (Lennox 2000; Chen et al. 2016; Newton
et al. 2016). To the extent that RMM disclosures convey negative news about a company, we would expect more auditor–client disagreements and more auditor turnover
13
446
RMM
(2)
(1)
0.026
VOLUME
VOLUME
− 0.005
Low
High
Firm size
Panel C: 3-day abnormal trading volume
446
0.092
483
0.095
Obs
R-square
(1.370)
Included
(0.247)
(2)
0.001
(1)
0.000
|CAR|
|CAR|
Included
Controls
RMM
Low
High
Firm size
Panel B: 3-day unsigned cumulative abnormal returns
0.098
483
0.068
Obs
R-square
(− 0.713)
Included
(− 1.002)
(2)
− 0.001
(1)
− 0.001
CAR​
CAR​
Included
Controls
RMM
Low
High
Firm size
Panel A: 3-day cumulative abnormal returns
− 0.004
(3)
VOLUME
High
Analyst following
0.111
504
Included
(0.837)
0.000
(3)
|CAR|
High
Analyst following
0.049
504
Included
(− 0.762)
− 0.001
(3)
CAR​
High
Analyst following
0.052
(4)
VOLUME
Low
0.088
425
Included
(− 0.236)
− 0.000
(4)
|CAR|
Low
0.140
425
Included
(0.299)
0.000
(4)
CAR​
Low
0.121
472
Included
(− 1.394)
− 0.001
(6)
CAR​
Low
0.117
472
Included
(0.341)
0.000
(6)
|CAR|
Low
− 0.015
(5)
VOLUME
High
0.028
(6)
VOLUME
Low
Large shareholders
0.101
457
Included
(1.071)
0.001
(5)
|CAR|
High
Large shareholders
0.077
457
Included
(− 0.146)
− 0.000
(5)
CAR​
High
Large shareholders
0.047*
(7)
VOLUME
High
Uncertainty
0.074
469
Included
(0.330)
0.000
(7)
|CAR|
High
Uncertainty
0.084
469
Included
(− 3.267)
− 0.003***
(7)
CAR​
High
Uncertainty
− 0.033
(8)
VOLUME
Low
0.117
462
Included
(0.828)
0.000
(8)
|CAR|
Low
0.092
462
Included
(1.218)
0.001
(8)
CAR​
Low
Table 3 Short window tests of the incremental information content of auditors’ RMM disclosures—cross-sectional analysis partitioning companies based on the information environment
Why are expanded audit reports not informative to investors?…
13
13
445
(− 0.272)
0.055
504
Included
(0.746)
0.040
(3)
VOLATILITY
High
Analyst following
0.194
504
Included
(1.239)
0.128
424
Included
(1.122)
0.083
(4)
VOLATILITY
Low
0.174
411
Included
(− 0.434)
(1.642)
0.020
467
Included
0.113
457
Included
(0.839)
0.056
(5)
VOLATILITY
High
0.124
472
Included
(0.347)
0.019
(6)
VOLATILITY
Low
Large shareholders
0.156
449
Included
(1.758)
0.110
469
Included
(0.392)
0.025
(7)
VOLATILITY
High
Uncertainty
0.242
462
Included
(− 1.400)
0.130
461
Included
(1.269)
0.075
(8)
VOLATILITY
Low
0.187
456
Included
Table 3 presents short-window (i.e., 3-day) tests of the incremental information content of the auditors’ RMM disclosures after partitioning companies on proxies for
their information environment. The cross-sectional partitions are based on above versus below median firm size, median analyst following, median percentage ownership
by large shareholders, and the median number of words expressing uncertainty included in the text of the audit report. ***, **, and * denote statistical significance at the
1%, 5%, and 10% levels, respectively (two-tailed). T-statistics are reported in parentheses below each coefficient. The estimated models use robust regression to reduce
the influence of outlier observations. The control variables are: SIZE, BTM, ANALYSTS, SEC_REG, PROBLEM, DISTRESS, LN_SUBS, GC, INVENTORY, RECEIVABLES, GOODWILL, FIXED_ASSETS, ACQ, LAG_ACQ, SALES_GROWTH, EXTR_ITEMS, and DEF_TAX, plus year fixed effects, auditor fixed effects, and industry fixed
effects. Variable definitions are in Appendix B
0.127
483
0.079
Obs
R-square
(0.933)
Included
(1.035)
(2)
0.066
(1)
0.055
VOLATILITY
VOLATILITY
Included
Controls
RMM
Low
High
Firm size
Panel D: 3-day abnormal trading volatility
433
0.127
482
0.208
Obs
R-square
(0.630)
Included
(− 0.380)
Included
Controls
Table 3 (continued)
C. S. Lennox et al.
Why are expanded audit reports not informative to investors?…
events after auditors disclose a larger number of RMMs. In contrast, if RMM disclosures do not convey new information, we would not expect to find a relationship between auditor turnover and RMM disclosures. Table 4 shows that the rate of
auditor turnover is unrelated to the number of RMMs. This finding corroborates the
main findings from our market reaction tests, which suggest that RMMs are not perceived by investors or companies as conveying negative news.
In the remainder of our analysis, we examine alternative explanations for why
RMMs are perceived as uninformative.
3.4 Do investors initially underreact to the information contained in the RMMs?
A potential explanation for the insignificant market reactions is that the RMM disclosures do contain valuable information but investors do not immediately respond
to it.For example, prior research documents that investors do not attend to all the
information at the audit report release date and that modified audit reports are associated with significantly negative long-window abnormal returns (Kausar et al.
2009; Czerney et al. 2018). To investigate this explanation, we construct long-window buy-and-hold abnormal returns (BHARs) for the 1-month, 3-month, 6-month,
and 12-month windows following the release of the expanded audit reports. As
shown in Table 5, we find the long-window buy-and-hold abnormal returns are not
significantly related to any of our RMM measures (i.e., RMM, RMM_ACCT​, RMM_
ENTITY, RMM_COMPANY, RMM_INDUSTRY​, RMM_PRED, RMM_UE, RMM_
TONE, and RMM_CHANGE). These results are inconsistent with the argument that
RMM disclosures are informative but investors do not immediately respond to them.
3.5 Do RMM disclosures lack value relevance?
A second potential explanation for the lack of significant information content is that
RMM disclosures fail to reflect risks that are value relevant. Auditors perform substantive
testing to detect and correct material misstatements and reduce financial reporting risks to
an acceptable level. Therefore, auditors’ RMM disclosures may reference risks that were
identified at the start of the audit but were appropriately mitigated during the audit and
thus are irrelevant to investors at the time the audited financial statements are released.
Theoretical and empirical research suggests that value relevance models are useful for assessing investor perceptions of the relevance of accounting numbers to firm
valuation (Ohlson 1995; Feltham and Ohlson 1995; Barth et al. 2001). Nevertheless,
there is an important distinction between value relevance and informativeness, because
accounting numbers can be value relevant but not incrementally informative if the
accounting information is preceded by more timely information (Barth et al. 2001).
We estimate value relevance models to test whether the RMM disclosures are
value relevant.
P = 𝛼0 + 𝛽1 E + 𝛽2 RMM + 𝛽3 E × RMM + CONTROLS + 𝜀
(2)
13
C. S. Lennox et al.
Table 4 Auditor change analysis (dependent variable = AUD_CHANGEt+1)
(1)
RMM
(2)
(3)
(4)
(5)
(6)
0.039
(0.340)
RMM_ACCT​
0.106
(0.884)
RMM_ENTITY
− 0.135
(− 0.524)
RMM_COMPANY
0.042
(0.355)
RMM_INDUSTRY​
0.010
(0.035)
RMM_PRED
− 0.169
(− 0.318)
RMM_UE
0.047
(0.401)
RMM_TONE
8.267
(0.498)
RMM_CHANGE
− 0.111
(− 0.539)
Controls
Included
Included
Included
Included
Included
Included
Observations
924
924
924
924
916
404
ROC
0.722
0.725
0.722
0.720
0.726
0.828
***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively (two-tailed).
Z-statistics are reported in parentheses below each coefficient. The auditor change models control for
SIZE, BTM, ANALYSTS, SEC_REG, PROBLEM, DISTRESS, LN_SUBS, GC, AC_SIZE, BUSY, and TENURE, plus year fixed effects, auditor fixed effects, and industry fixed effects. The sample size in Column
6 is reduced because the RMM_CHANGE variable is only available in year 2 and industry and audit firm
fixed effects perfectly predict the dependent variable for some observations
P = 𝛾0 + 𝛿1 BV + 𝛿2 RMM + 𝛿3 BV × RMM + CONTROLS + 𝜀
(3)
where:
P = company i’s stock price 30 trading days after the audit report is released to
investors;
E = earnings per share for company i in year t;
BV = net assets per share for company i in year t; and.
RMM = the number of risks of material misstatement disclosed in company i’s
audit report.
To the extent that RMMs are value relevant, we expect the valuation coefficients
on E and BV to be smaller when auditors report more RMMs. Hence, we predict
negative coefficients on the interaction terms E × RMM and BV × RMM; i.e., β3 < 0
and δ3< 0.
13
Why are expanded audit reports not informative to investors?…
We estimate Eqs. (2) and (3) separately because including E and BV (and their interactions with RMM) in the same model results in severe multicollinearity problems (some
variance inflation factors exceed 20 in the joint specification). The results are shown
in Columns (1) and (2) of Table 6. We find that the valuation coefficients on earnings
per share and net assets per share are significantly smaller when auditors disclose more
RMMs. Specifically, the coefficient on E × RMM is − 0.364 (t-stat. = − 3.687), and the
coefficient on BV × RMM is − 0.081 (t-stat. = − 4.079).6 These findings are inconsistent with RMM disclosures being uninformative because they fail to reflect value relevant information. To evaluate the economic significance of these results, we compute
the change in the valuation coefficients as the number of RMMs increases from the 25th
percentile value (3.00) to the 75th percentile (5.00). Over this range, the valuation coefficients on earnings per share (net assets per share) fall from 11.63 to 10.90 (1.43 to 1.27).
Therefore, the valuation coefficients remain positive, but their magnitude is significantly
attenuated when auditors disclose more risks of material misstatement.
To provide additional evidence that RMMs are value relevant, we examine
whether companies exhibit less persistent (i.e., more uncertain) performance when
they receive more RMMs. We test whether differences in the persistence of performance are related to RMMs by estimating Eqs. (4) and (5):
ROAit+1 = 𝛼0 + 𝛽1 ROAit + 𝛽2 RMMit + 𝛽3 ROAit × RMMit + CONTROLS + 𝜀it−1
(4)
CFOit+1 = 𝛼0 + 𝛽1 CFOit + 𝛽2 RMMit + 𝛽3 CFOit × RMMit + CONTROLS + 𝜀it−1
(5)
where:
ROA = a company’s return on assets (net income divided by total assets);
CFO = a company’s cash flow from operations (scaled by total assets); and.
E = earnings per share for company i in year t.
We expect the number of RMMs to be negatively related to the persistence in a
company’s performance (i.e., β3< 0).
The results for Eqs. (4) and (5) are reported in Table 7. As expected, the coefficients on ROAit × RMMit and CFOit × RMMit are negative and statistically significant (t-stats. = − 2.021 and − 5.621, respectively). Therefore, companies with more
RMMs exhibit a lower persistence in performance.7 In a further untabulated analysis,
we test whether the difference in persistence is related to different types of RMMs
(i.e., RMM_ACCT​ and RMM_ENTITY, RMM_COMPANY and RMM_INDUSTRY​,
RMM_PRED and RMM_UE, RMM_TONE and RMM_CHANGE). In these tests we
find that nine of the 18 interaction coefficients are statistically significant (p < 0.05
6
The significant positive coefficient on RMM reflects that larger companies receive more RMMs (see
Table 11 in Appendix C) and larger companies tend to have higher stock prices. However, the main
effect in these models captures the effect of RMM when earnings or book value equals zero, which is not
meaningful.
7
We also examine whether RMMs are predictive of future ROA and future CFO. After dropping the
interaction terms (ROAit × RMMit and CFOit × RMMit), we find that RMMs are negatively associated with
future CFO but not future ROA.
13
C. S. Lennox et al.
Table 5 Long window tests of the incremental information content of auditors’ RMM disclosures (1-, 3-,
6-, and 12-month cumulative buy and hold abnormal returns)
Panel A: Descriptive statistics for buy-and-hold abnormal returns (BHAR)
Std. Dev
25th Pctl
50th Pctl 75th Pctl
1 month
Obs
876
Mean
0.007
0.067
− 0.033
0.007
0.041
3 months
876
0.015
0.123
− 0.053
0.017
0.081
6 months
876
0.020
0.178
− 0.072
0.027
0.115
12 months
876
0.025
0.303
− 0.156
0.033
0.208
Panel B: Long window buy-and-hold abnormal returns (BHAR) with RMM as the independent
variable of interest
1 month
3 months
6 months
12 months
RMM
− 0.000
− 0.002
− 0.002
− 0.012
(− 0.078)
(− 0.725)
(− 0.434)
(− 1.497)
Controls
Included
Included
Included
Included
Observations
876
876
876
876
R-squared
0.098
0.107
0.168
0.181
Panel C: Long window buy-and-hold abnormal returns (BHAR) with account-level versus entitylevel RMMs as the independent variables of interest
1 month
3 months
6 months
12 months
RMM_ACCT​
− 0.000
− 0.004
− 0.003
− 0.010
(− 0.028)
(− 1.165)
(− 0.532)
(− 1.239)
RMM_ENTITY
0.002
0.009
0.007
− 0.011
(0.461)
(1.281)
(0.675)
(− 0.630)
Controls
Included
Included
Included
Included
Observations
876
876
876
876
R-squared
0.099
0.108
0.168
0.180
Panel D: Long window buy-and-hold abnormal returns (BHAR) with predicted and unexpected
RMMs as the independent variables of interest
1 month
3 months
6 months
12 months
RMM_PRED
0.007
0.015
0.044
− 0.027
(0.352)
(0.447)
(0.887)
(− 0.312)
RMM_UE
− 0.000
− 0.002
− 0.002
− 0.012
(− 0.070)
(− 0.712)
(− 0.466)
(− 1.499)
Controls
Included
Included
Included
Included
Observations
876
876
876
876
R-squared
0.098
0.108
0.169
0.181
Panel E: Long window buy-and-hold abnormal returns (BHAR) with company-level and
industry-common RMMs as the independent variables of interest
1 month
3 months
6 months
12 months
RMM_COMPANY
− 0.000
− 0.003
− 0.003
− 0.013
(− 0.024)
(− 0.824)
(− 0.715)
(− 1.562)
RMM_INDUSTRY​
− 0.001
0.003
0.014
− 0.000
(− 0.320)
(0.324)
(1.232)
(− 0.074)
13
Why are expanded audit reports not informative to investors?…
Table 5 (continued)
Controls
Included
Included
Included
Included
Observations
876
876
876
876
R-squared
0.098
0.108
0.171
0.181
Panel F: Long window buy-and-hold abnormal returns (BHAR) with RMM tone as the independent variable of interest
1 month
3 months
6 months
12 months
RMM_TONE
− 0.016
− 0.221
0.009
1.391
(− 0.068)
(− 0.501) (0.014)
(1.245)
Controls
Included
Included
Included
Included
Observations
870
870
870
870
R-squared
0.100
0.108
0.170
0.178
Panel G: Long window buy-and-hold abnormal returns (BHAR) with the change in RMMs from
year 1 to year 2 as the independent variable of interest
1 month
3 months
6 months
12 months
RMM_CHANGE
− 0.001
− 0.006
− 0.000
0.014
(− 0.179)
(− 1.220)
(− 0.054)
(1.080)
Controls
Included
Included
Included
Included
Observations
410
410
410
410
R-squared
0.162
0.205
0.261
0.190
***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively (two-tailed).
T-statistics are reported in parentheses below each coefficient. The models include the same controls as
Tables 2 and 3 plus fixed effects for the year-end announcement month to control for cross-sectional correlation in returns among sample observations. Models are estimated using robust regression to reduce
the influence of outlier observations. The analysis in Table 5 excludes observations that exit the sample
in the 12 months following the audit report release date. The sample size is smaller in Panel G because it
includes only observations from the second year of adoption
using two-tailed tests). Together, the analyses in Tables 6 and 7 do not support the
argument that RMMs are uninformative because they fail to reflect value relevant
information.
3.6 Did investors already know about the risks before auditors started disclosing
them?
Thus far, we have found that RMM disclosures do not convey significant incremental
information to investors (Tables 2, 3, 4 and 5), but are value relevant (Tables 6 and 7). A
potential answer to this conundrum is that auditors’ risk disclosures are relevant to investors but investors already knew about the risks before auditors began disclosing them. If
so, the RMM disclosures would be value relevant but not incrementally informative.
We take two complementary approaches to testing this explanation. First, we reestimate the value relevance models (Eqs. 2 and 3) with data on stock prices, earnings, and net assets from the prior year (i.e., year t-1) and RMM disclosures from
the current year (i.e., year t). This approach allows us to determine whether investors
13
C. S. Lennox et al.
Table 6 The value relevance of RMMs (dependent variable = Pit)
(1)
(2)
12.721***
Eit
(31.229)
Eit × RMMit
− 0.364***
(− 3.687)
BVit
1.681***
(21.754)
BVit × RMMit
− 0.081***
(− 4.079)
RMMit
0.331***
0.116
(4.668)
(1.365)
Constant
0.041
0.667
(0.069)
(1.046)
Observations
932
932
R-squared
0.869
0.750
Table 6 presents equity valuation models using RMMs reported in the first two years of adoption. ***,
**, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively (two-tailed). T-statistics are reported in parentheses below each coefficient. The models are estimated using robust regression
to reduce the influence of outlier observations. The models include controls for year fixed effects, industry fixed effects, auditor fixed effects, and going concern opinions. Variable definitions are in Appendix B
were pricing the risks before auditors started disclosing them in expanded audit
reports. In particular, we estimate the following equity valuation models:
Pit−1 = 𝛼0 + 𝛽1 Eit−1 + 𝛽2 RMMit + 𝛽3 Eit−1 × RMMit + CONTROLS + 𝜀it−1 (6)
Pit−1 = 𝛾0 + 𝛿1 BVit−1 + 𝛿2 RMMit + 𝛿3 BVit−1 × RMMit + CONTROLS + 𝜀 (7)
where:
Pit-1 = company i’s stock price, 30 trading days after the audit report for fiscal year
t-1 is publicly released to investors;
Eit-1 = earnings per share for company i in year t-1;
BVit-1 = net assets per share for company i in year t-1; and.
RMMit = the number of RMMs in company i’s audit report in year t.
If investors already knew in year t-1 which companies were more risky, we would
expect negative coefficients on Eit-1 × RMMit and BVit-1 × RMMit; i.e., β3 < 0 and δ3< 0.
The results for Eqs. (6 and 7) are shown in Columns (1) and (2) of Table 8. We find that
the coefficients for the interaction terms Eit-1 × RMMit and BVit-1 × RMMit are negative and
statistically significant (t stats = − 5.286 and − 8.587 respectively). These results suggest
that investors already knew which companies were more risky before auditors started disclosing the RMMs.
A plausible explanation for the results in Columns (1) and (2) is that investors
already knew about the risks because the RMMs in year 2 had already been disclosed
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Why are expanded audit reports not informative to investors?…
Table 7 Auditors’ RMMs and the persistence of accounting performance
RMMit
ROAit
(1)
(2)
ROAit+1
CFOit+1
− 0.000
0.002*
(− 0.244)
(1.670)
0.897***
(43.671)
RMMit × ROAit
− 0.009**
(− 2.021)
CFOit
0.963***
(25.209)
RMMit × CFOit
− 0.058***
(− 5.621)
Controls
Included
Included
Obs
921
739
R-square
0.910
0.811
***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively (two-tailed).
T-statistics are reported in parentheses below each coefficient. The estimated models use robust regression to reduce the influence of outlier observations. Variable definitions are in Appendix B. The control
variables are SIZE, BTM, ANALYSTS, SEC_REG, PROBLEM, DISTRESS, LN_SUBS, GC, INVENTORY,
RECEIVABLES, GOODWILL, FIXED_ASSETS, ACQ, LAG_ACQ, SALES_GROWTH, EXTR_ITEMS,
and DEF_TAX, plus year fixed effects, auditor fixed effects, and industry fixed effects. The sample sizes
are smaller in Table 7 due to missing one-year-ahead data and limited data coverage for operating cash
flows
by auditors in year 1 (i.e., in the first year of adoption). To ensure that this timing
issue is not driving our results, we distinguish between RMMs that the auditor had
already disclosed (RMM_PREV) and RMMs that the auditor had not already disclosed
(RMM_NPREV). In the first year of adoption (year 1), there are no previous RMM
disclosures, so RMM_NPREV = RMM for every observation. In year 2, we partition
the RMM disclosures into those that had previously been disclosed in year 1 (RMM_
PREV) and those that had not previously been disclosed in year 1 (RMM_NPREV).
We then re-estimate Eqs. (4 and 5), replacing the ­RMMit variable with RMM_PREVit
and RMM_NPREVit. The results are shown in Columns (3) and (4) of Table 8.
We find significant negative coefficients on Eit-1 × RMM_NPREVit
(t-stat. = − 3.053) and BVit-1 × RMM_NPREVit (t-stat. = − 7.405). Therefore, the valuation coefficients on earnings and net assets in year t-1 are significantly smaller for
companies whose auditors disclosed more RMMs in the following year (year t).
These findings are consistent with investors having information in year t-1 about
risks that auditors would not disclose until the following year (year t). Further, we
find significant negative coefficients on Eit-1 × RMM_PREVit (t-stat. = − 5.261) and
BVit-1 × RMM_PREVit (t-stat. = − 6.979). Therefore, the valuation coefficients on
earnings and net assets in year t-1 are significantly smaller for the companies whose
RMMs had already been disclosed in the previous year (year t-1). Overall, these
13
C. S. Lennox et al.
findings indicate that investors were informed about the risks in the year before the
auditors started disclosing them in expanded audit reports.8 These findings provide
our first evidence suggesting that investors were already aware of the heightened risks
at companies that went on to receive more RMMs from their auditors.
Because Table 8 (Columns 3 and 4) partitions RMMs into those disclosed (not disclosed) by the auditor in the previous year, all of the risks in the first year of adoption (i.e.,
year 1) are, by construction, not previously disclosed by the auditor (RMM_NPREVit). A
limitation of this analysis is that at least some of the year 1 risks may have been previously
disclosed by the company in the prior year. For example, companies may have disclosed
the risks in their previous annual reports, conference calls, or earnings announcements.
Therefore, in our next analysis we examine whether the year 1 RMMs were previously
disclosed by the company. We expect that an earlier disclosure would make the auditors’
RMM disclosures in year 1 largely redundant or superfluous. For instance, if the company
had already disclosed a risk relating to revenue recognition (e.g., that the company uses a
complex percentage of completion method for revenue recognition), the same disclosure
by the auditor in year 1 would be largely redundant even though the auditor had not previously disclosed the risk.
Thus, our next analysis focuses on the specific RMMs in year 1 that had already been
disclosed by companies prior to auditors disclosing them in expanded audit reports.9
Given the significant costs of hand-collecting the earlier disclosures, we perform this analysis on a random subset of 243 companies (approximately 50% of the sample). We code
each RMM disclosure as either “already disclosed” by the company (RMM_AD_Y1) or
not already disclosed (RMM_NEW_Y1): “already disclosed” means we are able to find
that the risk was already disclosed by the company prior to the auditor’s disclosure in
year 1, whereas “new” means we cannot find any evidence that the risk was disclosed by
the company before being disclosed by the auditor in year 1. We search for the “already
disclosed” RMMs by examining each company’s year t-1 annual report, prior earnings
announcement, or conference call transcript. These risk disclosures are typically contained in the audit committee report or the “key/critical accounting estimates and areas of
significant judgment” note to the financial statements.
Appendix D provides an example of an “already disclosed” RMM. The “new” RMMs
are the risks that we cannot find in the earlier disclosures. These “new” RMM disclosures
could be incrementally informative given that we find no evidence that they were previously disclosed. However, we cannot rule out the possibility that investors knew about
these “new” risks through other disclosure channels, because it is not feasible for us to
8
We perform one additional test to further ensure that the market already knew about the RMM information. Specifically, we convert Model 2 into a changes model where change in price from year t-1 to year
t is the dependent variable and unexpected earnings (year-over-year change from year t-1 to year t) and
unexpected RMMs (RMM_UE) are the independent variables of interest. We do not find significant coefficients on unexpected earnings, unexpected RMMs, or the interaction term, indicating that the market
was already aware of the RMM information. We thank the editor for suggesting this additional test.
9
We obtain all available prior earnings announcements and conference call transcripts from company
websites, Datastream, and the Thomson Reuters Eikon database. While not all conference call transcripts
were available, we do not expect the missing reports to affect our inferences because most of the “already
disclosed” disclosures were identified in prior earnings announcements or prior annual reports rather than
in prior conference calls.
13
Why are expanded audit reports not informative to investors?…
Table 8 Value relevance tests in year t-1 using year t RMM disclosures (dep. var. = Pit-1)
(1)
Eit-1
Eit-1 × RMMit
(2)
(3)
13.873***
13.581***
(32.497)
(31.060)
(4)
− 0.525***
(− 5.286)
Eit-1 × RMM_ NPREVit
− 0.325***
(− 3.053)
Eit-1 × RMM_PREVit
− 0.637***
(− 5.261)
BVit-1
BVit-1 × RMMit
1.648***
1.642***
(24.624)
(22.737)
− 0.142***
(− 8.587)
BVit-1 × RMM_NPREVit
− 0.134***
(− 7.405)
BVit-1 × RMM _PREVit
− 0.160***
(− 6.979)
RMMit
0.205***
0.211***
(3.177)
(2.914)
RMM_NPREVit
RMM_ PREVit
Constant
0.144**
0.209***
(2.124)
(2.665)
0.294***
0.320***
(3.800)
(3.464)
0.720
0.102
0.705
0.052
(1.380)
(0.193)
(1.325)
(0.093)
Observations
967
967
937
937
R-Squared
0.876
0.734
0.876
0.719
Table 8 presents equity valuation models for year t-1 using RMMs reported in year t. ***, **, and *
denote statistical significance at the 1%, 5%, and 10% levels, respectively (two-tailed). T-statistics are
reported in parentheses below each coefficient. The models are estimated using robust regression to
reduce the influence of outlier observations. The models include controls for year fixed effects, industry
fixed effects, auditor fixed effects, and going concern opinions. Variable definitions are in Appendix B
search every possible source of information. (The UK does not have an equivalent of
Regulation FD, which means that managers are permitted to communicate privately with
investors. Given that such communications would not be publicly observable, we cannot
include them in our search for “already disclosed” RMMs.) In the second year of adoption (year 2), RMM_AD_Y2 and RMM_NEW_Y2 are coded based on whether or not the
RMM was reported by the auditor in the prior year.
Table 9, Panel A reports descriptive statistics for the RMM_AD_Y1, RMM_NEW_Y1,
RMM_AD_Y2, and RMM_NEW_Y2 variables. Of the RMMs disclosed in year 1, we find
that 67.22% had already been disclosed by the company in the previous year (RMM_AD_
Y1). For the remaining 36.47% of year 1 RMMs, we were unable to find any evidence of
13
C. S. Lennox et al.
a prior disclosure by the company (RMM_NEW_Y1). Of the RMMs disclosed in year 2,
we find that 73.45% had already been disclosed by the auditor in year 1 (RMM_AD_Y2).
Only 26.54% of the year 2 RMMs had not already been disclosed by the auditor in year 1
(RMM_NEW_Y2). Overall, these descriptive statistics suggest that investors would have
known about the vast majority of RMMs before auditors disclosed them in either year
1 or year 2. In year 1, most of the risks had already been disclosed by companies, and in
year 2 most of the risks had already been disclosed by auditors in the prior year. Thus,
the majority of RMMs appear to be stale rather than informative. This is consistent with
our evidence that the RMMs do not appear to be incrementally informative (Tables 2, 3,
4 and 5), that RMMs are value relevant (Tables 6 and 7), and that investors seem to have
known which companies were more risky before auditors began disclosing the RMMs in
expanded audit reports (Table 8).
Table 9, Panel B presents the short-window market reaction tests for the “already
disclosed” RMMs in years 1 and 2 (RMM_AD_Y1 and RMM_AD_Y2) and the
“new” RMMs in each year (RMM_NEW_Y1 and RMM_NEW_Y2). As expected,
Table 9, Panel B shows that the short-window market reactions are insignificant for
the “already disclosed” RMMs in years 1 and 2 (RMM_AD_Y1 and RMM_AD_Y2).
This is not surprising, as the “already disclosed” RMMs are not expected to be incrementally informative, given that these risks had already been disclosed. We find the
“new” RMMs (RMM_NEW_Y1 and RMM_NEW_Y2) are also statistically insignificant in each year as well as in the two years combined (RMM_NEW_Y1&Y2).10 For
completeness, we also examine whether “new” and “already disclosed” RMMs are
associated with auditor changes and long-window abnormal returns. In both sets of
analysis (untabulated), we find the coefficients for “new” and “already disclosed”
RMMs are not statistically significant. Collectively, these results indicate that investors do not find RMMs to be incrementally informative even when we are unable to
find evidence that the RMMs had already been disclosed.
While it may seem surprising that the “new” RMMs are not incrementally informative, Table 9, Panel A shows that the average number of RMMs per company is 3.79
and the typical company has only 1.10 RMMs that had not already been disclosed (i.e.,
the mean value of RMM_NEW_Y1&Y2 is 1.10). From an investor’s perspective, that is
not a lot of “new” information to react to, especially when one considers that the earnings number is a conglomeration of many different accounts not labeled as RMMs. Thus,
investors may pay little attention to the “new” RMMs given that most RMMs had already
been disclosed. It is also possible that the “new” RMMs had not been disclosed previously because they were less important than the other RMMs, in which case the “new”
RMMs would be less likely to trigger a market reaction. Another possibility is that investors may have considered the “new” RMMs less important because they reflect one-off
temporary risks rather than long-term persistent risks. Finally, the “new” RMMs may not
10
In Table 9, we have sufficient power to detect a significant coefficient for a new RMM in the CAR
analysis that is greater than or equal to − 0.003 (i.e., a market reaction of − 0.3%) at conventional levels
(alpha equal to 0.05 and power equal to 80%). The corresponding effect size for |CAR| is a market reaction to a new RMM that is greater than or equal to − 0.22%. Due to the small economic magnitude of
these effect sizes, we conclude that we have adequate power to detect an economically meaningful market reaction, if one existed.
13
Why are expanded audit reports not informative to investors?…
Table 9 “New” versus “already disclosed” RMM disclosures
Panel A: Descriptive statistics for “new” versus “already disclosed” RMMs in the first and second years
of adoption
Mean per Firm-year
% of total RMMs
RMM_NEW_Y1
1.29
36.47%
RMM_AD_Y1
2.56
67.22%
Firm-year observations
235
RMM_NEW_Y2
1.00
26.54%
RMM_AD_Y2
2.77
73.45%
Firm-year observations
453
RMM_NEW_Y1&Y2
1.10
28.69%
RMM_AD_Y1&Y2
2.69
71.31%
Firm-year observations
688
Panel B: The short window market reactions of “new” and “already disclosed” RMMs
CAR​
|CAR|
VOLUME
VOLATILITY
(1)
(2)
(3)
(4)
Year 1
RMM_NEW_Y1
− 0.003
0.001
0.032
0.041
(− 1.567)
(0.598)
(0.637)
(0.318)
− 0.000
0.000
0.055
0.046
(− 0.018)
(0.486)
(1.306)
(0.435)
Controls
Included
Included
Included
Included
Observations
234
234
235
235
R-squared
0.184
0.179
0.322
0.136
RMM_AD_Y1
Year 2
RMM_NEW_Y2
− 0.000
0.001
− 0.009
0.053
(− 0.011)
(1.179)
(-0.291)
(0.705)
− 0.001
− 0.000
− 0.013
− 0.047
(− 0.919)
(− 0.607)
(− 0.457)
(− 0.681)
Controls
Included
Included
Included
Included
Observations
453
453
445
453
R-squared
0.047
0.110
0.195
0.082
RMM_AD_Y2
Year 1 & year 2
RMM_NEW_Y1&Y2
− 0.001
0.001
0.000
0.049
(− 0.621)
(1.075)
(0.005)
(0.804)
− 0.001
− 0.000
0.010
0.001
(− 0.777)
(− 0.276)
(0.438)
(0.026)
Controls
Included
Included
Included
Included
Observations
688
688
680
688
R-squared
0.058
0.078
0.171
0.056
RMM_AD_Y1&Y2
Table 9 presents short-window (i.e., 3-day) tests of the incremental information content of auditors’
RMM disclosures, partitioned on whether or not the RMM was previously disclosed. Given the significant costs of hand-collecting the earlier disclosures, the sample sizes related to the RMM_NEW_Y1 and
RMM_AD_Y1 variables represent a random subset of 243 companies (approximately 50% of the sample).
The 3-day window is centered on the annual report date (i.e., the date the audit report is disclosed to
the public). ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively
13
C. S. Lennox et al.
Table 9 (continued)
(two-tailed). T-statistics are reported in parentheses below each coefficient. The estimated models use
robust regression to reduce the influence of outlier observations. Variable definitions are in Appendix B.
The control variables are SIZE, BTM, ANALYSTS, SEC_REG, PROBLEM, DISTRESS, LN_SUBS, GC,
INVENTORY, RECEIVABLES, GOODWILL, FIXED_ASSETS, ACQ, LAG_ACQ, SALES_GROWTH,
EXTR_ITEMS, and DEF_TAX, plus year fixed effects, auditor fixed effects, and industry fixed effects.
|CAR| is included as a control variable when trading volume is the dependent variable. The sample size
for abnormal trading volume in smaller due to missing data on trading volume for some observations
be informative if investors were already informed about them through sources that we
were unable to capture in our data collection. We acknowledge the limitation that we are
unable to empirically validate why the “new” RMMs are uninformative. We encourage
future research to investigate these possibilities.
4 Conclusion
Gutierrez et al. (2018) investigate whether the introduction of expanded audit reports
in the UK had significant capital market consequences for premium-listed companies.
Using a difference-in-differences research design, with premium-listed companies as the
treatment group and companies listed on the Alternative Investment Market as the control group, their study finds that RMM disclosures have no significant effect on cumulative signed abnormal returns, cumulative absolute abnormal returns, abnormal trading
volume, and abnormal return volatility. We extend their cross-sectional tests to examine
whether there is any evidence of information content. Using a myriad of tests, we find that
the number and types of RMM disclosures are not incrementally informative to investors
even in poor information environments.
Next, we consider three possible explanations for why investors do not find
the RMM disclosures to be informative. We first investigate whether RMMs do
contain valuable information but investors fail to immediately appreciate it. Our
results using long-window buy-and-hold abnormal returns do not support this
explanation. Second, we investigate whether the RMM disclosures are uninformative because they lack value relevance. Using value relevance tests, we
find that the valuation coefficients on earnings and book value are lower as the
number of disclosed RMMs increases. Moreover, companies with more RMMs
exhibit significantly less persistence in their reported performance, which supports the view that such disclosures are relevant to valuation. Therefore, we
reject the argument that RMM disclosures are uninformative because they are
irrelevant to valuation.
Finally, we examine whether RMMs are uninformative because investors
already knew about the risks before auditors started disclosing them. We provide two pieces of evidence consistent with this story. First, when we estimate
value relevance regressions for the year prior to RMM disclosure (year t-1),
we find the valuation coefficients on earnings and book value are significantly
13
Why are expanded audit reports not informative to investors?…
smaller among the companies that receive more RMMs in the following year
(year t). This suggests that investors were already applying valuation discounts
to the reported earnings and book values of companies that later received more
RMMs. Second, we find that the vast majority of RMMs in years 1 and 2 had
already been disclosed either by the company or by the auditor. Together,
these two findings suggest that investors already knew about the vast majority of RMMs. Thus, standard-setters who are conducting post-implementation
reviews on the consequences of expanded audit reports should not expect the
change in the audit reporting standard to deliver new information to investors.
Although expanded audit reports do not seem to provide investors with new
information, we acknowledge that the new reporting requirements may have other
benefits, such as increasing the external monitoring of management and auditors
(Minutti-Meza 2020). For example, concurrent research suggests that managers are
disclosing more information following the introduction of expanded audit reporting (Burke et al. 2020; Andreicovici et al. 2020). In addition, concurrent research
indicates less earnings management when auditors are required to disclose RMMs
(Klueber et al. 2018; Reid et al. 2019; Santos et al. 2020; Drake et al. 2020). Thus,
although investors may not be directly informed by RMMs, investors may benefit
indirectly through improved manager disclosures or improved financial reporting.
Our study is subject to the caveat that the UK has important institutional characteristics that could limit the generalizability of our findings. The UK’s information
environment is less transparent than that of the US due to the UK’s less stringent
disclosure requirements. For example, US companies must file quarterly financial
reports and are subject to Regulation FD, but the same requirements are not imposed
on UK companies. In addition, many corporate governance practices required by
law for US companies are implemented in the UK on only a “comply or explain”
basis. Additionally, US companies and auditors report on the effectiveness of internal controls for accelerated filers, whereas the UK does not have this requirement.
Moreover, the UK is a less litigious environment than the US, and the FRC has
less enforcement power than the SEC and PCAOB. Therefore, UK auditors are less
likely to be concerned about shareholder litigation and regulatory sanctions, which
could affect their willingness to disclose informative RMMs. Finally, expanded
audit reports were introduced for premium-listed companies in the UK, and such
companies tend to be large and well known. This feature of the sample could limit
our ability to identify an investor reaction to auditor risk disclosures. We encourage
more research on expanded audit reporting in other jurisdictions whose institutional
arrangements are different from those observed in the UK.
13
C. S. Lennox et al.
Appendix A
See Table 10.
Table 10 Examples of auditors’ disclosures of the risks of material misstatement
Extract from the audit report issued by Deloitte to Chemring Group Plc (five risks of material misstatement)
1. Revenue recognition on:
• Significant long-term contracts, due to the financial effects of judgements, including future milestone success,
associated with determining the percentage of contract completion at the balance sheet date and risks associated with
completing the contract; and
• Delivery of goods, due to the complex contractual terms with regards to the transfer of risk and reward and therefore
the appropriate point at which revenue should be recognised.
2. Valuation of goodwill and intangible assets arising on historical acquisitions, due to management’s assertions
regarding their recoverable amount. There are a number of key judgments in determining the recoverable amount,
including growth rates in future cash flow forecasts and discount rates applied to these forecasts.
3. Taxation, specifically where provisions are made in respect of judgments taken in the various tax jurisdictions in
which the Group operates. These provisions are judgmental as a result of their nature and technical complexity.
4. Appropriateness and completeness of judgmental provisions made by management in respect of obsolete inventory,
product failures, and legal claims in respect of the outcome of these issues.
5. Appropriateness of the going concern principle.
Extract from the audit report issued by Ernst & Young to Aer Lingus Group (four risks of material misstatement)
1. Pension arrangements
We focused on this because, while the Group’s position is, supported by legal advice, that they have no legal or
constructive obligation in respect of the deficit in either scheme, the Group has indicated that it is willing to assist in
a process that will seek a solution that balances the interests of all parties, and such process may involve once off payments to a new scheme provided certain conditions are met. Industrial or other actions may arise.
2. Property, plant and equipment impairment assessment
The directors performed an impairment assessment as they identified an impairment trigger indicated by IAS 36
in the carrying amount of the Group’s net assets as more than its market capitalization as at December 31, 2013.
We focused on this area due to the identified impairment trigger, the size of this balance and because it involves
complex and subjective judgments by the directors about the future results of the business, in particular revenue and
costs, fleet composition and discount rate assumptions.
3. Fraud in revenue recognition
We focused on the timing of the recognition of revenue and its presentation in the income statement as the timing of
recognition is usually dependent on delivery of the service which, for the most part, occurs after payment has been
received for the service.
4. Risk of management override of internal controls
Extract from the audit report issued by PricewaterhouseCoopers to Pearson Plc (7 risks of material misstatement)
1. Risk of fraud in revenue recognition. Auditing standards require us to consider the risk of fraud in revenue recognition. We focused on material products and services where revenue recognition practices are particularly complex
and subject to estimates, being:
•Multiple element arrangements, such as the provision of supplementary (print or digital) materials or training with
textbooks, where revenue is recognized for each element as if it were an individual contractual arrangement requiring the estimation of its fair value and;
•Certain long term contracts in the Education segments, where revenue is recognized using percentage of completion
based on costs.
2. Risk of management override of internal controls. Auditing standards require us to consider the risk of management
override of internal controls.
3. Penguin Random House Transaction. On July 1, 2013, management announced the completion of the Penguin Random House combination which resulted in a number of complex accounting and business model changes as follows:
•Disposal accounting, including the gain on the sale;
•Valuation of the new business and intangible assets;
•And associate accounting, including alignment of accounting policies and presentation of results in the consolidated
financial statements.
We focused on this area because it was a complex transaction which required the directors to exercise a significant level
of judgement.
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Why are expanded audit reports not informative to investors?…
Table 10 (continued)
4. Provision for uncertain tax liabilities. The group is subject to several tax regimes due to the geographic diversity
of its businesses. The directors are required to exercise significant judgment in determining the appropriate amount
to provide in respect of potential tax exposures and uncertain tax provisions. The most significant of these relate
to U.S. tax. We focused on this area because of the inherent judgments required in estimating the amount of any
provision required. Changes in assumptions can materially impact the level of provisions recorded in the financial
statements.
5. Returns provision. We focused on this area because there are material judgmental provisions for anticipated book
returns within the Education segment particularly as the group transitions from print to digital.
6. Valuation of pre-publication assets and inventories. We focused on this area because there are material pre-publication assets and inventories within the Education segments. Judgment is required to evaluate the valuation of these
assets which may be more complex during the transition from print to digital.
7. Goodwill impairment reviews. We focused on this area because the Group carries significant goodwill and acquired
intangible asset balances. There is judgment in the identification and aggregation of cash generating units (CGUs)
and in the assumptions used in the annual goodwill impairment review.
Appendix B
Variable definitions
Dependent variables
CAR​= 3-day cumulative abnormal return (− 1, 1) centered on company i’s annual
report release date.
|CAR|= 3-day unsigned cumulative abnormal return (− 1, 1) centered on company i’s annual report release date.
VOLATILITY = abnormal 3-day stock return volatility (following Landsman et al.
2012).
VOLUME = abnormal 3-day trading volume (following Landsman et al. 2012).
Pit = company i’s stock price per share, 30 days after company i’s year t annual
report release date.
BHAR (1, 3, 6, 12 month) = buy and hold abnormal returns for company i calculated using the FTSEALL index over the window 1, 3, 6, or 12 months among
observations with one full year of returns following the annual report release date.
AUD_CHANGEt+1 = one if company i changes auditors between year t and year
t + 1, 0 otherwise.
ROAt+1 = return on assets for company i in year t + 1.
CFOt+1 = cash flow from operations scaled by total assets for company i in year
t + 1.
Test variables
RMM = the number of risks of material misstatement disclosed in company i’s
audit report.
RMM_ACCT​= the number of RMMs reported in company i’s expanded audit
report that pertain to specific accounts in the financial statements.
RMM_ENTITY = the number of RMMs reported in company i’s expanded audit
report that pertain to the entity as a whole.
13
C. S. Lennox et al.
RMM_COMPANY = the number of RMMs reported in company i’s audit report
that appear in fewer than 50% of the audit reports in company i’s industry in year t.
RMM_INDUSTRY​= the number of RMMs reported in company i’s audit report
that also appear in more than 50% of the audit reports in company i’s industry in
year t.
RMM_PRED = the predicted number of RMMs for company i in year t from the
model shown in Column 1 of Appendix C.
RMM_UE = the residual from estimating the model shown in Column 1 of
Appendix C that predicts the number of RMMs in company i’s audit report in year t.
RMM_TONE = the number of positive words in the RMM section of company i’s
audit report less the number of negative words in the RMM section of company i’s
audit report, scaled by total words in company i’s RMM section. Positive and negative words follow the 2014 update to the Loughran and McDonald (2011) word lists.
RMM_CHANGE = the number of RMMs reported in company i’s year t audit
report minus the number of RMMs reported in company i’s year t-1 audit report.
RMM_PREV = the number of RMMs reported in company i’s year t audit report
that were previously reported by the auditor in year t-1. In year 1, RMM_PREV = 0
because year 1 is the first year of RMM disclosure. In year 2, RMM_PREV = RMM_
NEW_Y2 because we use the year 1 audit report to identify any new RMMs in the
following year.
RMM_NPREV = the number of RMMs reported in company i’s year t audit
report that were not previously reported by the auditor in year t-1. In year 1, RMM_
NPREV = RMM because year 1 is the first year of RMM disclosure. In year 2,
RMM_NPREV = RMM_AD_Y2 because we use the year 1 audit report to identify
any new RMMs in the following year.
RMM_NEW_Y1 = the number of RMMs reported in company i’s year 1 audit
report where we are unable to find that the company previously disclosed the risk in
the prior year.
RMM_AD_Y1 = the number of RMMs reported in company i’s year 1 audit report
where we find that the company previously disclosed the risk in the prior year.
RMM_NEW_Y2 = the number of RMMs reported in company i’s year 2 audit
report that were not disclosed by the auditor in year 1.
RMM_AD_Y2 = the number of RMMs reported in company i’s year 2 audit report
that were already disclosed by the auditor in year 1.
RMM_NEW_Y1&Y2 = RMM_NEW_Y1 in year 1 or RMM_NEW_Y2 in year 2.
RMM_AD_Y1&Y2 = RMM_AD_Y1 in year 1 or RMM_AD_Y2 in year 2.
Control variables
ANALYSTS = the number of analysts following company i in year t.
ACQ = acquisitions amount divided by total assets for company i in year t. Missing values are set equal to zero.
AC_SIZE = audit committee size for company i in the first year of adoption.
BTM = the book to market ratio for company i at the end of year t.
BUSY = one if company i has a December year-end and zero otherwise.
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Why are expanded audit reports not informative to investors?…
BV = net assets per share for company i at the end of year t.
CATA​= current assets divided by total assets for company i in year t.
DEF_TAX = deferred taxes scaled by total assets for company i in year t. Missing
deferred tax accruals are set equal to zero.
DISTRESS = one if company i reports a negative value for net income or net
assets in year t and zero otherwise.
E = earnings per share for company i in year t.
EXTR_ITEMS = extraordinary items scaled by total assets for company i in year t.
Missing values are set equal to 0.
FIXED ASSETS = net fixed assets scaled by total assets for company i in year t.
GC = one if company i’s audit report includes an RMM or report modification
indicating substantial doubt about the entity’s ability to continue as a going concern
and zero otherwise.
GOODWILL = goodwill scaled by total assets for company i in year t.
INVENTORY = inventory scaled by total assets for company i in year t.
LAG_ACQ = acquisitions amount for company i in the prior year scaled by total
assets in the prior year.
SIZE = the natural log of the market value of equity for company i at the end of
year t.
LN_SUBS = the natural log of (one plus) the total number of subsidiaries for company i in year t.
LOSS = one if company i reported a loss in year t and zero otherwise.
PROBLEM = one if company i had an accounting problem during the two years
prior to year t’s audit report and zero otherwise. We identify a company as having
had an accounting problem if it announced a restatement to correct a previous error
or irregularity or if the company was named by the Financial Reporting Council as
having had an accounting problem.
RECEIVABLES = accounts receivable scaled by total assets for company i in year
t.
SALESGROWTH = percentage change in revenue for company i from year t-1 to
year t.
SEC_REG = one if company i is registered with the U.S. Securities and Exchange
Commission and zero otherwise.
TENURE = the natural log of audit firm tenure for company i in year t.
UNCERTAINTY = the number of uncertainty words divided by total words in the
risk of material misstatement section of company i’s audit report in year t. Uncertainty words are available at https://​www3.​nd.​edu/​~mcdon​ald/​Word_​Lists.​html.
Notes: The RMM test variables—PROBLEM, SEC_REG, AC_SIZE, AUD_
CHANGE, and AUDITOR_TENURE—are hand collected from companies’ annual
reports and other sources. Market variables are constructed from Compustat Global.
Other variables are sourced from FAME and Compustat Global, supplemented by
hand collection as needed to avoid sample loss.
13
C. S. Lennox et al.
Appendix C
See Table 11.
Table 11 RMM prediction model
SIZE
BTM
ANALYSTS
SEC_REG
PROBLEM
DISTRESS
LN_SUBS
GC
INVENTORY
RECEIVABLES
GOODWILL
FIXED ASSETS
ACQ
LAG_ACQ
EXTR_ITEMS
SALESGROWTH
DEF_TAX
Poisson Regression
OLS Regression
RMM
RMM
0.041***
0.155***
(3.131)
(3.079)
0.032**
0.123*
(2.023)
(1.928)
0.003
0.011
(1.045)
(1.136)
0.087
0.516*
(1.566)
(1.909)
0.195**
0.894**
(2.509)
(2.386)
0.124***
0.520***
(3.708)
(3.765)
0.032***
0.128***
(2.801)
(2.904)
0.097**
0.381**
(2.554)
(2.492)
− 0.119
− 0.440
(− 0.825)
(− 0.860)
0.107
0.373
(0.761)
(0.704)
0.138
0.506
(1.477)
(1.394)
0.152***
0.614***
(4.203)
(4.186)
1.390***
5.286***
(3.486)
(3.204)
− 0.490
− 1.960
(− 0.968)
(− 1.031)
− 2.368
− 3.375
(− 0.231)
(− 0.147)
− 0.073*
− 0.202*
(− 1.800)
(-1.695)
− 0.262
− 0.921
(− 0.574)
(− 0.498)
0.535***
0.861
(2.893)
(1.291)
Observations
932
932
Pseudo R-squared/R-squared
0.047
0.323
Constant
Year, auditor, and industry are included but omitted for brevity
***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively (two-tailed).
T-statistics are reported in parentheses below each coefficient. Variables are defined in Appendix B
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Why are expanded audit reports not informative to investors?…
Appendix D
Examples of previously disclosed risks of material misstatement
Example 1: Extract from the audit report issued by PricewaterhouseCoopers to ICAP
plc in 2014
“Area of Focus.
Goodwill impairment assessment.
We focused on this area due to the size of the goodwill balance and because the
directors’ assessment of the carrying value of the Group’s cash generating units
(CGUs) involves judgements about the future results of the business and the discount rate applied to future cash flow forecasts.” (p. 91).
Extract from the intangible assets footnote in the 2013 annual report of ICAP plc
“This process requires the exercise of significant judgement by management; if the
estimates made prove to be incorrect or performance does not meet expectations
which affect the amount and timing of future cash flows, goodwill and intangible
assets may become impaired in future periods.” (p. 98).
Example 2: Extract from the audit report issued by KPMG to Rathbone Brother plc
on April 15, 2014
“The risk: Note 32 refers to two related legal claims that are ongoing. The directors
have determined that no provision should be raised in respect of these claims as at
31 December 2013 as they believe that it is more likely than not that any final judgment in relation to these claims will not result in a liability against the group and, on
this basis, have disclosed them as a contingent liability. Due to the complexity of the
two cases and the risks concerning the sufficiency of the insurance cover, the assessment of whether a liability is probable, possible or remote is considered to be inherently subjective and the amounts involved are potentially material.” (p. 73).
Extract from the Rathbone Brothers plc earnings announcement issued
on February 20, 2014
“Legal proceedings: As reported in the 2012 report and accounts, a claim relating to
the management of a Jersey trust has been filed against a former employee (and director) of a former subsidiary and others (and that former subsidiary has recently been
joined in as a defendant). In addition, the company issued proceedings against certain
of its civil liability (professional indemnity) insurers in respect of the former employee’s potential liabilities arising out of the Jersey claim. In November 2013 the company announced that judgment had been handed down following the trial in the Commercial Court in London in respect of the insurance case. In December, the company
and the former employee in question decided to appeal subrogation aspects of the
13
C. S. Lennox et al.
judgment and our insurers also decided to appeal coverage aspects of the judgment.
The hearing of those appeals before the Court of Appeal is expected to take place in
the second half of 2014. The underlying Jersey claim is now expected to come to trial
towards the end of 2015. Further detail on these matters is set out in note 8.”
Acknowledgements We thank Mark DeFond, Jere Francis, Chris Hogan, Bill Kinney, Jeff Pittman, Joe
Schroeder (AAA discussant), Theodore Sougiannis, Mike Wilkins, and the workshop participants at Baylor University, the University of California at Riverside, the University of Illinois Urbana-Champaign,
the 2016 AAA Auditing Midyear Conference, and the 2016 International Symposium on Audit Research
for their helpful comments and suggestions. We are grateful for research funding from the McCombs
Research Excellence Grant Program and the McCombs Undergraduate Research Assistant Program. We
thank Emily Baker, Sid Chandrashekar, Diana Choi, Jessie Hu, Minjae Kim, John Menefee, Catherine
Quintana, Stephen Tran, Jinghua Xing, Tingya Hu, Julia Rossdeutscher, Ting-Ting Wang, Samantha
Wendt, and Qiang Wei for their research assistance.
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