lee et al - 2006 - auditor conservatism and audit quality - evidence from ipo earnings forecasts

Auditor Conservatism and Audit
Quality: Evidence from IPO
Earnings Forecasts
Philip J. Lee,
1
Sarah J. Taylor
2
and Stephen L. Taylor
3
1
University of Sydney, Australia
2
University of Melbourne, Australia
3
University of New South Wales and Capital Markets CRC Ltd, Australia
We investigate the relation between a proxy for differential
audit quality and both the (ex post) accuracy and conservatism
of audited earnings forecasts provided in Australian initial
public offering (IPO) prospectuses. For the period we examine,
most Australian IPO prospectuses include an earnings
forecast (i.e., disclosure is not ‘voluntary’), and the auditor
must be satisfied prior to signing off on the prospectus. After
controlling for other factors associated with forecast error,
there is some evidence that forecasts audited by Big 6 auditors
prove more accurate than those audited by a non-Big 6 auditor,
although this result is not robust across alternative measures of
forecast accuracy. In contrast, our finding of significantly less
optimistic bias for forecasts associated with Big 6 auditors is
robust to alternative measures of forecast bias. We interpret
these results as being consistent with the argument that the
economic demand for differential audit quality reflects the

same factors that underlie the demand for conservative
financial reporting.
Key words: audit quality, initial public offering, management
earnings forecasts, conservatism.
SUMMARY
Audit quality is not easily observed, and as a result
researchers have increasingly relied on measurable
attributes of audited financial statements. This is
premised on the assumption that the quality of
financial statement data is a joint function of
management representations and the audit process.
However, we observe that many prior studies rely
on the assumption that higher quality financial
statement data (and by inference, higher quality
auditing) will be reflected in greater accuracy. One
such example is the attempt to link proxies for
differential audit quality (e.g., Big 6 auditors) with
the absolute value of unexpected accruals. On the
other hand, extant research also suggests that
higher quality auditors may be associated with
more conservative financial reporting. We draw
attention to the potential conflict between the
accuracy and conservatism of audited financial
data, as conservatism implies a directional bias.
Such a bias implies that attempts to link audit
quality with accuracy may be confounded.
Correspondence to: Stephen L. Taylor, School of Accounting,
University of New South Wales, NSW 2052, Australia. Email:

International Journal of Auditing

Int. J. Audit. 10: 183–199 (2006)
ISSN 1090-6738
© 2006 The Author(s)
Journal compilation © 2006 Blackwell Publishing Ltd, 9600 Garsington Rd, Oxford OX4 2DQ,
UK and Main St., Malden, MA 01248, USA.
We test our theory on a relatively unique setting,
namely the quasi-compulsory provision of audited
earnings forecasts by Australian initial public
offerings (IPOs). By comparing the forecast result
with the actual result subsequently reported, we
are able to directly test the competing theories that
high quality auditing (in this case, Big 6 auditors)
are associated with either more accurate or more
conservative earnings forecasts. We control for
several other factors expected to be associated with
forecast accuracy and/or bias. Our evidence also
extends prior research which has relied on
measuring attributes of earnings forecasts made by
IPOs in environments where the provision of a
forecast is voluntary, rather than mandatory. By
examining mandatory forecasts, we effectively
control for factors that may be associated with
forecast accuracy and/or bias but which are also
determinants of the decision to voluntarily provide
such a forecast in the first place.
Our results support the view that high quality
auditing is associated with more conservative
reporting. Although we find some evidence that
forecasts audited by Big 6 auditors are more
accurate than forecasts audited by non-Big 6

auditors, this result is not robust to alternative
ways of measuring the forecast error. On the other
hand, evidence that forecasts audited by Big 6
auditors are more conservative proves to be highly
robust. We interpret our evidence as providing
support for the argument that the derived demand
for conservative financial reporting is also reflected
in the demand for high quality auditing, and so
audit quality is associated with conservative
reporting.
1. INTRODUCTION
It is widely accepted that auditors add value to
financial statements by reducing the likelihood of
deliberate misreporting (Watts & Zimmerman,
1986). However, although this suggests that higher
quality auditing should be associated with more
accurate (i.e., less biased) financial reporting, we
also note that at least one form of bias in financial
reporting, namely conservatism, has also been
argued to be associated with the quality of financial
reporting (Watts, 2003). Because conservative
accounting can facilitate the monitoring role of
financial reporting data (Ball & Shivakumar, 2005),
it is not surprising that the application of
conservatism within financial reporting has been
argued to have evolved in conjunction with the
demand for independent verification by external
auditors (Watts, 2003). Indeed, recent criticisms
of the accounting profession (e.g., Levitt, 1998),
and especially the controversy over auditor

independence, focus almost exclusively on alleged
overstatements of periodic results (Ruddock et al.,
2006). This further highlights the extent to which
auditors are presumed to ensure a certain degree of
conservatism in audited financial reports.
We investigate the extent to which a widely
used proxy for differential audit quality (i.e., Big 6
versus non-Big 6) is associated with more precise
and/or less optimistically biased financial data.
1
Our analysis is motivated by recognition that
accuracy (i.e., precision) and conservatism (i.e.,
less optimistic or ‘downward’ bias) are not the
same, and in fact are competing, rather than
complementary attributes of financial reporting
data. Put simply, if higher quality auditing is
associated with relatively more accurate financial
reporting, we would not expect to observe an
association between a proxy for audit quality and
a consistent conservative bias. Following the
arguments in Watts (2003), we expect that
conservatism associated with the use of Big 6
auditors is particularly valuable (and hence, most
likely to occur) where significant informational
asymmetries are present, and where audited
financial information is likely to be relied on. We
therefore examine the association between a proxy
for audit quality and both the accuracy and bias
(conservatism) in financial reporting data in such a
setting.

The setting we examine is the provision of
earnings forecasts in the prospectuses of Australian
initial public offerings (IPOs). Earnings forecasts
provided in Australian IPO prospectuses provide
a unique setting in which to examine the effect
of differential audit quality. Securities regulations
and the threat of litigation result in almost
no disclosures of forward looking financial
information by United States IPOs. In contrast,
relevant Australian securities regulations, operative
since 1991, are widely viewed as making the
provision of earnings forecasts ‘de facto’
mandatory for the majority of Australian industrial
IPOs (i.e., excluding mining IPOs), despite the
relatively imprecise wording of the legislation.
2
This influence is exacerbated by the relatively
severe penalties imposed by the Corporations Law
for the omission of ‘material information’ known
to the issuer. For the sample of IPOs we initially
184 P. J. Lee et al.
Int. J. Audit. 10: 183–199 (2006)© 2006 The Author(s)
Journal compilation © Blackwell Publishing Ltd. 2006
identify, almost 85% provide an explicit forecast of
expected earnings. This reduces the extent to which
any endogenous disclosure decision potentially
affects our tests relative to those that utilize less
frequent, voluntary disclosures.
3
Auditors also have less flexibility in reporting

on prospectus data than for periodic audits. Even
when the auditor does not provide an explicit
attestation to an earnings forecast, Australian
professional standards prohibit (and regulatory
guidelines reinforce) the auditor from signing the
prospectus if there are reservations about any
aspect of this document. They cannot provide a
‘qualified’ response.
4
We expect that the inability
to issue a qualified audit opinion (and thereby
signal uncertainty) will exacerbate the role of
conservatism as an attribute of audit quality.
Our expectation is that Big 6 auditors are at
least as concerned with avoiding reputation
costs that arise when forecasts (or, more generally,
assumptions implicit in historic audited results)
prove to be optimistic as they are with ensuring the
precision of the information to which they attest.
Likewise, we expect that the demand for audit
quality incorporates at least some expectation of
increased conservatism. Hence, attempts to identify
a relation between use of a Big 6 auditor and
forecast accuracy (i.e., the unsigned forecast error)
may be inconsistent with the underlying demand
for audit quality, and may be confounded by the
expected greater conservatism of Big 6 auditors.
Conversely, we would unambiguously expect to
find that forecasts audited by Big 6 auditors prove
to be more conservative, ex post, than those audited

by non-Big 6 auditors.
Our results are broadly consistent with these
predictions. Univariate tests show significant
differences when comparing measures of both
forecast accuracy and bias for Big 6 and non-Big
6 auditees. After controlling for other factors
expected to be associated with forecast accuracy we
find some evidence that forecasts attested to by Big
6 auditors are more accurate. However, this result
is not robust to alternative measures of forecast
accuracy. In contrast, we find consistent evidence of
greater conservatism (i.e., a lower optimistic bias)
among forecasts issued by IPO firms with Big 6
auditors, irrespective of the forecast error metric
used.
Our paper contributes in a number of ways. First,
we provide additional evidence of how auditors,
and differential audit quality, may add value
specifically in the IPO process. Although it has been
argued that the choice of a Big 6 auditor can serve
as a signalling mechanism for IPO firms (Datar
et al., 1991; Titman & Trueman, 1986), the process
by which auditors add value in such a setting has
been subject to relatively little empirical analysis.
5
Although a few studies examine earnings forecasts
voluntarily provided by Canadian IPOs (Davidson
& Neu, 1993; McConomy, 1998; Clarkson, 2000),
we argue that the conflicting results in these
studies reflect a failure to consider the incentives

which high quality auditors face in attesting to
accounting projections. The results of these studies
are inconsistent and sensitive to the exact model
of forecast error used (Clarkson, 2000). More
importantly, our sample largely avoids the
potential problem of endogenous voluntary
disclosure faced by these studies.
Second, we provide additional evidence
consistent with conservatism being one element
of auditor behaviour that underlies product
differentiation in auditing. Although a limited
number of studies examine the relation between
proxies for differential audit quality (i.e., Big 6) and
the output from the accrual accounting process,
these studies are limited to observing unusual
(i.e., unexpected) accruals, or rely on proxies for
identifying news dependent conservatism in
accounting.
6
In contrast, our use of audited
earnings forecasts allows us to directly measure the
extent of ex post accuracy and conservatism, and
consider the extent to which these are competing
objectives. We also examine the association
between audit quality and attributes of audited
information in an environment where expected
litigation costs are likely much lower than in the
United States. Although expected litigation costs
may be an important factor in creating a demand
for differential audit quality (Basu et al., 2001), we

also expect that the value of reputation effects, and
the underlying economic demand for conservatism
will result in evidence of an association between
audit quality and conservatism in a relatively low
litigation environment.
The remainder of the paper proceeds as follows.
Section 2 reviews prior evidence on the relation
between differential audit quality and conservative
financial reporting, and generates testable
hypotheses. Section 3 describes our data sources,
as well as providing evidence on the accuracy
and bias of earnings forecasts provided in IPO
prospectuses. Section 4 reports our primary results,
while Section 5 summarizes additional sensitivity
analysis. Section 6 concludes.
Auditor Conservatism and Audit Quality 185
Int. J. Audit. 10: 183–199 (2006)© 2006 The Author(s)
Journal compilation © Blackwell Publishing Ltd. 2006
2. BACKGROUND AND HYPOTHESES
2.1. Evidence of a link between audit quality
and reporting conservatism
As we have already noted, the underlying
contracting and informational demand for financial
reporting is also consistent with a demand for
conservatism, at least in so far as that conservatism
is a reflection of how the financial reporting
process reacts to new (or revised) information.
This is what Basu (1997) terms ‘news based’
conservatism, and what Ball & Shivakumar (2005)
describe as ‘conditional’ conservatism. At the same

time, it is logical to expect that the external
verification process (i.e., external auditors) will pay
heed to this demand for conservative reporting.
Although the auditor does not bear legal
responsibility for compiling the accounts, it is
beyond dispute that they are expected to influence
the outcome. Hence, it is expected that at least one
dimension of what is perceived as audit quality will
be the extent to which an ‘appropriate’ level of
conservatism is enforced by external auditors.
Evidence consistent with the conjecture that audit
quality is associated with increased conservatism
can be found in a number of forms.
First, there is evidence that the accrual
component of earnings, or at least the unexpected
component thereof, is inversely related to the
common auditor size-based proxy for audit quality.
Several studies focus on the link between audit
quality (proxied by Big 6) and the accrual
component of earnings.
7
Francis et al. (1999)
demonstrate that the decision to use a Big 6 auditor
is positively related to firms’ endogenous
propensity to generate accruals, as proxied by the
length of their operating cycle (current accruals)
and their capital intensity (non-current accruals).
Among studies that examine the link between Big 6
auditor choice and accruals, the most consistent
result is that Big 6 auditors are more conservative

than their non-Big 6 counterparts. Becker et al.
(1998) show that firms using Big 6 auditors typically
have lower unexpected accruals than other firms.
8
DeFond & Subramanyam (1998) report that firms
switching from a Big 6 to a non-Big 6 auditor appear
to implement more liberal accounting, as evidenced
by higher unexpected accruals. However, the
method used for estimating unexpected accruals
has been shown to have relatively low power in
identifying the unexpected component (Dechow
et al., 1995; McNicholls, 2001). As neither paper
identifies any specific incentive for managers to
exercise their discretion, interpretation of the
results is problematic.
Second, evidence of auditor conservatism is
evident in auditor reporting decisions. Francis &
Krishnan (1999) examine the relation between audit
firms’ propensity to issue modified audit reports
and the extent of accruals. They model the decision
to issue a modified report, and find that the
probability of a modified report increases with the
(absolute) level of accruals. However, the result is
strongest for firms with large positive accruals.
When firms are partitioned into those with Big 6
auditors and others, the relation between accruals
and the propensity to issue modified audit reports
is confined to Big 6 auditors. This is consistent with
Big 6 auditors being more conservative than
non-Big 6 auditors.

Third, there are studies that examine the extent
to which earnings incorporate economic losses on a
more timely basis than economic gains. Basu et al.
(2001) show that the asymmetric timeliness of bad
news in earnings, as reflected in unexpected stock
returns, is significantly greater for Big 6 auditees
than others.
9
This result largely reflects the effect of
more conservative operating accruals, rather than
extraordinary items or discontinued operations.
Basu et al. also show that negative earnings changes
are less persistent for Big 6 auditees than other
firms, which is consistent with greater
conservatism by Big 6 audit firms. However, tests
such as these rely on the identification of news
based conservatism from either contemporaneous
share price movements or from the time series
behaviour of earnings. We adopt a broader, but
nevertheless related perspective of conservatism,
and expect that forward looking financial
information attested by high quality auditors will
prove ex post to be relatively more conservative than
otherwise. This is consistent with the view that
conservative forecasts will be less likely to
anticipate gains than losses, and so by deliberate
understatement of the forecast and/or delayed
recognition of gains (or even expected gains) until
the first post-forecast result, the forecast result may
provetobeex post conservative. Whether this

results in reduced accuracy is an empirical question
that we also address.
2.2. IPO earnings forecasts and audit quality
Apart from the various methods discussed above
of identifying conservatism as one dimension of
186 P. J. Lee et al.
Int. J. Audit. 10: 183–199 (2006)© 2006 The Author(s)
Journal compilation © Blackwell Publishing Ltd. 2006
audit quality, there are also a small number of
studies that examine properties of Canadian IPO
earnings forecasts and their relation to either the
type of audit requirement and/or the identity of
the auditor. These studies reflect the frequent
voluntary provision of earnings forecasts at the
time of an IPO. McConomy (1998) argues that
auditing is expected to reduce the extent of any
positive bias which (otherwise unaudited)
information is likely to display. McConomy
compares the ex post accuracy and bias of earnings
forecasts prior to a 1989 requirement that the
forecasts be audited, rather than reviewed, and
finds a significant reduction in optimistic bias, but
relatively little improvement in accuracy. Our
interpretation of these results is that auditors adopt
a more conservative approach as their degree of
responsibility increases, although it is also possible
that firms most likely to provide optimistic
forecasts elected not to do so after the introduction
of the audit requirements.
Using a sample of Canadian IPOs from 1983–87,

Davidson & Neu (1993) show that earnings
forecasts reviewed by Big 6 auditors are
significantly less accurate than those reviewed by
non-Big 6 auditors. They explain this result as a
product of less post-listing earnings management
by Big 6 auditees. In effect, Davidson & Neu
assume that differential audit quality has no direct
effect on the quality of earnings forecasts, but at the
same time does act to constrain opportunistic
earnings management in the period following the
IPO. Exactly why auditors would not care about
earnings forecasts, yet actively intervene to
constrain accounting policies is not clear, except
that the review (as distinct from audit) requirement
that applied to Canadian IPO earnings forecasts
may effectively reduce the significance of auditor
reputation.
Another explanation for the result reported by
Davidson & Neu (1993) is that the relation between
differential audit quality and forecast properties
may be sensitive to the choice of variables used
to control for firm-specific and period-specific
uncertainty. Clarkson (2000) examines forecast
accuracy and bias in both the review (1984–87) and
audit (1992–95) regimes, and shows that Davidson
& Neu’s primary result is sensitive to the choice
of variables used to control for business risk, which
in turn is expected to affect forecast accuracy.
When a similar test is performed on the audit
regime sample, earnings forecasts audited by Big

6 auditors are significantly more accurate than
others. For a measure of forecast bias, Clarkson
finds no significant difference between those
audited by Big 6 and non-Big 6 auditors, in either
the audit or review regimes.
10
However, while the results reported by Clarkson
(2000) suggest that forecasts audited by Big 6
auditors are significantly more accurate but not
significantly less biased, we note that the decision
by Canadian IPO firms to provide an earnings
forecast is voluntary. Canadian IPO firms that hire
non-Big 6 auditors are typically riskier (Clarkson &
Simunic, 1994), and auditors may be relatively less
willing to face possible adverse effects of attesting
to forecasts by risky firms. Hence, the endogenous
forecast decision potentially biases Clarkson’s tests
towards finding that forecasts audited by Big 6
auditors are significantly more accurate. A better
specified test is possible in an environment where
the earnings forecasts are a ‘routine’ part of the
prospectus, as they are for the Australian IPOs we
examine.
11
2.3. Hypotheses
Based on the evidence outlined above, we adopt
the view that differential audit quality acts to
constrain, or at least delay, relatively aggressive
reporting practices. Hence, the primary evidence of
audit quality effects is most likely to occur as

greater conservatism, rather than improved
accuracy. If high quality auditing results in the
application of relatively conservative constraints,
then users have less concern at possible
‘overstatements’ than otherwise. In the context of
IPOs providing earnings forecasts at the time of
going public, we expect that conservatism will be
realized via forecasts which prove, ex post,tobe
more conservative. Even if ex post evidence of
conservative forecasts reflects some degree of
upwards earnings management in the first
post-listing result, this still reflects the deferral of
‘good news’ and/or more aggressive reporting to a
later point than explicitly incorporating it in the
forecast result. Moreover, it is hard to imagine that
an auditor who attests to conservative forecasts
would simply allow aggressive accounting in the
subsequent result. High quality auditors also will
likely face higher costs where they are found to
have endorsed over-optimistic forecasts, as
compared to ‘excessive’ conservatism.
On the other hand, systematically conservative
estimates of future results would be expected to
reduce the overall level of accuracy that would
Auditor Conservatism and Audit Quality 187
Int. J. Audit. 10: 183–199 (2006)© 2006 The Author(s)
Journal compilation © Blackwell Publishing Ltd. 2006
arise from otherwise unbiased estimates, with the
result that higher quality auditors may not be
associated with forecasts which prove, ex post,tobe

more accurate. Ultimately, the extent to which audit
quality is associated with either conservatism or
accuracy is an empirical question. Hence, we test
the following two hypotheses, both of which are
stated in the null form:
H1: There is no association between audit quality
and forecast accuracy.
H2: There is no association between audit quality
and forecast bias.
3. DATA
3.1. Sample
Our sample begins with all Australian industrial
IPOs between 1991 and 1998. Our data begins at
1991 to reflect the effect of changes to the
Corporations Law. Mining IPOs are excluded, as
they typically do not forecast earnings, primarily
because this practice is actively discouraged for
exploration firms. Trusts and pooled development
funds are also excluded, due to the differences in
operating structure and taxation treatment of
dividends and capital distributions. The resulting
sample of IPOs comprises 220 firms, of which 184
provide earnings forecasts with a horizon of at least
60 days. However, five of these firms were
eliminated because the company was delisted prior
to the end of the forecast period and/or the
forecast could not be matched with actual results.
Table 1 provides a summary of the temporal
distribution and industry membership of the
sample firms. Panel A shows that there is some

temporal clustering, consistent with the existence
of ‘hot’ and ‘cold’ issue markets. Panel B indicates
that the sample is relatively evenly distributed
across the five broad industry groupings, with the
exception of Financial services, which has a lower
representation.
In order to test our hypotheses, a proxy for
differential audit quality is required. Consistent
with theory (DeAngelo, 1981) and an over-
whelming amount of empirical evidence (Hay et al.,
2006), we use the conventional Big 6/non-Big 6
distinction. Our focus on the most common
method of identifying high quality auditors is also
motivated by our desire to highlight the underlying
tension between the effects of audit quality (as
conventionally measured) on forecast accuracy as
distinct from the effect of forecast bias (i.e.,
conservatism effects), rather than complicating the
analysis with more equivocal proxies for
differential audit quality. Other possible indicators
of high quality auditors such as client industry
specialization have mixed support. For example,
although Craswell et al. (1995) report evidence that
industry specialist auditors earn significant audit
fee premiums, more recent evidence suggests that
this premium was eroded as the audit market was
consolidated from a Big 8 through to a Big 6 and,
finally, a Big 4 (Ferguson & Stokes, 2002). This is
consistent with a reduced number of large
international audit firms making it more difficult

for any one of those firms to be seen as an industry
specialist, simply because the ‘random’ market
share in each client industry increases as the
number of competing audit firms declines.
Table 1: Summary of temporal distribution and
industry group membership details of 179 initial
public offerings for the period January 1991–June
1998
Panel A: Temporal distribution of industrial ipos
disclosing an earnings forecast
Year Number of firms Per cent
1991 4 2.2
1992 19 10.6
1993 54 30.2
1994 36 20.1
1995 12 6.7
1996 20 11.2
1997 29 16.2
1998 5
2.8
Total 179 100
Panel B: Industry group distribution
Group Number of firms Per cent
Services 50 27.9
Construction &
Development
39 21.8
Retail & Consumer/
Household Goods
29 16.2

Financial 11 6.1
Industrials 50
27.9
Total 179 100
Firms are classified as belonging to one of five
industry groups which are formed based on
Australian Stock Exchange Industry Classifications.
These groups are: Services, Construction
Development, Retail Consumer/Household Goods,
Financial, and Industrials. Mining firms are excluded
from the sample.
188 P. J. Lee et al.
Int. J. Audit. 10: 183–199 (2006)© 2006 The Author(s)
Journal compilation © Blackwell Publishing Ltd. 2006
3.2. Forecast errors
We hand collect data, and so carefully match the
forecast income number with the actual result. In
many cases, forecasts are provided for several
income definitions. When this occurs we match
forecast operating profit before tax (OPBT) with
actual. We prefer this measure relative to after-tax
earnings because discussion in several prospectuses
suggests that firms often forecast tax using the
nominal corporate tax rate rather than the expected
rate applicable to the calculation of income tax
expense.
12
Where firms do not forecast OPBT, an
alternative definition is used, either operating profit
after tax but before abnormal and extraordinary

items, operating profit after tax, or earnings before
interest and tax.
13
In all cases, forecast error is the
difference between forecast and actual, so that a
positive forecast error indicates optimism.
Descriptive data for forecast accuracy (i.e.,
absolute error) and forecast bias (i.e., signed error)
are presented in Table 2. Earnings forecast errors
are scaled two ways. First, we utilize issue size as a
deflator. This gives a feel for the possible ‘economic
significance’ of these forecast errors, relative to the
funds raised through the IPO. However, earnings
are for the firm as a whole, but the use of issue size
as a deflator reflects only the interest of the ‘new’
shareholders. Hence, we also measure earnings
forecast errors on a per share basis, where the
deflator is the issue price per share. Both of the
forecast error measures we report provide a more
intuitively ‘economic’ measure than simple error
percentages with respect to actual or forecast
earnings, and most closely corresponds with the
measures of forecast error (or ‘earnings surprise’)
used in other studies.
14
From Panel A of Table 2, the absolute forecast
error expressed relative to share price at the time
of the offering has a mean (median) error of
4.93% (1.48%). Turning to the extent of possible
bias, the data are consistent with forecast

errors being, on average, optimistic. However, the
median forecast error is negative which
Table 2: Descriptive statistics of forecast accuracy (Panel A) and bias (Panel B) for 179 IPO firms
Forecast accuracy is measured as the absolute value of (Forecast earnings less Actual earnings), while bias is
measured as (Forecast earnings less Actual earnings). Reported forecast measures (accuracy and bias) are scaled
by two alternate deflators (i) issue size and (ii) on a per share basis with the deflation by the issue price per share.
This results in two measures of forecast accuracy: error as a percentage of issue size and per share error deflated
by issue price. All figures are expressed as percentages.
Panel A: Forecast error for full sample (n = 179)
Mean Std. Dev Min. Median Max.
Abs (F-A)/Issue size 13.65 27.56 0 3.18 246.15
Abs (F-A) per share/Issue price per share 4.93 8.37 0 1.48 61.48
(F-A)/Issue size 2.46 30.68 -246.15 -0.33 107.71
(F-A) per share/Issue price per share 2.00 9.51 -34.22 -0.14 61.48
Panel B: Forecast error for non-Big 6 auditees (n = 54)
Mean Std. Dev Min. Median Max.
Abs (F-A)/Issue size 19.08 29.47 0 4.78 107.71
Abs (F-A) per share/Issue price per share 7.76 12.20 0 2.29 61.48
(F-A)/Issue size 13.70 32.38 -41.08 0.21 107.71
(F-A) per share/Issue price per share 5.34 13.46 -15.41 0.12 61.48
Panel C: Forecast error for Big 6 auditees (n = 125)
Mean Std. Dev Min. Median Max.
Abs (F-A)/Issue size 11.31* 26.48 0.03 2.29* 246.15
Abs (F-A) per share/Issue price per share 3.71*** 5.65 0.02 1.08* 34.22
(F-A)/Issue size -2.40*** 28.72 -246.15 -0.38 65.40
(F-A) per share/Issue price per share 0.55*** 6.74 -34.22 -0.22 23.09
*/**/*** = statistically significantly different from non-Big 6 auditees at 10%, 5%, 1% levels.
Auditor Conservatism and Audit Quality 189
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indicatesthat there are actually more forecasts
which are
ex post pessimistic than optimistic (100/179).
Expressed on a per share basis, the mean (median)
signed forecast error measured as a percentage
of the issue price is 2.00% (-0.14%).
15
Panel B of
Table 2 contains descriptive statistics for accuracy
and bias of non-Big 6 auditees while data for Big 6
auditees is contained in Panel C. Both measures of
absolute forecast error (i.e., forecast accuracy) have
means and medians that are significantly lower for
Big 6 auditees. However, for signed forecast errors
(i.e., forecast bias), only the mean is significantly
lower for Big 6 auditees. This is consistent with
the distribution of forecast errors for non-Big 6
auditees being more skewed, with a relatively
larger proportion of forecasts that prove, ex post,to
have large optimistic errors. However, given the
numerous systematic differences between Big 6
and non-Big 6 auditees documented in Table 3,
which may also be associated with the sign and size
of forecast errors, we caution against relying on
these univariate comparisons.
3.3. Control variables
In order to identify the effect of differential audit
quality on earnings forecast accuracy and bias,
we regress measures of forecast error on our
proxy for audit quality (i.e., a Big 6 dummy

variable), a measure of underwriter quality and
three composite control variables. Because we are
interested in the incremental effect of differential
audit quality rather than the determinants of
forecast error per se, we use principal component
analysis (Harman, 1976) to construct three
composite control variables (‘factors’), which
are intended to capture firm specific risk
(FIRMRISK), forecast characteristics (FORECAST)
and managerial incentives (INCENTIVES),
respectively.
16
The objective is simply to establish a limited
number of composite control variables where
each composite variable comprises a number of
measures that intuitively capture similar attributes.
We do not use orthogonalization procedures to
specifically minimize factor correlation.
17
Rather,
we pre-select the components of each factor, and
use principal component analysis to create the three
factors purely to simplify the presentation of our
analysis and to reduce the focus on individual
determinants of forecast error and bias. Of course,
where the components of a factor are highly
correlated, there are some efficiency gains from
simply maximizing the extent of the explained
dispersion across this set of variables before
attempting to explain the variation in forecast error

or bias.
18
Principal component analysis allows us to
isolate linear combinations of the potential control
variables that are likely to capture similar aspects
of the firm, its forecasting environment or the
incentives to make more accurate and/or less
biased forecasts. Hence, we construct an artificial
variable (i.e, factor) that is an optimally weighted
linear combination of the original variables. Each
factor is the normalized linear combination of the
assigned set of control variables with maximum
variance. Importantly, all of our results with respect
to the relation between audit quality and forecast
accuracy or bias are robust to simply estimating a
model with all of the individual control variables
rather than the three factors as independent
variables.
Our selection of possible control variables (and
the three resulting factors) is guided by prior
evidence on the determinants of IPO earnings
forecast accuracy and/or bias (e.g., Clarkson,
2000), as well as prior studies showing a relation
between proxies for firm-specific risk and
complexity that are ‘priced’ by auditors (Craswell
et al., 1995). Due to uncertainty about the future
and the inherent complexity of the firm’s
operations, managers typically forecast earnings
with some error. Possible proxies for firm specific
risk and complexity include the age of the firm,

firm size, leverage, the number of subsidiaries of
the IPO firm, whether or not the firm has foreign
operations, proportion of issue price not backed by
net tangible assets (i.e., a measure of ‘growth
options’ for the firm),
19
whether or not the firm had
a loss in the previous three years and the number
of risk factors highlighted in the IPO prospectus.
We include each of these measures in our first
estimated composite proxy, which we label
FIRMRISK.
20
Many of these variables are also used
to control for aspects of audit risk (i.e., number of
subsidiaries) in audit pricing models, which also
demonstrate evidence of differential audit quality
(Craswell et al., 1995).
In addition to the firm specific characteristics
included in our composite FIRMRISK measure
described above, forecast specific characteristics
may also be associated with the size and/or sign of
forecast errors. The length of time between the date
of making the forecast and the end of the period to
which it relates will affect the degree of confidence
190 P. J. Lee et al.
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Journal compilation © Blackwell Publishing Ltd. 2006
with which predictions can be made about the
future. Accordingly, the forecast horizon, measured

as the number of months between the prospectus
date and the end of the forecast period, is included
in our second composite proxy. We also control for
the level of detail with which the forecast is made.
A more detailed forecast likely reflects greater
confidence in the accuracy of the forecast. Forecast
detail is captured using a self constructed index,
which awards a score from 1 to 9 based on how
many of the following are disclosed in the forecast:
earnings, revenues, expenses, capital expenditures,
financing details, cash flows, dividends,
assumptions used in forecasting and sensitivity
analysis.
21
Finally, a dummy variable is used to
indicate whether the firm provided forecasts for
more than one financial year. Firms providing
forecasts for multiple years are expected to be more
confident about future outcomes, and as such are
likely to have lower forecast errors. These three
measures are combined in our second composite
proxy, which we label FORECAST.
It is also possible that IPO earnings forecasts
reflect some degree of moral hazard on the part of
those providing them. Lack of publicly available
information may provide managers with
opportunities to exploit investors, since the costs
of relying on an inaccurate earnings forecast are
generally borne by new investors. In contrast, if
managers intend to return to the capital market

they will have incentives to provide more accurate
forecasts to maintain investor confidence.
Accordingly, four variables are used to proxy
for competing managerial incentives: the level of
retained ownership, the proportion of newly-
raised funds paid to vendor shareholders, and
dummy variables for whether the firm conducted
a seasoned equity offering (SEO) in the two
years following the IPO and whether or not the
offer is a packaged or unit offer. The distinction
between IPOs where the offering is a package of
current (i.e, shares) and deferred (e.g., options)
equity purchase reflects evidence that among
Australian IPOs, unit IPOs represent a signalling
strategy intended to address concerns about the
quality of the firm’s business model that would
otherwise result in increased underpricing (Lee
et al., 2003a). We combine these four measures into
our third composite proxy, which we label
INCENTIVES.
Apart from our focus on the effect of differential
audit quality, another external party expected to
serve a monitoring function in relation to the
prospectus is the underwriter. Although not
directly responsible for the forecast provided in the
prospectus, underwriters typically have access to
superior information about the strategy and future
prospects of the firm which is relevant to valuation
of the IPO. Similar to Big 6 auditors, high quality
underwriters likely have their reputation at stake in

the event that a forecast is found to be extremely
inaccurate/biased. This leads to an expectation that
high quality underwriters will encourage IPO firms
to provide more accurate earnings forecasts. A
dummy variable is used to indicate if the IPO firm
used a high quality underwriter or not.
22
Finally, we include industry dummies in our
regressions to reflect possible industry-specific
variation in forecast attributes. This is especially
relevant to IPOs, where it is also possible that IPOs
cluster by type according to market conditions. We
use the broad industry groupings summarized in
Table 1.
Our model used to identify the effect of audit
quality on forecast accuracy and bias is therefore as
follows:
Forecast e rror a b IA_Big6 b FIRMRISK
b FORECAST b INCENTIV
12
34
=+ + +
+ EES
bUWQ bINDUSTRY e
56
+
++
(1)
where:
IA_BIG 6 equals 1 if the auditor is a BIG 6 auditor,

otherwise 0;
FIRMRISK is a composite factor capturing
firm-specific attributes that are likely to be
associated with the riskiness of the forecasting task;
FORECAST is a composite factor capturing
attributes of the forecast which are likely to be
associated with increased variation and/or bias;
INCENTIVES is a composite factor capturing
variables which are likely to be associated with
managers’ incentives to make accurate and/or
biased forecasts;
INDUSTRY is a numeric variable distinguishing
sample firms on industry groupings as outlined in
Table 1;
and e is an error term.
Descriptive data on the variables used to construct
our composite proxies, the underwriter quality
proxy and the auditor quality variable is reported
in Table 3. Panel A reports mean, standard
deviation, minimum, median and maximum values
for the continuous variables for the full sample and
the Big 6/non-Big 6 sub-samples. Data relating to
Auditor Conservatism and Audit Quality 191
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Table 3: Descriptive statistics of firm characteristics for a sample of 179 Australian industrial IPOs with a matched earnings forecast between
January 1991 and June 1998
Panel A contains mean, standard deviation, minimum, median and maximum for continuous variables for the full sample and the Big 6 and non-Big
6 audit sub-samples, as well as t-statistics comparing mean values for Big 6 and non-Big 6 auditees. Panel B contains frequency data for binary
variables, partitioned by audit quality and a chi-squared statistic comparing frequencies between Big 6 and non-Big 6 auditees.

Panel A: Descriptive statistics – continuous variables
Variable Mean Std. Dev Min. Median Max. Mean diff Student T Prob (t)
Firm age All 7.90 3.27 0 10 10 -1.05 -1.99 0.05**
Big 6 8.22 3.02 0 10 10
non-Big 6 7.17 3.73 0 10 10
Total assets ($’000) All 549,290 3,108,500 2,058 33,682 30,218,000 -725,000 -1.44 0.15
Big 6 767,867 3,702,444 3,358 46,243 30,218,000
non-Big 6 43,324 82,082 2,058 18,453 543,400
Issue size ($’000) All 147,440 1,066,600 61 14,000 14,153,260 -187,000 -1.08 0.28
Big 6 203,908 1,273,569 61 20,000 14,153,260
non-Big 6 16,730 33,801 1,300 8,000 235,000
Leverage (%) All 39.59 23.90 0 41.73 94.95 -6.82 -1.76 0.08*
Big 6 41.64 23.38 0 43.80 94.95
non-Big 6 34.83 24.63 0 36.86 91.94
No. of subsidiaries All 6.70 12.29 0 4 94 -3.39 -1.7 0.09*
Big 6 7.72 13.60 0 5 94
non-Big 6 4.33 8.11 0 3 58
Growth options All 0.61 0.34 0 0.67 1 -0.02 -0.27 0.78
Big 6 0.62 0.33 0 0.66 1
non-Big 6 0.60 0.34 0 0.69 1
Risk factors All 8.20 4.65 0 8 32 0.03 0.04 0.96
Big 6 8.19 4.95 0 8 32
non-Big 6 8.22 3.93 0 9 18
Retained ownership (%) All 45.47 26.90 0 50 99.20 4.23 0.97 0.34
Big 6 44.19 28.02 0 49.50 99.20
non-Big 6 48.42 24.09 0 50.50 95.40
Funds to vendor (%) All 26.54 39.54 0 0 100 -17.20 -2.72 0.01***
Big 6 31.72 41.87 0 0 100
non-Big 6 14.53 30.64 0 0 100
Forecast detail index All 4.93 1.45 1 5 9 -0.80 -3.47 0.00***

Big 6 5.17 1.44 1 5 9
non-Big 6 4.37 1.34 2 4 7
Horizon (months) All 8.66 3.36 2.1 8.4 18.2 0.16 0.29 0.77
Big 6 8.61 3.49 2.1 8.3 18.2
non-Big 6 8.77 3.06 2.5 8.4 16.6
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Journal compilation © Blackwell Publishing Ltd. 2006
Variable defintions
Age The length of prior operating history measured in years from 0 to 10 years. Firms older than 10 years are given a value of 10.
Assets Dollar value of assets immediately after the IPO is completed.
Issue size Dollar value of funds raised in the IPO.
Leverage Total liabilities/total assets.
Subsidiaries The number of subsidiaries the IPO firm has.
Growth options
1 −
()
Net tangible assets per share excluding cash
Issue price per sha
rre
.
Risk factors The number of risk factors specifically highlighted in the prospectus.
Retained ownership Proportion of equity retained by previous owners (i.e., 1 - (Proportion of post-listing paid-up equity sold to the public)).
Vendor funds Proportion of funds raised paid to vendor shareholders for sale of shares.
FC detail index A score from 1–9 used to capture the detail of forecast information provided.
Horizon The number of months between the prospectus date and the end of the financial period to which the forecast relates.
*/**/*** Statistically significant at 10%, 5%, 1%.
Panel B: Frequency distributions – binary variables partitioned by audit quality
Non-Big 6 Big 6 Chi Square (Prob)
UWQ Low 34 (19%) 46 (26%) 10.44

UWQ High 20 (11%) 79 (44%) (0.00)***
No expert attest FC 4 (2%) 3 (2%) 2.52
Expert attest FC 50 (28%) 122 (68%) (0.11)
No multi-year FC 25 (14%) 65 (36%) 0.49
Multi-year forecast 29 (16%) 60 (34%) (0.48)
No loss previous 3 years 39 (22%) 97 (54%) 0.60
Loss in previous 3 years 15 (8%) 28 (16%) (0.44)
No foreign operations 30 (17%) 60 (34%) 0.86
Foreign operations 24 (13%) 65 (36%) (0.35)
Not a unit offer 46 (26%) 110 (61%) 0.27
Unit offer 8 (4%) 15 (9%) (0.61)
No SEO in next 2 years 31 (17%) 67 (37%) 0.22
SEO in next 2 years 23 (13%) 58 (33%) (0.64)
Variable definitions
Big 6 = 1 if the firm had a Big 6 auditor, 0 otherwise.
UWQ = 1 if the firm had a high quality underwriter, 0 otherwise.
Expert attest = 1 if an expert attested to the earnings forecast, 0 otherwise.
Multi-year FC = 1 if the firm provided forecasts for multiple financial years, 0 otherwise.
Loss = 1 if the firm had a loss in the previous three years, 0 otherwise.
Foreign op. = 1 if the firm had substantial foreign operations, 0 otherwise.
Unit offer = 1 if the IPO was a unit offer consisting of a package of shares and options, 0 otherwise.
SEO = 1 if the firm had a seasoned equity offering in the two years following the IPO, 0 otherwise.
*/**/*** = Statistically significant at 10%, 5%, 1%.
Auditor Conservatism and Audit Quality 193
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total assets and issue size illustrates the fact that the
sample is skewed with median values representing
less than 10% of the mean. Similarly, although
the proportion of funds received by vendor

shareholders is approximately 26% on average,
more than half the IPOs in this sample retain within
the firm all funds raised from the IPO. Most
other variables are more normally distributed. A
comparison of continuous variables for Big 6 and
non-Big 6 auditees reveals a number of differences
between the two groups with the most significant
being that Big 6 auditees are typically older firms,
provide more detailed forecast information and
a greater proportion of funds from the offer are
paid to the vendor shareholder. Because these
differences would also be expected to be associated
with the sign and size of forecast errors, controlling
for these variables in tests of differences in forecast
errors between Big 6 and non-Big 6 auditees is
essential.
Panel B of Table 3 contains descriptive data
relating to the binary variables, partitioned into
Big 6 and non-Big 6 auditees. For the sample
companies, approximately 70% have a Big 6
auditor, with 55% having a high quality
underwriter. There is a statistically significant
association between auditor and underwriter
quality (chi-square significant at the 1% level),
consistent with the argument that prestigious
underwriters are more likely to rely on high quality
auditors as a means of reducing the underwriting
risk (Willenborg, 1999). Roughly half the sample
companies provided forecasts for more than one
financial year.

4. RESULTS
In Table 4 we report the results of White’s (1980)
adjusted OLS regressions explaining variation in
forecast accuracy (Panel A) and bias (Panel B).
23
For
each of the two forecast attributes we report results
using both measures of forecast error, namely
forecast error deflated by issue size and forecast
error per share deflated by the issue price per
share. For convenience we delete the coefficients
for the industry dummies, which are typically
insignificant, save for the industrials dummy in the
first regression reported in Panel B, where the
coefficient is negative and significant at the 10%
level. The results for our three control variables
constructed as factors are frequently significant,
and the intuitive nature of these results provides
support for our attempt to adequately control for
‘other’ determinants of forecast accuracy and bias.
24
In regressions explaining forecast accuracy, our
proxy for firm-specific risk (FIRMRISK) is not
significant. However, our FORECAST factor has a
significant positive coefficient, consistent with the
expectation that forecast errors will be larger for
firms which provide relatively less detailed
forecasts. When we use forecast error scaled by
issue size, we also find that errors are larger where
the incentives to make accurate forecasts are

relatively low (INCENTIVES). However, when we
use forecast error per share scaled by issue price,
the t-statistic is below normally acceptable levels of
statistical significance. For models of forecast bias,
we find that our FIRMRISK factor has a significant,
positive coefficient. Hence, to the extent that risk is
associated with larger forecast errors, this evidently
occurs via larger optimistic (rather than
pessimistic) errors.
We also note that our proxy for underwriter
reputation is significantly negatively associated
with absolute forecast errors (Panel A), consistent
with underwriters being concerned with the
accuracy of the forecasted earnings, which
presumably impact upon valuation. In contrast,
there is no statistically significant association
between underwriter reputation and forecast bias
(Panel B), suggesting that underwriters are equally
concerned about positive and negative forecast
errors.
Turning to our results for differential audit
quality, the regression results reported in Panel A of
Table 4 show a negative relation between absolute
forecast error and audit quality. However, this
result is only significant in tests where the
dependent variable is measured as the price
deflated forecast error per share. Hence, it appears
that audit quality does impact positively on forecast
accuracy, but this influence is not robust to the
method of measuring forecast error. In contrast, the

results in Panel B show both measures of forecast
bias are significantly negatively associated with
our audit quality indicator, consistent with the
argument that Big 6 auditors are associated with
more conservative forecasts. This result differs from
Clarkson (2000), although as we have noted,
the endogenous voluntary disclosure decision
potentially biases against him finding such a result.
Overall, the results for our audit quality proxy
are strongly supportive of our expectation that
audit quality and conservatism are synonymous.
Irrespective of which forecast error measure we use,
there is a statistically significant association. In other
194 P. J. Lee et al.
Int. J. Audit. 10: 183–199 (2006)© 2006 The Author(s)
Journal compilation © Blackwell Publishing Ltd. 2006
words, we consistently reject the null form
of hypothesis 2. On the other hand, evidence of
a link between our proxy for audit quality and
forecast accuracy (i.e., rejection of the null form
of hypothesis 1) is contingent on the measure
of forecast error used. Moreover, the coefficient
values for the Big 6 dummy in bias regressions
are consistently larger (in absolute value) than for
those which occur in the absolute error (i.e.,
accuracy) regressions. We interpret this as further
evidence that differential audit quality, at least in
the context of information in IPO prospectuses, is
more closely related to attesting to a lower
likelihood of deliberate optimism, rather than

absolute accuracy.
5. FURTHER TESTS
We perform a number of additional tests to ensure
the robustness of our conclusions. These involve
the use of additional forecast error metrics, the
possible effect of outliers, the extent to which
temporal clustering of IPOs influences our results,
the sensitivity of our results to the use of differing
measures of income, and the possibility that
endogenous disclosure choice may still affect our
Table 4: Multivariate analysis of determinants of forecast error for a sample of 179 Australian industrial IPOs
between January 1991 and June 1998
Panel A reports regression models of determinants of accuracy, while Panel B reports regression models of
determinants of bias. The first model in each of Panels A and B (i.e., Models 1 and 3) report results using forecast
error measured as a percentage of issue size for the dependent variable. The second model in Panels A and B (i.e.,
Models 2 and 4) use forecast error per share deflated by issue price per share as the dependent variable.
Independent variables are calculated using factor analysis of a range of variables expected to proxy for potential
determinants of forecast accuracy. Industry group dummy variables are included in the estimation of all models
but are not reported due to insignificant t-statistics for the variables in all but one of the models. T-statistics are
calculated using White’s (1980) heteroscedasticity-consistent standard errors and are reported in parentheses
below parameter estimates.
Panel A: Determinants of forecast accuracy
Intercept FIRMRISK FORECAST INCENTIVES IA_BIG 6 UWQ Adj. R
2
Model 1
Abs. FE
Issue size
18.4357
(3.71)***
-0.0323

(-0.44)
3.0314
(2.79)**
0.1332
(3.37)***
-3.8457
(-1.06)
-6.5260
(-2.03)**
0.16
Model 2
Abs. FE per share
Issue price per
share
7.8915
(4.04)***
-0.0077
(-0.27)
1.2912
(3.03)***
0.0076
(0.49)
-3.1640
(-2.23)**
-1.7338
(-1.38)
0.09
Panel B: Determinants of forecast bias
Model 3
FE

Issue Size
22.9283
(3.92)***
0.1895
(2.20)**
1.2832
(1.01)
-0.0288
(-0.62)
-14.5927
(-3.44)***
2.7138
(0.72)
0.12
Model 4
FE per share
Issue price per
share
9.0313
(4.17)***
0.0934
(2.93)***
1.0220
(2.16)**
-0.0240
(-1.39)
-4.6975
(-2.98)***
0.5697
(0.41)

0.14
Independent variables
FIRMRISK – This factor is estimated as a combination of the following variables as defined in Table 3: firm age,
size, leverage, number of subsidiaries, foreign operations dummy, growth options, loss dummy and risk factors.
FORECAST – This factor is estimated as a combination of the following variables as defined in Table 3: horizon,
multi-year forecast dummy and forecast detail index.
INCENTIVES – This factor is estimated as a combination of the following variables as defined in Table 3: retained
ownership, vendor shares sold, SEO dummy and unit offering dummy.
IA_BIG6 equals 1 if the audit firm is one of the Big 6.
UWQ equals 1 if the IPO underwriter is ranked as a high quality underwriter.
*/**/*** = statistically significant at 10%, 5%, 1%.
Auditor Conservatism and Audit Quality 195
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results, even though the vast majority of IPO firms
provide an earnings forecast in the prospectus.
The results reported in Table 4 use the two
measures of forecast error that we believe are most
amenable to an economic interpretation, and which
are used almost exclusively in studies of forecast
error and more broadly the capital market effects
thereof (Kothari, 2001). However, some of the
studies we have cited which examine the forecast
accuracy of Canadian IPO earnings forecasts use
a variety of measures, including forecast error
deflated by forecast earnings, forecast error deflated
by actual earnings, and the log of forecast error
(adjusted to deal with negative errors – see
Clarkson, 2000). Although there are some variations
in the (unreported) individual regression results,

our primary results are qualitatively similar.
25
Most
importantly, all these approaches yield the same
conclusion for the effect of differential audit quality,
namely that high quality auditors are not associated
with significantly improved forecast accuracy, but
are associated with ex post evidence of significantly
more conservative forecasts.
The descriptive data provided in Table 2
demonstrates that there are a number of relatively
extreme forecast errors. In order to verify that our
results are not unduly affected by the inclusion of
these observations, we repeat the analysis in Table 4
after winsorizing all extreme values at the
equivalent of three standard deviations above or
below the mean. Our results remain qualitatively
similar to those reported. We also perform the same
tests with these values deleted, and once again find
qualitatively similar results.
In Table 1 we show that there is some temporal
clustering of IPOs, consistent with the popular
argument that IPO markets are cyclical. However,
when we break our sample of IPOs into three
time periods (1991–93 (n = 79), 1994–95 (n = 49),
and 1996–98 (n = 56)), our results are largely
unchanged.
Given that not all of our sample IPOs have the
earnings forecast error measured using operating
profit before tax, we repeat our tests using only

those firms for which OPBT is the measure of
income used to calculate forecast errors. For the
reduced sample of 164 firms, our results are
qualitatively similar to those reported in Table 4.
The sample of earnings forecasts we examine
represent approximately 85% of identifiable
industrial IPOs for the period from which we draw.
Although the very high rate of forecast disclosure is
expected to substantially reduce concerns about
self-selection, we make comparison of those IPO
firms providing a forecast and those that do not, on
each of the independent variables utilized in the
regressions in Table 4. There is weak evidence
that disclosers are larger and more leveraged, but
essentially the two groups are similar on the
dimensions tested.
6. CONCLUSION
This paper is motivated by two concerns. First,
regulators and other interest groups have criticized
the reliability (i.e., accuracy and bias) of earnings
forecasts, which are ‘de facto’ mandatory in IPO
prospectuses. Second, while there is an extensive
literature examining indirect evidence of
differential audit quality, there is relatively little
direct evidence based on ‘output’ from the audit
process. Given that there is evidence consistent with
IPO firms choosing audit quality based on
signalling considerations, the properties of the most
observable financial output in that setting (i.e.,
earnings forecasts) is likely of considerable interest.

Our paper is also motivated by concerns that the
definition of differential audit quality is somewhat
confused. We argue that high quality auditors invest
in reputation, and are concerned that investment
be maintained. Hence, they likely place greater
weight on failing to identify excessively optimistic
financial reporting than avoiding conservatism.
The economic factors common to both the demand
for financial reporting and its verification by
independent auditors point to conservatism as an
important attribute of audited financial data (Watts,
2003). This leads to the prediction that earnings
forecast errors will be relatively more conservative,
but not necessarily more accurate, when they are
audited by a high quality auditor. Using the Big 6 as
a proxy for expected audit quality, our results
support this prediction.
Our results also potentially explain why
McConomy (1998) finds that a shift from a review
requirement for Canadian IPO earnings forecasts to
a requirement for a full audit is associated with
reduced bias but no greater accuracy. The greater
liability potentially faced by auditors under the
latter regime (irrespective of whether they are Big 6
or otherwise) is expected to increase the weight
auditors place on avoiding excessively optimistic
statements relative to conservatism (Basu et al.,
2001). Also, our results may reflect a better specified
test of the relation between forecast bias and
differential audit quality than Clarkson (2000), who

196 P. J. Lee et al.
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Journal compilation © Blackwell Publishing Ltd. 2006
is faced with the potentially confounding effect of an
(endogenous) voluntary disclosure decision.
There are a number of possible extensions to our
research. Because our focus is on the incremental
role of differential audit quality, we pay little
attention to the specific role of various other
influences on either the accuracy or bias of IPO
earnings forecasts. The relatively low explanatory
power of our models of forecast accuracy and
bias (all less than 20%) suggests the need for
some caution in interpreting our results, as well
as identifying an opportunity for subsequent
research. At the same time, it is also possible
that earnings management occurs in response to
the prior disclosure of an earnings target. The
incentives for such earnings management and
their role in determining the extent of earnings
management would appear to be worthy of further
investigation, as would the extent of any relation
between IPO firms’ choice of auditor and any
earnings or forecast management.
ACKNOWLEDGEMENTS
We acknowledge the financial support of the
Accounting Foundation within the University of
Sydney and the School of Accounting, University
of Technology, Sydney. We are grateful for the
comments of two anonymous referees, Stuart

Turley (editor), Sudipta Basu, Peter Clarkson, Julie
Cotter, Jeff Coulton, Zoltan Matolcsy, Don Stokes,
Julie Walker and Ron Webber, as well as seminar
participants at University of Technology, Sydney,
University of Queensland, the 2001 AAANZ
conference and the 2001 International Symposium
on Audit Research.
NOTES
1. Throughout the paper we use the term Big 6 to
refer to the large international audit firms, as
this is consistent with the circumstances that
prevailed during the period we examine.
2. The relevant legislation is contained in Section
710 of the Corporations Law (previously s.
1022), which requires that a prospectus contain
‘. . . all such information as investors and
professional advisors would reasonably
require and expect in the prospectus for the
purpose of making an informed assessment as
to: a) the assets and liabilities, financial
position, profits and losses and prospects of the
corporation; and b) the rights attaching to the
securities’.
3. If forecast disclosure is voluntary, then it is
possible that the disclosure decision, the choice
of auditor, and the optimal degree of
conservatism (or accuracy) are endogenously
determined. Of course, by focusing on IPO
forecasts, we may not be able to generalize to
other settings such as forecasts made by listed

firms, where the incentives for making accurate
and/or biased forecasts may be somewhat
different from the IPO setting.
4. Australian Auditing Standard (AUP) 3.1
‘Special Purpose Auditor’s Report’ states (para.
23):
Where the auditor has reason to believe that
there may be matters contained in or omitted
from the offer document which could cause
the offer document or report to be
misleading, they should bring the matters to
the attention of the directors or promoters. If
the matters are not resolved to the auditor’s
satisfaction, they should not consent to the
inclusion of their report in the offer
document.
5. We note in passing that signalling models such
as Datar et al. (1991) typically assume that audit
quality is associated with greater information
precision. To the extent that audit quality is
more strongly associated with conservatism, a
key assumption underlying these models may
be violated.
6. Examples include Becker et al. (1998), DeFond
& Subramanyam (1998), Francis et al. (1999),
Francis & Krishnan (1999), Lennox (1999), and
Basu et al. (2001).
7. Apart from the papers we review, there are
several studies which demonstrate possible
auditor conservatism. These are reviewed by

Kinney & Martin (1994).
8. Becker et al. (1998) also report that the absolute
value of discretionary accruals is lower for Big 6
auditees.
9. The tests used by Basu et al. (2001) reflect a
‘news based’ definition of conservatism, which
is premised on the assumption that stock prices
rapidly and asymmetrically reflect both good
and bad news. Hence the test of conservatism
becomes the extent to which good versus
bad news in stock prices is reflected in
contemporaneous earnings.
10. For both the audit and review regimes,
Clarkson reports a negative, but insignificant
coefficient for a dummy variable coded 1 for
Big 6 auditors.
Auditor Conservatism and Audit Quality 197
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Journal compilation © Blackwell Publishing Ltd. 2006
11. Brown et al. (2000) provide some evidence on
differential audit quality and Australian IPO
forecast properties. However, their primary
focus is on dividend forecasts, and they pool
observations before and after the regulatory
changes in 1991, which we argue introduced de
facto mandatory earnings forecasts.
12. Several firms acknowledged in the discussion
accompanying their forecasts that tax expense
was not expected to correspond with the
forecast.

13. We consider the possible influence of variation
in the measurement of forecast earnings in
Section 5.
14. See, for example, the studies cited by Kothari
(2001) in his extensive literature review of
capital markets research in accounting.
15. We consider the effect of outliers on our results
in Section 5.2.
16. We are grateful to Ron Webber for suggesting
this approach. Full details of the factor analysis
procedure and the factor weights are available
from the authors.
17. The Pearson correlation coefficients between
our three factors are as follows: FIRMRISK/
FORECAST 0.07; FIRMRISK/INCENTIVES
0.30; FORECAST/INCENTIVES 0.17. None of
the three factors displays significant skewness
or kurtosis. When we apply orthogonalization
procedures we are less certain of the economic
interpretation of our three factors. However,
our primary results with respect to the effect of
audit quality on forecast error and bias are
robust to this choice.
18. The highest correlation among our control
variables occurs for age, size and leverage. The
Pearson correlation coefficients are as follows:
age/size 0.29, age/leverage 0.44, leverage/size
0.38. No other control variables have
correlation coefficients above 0.3.
19. GOPT = 1 - [Net tangible assets per share

(excluding cash)/Issue price per share].
20. In our factor estimation procedure we use raw
values of age and firm size rather than the
natural log. Additional analysis shows all of
our conclusions are robust to this choice.
21. This measure has previously been used in an
examination of the relation between audit
quality, IPO pricing and voluntary disclosure
(Lee et al., 2003b).
22. We are unaware of any ‘established’ rankings
for underwriters of Australian IPOs, and so we
designate underwriters as high quality if they
have bank and/or international ownership,
or they have a national distribution capability
(i.e., offices in all major Australian cities). This
approach attempts to catch ‘deep pockets’
through reputation at risk, and it has also been
used in other published Australian IPO
research (Lee et al., 2003a, 2003b). It has also
been shown (for an earlier time period) to be
closely related to underwriting market share in
the Australian IPO market, measured either by
number of clients or percentage of funds raised
among IPOs (Taylor, 1991).
23. Of the four regressions reported in Table 4,
only the test of forecast accuracy using
price-deflated forecast error displays
significant heteroscedasticity. Nevertheless, we
report White’s (1980) adjusted t-statistics for
consistency across all four regressions.

24. Cronbach’s alpha measure of internal
consistency for the three factors are -0.17, 0.66
and 0.58, respectively. Variance inflation factors
for the three factors are all acceptably close to 1,
and all models have significant f-statistics.
25. Full details are available from the authors.
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AUTHOR PROFILES
Philip Lee holds a PhD from the University of
Sydney, where he is currently an Associate
Professor of Accounting.
Sarah Taylor is a lecturer in Accounting at the
University of Melbourne and a doctoral candidate
at the University of Technology, Sydney.
Stephen Taylor is Professor of Accounting in the
Faculty of Business, University of New South
Wales, and is also a project leader with the Capital
Markets CRC, a research centre funded by the
Federal Government of Australia. He holds a PhD
from the Australian Graduate School of
Management.
Auditor Conservatism and Audit Quality 199
Int. J. Audit. 10: 183–199 (2006)© 2006 The Author(s)
Journal compilation © Blackwell Publishing Ltd. 2006