AN EMPIRICAL EXAMINATION OF AUDIT REPORT LAG
USING CLIENT AND AUDIT FIRM CYCLE TIMES
John G. Wermert*
Assistant Professor
School of Accounting
College of Business & Public Administration
Drake University
2507 University Avenue
Des Moines, IA 50311 USA
Office: (515) 271-3899
E-mail: [email protected]
James L. Dodd
Visiting Fulbright Professor
Department of Industrial Economics and Technology Management
Norwegian University of Science and Technology (NTNU), Gløshaugen
N-7491 Trondheim, Norway
Office: (country code 47) 73 59 12 67
E-mail: [email protected]
and
Thomas A. Doucet
Associate Professor
Department of Finance & Accounting
School of Business & Public Administration
California State University, Bakersfield
9001 Stockdale Highway
Bakersfield, CA 93311 USA
Office: (661) 664-2337
E-mail: [email protected]
* Corresponding Author
An Empirical Examination of Audit Report Lag
Using Client and Audit Firm Cycle Times
Abstract
This paper examines the factors that affect audit report lag (ARL), the period of
time between a company’s fiscal year end and the completion of audit field work. While
several prior studies have investigated ARL, this study extends prior research by breaking
ARL into two components: (1) the time required by the client to close its books, referred
to as client cycle time (CCT) and (2) the time required by the auditor to complete the
audit after the client’s books are closed, referred to as firm cycle time (FCT). Numerous
hypotheses are advanced that predict how nine explanatory variables will affect both CCT
and FCT. The data from a sample of 71 firms were analyzed using canonical correlation
analysis. The pooled R2 for the model was 0.68, indicating that the model was successful
in explaining a large portion of the variance. As hypothesized, the degree to which
explanatory variables used in prior ARL studies affected FCT and CCT varied greatly.
While some variables affected both FCT and CCT, others primarily affected either FCT
or CCT. In addition, this study found that the importance placed by top management on
timely reporting, a variable not investigated in prior ARL studies, has a significant effect
on CCT and FCT.
An Empirical Examination of Audit Report Lag
Using Client and Audit Firm Cycle Times
I. Introduction
Prior research in accounting and finance has documented the importance of timely
audit and earnings information. Capital markets based research has found that markets
react to the issuance or removal of audit qualifications, thus underscoring the importance
of their timely release (Fields and Wilkins 1991; Frost 1991, 1994; Loudder et al. 1992).
In addition, Bamber and Stratton (1997) found that the uncertainty modified audit reports
influenced loan officers’ decisions regarding risk assessment, interest rates, and whether
to grant the loan. Other research has shown that the timing of earnings releases is also
informative to the security markets. Companies releasing information 'early' are generally
viewed more favorably than firms releasing information 'late' (Givoly and Palmon 1982,
Chambers and Penman 1984, Kross and Schroeder 1984). Furthermore, structural
changes in the way companies conduct business is making the timely release of earnings
information increasingly more important (Wallman 1995).
One of the most important factors that affects the timeliness of information
releases is the timeliness of the annual audit. Bamber et al. (1993) report that over 70
percent of all companies wait until at least the annual audit report date before announcing
earnings. Given the importance of timely audit and earnings information, and the role of
the annual audit in determining the timing of information releases, it is not surprising that
ARL has received considerable attention (Givoly and Palmon 1982, Whittred 1980,
Ashton et al. 1987, Ashton et al. 1989, Newton and Ashton 1989, Kinney and McDaniel
1993, Bamber et al. 1993, Simnett et al. 1995, Schwartz and Soo 1996). Generally, prior
ARL studies have investigated the determinants of audit lag by regressing ARL on a set
of variables that were believed to be related to audit lag. The explanatory power of the
early studies was relatively low, with R2s generally less than 0.25. Later studies using
more comprehensive models were more successful in explaining ARL, yet a large portion
of ARL remains unexplained (Simnett et al. 1995: 18).
While prior research has provided important insights into ARL, a potential
limitation of prior research is that ARL has been viewed as a single time period resulting
from a single process (i.e. the annual audit process). Accordingly, prior research has tried
to explain the entire period from the fiscal year end to the end of the auditors’ field work
using variables that were expected to increase or decrease the length of the audit process.
However, in reality there are two different, but related, processes occurring during the
ARL time period: the client's financial statement closing process and the annual audit
process. Both of these processes will have significant effects on ARL.
It is important to point out that we are not asserting that the audit process cannot
commence until the client's year-end closing process is completely finished. Rather, the
audit process and the client's closing process should be viewed as related processes, with
the audit process being influenced by the client's closing process in two ways. First, the
timing of many of the year-end substantive audit procedures will be affected by the timing
of the closing process. For example, a search for unrecorded liabilities (see Arens and
Loebbecke 1997: 592) cannot be performed until the list of (recorded) accounts payable
has been finalized. Second, the length of the closing process will likely influence the
time required by the auditor to complete the audit. If the client hastily closes its
accounting books, making numerous errors in the process, the time required to audit the
financial statements could increase precipitously. The opposite should also be true. A
diligent closing of the client's books may require more time on the part of the client, but it
should promote a smooth, timely audit. Thus, the client's closing process and the audit
process should be viewed as two related processes, and examining these related processes
simultaneously should provide an enhanced understanding of the determinants of audit
delay.
This study extends prior research in two major ways. First, we break ARL into
two components: (1) the period of time that it takes the client to close the company’s
books, hereafter referred to as the client's cycle time (CCT), and (2) the period of time it
takes the auditor to complete the audit after the client’s books are closed, hereafter
referred to as the accounting firm's cycle time (FCT). We believe this is an important
extension of prior research that will allow us to better understand the effects of
explanatory variables that were used in prior ARL research. In other words, we can
determine if the variables used in previous studies were primarily influencing the client's
closing process (as proxied by CCT), the year end audit process (as proxied by FCT), or
influencing both processes. Treating ARL as a single period, as has been done in prior
research, does not allow for such relationships to be examined. Furthermore, including
CCT as well as FCT in our model facilitates the inclusion of variables that are expected
to primarily affect CCT.
A second major contribution of this paper is the inclusion of an important, new
variable. Simnett et al. (1995: 18) note that while prior models are able to explain a
significant amount of audit delay, a considerable portion of audit delay remains
unexplained. They suggest that future research examine the effect of management
attitudes regarding the timeliness of financial reporting . Accordingly, we use such a
variable in our study.
In summary, this study examines the interrelationship of CCT, FCT and nine
explanatory variables that were chosen based upon prior research and data availability.
Not all variables used in prior research were examined in this study to keep the number of
variables to a manageable set. We propose and test 19 hypotheses in the following
section. Further research should explore other factors that we were not able to include in
our study.
II. Hypotheses
A rather consistent finding of prior research is that ARL is inversely related to
client size. Generally, the explanation proffered by prior researchers is that large firms
face greater pressures to report earnings early and that large firms can pressure their
auditors to complete their audits on a more timely basis than small firms (Dyer and
McHugh 1975, Newton and Ashton 1989, Bamber et al. 1993). Accordingly, we expect
FCT to be inversely related to client size. However, large firms are also more likely to
have advanced accounting systems and better internal controls, and they are also more
likely to have formal policies and procedures that will lend themselves to timely closings.
Accordingly, we also expect ARL to be inversely related to CCT, ceteris paribus. Our
first two hypotheses are (in alternative form):
H1a: CCT is inversely related to client size.
H1b: FCT is inversely related to client size.
Prior research has found that the use of structured audit technology is associated
with longer ARL (Newton and Ashton 1989, Bamber et al. 1993, Swartz and Soo 1996).
Ostensibly, this is due to the increased planning and documentation requirements
associated with structured audit approaches and time requirements of reconciling
administrative (i.e. national office) policies and daily work practices of individual
auditors (Bamber et al 1993). Furthermore, Cushing (1989) notes that of the 18 elements
of audit structure, eleven are expected to increase audit delay, while only three are
expected to reduce delay. Thus, we expect the use of a structured audit methodology to
be associated with increased FCT.
Prior research has not considered what effect the use of a structured audit
methodology may have on the time required for a client to close its books. While the
primary effect of audit structure is expected to be on FCT, we also expect audit structure
to have a less pronounced, secondary effect on CCT for two reasons. First, Cushing and
Loebbecke (1986) observe that firms that use structured audit approaches have detailed
guidance about the requirements for content, preparation, and administration of working
papers. Some of these working papers would most likely be prepared by client personnel
during the closing process, thus resulting in longer CCT. In addition, Icerman and
Hillison (1991) found that firms using structured audit approaches tend to book a greater
percentage of the errors discovered during audits than do firms using less structured audit
methodologies. Firms with auditors that more routinely book discovered errors would
likely exercise more care during the closing process to avoid a large number of proposed
audit adjustments. Such increased attention to detail would tend to increase CCT.
However, this is expected to be a less pronounced, secondary effect. The primary effect
of audit structure is still expected to be on FCT. This leads to the following hypotheses:
H2a: FCT is an increasing function of audit firm structure.
H2b: CCT is an increasing function of audit firm structure.
H2c: The effect of audit firm structure will be greater for FCT than CCT.
Prior research has found that firms that experience losses experience longer ARLs
(Bamber et al. 1993, Schwartz and Soo 1996). Auditors often use lower materiality
thresholds for companies experiencing losses (Bamber et al. 1993) and lower materiality
thresholds will result in increased evidence collection (Arens and Loebbecke 1997). This
may be due, in part, to greater perceived auditor business risk for audits of companies
reporting losses. Furthermore, losses are often associated with complex audit issues such
as inventory obsolescence or recoverability of assets, also requiring additional substantive
evidence collection. As for CCT, losses are expected to have a lesser effect. While the
basic closing process should remain unchanged, clients with complex audit issues, such
as inventory obsolescence, will require additional time to prepare analyses to justify their
accounting treatment. In addition, companies with income that was lower than expected
may spend additional time verifying reported results or searching for unrecorded income.
This leads to the following three hypotheses:
H3a: CCT will be longer for companies reporting a net loss than companies
reporting net income.
H3b: FCT will be longer for companies reporting a net loss than companies
reporting net income.
H3c: The effect of reporting a net loss will be greater for FCT than CCT.
Issuance of a going concern opinion is expected only after careful consideration of
the conditions and events that indicate there could be substantial doubt about the ability
of the client to continue as a going concern, as well as consideration of management’s
plans for dealing with the adverse conditions or events. For example, in the United States
auditing standards suggest that auditors evaluate management’s plans to dispose of assets,
including evaluating any restrictions on such transactions imposed by loan covenants
(SAS No. 59, AICPA 1988). The auditors would also need to review the client’s plans to
borrow money or restructure debt, to reduce or delay expenditures, or to sell stock.
Auditors may also perform an in-depth analysis of the company’s cash flow projections.
All of these activities would result in increased FCT. On the other hand, we do not
expect the issuance of a going concern opinion to affect CCT. The work required by the
client to substantiate management plans would generally involve prospective information,
and thus, should not impede the preparation of historical financial statements. Such
information would normally be prepared after the financial statements have been prepared
and the auditors have requested specific analyses regarding future plans. This leads to the
following hypotheses:
H4a: Issuance of a going concern opinion will have no effect on CCT.
H4b: Issuance of a going concern opinion will be associated with increased FCT.
A factor that is expected to increase the complexity of the client’s closing process
as well as the external audit is the number of different lines of business in which the
client operates. Ceteris paribus, the work required by the client to compile and
consolidate information increases as the number of segments increases and similar
incremental effort is required to audit such information. In addition, control and coordination requirements increase for both the client and the auditor as the number of
segments increases (Bamber et al. 1993, Bamber and Bylinski 1982), and such
requirements are expected to increase both CCT and FCT. This leads to the following
two hypotheses:
H5a: CCT will be an increasing function of the number of business segments.
H5b: FCT will be an increasing function of the number of business segments.
An additional factor that may affect both CCT and FCT that has not been
addressed by previous ARL research is management's attitude regarding financial
reporting. For years, auditors have realized the importance of the control environment in
assessing the likely operating effectiveness of internal controls. In fact, the control
environment is often referred to as the 'tone at the top.' In an analogous manner, the 'tone
at the top' with regard to the timeliness of financial reporting is likely to differ across
firms. Management at some client firms are likely to place more importance on timely
reporting than at other firms (Simnett et al. 1995). For example, Ettorre (1995) explains
how Motorola’s top management dictated very tight reporting deadlines to its operating
sectors to allow Motorola to close its books in two business days. Furthermore, it is
possible that such an attitude by management could have a lesser, secondary effect on the
auditors. Client management that emphasizes timely reporting may expect their auditors
to conduct their audit in a very timely manner, perhaps by assigning more personnel to
the engagement. This leads to the following hypotheses:
H6a: CCT will be inversely related to the importance placed by top management
on timely financial reporting.
H6b: FCT will be inversely related to the importance placed by top management
on timely financial reporting.
H6c: The effect of the importance placed by top management on timely financial
reporting will be greater for CCT than FCT.
Several prior studies have found that ARL is related to industry membership. In
particular, ARL for financial institutions is less than that for non-financial companies
(Ashton et al. 1989, Bamber et al. 1993, Schwartz and Soo 1996). Bamber et al. (1993:
6) note that 'banks’ accounting systems are generally highly centralized and automated,
and banks hold little inventory or fixed assets.' On the hand, non-financial companies are
more likely to have diverse transactions, as well as material levels of inventories and
fixed assets. Thus, we expect the CCT for financial firms to be less than the CCT of nonfinancial firms, ceteris paribus. In addition, on a dollar for dollar basis, non-financial
assets are likely to be more time consuming to audit than financial assets (Ashton,
Willingham and Elliott 1987) resulting in increased FCTs for non-financial companies
relative to financial companies. This lead to the following two hypotheses.
H7a: The CCT of financial institutions will be less than the CCT of non-financial
institutions.
H7b: The FCT of financial institutions will be less than the FCT of non-financial
institutions.
The final explanatory variable that we explore is the client’s fiscal year end. It is
common in many jurisdictions for companies to have a common year-end. For example,
Schwartz and Soo (1996) reported that 53% of the firms in their sample had either a
December or January year-end. It is expected that firms that share a common year-end
will have to wait, on average, longer for their annual audit (Simnett et al. 1995).
Accordingly, we expect the FCT to be longer for clients with either a December or
January year-end than for clients with other year-ends. On the other hand, we do not
expect the fiscal year end to affect the length of time it takes a client to close its books.
This leads to the final two hypotheses.
H8a: There is no association between CCT and fiscal year end.
H8b: The FCT for companies with December or January fiscal year ends will be
longer than for companies with other year-ends.
A summary of the hypotheses is included as Table 1. Also shown are the variable
names and descriptions as well as the predicted relationships between CCT, FCT and the
explanatory variables.
III. Sample and Variable Measurement
Using systematic sampling, a pilot sample of 100 public companies with two-digit
primary Standard Industrial Classification (SIC) codes from 10-89 was selected from the
Disclosure CD-ROM Database. The company controller of each of these companies was
sent a mail survey that requested information related to the length of the company’s
interim and year-end accounting cycles. Two weeks following the initial mailing a
follow-up post card reminder requested subject companies to participate. Pilot sample
responses were used to refine the survey instrument and estimate response rates for the
larger mailing.
Using the same procedures as in the pilot study, 325 companies were selected for
the survey. Respondent questionnaires were matched with appropriate financial
statement information in annual reports and Securities and Exchange Commission (SEC)
filings that were available either on NAARS or on microfiche at the Indiana University
library. Respondents were asked to indicate on the questionnaire the fiscal year for which
their responses applied and financial statement information was carefully matched on this
basis. Most firms provided information for fiscal years ending in 1991. Firms for which
matching financial statement information could not be located were not included in the
sample. The resultant sample of 71 companies represents a 22% usable response rate,
which compares favorably to other studies using survey methods.
Eleven different variables were used in the study. CCT, the number of calendar
days needed by the company to close its books, was provided by the responding firms on
the questionnaire. Auditors’ report dates were obtained from the appropriate auditors’
opinions in the annual reports. These report dates were used to calculate ARL, and FCT
was calculated as ARL less CCT. The client’s size (SIZE) was calculated as the natural
log of total assets. Two variables were used to capture the degree to which the auditors
used a structured audit methodology. STRUCT is a dummy variable that takes the value
of 1 for those firms that use a highly structured audit methodology, and 0 otherwise.
INTERM is a dummy variable that takes the value of 1 for those firms that use an
intermediate structured audit methodology, and 0 otherwise. GCUOP and LOSS are
dummy variables that take the value of 1 for firms that received a going concern opinion
or reported a net loss from continuing operations, respectively. SEGMENTS is the
number of lines of business that were reported by the company in its SEC filings or
annual report. IMPORT measures the emphasis that top management places on timely
financial reporting. This measure was the controller’s response, measured on a five-point
scale, to a question regarding his/her agreement to a statement that upper management
requires that they receive financial statements as soon as possible after period-end.
Responses ranged from one to five, with higher scores indicating stronger agreement
regarding top management’s emphasis on timely reporting. FYE is a dummy variable that
assumes the value of one if the company has either a December or January fiscal year
end, or 0 otherwise. Finally, FININST is a dummy variable that assumes a value of one
for financial institutions, and 0 otherwise.
Descriptive Statistics:
Panel A of Table 2 provides descriptive statistics for each of the variables used in
the study. The firms varied considerable in size from a low of $1.6 million in total assets
(American Cytogenetics, Inc.) to a high of $31.7 billion (Kraft, Inc.). Accordingly, the
natural log of total assets (in thousands) was used as a measure of SIZE to reduce
heteroscedasticity.
The sample was approximately evenly distributed among
unstructured, intermediate structured and structured firms. The average firm had
approximately 1.5 segments, with the most segments reported by any one firm being five.
Thirty-one percent of the firms had reported a loss and approximately 10% of the firms
had received a going concern opinion. Firms having December or January year-ends
accounted for 61% of the sample.
The average audit lag consisted of a client cycle time (CCT) of 22.5 days and a
firm cycle time (FCT) of 27.3 days. Thus, on average the CCT represented 45.2% of the
total ARL whereas FCT represented 54.8%. The client’s cycle time varied from 1 to 60
days, whereas the firm’s cycle time ranged from 0 to 84 days. The distribution of FCT
and CCT are provided in Panel B of Table 2.
The bivariate correlations between all variables are reported in Table 3. As
expected, FCT and CCT are significantly correlated with many of the explanatory
variables. Furthermore, the correlation between FCT and CCT of -0.18, although not
significant at traditional levels (two tailed, p<0.14), leads one to believe that there may be
some interdependence between FCT and CCT. In addition, many of the correlations
among the explanatory variables are significant. Some of these significant correlations
are rather straightforward, such as the significant positive correlation between LOSS and
GCUOP -- firms reporting losses are more likely to receive going concern opinions.
Another correlation worth noting is the significant correlation between SIZE and LOSS,
indicating that smaller firms were more likely to report a loss. This makes the significant
negative correlation between SIZE and GCUOP more understandable.
IV. Results
Canonical correlation analysis was used to analyze the data. Canonical correlation
is a multivariate statistical procedure employed to study the relationships between two
sets of variables when each set includes at least two variables. With canonical
correlation, linear combinations of each set of variables -- called variates -- are created
such that the bivariate correlation between the variate scores is maximized (Marascuilo
and Levin, 1983). The bivariate correlation is referred to as the canonical correlation and
the weights applied to the variables in the linear combinations are referred to as canonical
weights (Fornell and Larcker 1980) or canonical function coefficients (Thompson 1984).
The canonical correlation procedure continues by finding a second set of canonical
variates, orthogonal to the first pair, that maximizes the bivariate correlation between the
second pair of variates. This procedure of constructing canonical functions (with the
variates of each new function being orthogonal to all previous functions) continues until
the number of canonical functions equals the number of variables in the smaller set
(Marascuilo and Levin, 1983).
Use of canonical correlation in this study allows audit report lag to be treated as a
multidimensional phenomenon consisting of the audit firms’ cycle time and the clients’
cycle time, instead of a unitary measure. An alternative approach would be to separately
regress each of the cycle time variables on the full set of explanatory variables and to
combine the results into some overall explanation. However, Fornell and Larker (1980:
456) note this is inappropriate when the dependent variables are conceptually related,
which is true in this setting. As noted in Section I, CCT and FCT are expected to be
related. Furthermore, canonical correlation allows for a truly multivariate analysis of
CCT and FCT since the canonical functions reveal the contribution of the variables in the
presence of all of the other variables (both 'dependent' and 'independent').
The canonical correlation analysis resulted in two significant canonical functions,
the maximum possible since the smaller variable set included only two variables (FCT
and CCT). The canonical correlations, squared canonical correlations, significance
levels, as well as function coefficients for both canonical functions are reported in Table
4. The canonical correlation associated with the first and second canonical functions
were 0.66 and 0.49, respectively. The squared canonical correlations, which indicate the
shared variance of the canonical variates, were 0.44 and 0.24 respectively. Thus, each
function captures a large portion of the variance of the cycle time variables. The
statistical tests of significance of the canonical correlations test the null hypothesis that
the canonical correlation for a particular canonical function, as well as the canonical
correlations for all higher level canonical functions, are equal to zero (Marascuilo and
Levin, 1983). For the current study, the null hypothesis that the first and second
canonical correlation are equal to zero is rejected at the 0.001 level, thus we can be
certain there is a linear relationship between the two sets of variables. The null
hypothesis that the second canonical correlation (independently) is equal to zero is
rejected at the 0.027 level. Thus overall, both canonical functions appear significant and
contribute to understanding the relationship between the cycle time and explanatory
variables.
The explanatory power of the model compares quite favorably with prior studies.
While many of the early studies of ARL (e.g. Ashton et al. 1988, Newton and Ashton
1989) had relatively low explanatory power, later more comprehensive models of ARL
(e.g. Bamber et al. 1993; Simnett et al. 1995) were able to explain approximately half of
the variance in ARL. By comparison, the first canonical function of our model explains
44% of the variance in the variables, which is comparable to the results found in previous
studies. However, the second canonical function -- which is orthogonal to the first -explains an additional 24% of the variance. Thus, overall, our model explains 68% of the
variance in the variables.
Interpretation of the Canonical Functions
Statisticians often disagree about the best way to interpret canonical functions and
variates (Rencher 1992). This study relies upon interpretation of the standardized
coefficients, sometimes referred to as pattern interpretation (Maraschuilo and Levin
1983), to analyze the results. Rencher (1995) notes that the standardized coefficients
from the canonical function reveal the contribution of each of the variables in the
presence of each of the others. This is 'precisely the behavior we desire in a multivariate
setting. They provide a pertinent multivariate approach to interpretation of the
contribution of the variables acting in combination' (Rencher 1995: 406). However, with
canonical correlation analysis, it is not possible to perform tests of statistical significance
on individual standardized coefficients. Rather, one must evaluate the importance and
contributions of variables based on the relative magnitudes of the standardized
coefficients.
In the first canonical function, both of the cycle time variables have large positive
function coefficients of approximately equal magnitude, with the coefficient of FCT
being 0.76 and CCT being 0.80. We interpret this first function to mean that large
portions of both FCT and CCT have common causes (e.g. predictors). Thus, this function
should capture those portions of the explanatory variables that affect CCT and FCT in a
similar manner. For example, an increase in the number of segments may have a similar
impact on FCT and CCT. If so, this effect would be captured by the first canonical
function.
In the second canonical function, the standardized coefficients of FCT and CCT
are 0.68 and -0.62, respectively, indicating that this canonical function is capturing the
effects of factors that result in a long FCT, relative to the CCT . Or alternatively,
negative coefficients on the explanatory variables in this function would indicate a longer
CCT relative to the FCT. The difference that this canonical function is capturing is
referred hereafter as incremental auditor effort -- an amount of effort expended by the
auditor, not matched by the client.
Client Size
Overall, the most influential variable in defining the canonical solution is SIZE.
In the first canonical function, the standardized coefficient of -0.75 indicates that SIZE
has a strong joint-influence on CCT and FCT and that the relationship is inverse. Larger
firms have shorter CCTs and FCTs. This result is consistent with prior ARL research
which has found that overall audit lag (FCT + CCT) was inversely related to SIZE
(Schwartz and Soo 1996, Bamber et al. 1993). While the -0.75 coefficient in the first
canonical function is consistent with larger firms having smaller FCTs, it is also
important to note that a large portion of the SIZE effect noted in this study is due to the
relationship of SIZE to CCT. In fact, review of the simple, bivariate correlations in Table
3 seems to confirm the importance of CCT’s influence on SIZE, with the correlation
between SIZE and CCT being -0.42. Thus, H1a and H1b are strongly supported.
In the second canonical function, SIZE has a function coefficient of 0.32
indicating that as SIZE increases, FCT decreases less than CCT. While this result was not
hypothesized, it does point again to the strong influence that CCT plays in the
relationship between overall ARL and SIZE. Investigation of the interactive effects of
CCT, FCT and SIZE are left to future research.
Use of Structured Audit Methodology
As noted earlier, the audit firms were categorized into three levels of structure
using Kinney’s (1986) structure scale. Hypotheses H2a and H2b predict that CCT and
FCT are increasing functions of the degree to which the auditor uses a structured audit
methodology. Hypothesis H2c predicts that the effect of audit structure will be less for
CCT than for FCT.
The STRUCT and INTERM coefficients of 0.12 and 0.06 in the first and second
canonical functions are consistent with a relatively minor joint influence of auditor
structure on CCT and FCT. However, the coefficients of 0.65 for STRUCT and 0.16 for
INTERM in the second canonical function indicates that use of a structured audit
approach has a strong influence on the incremental auditor effort, without a
corresponding increase in effort by the client firm. In addition, as would be expected, the
effect of STRUCT on CCT and FCT is greater than that of INTERM. Overall, STRUCT
(and INTERM) is the most influential explanatory variable in the second canonical
function. Hypotheses H2a, H2b and H2c are also supported.
Losses Reported by Client
Hypotheses H3a and H3b predict that CCT and FCT will be longer for companies
reporting a net loss than companies reporting net income. Further, hypothesis H3c
predicts that the effect of a net loss will be greater for FCT than for CCT. The coefficient
of LOSS in the first canonical function is near zero, indicating LOSS had no significant
joint influence on CCT and FCT. However, the coefficient of LOSS in the second
canonical function is 0.22 indicating that LOSS is associated with incremental auditor
effort. These results do not support H3a, but do support H3b and H3c.
Interpretation of these results is not entirely clear. One potential explanation is
that companies incurring a net loss can usually anticipate the loss in advance of the fiscal
year end, since most companies in our sample are required to file financial statements on
a quarterly basis with the SEC and many probably report internally on an even more
frequent basis. Thus, the companies would not likely be 'surprised' by the loss. Client
efforts to deal with the complex issues associated with a loss, as well as any search for
unrecorded income, may be conducted before the end of the year resulting in little effect
on CCT. On the other hand, it may be more difficult for the auditors to move the timing
of their tests before year-end when complex issues, such as inventory obsolescence, are
involved. Furthermore, when losses are incurred, thereby increasing the auditors’
business risk, auditors’ may seek more persuasive evidence (Arens and Loebbecke 1997).
One such way to do so is to gather substantive evidence (e.g. with regard to inventory
obsolescence) closer to year-end, thereby resulting in increased FCT.
Issuance of a Going Concern Opinion
H4b predicts that the issuance of a going concern opinion will result in increased
FCT, while H4a predicts that such opinions should have no effect on CCT. The
coefficient of GCUOP of 0.16 in the first canonical function indicates that the issuance of
a going concern opinion has some joint influence on CCT and FCT, which is inconsistent
with H4a. However, the 0.56 coefficient in the second function indicates that the
issuance of a going concern opinion results in significant incremental auditor effort. This
result combined with 0.16 coefficient in the first function clearly indicates that going
concern opinions are associated with increased FCT. H4b is strongly supported.
Segments
According to hypotheses H5a and H5b, an increase in the number of segments
should be associated with an increase in both CCT and FCT. Analysis of the canonical
function coefficients is generally consistent with this interpretation, with the coefficients
of the first and second canonical functions being 0.18 and 0.01, respectively. While
SEGMENTS was not very influential in defining the canonical solution, these results do
indicate that CCT and FCT were moderately affected by the number of business
segments. Hypotheses H5a and H5b are supported.
Importance Top Management Places on Timely Financial Reporting
Hypotheses H6a and H6b predict that management commitment to timely
reporting will decrease both CCT and FCT, with H6c predicting that the effect will be
greater for CCT than FCT. Examination of the first canonical function reveals IMPORT
having a canonical function coefficient of -0.42. This indicates a strong, inverse
relationship between perceived importance of timely financial reporting by management
and the length of both FCT and CCT. In other words, management commitment to timely
reporting affected not only the client’s closing schedule, but the pace of the annual audit
as well. Hypotheses H6a and H6b are strongly supported.
Analysis of the second canonical function reveals a canonical coefficient of -0.13.
While the magnitude of the coefficient is fairly low, it does seems to indicate that
management commitment to timely reporting has a slightly greater effect on FCT than on
CCT. This is contrary to what was expected and H6c is not supported.
Client’s Industry
H7a and H7b, predict an industry effect for financial institutions. More
specifically, financial institutions are expected to have shorter CCTs and FCTs.
Inspection of the first and second canonical function shows function coefficients of -0.25
and -0.03 for FININST. These results support both hypotheses.
Client’s Fiscal Year End
The last two hypotheses, H8a and H8b, predict that firms with fiscal years ending
in December or January will have longer FCTs, but that CCT will be unaffected. The
coefficient on FYE in the first canonical function is near zero while the coefficient in the
second function is 0.55. These results are consistent with H8a and H8b. FYE did not
seem to have an effect on CCT, but the incremental auditor effort was very significantly
affected.
V. Discussion and Conclusion
The results reported here using canonical correlation analysis provide new insights
into the determinants of ARL. ARL should not be viewed as a single time period, but
rather as two distinct, but yet interrelated, periods: the time required by the client to close
its books (CCT) and the time required by the auditor to finish their audit once the books
are closed (FCT). The results reported here show that the variables that have been found
to be significant in prior ARL studies often affect CCT and FCT in different ways. In
addition, this study includes an important, additional variable that affects both CCT and
FCT: the importance placed by top management on timely reporting.
Overall, the most significant variable in defining the canonical solution is the
client’s size. Ceteris paribus, large clients close their books more quickly and their audits
are completed on a more timely basis than smaller clients. The use of a structured audit
methodology was also found to be important, with increased structure being associated
with increased FCT, and to a lesser extent increased CCT. Going concern opinions,
client losses, and fiscal year ends in December or January all contributed to increased
FCTs, and going concern opinions also appeared to contribute to increased CCTs. The
number of business segments had a modest effect on both FCT and CCT. As expected,
the FCT and CCT increased as the number of segments increased. Industry was also an
important variable, with financial institutions having reduced FCTs and CCTs. Finally,
management’s commitment to timely financial reporting, a variable not considered in
prior studies, was an important variable. FCT and CCT were lower in companies where
management was more committed to timely reporting.
VI. Limitations
As with most studies, the results reported here are subject to limitations. First,
this study used a smaller sample than many previous studies of audit report lag, and prior
studies often used data from numerous years. Large samples were often possible in prior
research because the data was generally taken from publicly available sources.
Unfortunately, that is not possible in this study because CCT data is not publicly
available. CCT data was collected via a questionnaire that was voluntarily completed.
Obtaining a sample size comparable to previous studies would be very difficult, if not
impossible, and cost prohibitive. Additional studies, perhaps in specific industries, could
be used to further validate the results reported herein.
In addition, while the canonical correlation results reported in Table 4 provide
insights beyond those from ordinary regression methods, it is not possible to subject
individual function coefficients to statistical significance tests when canonical correlation
is used. The importance and contributions of variables was based on the relative
magnitudes of the standardized coefficients. Accordingly, additional research would be
helpful in assessing the robustness of the results reported here.
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Table 1
Summary of Hypotheses
Predicted Relationship to
Variable Name
CCT
FCT
Effect
greater
for
SIZE
(H1a)
(H1b)
No
Prediction
Degree of auditor structure
STRUCT/INTERM
+
(H2a)
+
(H2b)
FCT
(H2c)
Reported net loss by client
LOSS
+
(H3a)
+
(H3b)
FCT
(H3c)
GCUOP
No
effect
(H4a)
+
(H4b)
FCT
(N/A1)
SEGMENTS
+
(H5a)
+
(H5b)
No
prediction
IMPORT
(H6a)
(H6b)
CCT
(H6c)
FININST
(H7a)
(H7b)
No
prediction
FYE
No
effect
(H8a)
+
FCT
(N/A)
Variable Description
Firm Size
Going concern opinion
issued by the auditor
Number of business
segments the client operates
in
Importance placed by top
management on timely
reporting
Client is a financial
institution
Client has a December or
January fiscal year end
1
(H8b)
It is quite obvious that if no effect is predicted for CCT and a direct effect is predicted for FCT, that the
effect for FCT is predicted to be greater than that of CCT. However, no separate hypothesis is included
when the predictor variable is expected to have no effect on CCT.
TABLE 2
Descriptive Statistics and Cycle Time Distributions
(n=71)
Panel A: Descriptive Statistics
Variables
Mean
Std. Dev.
Min.
Max.
Cycle Time Variables:
FCT
CCT
27.31
22.52
16.92
12.46
0.00
1.00
84.00
60.00
11.81
0.31
0.34
0.10
1.49
0.31
4.48
0.61
0.13
2.29
0.47
0.48
0.30
0.97
0.47
0.71
0.49
0.34
7.38
0.00
0.00
0.00
1.00
0.00
2.00
0.00
0.00
17.27
1.00
1.00
1.00
5.00
1.00
5.00
1.00
1.00
Cycle Time Determinants
SIZE
STRUCT
INTERM
GCUOP
SEGMENTS
LOSS
IMPORT
FYE
FININST
Panel B: Distribution of Firm Cycle Time and Client Cycle Time
Less than 10 days
11-20 days
21-30 days
31-40
41-50
Over 50 days
Firm Cycle Time
10 (14.1%)
18 (25.4%)
14 (19.7%)
16 (22.5%)
6 (8.5%)
7 (9.9%)
________________________________
For variable names and descriptions, see Table 1.
Client Cycle Time
10 (14.1%)
26 (36.7%)
20 (28.2%)
10 (14.1%)
3 (4.2%)
2 (2.8%)
TABLE 3
Correlation Matrix
(n=71)
Variables
FCT
CCT
SIZE
STRUCT
INTERM
LOSS
GCUOP
SEGMENTS
IMPORT
FININST
FYE
Cycle Times:
FCT
1.00
CCT
-0.18
1.00
SIZE
-0.28a
-0.42a
1.00
STRUCT
0.29b
-0.16
-0.07
INTERM
c
-0.05
b
-0.48a
1.00
a
0.01
-0.29b
1.00
0.08
-0.14
0.49a
1.00
b
-0.17
1.00
Predictor Variables:
-0.20
0.26
LOSS
0.34
a
0.13
-0.50
GCUOP
0.32a
-0.01
-0.34a
a
0.04
-0.06
-0.25
SEGMENTS
-0.01
-0.10
0.44
IMPORT
0.23c
0.18
-0.04
0.11
-0.19
0.19c
-0.11
0.06
1.00
b
b
-0.09
-0.16
0.02
-0.11
-0.10
1.00
-0.02
0.03
-0.02
-0.22c
0.17
-0.10
-0.22c
a
FININST
-0.08
-0.33
FYE
0.06
-0.31b
0.28
0.25b
_______________________________
For variable names and definitions, see Table 1.
a
p<0.01
p<0.05
c
p<0.10
b
1.00
0.29
1.00
TABLE 4
Canonical Correlation Analysis Results
Canonical Variate Functions
Canonical correlation (Rc)
Squared canonical correlation (Rc2)
Significance level of (Rc)a
Cycle Time Variables:
FCT
CCT
Predictor Variables:
SIZE
STRUCT
INTERM
LOSS
GCUOP
SEGMENTS
IMPORT
FININST
FYE
I
0.66
0.44
0.001
II
0.49
0.24
0.027
0.76
0.80
0.68
-0.62
-0.75
0.12
0.06
0.03
0.16
0.18
-0.42
-0.25
-0.02
0.32
0.65
0.16
0.22
0.56
0.01
-0.13
-0.03
0.55
______________________
For variable names and descriptions, see Table 1.
a
F statistic based on Rao’s approximation (Rao, 1973). The null hypothesis is that the canonical correlation
for the variate function and all higher canonical variate functions are zero.