PREDICTING A MODIFIED AUDITORS REPORT – A MODEL FOR SOUTH AFRICADocument Transcript
PREDICTING A MODIFIED AUDITORS REPORT – A MODEL FOR SOUTH
By – Dr. Steven Firer
School of Accountancy University of the Witwatersrand
In this study the extent to which combinations of financial and non-financial information
can be used to enhance the ability to discriminate between the choices of a modified or
unqualified audit report. An examination was made of financial statements, opinions of
auditors and notes to financial statements for companies that received modified a audit
report and for those that received an unqualified audit report. The data are taken from 67
South African listed companies. Logistic regression was used to estimate the effect of
firm litigation and financial information on modified audit reports. The modified audit
report is associated with financial information such as solvency. The model developed is
accurate in classifying the total sample correctly with a rate of 92.5%. This kind of model
can serve as a decision aid for auditors when predicting what opinion other auditors
would issue in similar circumstances, when evaluating potential clients, and to control
audit quality within audit firms, and as a defence in lawsuits
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Upon completion of a professional engagement, the auditor is obliged to inform the users
of the financial statements about the nature of the work performed and the conclusions
that have been reached thereon. The contents, format and type of the report will be
determined by the nature of the work performed as agreed in the engagement letter.
SAAS 200 (p02) states that the objective of an audit of the financial statements is to
enable the auditor to express an opinion as to whether the financial statements fairly
present, in all material respects, the financial position of the entity at a specific date, and
the results of its operations and cash flow. If this is not the case, or the auditor is not able
to determine whether this is the case, this will lead to a modified audit report. SAAS
700(p27-45) details the possible type of modified opinion”: Qualified, Adverse opinion
or Disclaimer of opinion arising from a disagreement or scope limitation and / or an
Emphasis of matter to draw attention to an important matter in the FS or matters not
disclosed in the FS that the auditor is required to report, but which do not affect the audit
opinion. For the purposes of this research modified reports only include Qualified and
Adverse opinions and Disclaimer of opinions. SAAS 700 (p04) states that the auditor’s
report should contain a clear written expression of opinion on the financial statements
taken as a whole.
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The audit of a company is required in terms of section 282 of the Companies Act to
report to its members. Section 301 of the Companies Act requires the auditor to report to
the members of the company upon completion of the audit, and to qualify the report if
The objective of this study is to develop a model based on financial information and other
indicators, such as firm litigation to explain and predict an audit qualification of South
African companies. This kind of model can serve as a decision aid for auditors when
predicting what opinion other auditors would issue in similar circumstances, when
evaluating potential clients, and to control audit quality within audit firms, and as a
defence in lawsuits.
Factors used as possible indicators of going concern problems for the firm that may result
in a going concern modification in the audit report in terms of SAAS 570 include firm
litigation, financial distress, and liquidity. This study has implications for auditors
internal and external, company decision makers, investors, financial analysts and
researchers. It helps company auditors evaluate their clients and the importance of the
financial and non-financial factors used in their evaluation. It can also be used to predict
the possible outcome ahead of the external audit.
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The remainder of the paper is organised as follows: second section reviews research on
modified audit reports. The third section underlies the methodologies employed, the
variables, the method and the data used in the study.
The fourth section describes the empirical results and discussion obtaining from the use
of the univariate and multivariate analyses. Finally, the fifth section presents conclusions
In a South African context there has been very little debate (academic or political) as to
the information contained in audit reports. Auditing has a role in reducing agency
problems stemming from the separation of management and control and the lender-
borrower conflict. The theory (Spathis, 2003) suggests that as agency costs increase;
there is a demand for higher quality audits either voluntary undertaken by the managers
or externally imposed by shareholders or creditors (Watts & Zimmerman, 1986).
The debate that has taken place in South Africa; has mainly dealt with the question as to
whether audit reports give a fair presentation of the economic situation of the company
for decision making by the users of financial information. In South Africa the
modification of audit reports has lately been brought into the limelight in connection
primarily with the number of company failures. This is evident from the appointment of
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the GAAP monitoring panel (JSE, 2000), the Accounting Review Panel by Finance
Minister Trevor Manuel (Wits, 2003 ) and the Nel Commission Reports into the collapse
of Masterbond (Nel, 1990)
Several models have been developed to explain modifications in audit reports. The
general consensus of these models has been that financial and non-financial factors
dominate the audit opinion decision.
In South Africa, SAAS 570 provides guidance for auditors to provide more critical
evaluations to identify the possibility of going concern problems which may result a
modified audit report. SAAS 570 (p06) identifies the conditions that an auditor should
consider in evaluating the going concern status of an entity. These conditions include
financial problems (short-term liquidity) and operating problems (profitability and cash
flow). SAAS 570 (p06) also provides guidance on two other types of information,
negative trends (operating losses) and other indicators such as legal proceedings.
Dopuch et al (1987) investigated the extent to which models based on financial and
market variables predict auditors’ decisions to issue modified audit reports. The results
showed that the most significant variables in a modified prediction are current year loss,
industry return, and the change in the ratio of total liabilities to total assets.
Keasy et al (1988) found that the likelihood of a company receiving a modified audit
report was significantly greater if a large firm of auditors had audited the company, if the
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company had few directors, a secured loan, and if there was a long delay between the
financial year-end or completion of the audit) and the issuance of the audited accounts.
Spathis (2003) tested the extent to which combinations of financial and non-financial
information can be used to enhance the ability to discriminate between the choices of a
qualified or unqualified audit report. This study found that the qualification decision is
associated with financial information such as financial distress and by non-financial
information such as firm litigation.
CONTRIBUTION OF THIS STUDY
The major feature of this study is its focus on South Africa. Data drawn from South
African sources - rather than data from developed Western economies - has been utilised.
A number of key reasons support this focus: auditing research initially evolved in the
United States and the United Kingdom and more recently, in other developed nations
such as Greece and Spain. Knowledge of the understanding and impact of auditing in
developing economies such as South Africa is, in contrast, still in its infancy. Given the
significance of emerging economies to the overall well being and balance of the global
economy, it is important to establish and develop an audit research framework in South
The model developed in this study can be used as a quality control tool in the planning
or final stage of an engagement. Audit practitioners are beginning to feel the burden of
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professional responsibility. There is increased anxiety as a result of the implementation of
the practice review and the issue of new auditing standards on quality control.
Audit quality has become an important issue as the accounting profession faces intense
criticisms from financial statement users, regulators, and the courts. Increases in
litigation, governmental pressure, and other external criticisms provide an array of signals
indicating dissatisfaction with the quality of service provided by the accounting
Committed, experienced audit professionals should be committed to producing good
quality work and must continue to explore ways to improve the audit that they perform.
This audit report prediction model is an example of this exploration of achieving the
highest audit quality.
Due to the difficulty in acquiring information from private companies, it was decided to
limit this study to public companies that are listed on the JSE Securities Exchange. For
the purposes of this study, the extent of an audit qualification is measured using statutory
annual reports. Data was collected from the 1998-2002 fiscal year annual reports of
publicly traded companies listed on the JSE Securities Exchange. The primary source of
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information for this study was the use of the secondary database from McGgregor BFA
(McGregor’s, 2002). McGregor BFA supplies real-time and historical fundamental
information on South African listed companies.
The sample of 67 publicly listed firms includes 20 with qualifications and 47 without
qualifications. This an extremely limited amount of audit modifications on the JSE
securities exchange. As a result the sample was restricted to companies selected from the
Venture Capital sector to first, eliminate structural differences with other sectors and
second to ensure that an adequate number of modified audit reports were selected
(companies listed in the venture capital sector of the JSE Securities Exchange have the
most modified audit reports).
The probability of a qualified opinion was measured using a logistic regression approach.
Logistic regression is designed to use a mix of continuous and categorical predictor
variables to predict a categorical outcome or dependent variable (SPSS, 2004) – in this
study the dependent variable is an audit qualification, which is measured using a dummy
variable 1, if a company received an audit qualification and 0 if not. The following model
is estimated (based on Spathis, 2003; Dopuch et al, 1987; Keasy et al, 1988):
Pr ob(Qual i ) = β 0 + β 1CRi + β 2 DERi + β 3 FDi + β 4 TDCFi + β 5 ROAi + β 6 ROEi + β 7 LOSS i + β 8 LIT + ε i
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Variable – Abbreviation Full Description Reason for Use
Prob (Qual) Probability of Audit Qualification: Spathis (2003) – prior literature
0 – Unqualified
1 - Qualified
CR Current Ratio Spathis (2003) – prior literature
DER Debt Equity Ratio Measure of level debt compared to
FD Financial Distress – K Score (South Spathis (2003) – prior literature
African version of Altman Z Score)
QR Quick Ratio Measure of short term liquidity
TDCF Total Debt / Cash Flow Keasy et al (1988) – prior literature
ROA Return on Assets Firer (2003) – measure of company
ROE Return on Equity Firer & Williams (2003) – measure of
LOSS Current Years Loss: Spathis (2003) and Dopuch et al (1987)
0 – No loss – prior literature
1 – Loss in current year
LIT Litigation: Spathis (2003) – prior literature
0 – No litigation
1 – Litigation against company
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Insert Appendix 2
Appendix 2 reports the mean values standard deviation and t-tests of ratios for qualified
and unqualified companies and indicates the magnitude of the differences in the variables
between the two types of reports. The univariate tests suggest several variables may be
helpful in audit opinion qualification. The large differences in values of ratios between
qualified and unqualified companies and the high statistical significance ( ρ < 0.05)
indicate that certain ratios such as QR and CR may indeed be related audit opinion
decisions. The companies with qualified audit reports show lower K-Scores (high
financial distress) and lower liquidity.
Appendix 2 shows that only 1 of the 11 instances with firm litigations received a
qualified audit report (chi-square 30.224, ρ < 0.000). The chi-square statistics indicate
that there is a significant difference between qualified and unqualified audit reports in
relation to company litigation. The same result also holds for current years losses. 18 out
of the 46 companies with losses in the current year received a qualified audit report. The
difference between the two groups of companies reported is significant (chi-square 9.328,
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ρ < 0.000). These differences are evidence that the qualified companies related
significantly with litigation or losses.
Initial Summary – Multivariate Analysis
The model chi-square is a statistical test of the null hypothesis that the coefficients for all
the terms in the model are zero. It is equivalent to the overall F test in OLS regression. Its
value, 54.249, is the difference between the initial and final -2LL. It has nine degrees of
freedom, which is the difference between the number of parameters in the two models.
The null hypothesis is rejected because the significance is low .000 (to 3 decimals), and
conclude that the set of variables improves the prediction of the log odds.
The Hosmer and Lemeshow goodness-of-fit test and table summaries are shown in
appendix 2. The goodness-of-fit statistic is 2.170, distributed as a chi-square value, with a
significance of 0.975. When comparing observed values and expected events in the
context of testing goodness-of-fit, it is hoped to find a non-significant probability, which
indicates that the expected and observed events are close, in turn implying that the model
is a good fit (SPSS, 2004). Here, the model does appear to fit, confirming the change in
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-2LL test (test of model). For the Homer and Lemeshow to provide robust statistical
results it is necessary that the expected number of events be greater than five (SPSS,
2004), which is the case in this research study.
Accuracy of Prediction – Multivariate Analysis
A measure of how well the model performs is in its ability to accurately classify cases
into the two categories of the variable qualified audit report (whether or not a company
had a qualified audit report) (SPSS, 2004). The overall predictive accuracy is 92.5%. The
model is much stronger for predicting unqualified audit reports, as the model predicted 45
out of 47 or 95.7% of these cases. However, the model does an excellent job for
predicting qualified audit reports, getting 17 out of 20, or 85% correct.
The setting of this research study is in predicting modified audit reports, so the current
model would be acceptable. This illustrates the high correspondence between the
statistical fit of the model from the likelihood statistics, and the significance of the
individual variables, and the predictive ability of the model.
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Interpreting Logistic Regression Coefficients – Multivariate Analysis
Logistic regression provides two measures that are analogs (Cox & Snell and
Nagelkerke) to R 2 in OLS regression. According to SPSS (2004) the preferred measure
is the pseudo R 2 of Nagelkerke. The R 2 of Nagelkerke is 0.788, which indicates the
independent variables can explain a large amount of the variance, which shows that the
model is robust and reflects a strong relationship between the dependent and independent
variables. The results in the Variables in the Equation table indicate that five variables
with significant associations were included in the model: CR, DER, QR, LOSS, and LIT.
CR (define these – refer earlier comment) has an increased probability of being classified
with qualified companies ( Β = 12.411, ρ = .002) and this ratio has a positive effect.
DER has an increased probability of being classified with qualified companies ( Β =
-.595, ρ = .012) and this ratio has a negative effect. QR has an increased probability of
being classified with qualified companies ( Β = -14.059, ρ = .002) and this ratio has a
negative effect. LOSS has an increased probability of being classified with qualified
companies ( Β = 7.411, ρ = .027) and this ratio has a positive effect. LIT has an
increased probability of being classified with qualified companies ( Β = -5.357, ρ = .008)
and this ratio has a negative effect.
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These results confirm the findings in Dopuch et al (1987), which showed that the most
significant variable in qualification prediction is the current year’s loss. However these
results are contrary to the findings of Spathis (2003). Spathis finds that FD has a
significant association with predicting an audit qualification, while this research study
does not. There is a common finding in terms of FD, in that both these studies do indicate
that companies with high K-Scores have an increased probability of being classified into
the unqualified companies. This indicates that companies with low K-Scores have
received an audit qualification. Mutchler (1995) posits the view that in these situations
contrary information (bad news) was the driving factor in the auditor’s decision.
In terms of developing a model in a South African context, this study has identified three
variables that can be used as predictors of an audit qualification: DER, QR, and LOSS.
DER is the debt equity ratio. This study found that the lower this ratio the higher the
probability it will be associated with a qualified audit report.
A possible reason for this; companies with low debt equity ratios are using equity to
finance the company operations, and this could be an indication that a company may not
be able to raise finance and hence may have a going concern problem.
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QR is the quick ratio. The quick ratio is a proxy for short-term liquidity. A declining
quick ratio is an indication that a company will not be able to meet its short-term
obligations. Auditors may see this as a going concern problem, and may necessitate an
audit qualification. LOSS is the current year’s loss. If a company made a loss this might
be an indication of insolvency and together with the other going concern issues, an audit
report qualification may result.
CONCLUSIONS AND IMPLICATIONS
The primary objective of this study has been the development of a reliable model based
on financial statement information and non-financial information such as firm litigation,
to identify an audit qualification opinion for South African companies.
In order to achieve this goal logistic regression analysis was used to develop a model that
identifies factors associated with qualified audit reports. Eight variables (six financial
ratios and two dummy variables) were selected for examination as potential predictors of
qualified audit reports.
These variables appeared to be important in prior research and constitute ratios derived
from published financial statements. The major explanatory variables were firm
litigation, financial distress, and current year’s losses.
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The model of logistic regression is accurate in classifying the total sample correctly with
a rate of 92.5%. The results of this model suggest there is potential in detecting qualified
audit reports through analysis of publicly available financial statements.
Despite possible limitations of using a relatively focused sample and a single domestic
location, the results of the present study provide valuable insights into the development of
an audit opinion prediction model. Furthermore, this study contributes to the expansion of
the current research agenda within the auditing discipline (specifically in the South
African context) towards alternative areas of interest. Auditor’s skills and abilities, the
social contract (trust, and self regulation) have not been examined. There are several
variables that remain for future study.
These variables include number of directors, number of employees, the rate of turnover
of the financial manager, the type of auditor and the frequency with which auditors are
changed. This research study points to some compelling links between financial
statement variables and modified audit reports. This connection is found in the empirical
evidence that company’s with poor liquidity and that incur a loss in the current year have
an increased probability of their auditors furnishing a qualified auditors report. The
results of this inaugural, exploratory study in South Africa are clearly thought provoking.
However, they represent only another step in the process of creating and setting auditing
standards (No – may open the debate around the auditing standards but not part of the
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There is nevertheless compelling evidence that financial statement variables can be used
to predict an audit qualification. If this model is used by auditors, it will result in
profound changes in the way audit quality can be monitored in South Africa.
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SAAS 200 – South African Auditing Standards: The objective and general principals
governing an audit of financial statements, July, 1996, issued by the Auditing Standards
Committee on behalf of the Public Accountants’ and Auditors’ Board, SAICA
Handbook Volume 2 Auditing, the South African Institute of Chartered Accountants,
SAAS 570 – South African Auditing Standards: Going Concern, February, 2000, issued
by the South African Institute of Chartered Accountants.
SAAS 700 – South African Auditing Standards: The auditors report on financial
statements, December, 2000, issued by South African Institute of Chartered Accountants.
Firer, S. (2003): Exploring the Intellectual Capital Contribution to Company
Performance in South Africa, Unpublished Thesis?, Doctor of Business Administration,?
Durban: University of Natal.
Firer, S., Mitchell Williams, S. (2003): Intellectual capital and traditional measures of
corporate performance, Journal of Intellectual Capital, 4(3), 348-360.
SPSS (2004): Advanced Statistical Analysis Using SPSS, SPSS – SA, Cape Town.
Mutchler, J (1985): A multivariate analysis of the auditor’s going concern opinion
decision, Auditing: A Journal of Practice and Theory, Fall, pp 148-163.
Spathis, T. (2003): Audit Qualification, Firm Litigation, and Financial Information: an
Empirical Analysis in Greece, International Journal of Auditing, Vol 7 (1), pp 71-85
Dopuch, N., Holthausen, R., Leftwich, R. (1987): Predicting audit qualifications with
financial and market variables, The Accounting Review, Vol 62(3), pp.431-454.
predicting-a-modified-auditors-report-a-model-for-south-africa3371.doc Page 18 of 23
Keasy, K., Watson, R., Wynarzcyk, P. (1988): The small company audit qualification: A
preliminary investigation, Journal of Accounting Research, Autumn, pp.506-523.
JSE (2000) – Gaap Monitoring Panel of the JSE Securities Exchange, available at
Wits (2003): School of Accountancy, Faculty of Commerce, Law, and Management, The
University of the Witwatersrand, Final Research Report, to the Ministerial Panel for the
Review of the Draft Accountancy Professions Bill and National Treasury – Unpublished
Watts, R., Zimmerman, J. (1986): Positive Accounting Theory, Englewood Cliffs, NJ:
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Independent Samples Test
Levene's Test for
Equality of Variances t-test for Equality of Means
Interval of the
Mean Std. Error Difference
F Sig. t df Sig. (2-tailed) Difference Difference Lower Upper
QR Equal variances
10.828 .002 -2.173 65 .033 -1.4255 .65600 -2.73559 -.11533
-2.968 64.637 .004 -1.4255 .48021 -2.38461 -.46631
CR Equal variances
3.589 .063 -1.433 65 .157 -.9847 .68701 -2.35677 .38733
-1.739 57.255 .087 -.9847 .56612 -2.11824 .14880
FD Equal variances
2.838 .097 -.952 65 .345 -48.0994 50.52866 -149.012 52.81335
-.717 22.246 .481 -48.0994 67.07560 -187.117 90.91783
DER Equal variances
.452 .504 -.935 65 .353 -5.7328 6.12981 -17.97491 6.50925
-1.359 57.870 .179 -5.7328 4.21839 -14.17727 2.71161
TDCF Equal variances
1.385 .244 .540 65 .591 22.6240 41.93340 -61.12275 106.37080
.829 46.471 .411 22.6240 27.29180 -32.29651 77.54455
ROA Equal variances
1.818 .182 -.727 65 .470 -226.4592 311.67329 -848.914 395.99538
-.585 24.297 .564 -226.4592 386.80163 -1024.26 571.34349
ROE Equal variances
1.778 .187 .818 65 .417 1279.5886 1564.9688 -1845.87 4405.047
1.250 47.502 .217 1279.5886 1023.2746 -778.400 3337.577
Discuss sample selection / justification for predictability – refer page 7
N Mean Std. Deviation Minimum Maximum
LOSS 67 .6866 .46739 .00 1.00
LIT 67 .1642 .37323 .00 1.00
Observed N Expected N Residual
.00 21 33.5 -12.5
1.00 46 33.5 12.5
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Observed N Expected N Residual
.00 56 33.5 22.5
1.00 11 33.5 -22.5
Chi-Square a 9.328 30.224
df 1 1
Asymp. Sig. .002 .000
a. 0 cells (.0%) have expected frequencies less than
5. The minimum expected cell frequency is 33.5.
Omnibus Tests of Model Coefficients
Chi-square df Sig.
Step 1 Step 54.249 9 .000
Block 54.249 9 .000
Model 54.249 9 .000
-2 Log Cox & Snell Nagelkerke
Step likelihood R Square R Square
1 27.437 .555 .788
Hosmer and Lemeshow Test
Step Chi-square df Sig.
1 2.170 8 .975
Observed .00 1.00 Correct
Step 1 QUAL .00 45 2 95.7
1.00 3 17 85.0
Overall Percentage 92.5
a. The cut value is .500
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Variables in the Equation
B S.E. Wald df Sig. Exp(B)
CR 12.411 4.097 9.176 1 .002 245506.0
1 DER -.595 .236 6.367 1 .012 .552
FD -.056 .039 2.027 1 .155 .945
QR -14.059 4.564 9.491 1 .002 .000
TDCF .004 .010 .207 1 .649 1.004
ROA .009 .007 1.739 1 .187 1.009
ROE .001 .001 1.247 1 .264 1.001
LOSS 7.411 3.346 4.906 1 .027 1654.411
LIT -5.357 2.016 7.062 1 .008 .005
Constant -7.758 3.465 5.013 1 .025 .000
a. Variable(s) entered on step 1: CR, DER, FD, QR, TDCF, ROA, ROE, LOSS, LIT.
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