PREDICTING A MODIFIED AUDITORS REPORT – A MODEL FOR SOUTH AFRICA
Upcoming SlideShare
Loading in...5
×
 

PREDICTING A MODIFIED AUDITORS REPORT – A MODEL FOR SOUTH AFRICA

on

  • 1,150 views

 

Statistics

Views

Total Views
1,150
Views on SlideShare
1,150
Embed Views
0

Actions

Likes
0
Downloads
5
Comments
0

0 Embeds 0

No embeds

Accessibility

Categories

Upload Details

Uploaded via as Microsoft Word

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment

PREDICTING A MODIFIED AUDITORS REPORT – A MODEL FOR SOUTH AFRICA PREDICTING A MODIFIED AUDITORS REPORT – A MODEL FOR SOUTH AFRICA Document Transcript

  • PREDICTING A MODIFIED AUDITORS REPORT – A MODEL FOR SOUTH AFRICA By – Dr. Steven Firer School of Accountancy University of the Witwatersrand ABSTRACT 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 predicting-a-modified-auditors-report-a-model-for-south-africa3371.doc Page 1 of 23
  • INTRODUCTION 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. predicting-a-modified-auditors-report-a-model-for-south-africa3371.doc Page 2 of 23
  • 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 necessary. 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. predicting-a-modified-auditors-report-a-model-for-south-africa3371.doc Page 3 of 23
  • 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 and implications. PREVIOUS RESEARCH 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 predicting-a-modified-auditors-report-a-model-for-south-africa3371.doc Page 4 of 23
  • 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 predicting-a-modified-auditors-report-a-model-for-south-africa3371.doc Page 5 of 23
  • 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 Africa. 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 predicting-a-modified-auditors-report-a-model-for-south-africa3371.doc Page 6 of 23
  • 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 profession. 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. METHODOLOGY Data 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 predicting-a-modified-auditors-report-a-model-for-south-africa3371.doc Page 7 of 23
  • 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). Method 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 predicting-a-modified-auditors-report-a-model-for-south-africa3371.doc Page 8 of 23
  • Table 1 Variables 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 equity 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 profitability ROE Return on Equity Firer & Williams (2003) – measure of company profitability 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 predicting-a-modified-auditors-report-a-model-for-south-africa3371.doc Page 9 of 23
  • RESULTS Insert Appendix 2 Univariate Results 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, predicting-a-modified-auditors-report-a-model-for-south-africa3371.doc Page 10 of 23
  • 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 predicting-a-modified-auditors-report-a-model-for-south-africa3371.doc Page 11 of 23
  • -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. predicting-a-modified-auditors-report-a-model-for-south-africa3371.doc Page 12 of 23
  • 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. predicting-a-modified-auditors-report-a-model-for-south-africa3371.doc Page 13 of 23
  • 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. predicting-a-modified-auditors-report-a-model-for-south-africa3371.doc Page 14 of 23
  • 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. predicting-a-modified-auditors-report-a-model-for-south-africa3371.doc Page 15 of 23
  • 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 “formal process”). predicting-a-modified-auditors-report-a-model-for-south-africa3371.doc Page 16 of 23
  • 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. predicting-a-modified-auditors-report-a-model-for-south-africa3371.doc Page 17 of 23
  • REFERENCES 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, Johannesburg. 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 www.saica.co.za 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: Prentice Hall. predicting-a-modified-auditors-report-a-model-for-south-africa3371.doc Page 19 of 23
  • APPENDICES Appendix 2 Group Statistics Std. Error QUAL N Mean Std. Deviation Mean QR 1.00 20 .8505 1.09134 .24403 .00 47 2.2760 2.83540 .41359 CR 1.00 20 1.4340 1.71636 .38379 .00 47 2.4187 2.85309 .41617 FD 1.00 20 -72.6885 288.16570 64.43581 .00 47 -24.5891 127.73669 18.63231 DER 1.00 20 -1.6190 6.87118 1.53644 .00 47 4.1138 26.93338 3.92864 TDCF 1.00 20 -4.8830 8.72618 1.95123 .00 47 -27.5070 186.62436 27.22196 ROA 1.00 20 -406.2630 1623.62819 363.05430 .00 47 -179.8038 914.84020 133.44316 ROE 1.00 20 -.0465 583.65744 130.50977 .00 47 -1279.64 6957.92563 1014.918 predicting-a-modified-auditors-report-a-model-for-south-africa3371.doc Page 20 of 23
  • Independent Samples Test Levene's Test for Equality of Variances t-test for Equality of Means 95% Confidence 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 assumed Equal variances -2.968 64.637 .004 -1.4255 .48021 -2.38461 -.46631 not assumed CR Equal variances 3.589 .063 -1.433 65 .157 -.9847 .68701 -2.35677 .38733 assumed Equal variances -1.739 57.255 .087 -.9847 .56612 -2.11824 .14880 not assumed FD Equal variances 2.838 .097 -.952 65 .345 -48.0994 50.52866 -149.012 52.81335 assumed Equal variances -.717 22.246 .481 -48.0994 67.07560 -187.117 90.91783 not assumed DER Equal variances .452 .504 -.935 65 .353 -5.7328 6.12981 -17.97491 6.50925 assumed Equal variances -1.359 57.870 .179 -5.7328 4.21839 -14.17727 2.71161 not assumed TDCF Equal variances 1.385 .244 .540 65 .591 22.6240 41.93340 -61.12275 106.37080 assumed Equal variances .829 46.471 .411 22.6240 27.29180 -32.29651 77.54455 not assumed ROA Equal variances 1.818 .182 -.727 65 .470 -226.4592 311.67329 -848.914 395.99538 assumed Equal variances -.585 24.297 .564 -226.4592 386.80163 -1024.26 571.34349 not assumed ROE Equal variances 1.778 .187 .818 65 .417 1279.5886 1564.9688 -1845.87 4405.047 assumed Equal variances 1.250 47.502 .217 1279.5886 1023.2746 -778.400 3337.577 not assumed Discuss sample selection / justification for predictability – refer page 7 Descriptive Statistics N Mean Std. Deviation Minimum Maximum LOSS 67 .6866 .46739 .00 1.00 LIT 67 .1642 .37323 .00 1.00 LOSS Observed N Expected N Residual .00 21 33.5 -12.5 1.00 46 33.5 12.5 Total 67 predicting-a-modified-auditors-report-a-model-for-south-africa3371.doc Page 21 of 23
  • LIT Observed N Expected N Residual .00 56 33.5 22.5 1.00 11 33.5 -22.5 Total 67 Test Statistics LOSS LIT 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 Model Summary -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 Classification Tablea Predicted QUAL Percentage 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 predicting-a-modified-auditors-report-a-model-for-south-africa3371.doc Page 22 of 23
  • Variables in the Equation B S.E. Wald df Sig. Exp(B) Step a 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. predicting-a-modified-auditors-report-a-model-for-south-africa3371.doc Page 23 of 23