A Multivariate Analysis of the Auditor's Going-Concern Opinion Decision

September 5, 2017 | Author: Renhard Sirait | Category: Auditor's Report, Going Concern, Audit, Financial Audit, Skewness
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Journal of Accounting Research Vol. 23 No. 2 Autumn 1985 Printed in U.S.A.

A Multivariate Analysis of the Auditor's Going-Concern Opinion Decision JANE F. MUTCHLER*

I. Introduction The Auditing Standards Board (ASB) recently attempted to eliminate the subject-to opinion, including those issued for going-concern uncertainties. Financial statement users expressed strong opposition to this move, partly because they believed that auditors are privy to inside information (AICPA [1982; 1983]). Clearly, if an auditor's loss-likelihood judgment is made with greater precision because of access to inside information, the audit opinion would have information content. On the other hand, if auditors' opinions merely refiect what can be gleaned from publicly disclosed information, then the opinion itself could be redundant.^ The research described in this paper was designed to examine the relationship beween the going-concern opinion and publicly available information. Discriminant analysis was used to test models of the goingconcern opinion decision with a sample of manufactinring companies that received a going-concern opinion (GCAR companies) and a sample of manufacturing companies that exhibited potential going-concern diffi* Assistant Professor, Ohio State University. I wish to acknowledge the useful comments and suggestions of Frederick Neumann, James McKeown, William Hopwood, Charles Boynton, Richard Murdock, the Ohio State Ph.D. Seminar students, and an anonymous reviewer. The research described here is based on my Ph.D. dissertation, completed at the University of Illinois, and was supported by a grant from the AICPA. [Accepted for publication December 1984.] ' This statement ignores the situation where the threat of qualification itself forces information disclosures that may otherwise not be forthcoming. 668 Copyright ©, Institute of Professional Accounting 1985



culties but that did not receive a going-concern opinion {NGCAR companies). The independent variables were selected from information available from companies' 8-Ks, 10-Ks, or annual reports. The selections were based on interviews and questionnaire responses from a sample of auditors. The modeling process and testing took place in three stages. In the first stage I used an auditor-chosen set of ratios in the discriminant model. At the second stage I added "contrary information" items and "mitigating factors" as described in Statement on Auditing Standards (SAS) No. 34 (AICPA [1981]).^ Finally, at stage three I added still more variables deemed important by auditors. Although the results suggest that the going-concein opinion is redundant to a certain degree, there are several cases in which the opinion seems to have marginal information content for the financial statement user. My approach is designed to complement studies which assessed the information content of qualified opinions in terms of their associations with unexpected returns on securities. Firth [1978], Ball, Walker, and Whittred [1979], and Chow and Rice [1982] concluded that qualified opinions had information content. Similarly, Banks and Kinney [1982] showed negative returns both for companies that had received an uncertainty qualification and for companies that had received an unqualified opinion and had uncertainties disclosed in a footnote. In contrast, Elliott [1982] and Dodd et al. [forthcoming] showed that abnormal returns occurred prior to the announcement of the qualified opinion and concluded that the opinion itself did not seem to have significant information content. These researchers, along with Bailey [1982], point out various difficulties in isolating market reactions to the audit opinion. The fact is that the audit opinion is so closely tied to the financial statement results that it is extremely difficult, perhaps impossible, to institute enough controls to determine the information content of the audit opinion itself through securities price studies. My research adopted a different approach from that of the market research in that I tried to disaggregate the opinion effect and the financial information effect by examining the relationship between the going-concern opinion and publicly available information. As indicated above, my results are more consistent with those reported by Elliot [1982] and Dodd et al. [forthcoming] in that my discriminant model using publicly available information explained a high proportion of qualified opinions. In section 2 I describe the research methodology. The third section provides details on the research results. A summary and conclusions appear in the final section. ^ SAS No. 34 is intended to provide guidance to auditors in situations where there were doubts about the continued existence of an entity.



2. Research Methodology 2.1 SAMPLE

Auditor decision making in the presence of going-concern uncertainties can be characterized as a two-stage process in which the auditor recognizes that a company has a problem, and then uses subsequent information cues to determine whether to issue a going-concern qualification. In general, the problem/nonproblem status of a company is a matter of degree. As used here, however, it was looked upon as the event that triggers an analysis on the part of the auditor. For example, assume a company has a loss in the current year after several years of continual gains. That company may have product obsolescence problems, and if there is no new product on the horizon and/or poor management, etc., the situation may be such that the auditor would consider issuing the going-concern opinion. In contrast, another company in the same situation might have several promising products on the horizon, good management, etc., in which case, the auditor would be less likely to issue a qualification. The main point is that some event (e.g., a current-year loss) triggers further analysis on the part of the auditor. Prior to my sample selection, I attempted to identify a set of events that constituted a problem in the mind of an auditor. This was done through an interview and questionnaire process using two auditors from each of the Big Eight firms (Mutchler [1984]).''' In order to test a model of auditor decision making in the presence of going-concern uncertainties I selected two samples of problem companies—those that received a going-concern opinion (GCAR companies) and those that did not receive a going-concern opinion (NGCAR companies) even though they exhibited similar kinds of problems. In order to reduce confounding effects and to ensure that sufficient data would be available, the samples were restricted to those companies with two-digit SIC codes from 20-39 (manufacturing companies) and with three years of data on the Disclosure II Database.'' The base year from which the audit opinion type and other information was taken was the companies' fiscal years ending between the dates March 31, 1981 and February 28, 1982. This period was chosen because SAS No. 34 was in effect during that time. The initial set of GCAR companies was identified with reference to the audit opinion field on the Disclosure II Database. Rather than rely only on this data base I examined the complete copy of the audit opinion in order to make sure that the words "unable to continue in existence" or •' All subjects interviewed were administrative partners, some at the regional but most at the national level. * This data base (June 1982 version) contains descriptive and financial statement data on 8,443 SEC registrants including auditor name and current-year audit opinion type. It does not include company records for management investment companies, real estate limited partnerships, or oil and gas drilling funds.



"going-concern" were used and the intentions of the auditor were obvious. In cases where the meaning was ambiguous (e.g., the words "ability to obtain future financing" were used) I contacted the auditing firm to determine if its opinion was meant to be a going-concern qualification. The final set of GCAR companies consisted of 119 manufacturing companies that received a going-concern opinion as defined and that met the sample restrictions described above. Because 65% of the GCAR companies had received the going-concern opinion over two or more consecutive years, separate analyses were conducted on both the full set of 119 GCAR companies (along with 119 NGCAR companies) and on the set of 42 GCAR companies (along with 42 NGCAR companies) that were initial recipients of the going-concern opinion. For ease of exposition the full sample of 238 companies will be called Sample Set 1 and the set of 84 companies will be called Sample Set 2. The selection of the NGCAR set of companies was based on the set of problem company criteria determined through the auditor interviews. These appear in table 1. In order to increase the potential set of firms with problems but not with a going-concern qualification, the NGCAR firms were selected based only on criteria 5-11.® They were selected from a random sampling of 2,855 manufacturing companies from the Disclosure II Database, provided they met at least one of the criteria numbered 5 to 11. Since equal-sized groups were used in the discriminant model, 119 NGCAR companies were selected." They were also broken up into Sample 1 (all 119) and Sample 2 (a subset of 42). Table 2 describes the distribution of the problem criteria across the entire set of companies weighted to refiect the population. The probabilities are based on relative frequencies. 2.2 MODELING PROCESS

The purpose of the modeling phase of the research was to construct a model of the information cues used by auditors to determine whether a problem company would receive a going-concern opinion. The cues used were also determined through the interview process using the same 16 auditor subjects referred to earlier. In the first stage, the model used the top six ratios as ranked by the auditor subjects. These were: (1) Cash Flow (working capital from operations)/Total Liabilities {CFTL); (2) Current Assets/Current Liabilities {CACL); (3) Net Worth/Total Liabilities {NWTL); (4) Total Long-Term '' Note that all companies meeting criteria 1-4 also met at least one of the criteria 5-11. ^ The total set of manufacturing companies represents 33.8% of the entire data base. The total problem group represents 28% of the entire set of manufacturing companies. The GCAR group in total represents 5% of the manufacturing population, with first-time recipients representing 2%. A total of 684 companies was examined before 119 NGCAR companies were identified.


JANE F. MUTCHLER TABLE 1 Problem Company Criteria

1. 2. 3. 4.

Enter Receivership Enter Reorganization Inability to Meet Interest Payments Going-Concern Opinion in the Previous Year 5. In Liquidation 6. Negative Net Worth 7. Negative Cash Flow

8. Negative Income From Operations 9. Negative Working Capital 10. Current Year Loss, including cases where there were two and three straight loss years 11. Current Year Retained Earnings Deficit, including cases where there were two and three straight deficit years

TABLE 2 Distribution of Problem Company Characteristics Weighted to Reflect Population Proportions Probabilities C harac tcris t ic s

1. Three Straight Years of Losses 2. Three Straight Years Deficit 3. In Liquidation 4. Two Straight Years of Losses 5. Two Straight Years of Deficit 6. Current Year Loss 7. Current Year Deficit 8. Negative Net Worth 9. Negative Cash Flow 10. Negative Income From Operations 11. Negative Working Capital 12. Going-Concern Opinion in Previous Year (Xi) P(_Xi) P{Xi IG) P(Xi I NG) P(G I Xi) P(NG I Xi)



P{x, 1 NG)




1 45.5


13.3 .3 11.2

73.9 2.5 68.1

42.0 .8 34.5

28.2 40.1 30.6

71.8 59.9 69.4






19.7 15.6 2.1 13.8 13.2

84.9 94.1 38.7 77.3 65.5

68.1 47.9 .8 43.7 43.7

21.7 30.5 91.1 28.3 25.1

78.3 69.5 8.9 71.7 74.9

3.0 4.4

37.0 66.7

5.0 4.6

62.0 76.2

38.0 23.8

PiNG 1 Xi)

= Characteristic i; i — 1,12. — The relative frequency of characteristic i in the population of problem companies. = The relative frequency of characteristic i in the population of problem companies that received a going-concern opinion. = The relative frequency of characteristic i in the population of problem companies that did not receive a going-concern opinion. = The relative frequency of companies that received a going-concern opinion in the population of companies exhibiting characteristic i. = The relative frequency of companies that did not receive the going-concern opinion within the population of companies exhibiting characteristic i.

Liabilities/Total Assets {LTDTA); (5) Total Liabilities/Total Assets {TLTA); (6) Net Income Before Tax/Net Sales {NIBTS). The mean values of each ratio of the sample groups are shown in table 3, along with the mean asset sizes of each group. Previous research has documented



TABLE 3 Mean Ratio and Asset Values Sample Set 1 N = 238

Variable CFTL CACL


Sample Set 2 N = 84





-.345 1.666 .515 .347 1.352 -.745 $8,766,394

-.301 2.558 1.334 .254 .590 -.062 $299,083,193

-.316 1.260 .211 .309 .996 -.621 $20,070,381

-.735 3.053 2.076 .205 .508 -.230 $56,794,262

Range of Asset Values Sample Set 1 GCAR NGCAR Sample Set 2 GCAR NGCAR

$ 96,000-$ 6,270,000,000 $180,000-$23,021,400,000 $369,000-$303,430,000 $180,000-$472,935,000

that various accounting ratios are capable of predicting bankruptcy (e.g., see Zavgren [1983] for a review). While there is not a one-to-one correspondence between the going-concern opinion and bankruptcy (Altman and McGough [1974]), the two events are related. As a result, I expected this ratio set to predict the opinion decision with a relatively high degree of accuracy. I also expected that the ratio model with Sample Set 2 data would produce higher overall predictive accuracy than with Sample Set 1 data since the latter included companies that had successive going-concern opinions, even though some of them were most likely improving their operations and performance. Auditors apparently find it easier not to remove going-concern opinions until companies are clearly out of trouble. Sample Set 2, on the other hand, consists only of initial recipients of the going-concern opinion, which in the auditor's mind clearly exhibited survival difficulties. The predictive accuracy across the two samples of firms from the NGCAR group should be no different except for random fiuctuations. Altman and McGough [1974] observed cases where a bankrupt company had not received a prior going-concern qualification, and cases where a going-concern qualification had been issued but the company subsequently did not go bankrupt. Generally, the auditor does not know, at the time of the going-concern decision, whether a company will subsequently file for bankruptcy. Going-concern states are assessed and, according to SAS No. 23, the auditor then decides whether there are factors that may either mitigate any observed apparent problems, or actually make a declining situation even worse. As a result, we can expect to observe companies that look "bad" in terms of a ratio analysis but do



not receive qualifications, and companies that look relatively "good" but do receive the qualification. Figure 1 illustrates the nature of the inference problem facing the auditor. Companies whose composite rating (e.g., a discriminant score) falls to the left of the cutoff shown would normally receive a qualification. But the distribution of NGCAR scores which fall in area A represents companies that have low scores but did not receive a qualification. Similarly, scores which fall to the right of the cutoff would warrant unqualified opinions, but the distribution of GCAR companies in area B of figure 1 represents companies that received a going-concern opinion even though they did not have composite scores as bad as the rest of the GCAR companies. In each case we would assume that other mitigating factors account for these inconsistent observations. Such factors were identified in the following manner. The discriminant procedure (to be described) was run using the ratio set above and any misclassified companies falling in areas A and B of figure 1 were identified. Using information gathered during the auditor interviews along with SAS No. 34 as guides, I examined the management discussion fields of Disclosure II Database for these companies in order to identify factors associated with each. The factors were classified into sets. These sets of items constituted the good news (no qualification) and bad news (qualification) factors. These are shown in table 4. Once this list was compiled, I then examined information for all companies in the sample, whether misclassified or not, to determine which of the companies exhibited any of the same factors. If they exhibited any good news items, they were coded with a 1 on the good news variable; otherwise they were coded with a 0. A similar 0, 1 coding was employed for the bad news variables for each firm exhibiting any of the bad news factors. Table 5 presents the mean values of the good news and bad news variables for both groups and across both sample sets. Distribution of Discriminant Scores NGCAR


Cutpoint FIG, 1



TABLE 4 Good News and Bad News Variables Good News

Bad News

Line of credit available Successful new product Increase in research and development expenditures Sale of common stock Issue of new debt Forgiveness of debt including preferred dividends Restructuring of debt Waivers obtained for violation of debt covenants Obtained employee and supplier concessions

Default on debt Inventory obsolescence Loss of major customer Accounts receivable factoring Preferred dividend arrearages Employee strike Federal tax lien Product obsolescence Lost money on a fixed-price contract Loss of purchase discounts from suppliers In reorganization

TABLE 5 Mean Values for Good News and Bad News Variables Sample Set 2 iV=84

Sample Set 1 JV = 238 Good News Bad News





.376 .305

.370 .140

.352 .414

.357 .213

The final phase of the modeling process included other variables that were suggested to be important by the auditor subjects. These subjects indicated that although a company may look bad on the surface, its performance may have improved over the previous year and it may not receive the qualification. To capture this feature I incorporated an improvement variable into the model which indicated whether a company's performance had improved over the previous years. The measure was calculated as follows: IMPROVE = NI denotes net income, EA ending assets, and i the current fiscal year. A positive number indicated that the company's performance had improved over the previous year. An analysis of the auditor interview responses also suggested that the prior-year opinion type might have an effect on the current year's opinion. In particular, a company with a going-concern qualification in the prior year was likely to receive the same qualification in the current year. For Sample Set 1, each company that had a going-concern opinion in the prior year was coded 1 on the PYAR variable and all others were


JANE F. MUTCHLER TABLE 6 Mean Values for PYAR and IMPROV Variables Sample Set 1 JV = 238


Sample Set 2 iV = 84





.647 -.104

.042 -.027

NA -.185

NA -.032

coded 0. Since companies in Sample Set 2 all had unqualified opinions in the prior year, the models tested with that sample group did not contain the PYAR variable. Table 6 presents the mean of the IMPROV and PYAR variables. Note that a higher percentage of the GCAR companies received a going-concern opinion in the previous year, and also had more negative values for the 7MPi?0y variable. The model was estimated using ten separate discriminant runs. For each discriminant run, one-half of the total sample was used as a derivation (estimation) sample and one-half as a validation (holdout) sample. Each of the ten separate derivation and validation samples were chosen by using the SPSSX [1983] random sampling procedure. The results from these ten runs were then pooled to produce an average set of results.' Two of the basic assumptions of the discriminant model are that the independent variables are multivariate normal and that the covariance matrices are equal. In order to ensure that the assumptions of the discriminant technique were met, I employed an outlier identification and truncation process. Barnett and Lewis [1978], Frecka and Hopwood [1982], and Hopwood, McKeown, and Mutchler [1984] all provide information about the infiuence of outliers on the estimating and prediction processes. The outlier identification and truncation process described in detail in the last paper was used in this research. The process proceeds as follows. Descriptive statistics were calculated for all of the ratios in both their raw and square root forms. The skewness of each form was assessed, and the form with the lowest absolute skewness coefficient was selected for the outlier identification process. Outlying observations were sequentially deleted until the skew was at or below a critical value. If skew was positive, the highest value was deleted; if negative, the lowest value was deleted. A critical value of .05 (see table XIVA in Barnett and Lewis [1978]) was used and represents that level of skewness such that 95% or less of normally distributed samples would have skew less than that value. Once the skewness had reached the critical level, the highest 'Each separate discriminant run produced a different accuracy level. I could simply have chosen the highest and reported that result. A more accurate representation of the predictive power of the model, however, is obtained by pooling the results over several different random samples. The range of the predictive accuracies across the ten runs is reported in notes 8 and 9.



TABLE 7 Ratio Model Prediction

~ "^


Sample Set 1 iV=238

Sample Set 2

Predicted GCAR NGCAR

Predicted GCAR NGCAR

424 72.4%

162 27.6%

86 15.0%

490 85.0%

Overall = 82.8%


174 79.1%

46 20.9%


35 16.7%

174 83.3%


Overall = 83.0%

and lowest values of observations constituted the truncated distribution. A separate outlier identification and truncation process was followed for Sample Set 1 (238 companies) and for Sample Set 2 (84 companies).

3. Research Results The prediction accuracy results reported below are based on the validation (holdout) samples. The number of companies in each cell represents the total classifications across the ten separate discriminant runs, and the percentages the percent classified for each category of company. The overall percentage is based on tbe overall correct classifications and is restated to refiect what one would expect on a random sample from the population. Table 7 presents the prediction accuracy results for the auditor ratio model.® As expected the ratio model is able to predict the opinion decision with a relatively high degree of accuracy (approximately 83%). The accuracy on an overall basis for Sample Set 2 is only .2 percentage points different than that of Sample Set 1, representing the combined effect of the 6.7 percentage point increase for the GCAR group and the 1.7 percentage point decrease for the NGCAR group. The significant increase in the predictive accuracy for the GCAR group with Sample Set 2 data was expected. Table 8 presents the predictive accuracy results for the model containing both ratios and the good news and bad news variables. Contrary to expectations, the addition of the good news and bad news variables decreased the predictive accuracy for both sample sets. Although these results could indicate the fact that these variables are not used by auditors in their opinion decisions, there are other possible explanations of the * None of coefficients for the variables in the models is reported. Of interest here was the relative predictive power or increase in predictive power of given sets of variables. The importance of specific variables was immaterial. The ratios, for example, were tested individually and in various combinations and none predicted better than the total set of six ratios chosen by the auditors.



decrease in predictive accuracy. Two in particular stand out. One is the fact that the good news variable means were about equal for the GCAR and the NGCAR groups. This would contribute to lower predictive power. Second, no attempt was made to weight the relative importance of any of the events included in the model. Nor would I know how to do this. I did try to model multiple occurrences of these variables, but to no avail. Perhaps the only way to model these variables successfully in terms of the auditors' opinion decisions would be to examine the audit work papers for each specific case. Table 9 presents the results for the model with the ratios and the /MPfiOV variable. For both sample sets there is little difference between the results with this model and the simple ratio model. This could indicate that when making the going-concern opinion decision the auditor is more interested in what the improvement will be between the current year and the next, not the current improvement. The auditor subjects interviewed stated that they carefully looked at cash fiow forecasts which would have an impact on their decisions. Cash fiow forecasts, of course, are not generally available and could be a major item of inside information that is impounded in the opinion decisions. Table 10 presents the results for the model with the ratios and the PYAR variable. A 9.1 percentage point increase in predictive accuracy TABLE 8 Model with Ratios and Good News and Bad News Variables



Sample Set 1 AT =238

Sample Set 2

Predicted GCAR NGCAR 426 160 72.7% 27.3%

Predicted GCAR NGCAR

101 17.5%

475 82.5%


157 71.4%

63 28.6%


40 19.1%

169 80.9%

Overall = 80.7%

Overall = 80.2%

TABLE 9 Model with Ratios and IMPROV Variable


Sample Set 1 A'=238

Sample Set 2 N^84


Predicted GCAR NGCAR 152 434 25.9% 74.1%


Predicted GCAR NGCAR 174 46 21.0% 79.0%


89 15.5%


38 18.2%

487 84.5%

Overall = 82.6%


171 81.8%

Overall = 81.8



TABLE 10 Model with Ratios and PYAR Variables Sample Set 1 AT = 2 3 8





Predicted GCAR NGCAR 170 416 71,0% 29,0% 34 5,9%

542 94.1%

Overall = 89.9%

occurred for the NGCAR companies, while a 1.7 percentage point decrease occurred for the GCAR group compared to the results in table 7. This results in an overall increase in predictive accuracy of 7.1 percentage points. Clearly, knowledge about the ratio values and the prior-year opinion type is useful in predicting the opinion decision. Although the models tested in this research exhibit a relatively high degree of predictive accuracy in each sample group and on an overall basis, there are cases where it appears the opinion decision has information content beyond the items tested here. Moreover, there are cases in which companies were classified correctly with the ratio model, but then misclassified with the model containing the ratios and the good news and bad news variables. This suggests that clues to information content of the going-concern opinion may be linked to specific cases. The auditor subjects interviewed suggested that there are cases in which inside information is embedded in the opinions. For example, if management is preparing to place in action a plan whereby certain operations would be shut down and employees terminated, the auditor would attempt to determine whether the completion of such a plan would improve or reduce the chances that the company will continue in existence. At the time the opinion is issued, however, these plans can only be publicized in general terms. Thus, in this hypothetical example the fact that a "bad" company received an unqualified opinion would have information content for external parties. In contrast, it is difficult to imagine cases in which companies would look "good" and yet receive the goingconcern opinion for the first time. If they did, we would expect disclosures of whatever event caused the issuance of such an opinion. Almost 80% of the GCAR companies in Sample Set 1 that were misclassified had received a going-concern opinion in the previous year. This suggests that these companies were improving operations, and except for the fact that they already had received the qualification would probably not have received one in the current year. Of more interest to external parties, however, is the result that 79% of companies that initially received going-concern opinions (Sample Set 2)



could be identified using the ratio model. This suggests that the goingconcern opinion is redundant information for these companies in that the financial statement user could make the same decision as the auditor using the same ratio sets. The clues about any information content of the going-concern opinion lie in the companies that were misclassified using the ratio model. Two of the nine companies misclassified in that set were in liquidation, which would account for more liquid ratios. Any investor or lender would know the company is in liquidation so a goingconcern opinion issued because of that fact would also yield no additional information. Overall, then, only 7 out of 42 GCAR companies were seriously misclassified using ratio data alone.

4. Summary and Conclusions The purpose of this research was to determine the extent to which auditors' going-concern opinion decisions could be predicted using publicly available information. Modeling of the opinion decision was conducted in three stages, with the first stage using only a set of auditorchosen ratios, and the second and third stages adding SAS No. 34 type variables (mitigating factors and contrary information) and a trend measure (IMPROV) and the prior-year opinion type (PYAR) to the model, respectively. All variables used in the models were determined with reference to auditor subjects. The sample of companies used in the research was composed of only problem companies because the auditor must first identify a company as having a problem in some sense prior to making the going-concern opinion decision. The final sample consisted of 119 problem companies that had received a going-concern opinion (GCAR companies) and 119 problem companies that did not receive a going-concern opinion (NGCAR companies). The models were tested using two sample sets, the entire group of 119 GCAR companies (along with 119 NGCAR companies) and a subset of 42 GCAR companies (along with 42 NGCAR companies) that had received the qualification for the first time. The models were tested for violation of the assumptions of discriminant analysis, and an outlier identification and truncation procedure was followed to the extent appropriate. The accuracy of each model was assessed by pooling the results of ten separate discriminant runs, each of which was validated on a separate validation sample. The model with the ratios and prior-year opinion variable had the highest overall predictive accuracy. The rate for the entire sample (238 companies)^ was 89.9% and for the smaller sample set (companies that ^ The range of predictive accuracy across the ten separate discriminant runs for each group and on an overall basis is as follows: GCAR 64.2-79.2%; NGCAR 88.6-96.8%; overall 86.7-93.6%.



had received the qualification for the first time) it was 83%.^° While the going-concern opinion does not appear to have additional information content for the majority of companies, there are specific cases in which the qualification has marginal information content. But each case appears unique, which presents modeling difficulties. This study was designed to test the extent to which the going-concern opinion could be predicted using only publicly available information. The results are limited in the sense that even with a 100% prediction accuracy rate, they could not resolve the question of whether the public information plus the qualification was important to financial statement users. The type of audit opinion could be used as a reinforcing signal, and cases in which the information and the opinions were consistent would justify more reliance on their own decisions. Cases in which the two sources of signals were inconsistent might trigger the search for additional data before decisions are made. This possibility only raises further questions, including whether the opinion is used as a signal of other more broader issues in the evaluation of business risk. To test the overall function of qualified opinions will require additional studies incorporating more controls. REFERENCES AMERICAN INSTITUTE OF CERTIFIED PUBLIC ACCOUNTANTS. Statement on Auditing Stand-

ards No. 34: The Auditor's Considerations When a Question Arises About an Entity's Continued Existence. New York: AICPA, 1981. . "News Report Section." Journal of Accountancy (August and September 1982). . "News Report Section." Journal of Accountancy (February 1983). ALTMAN, E . I., AND T. P. McGoUGH. "Evaluation of a Company as a Going Concern." Journal of Accountancy (December 1974): 59-67. BALL, R., R. G., WALKER, AND G. P. WHITTRED. "Audit Qualifications and Share Prices."

Abacus (June 1979): 23-34. BAILEY, W . J. "An Appraisal of Research Designs Used to Investigate the Information Content of Audit Reports." The Accounting Review (January 1982): 141-46. BANKS, D . W., AND W . R. KINNEY. "LOSS Contingency Reports and Stock Prices: An

Empirical Study." Journal of Accounting Research (Spring 1982): 240-54. BARNETT, V. D., AND T . LEWIS. Outliers in Statistical Data. New York: Wiley, 1978. CHOW, C. W., AND S. J. RICE. "Qualified Audit Opinions and Share Prices—An Investigation." Auditing: A Journal of Theory and Practice (Winter 1982): 35-53. DISCLOSURE INCORPORATED. Disclosure II Database. Washington, D.C.: Disclosure Inc., 1982. DoDD, P., ET AL. "Qualified Audit Opinions and Stock Prices: Information Content, Announcement Dates, and Concurrent Disclosures." Journal of Accounting and Economics (forthcoming). ELLIOTT, J. A. "'Subject to' Audit Opinions and Abnormal Security Returns: Outcomes and Ambiguities." Journal of Accounting Research (Autumn 1982, pt. II): 617-38. FIRTH, M . "Qualified Opinions: Their Impact on Investment Decisions." The Accounting Review (July 1978): 642-50. '" The range of predictive accuracy across the ten separate discriminant runs for each group and on an overall basis is as follows: GCAR 68.2-90.5%; NGCAR 68.0-100%; overall 75.0-89.0%.



FRECKA, T . J., AND W . S. HOPWOOD. "The Effects of Outliers on the Cross-Sectional Distributional Properties of Financial Ratios." The Accounting Review (January 1982): 115-28. HOPWOOD, W., J. MCKEOWN, AND J. MUTCHLER. "The Impact of the Cross-Sectional

Distribution of Ratios on Financial Distress Prediction." Working paper WPS 84-10, Ohio State University, January 1984. MUTCHLER, J. F. "Auditors Perceptions of the Going-Concern Opinion Decision." Auditing: A Journal of Practice and Theory (Spring 1984): 17-30. SPSSX. A Complete Guide to SPSSX Language and Operations SPSSX User Guide. New York: SPSS, Inc., 1983. ZAVGREN, C. V. "The Prediction of Corporate Failure: The State of the Art." Journal of Accounting Literature (Spring 1983): 1-38.

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