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

Journal of Accounting ResearchVol. 23 No. 2 Autumn 1985

Printed in U.S.A.

A Multivariate Analysis of theAuditor's Going-Concern Opinion

Decision

JANE F. MUTCHLER*

I. Introduction

The Auditing Standards Board (ASB) recently attempted to eliminatethe subject-to opinion, including those issued for going-concern uncer-tainties. Financial statement users expressed strong opposition to thismove, partly because they believed that auditors are privy to insideinformation (AICPA [1982; 1983]). Clearly, if an auditor's loss-likelihoodjudgment is made with greater precision because of access to insideinformation, the audit opinion would have information content. On theother hand, if auditors' opinions merely refiect what can be gleaned frompublicly disclosed information, then the opinion itself could be redun-dant.^

The research described in this paper was designed to examine therelationship beween the going-concern opinion and publicly availableinformation. Discriminant analysis was used to test models of the going-concern opinion decision with a sample of manufactinring companies thatreceived a going-concern opinion (GCAR companies) and a sample ofmanufacturing companies that exhibited potential going-concern diffi-

* Assistant Professor, Ohio State University. I wish to acknowledge the useful commentsand suggestions of Frederick Neumann, James McKeown, William Hopwood, CharlesBoynton, Richard Murdock, the Ohio State Ph.D. Seminar students, and an anonymousreviewer. The research described here is based on my Ph.D. dissertation, completed at theUniversity of Illinois, and was supported by a grant from the AICPA. [Accepted forpublication December 1984.]

' This statement ignores the situation where the threat of qualification itself forcesinformation disclosures that may otherwise not be forthcoming.

668Copyright ©, Institute of Professional Accounting 1985

AUDITOR'S GOING-CONCERN OPINION 669

culties but that did not receive a going-concern opinion {NGCAR com-panies). The independent variables were selected from information avail-able from companies' 8-Ks, 10-Ks, or annual reports. The selections werebased on interviews and questionnaire responses from a sample of audi-tors.

The modeling process and testing took place in three stages. In thefirst stage I used an auditor-chosen set of ratios in the discriminantmodel. 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 morevariables deemed important by auditors. Although the results suggestthat the going-concein opinion is redundant to a certain degree, thereare several cases in which the opinion seems to have marginal informationcontent for the financial statement user.

My approach is designed to complement studies which assessed theinformation content of qualified opinions in terms of their associationswith unexpected returns on securities. Firth [1978], Ball, Walker, andWhittred [1979], and Chow and Rice [1982] concluded that qualifiedopinions had information content. Similarly, Banks and Kinney [1982]showed negative returns both for companies that had received an uncer-tainty qualification and for companies that had received an unqualifiedopinion and had uncertainties disclosed in a footnote. In contrast, Elliott[1982] and Dodd et al. [forthcoming] showed that abnormal returnsoccurred prior to the announcement of the qualified opinion and con-cluded that the opinion itself did not seem to have significant informationcontent.

These researchers, along with Bailey [1982], point out various difficul-ties in isolating market reactions to the audit opinion. The fact is thatthe audit opinion is so closely tied to the financial statement results thatit is extremely difficult, perhaps impossible, to institute enough controlsto determine the information content of the audit opinion itself throughsecurities price studies. My research adopted a different approach fromthat of the market research in that I tried to disaggregate the opinioneffect and the financial information effect by examining the relationshipbetween the going-concern opinion and publicly available information.As indicated above, my results are more consistent with those reportedby Elliot [1982] and Dodd et al. [forthcoming] in that my discriminantmodel using publicly available information explained a high proportionof qualified opinions.

In section 2 I describe the research methodology. The third sectionprovides details on the research results. A summary and conclusionsappear in the final section.

^ SAS No. 34 is intended to provide guidance to auditors in situations where there weredoubts about the continued existence of an entity.

670 JOURNAL OF ACCOUNTING RESEARCH, AUTUMN 1985

2. Research Methodology2.1 SAMPLE

Auditor decision making in the presence of going-concern uncertaintiescan be characterized as a two-stage process in which the auditor recog-nizes that a company has a problem, and then uses subsequent infor-mation cues to determine whether to issue a going-concern qualification.In general, the problem/nonproblem status of a company is a matter ofdegree. As used here, however, it was looked upon as the event thattriggers an analysis on the part of the auditor. For example, assume acompany has a loss in the current year after several years of continualgains. That company may have product obsolescence problems, and ifthere is no new product on the horizon and/or poor management, etc.,the situation may be such that the auditor would consider issuing thegoing-concern opinion. In contrast, another company in the same situa-tion might have several promising products on the horizon, good man-agement, etc., in which case, the auditor would be less likely to issue aqualification. The main point is that some event (e.g., a current-yearloss) triggers further analysis on the part of the auditor.

Prior to my sample selection, I attempted to identify a set of eventsthat constituted a problem in the mind of an auditor. This was donethrough an interview and questionnaire process using two auditors fromeach of the Big Eight firms (Mutchler [1984]).'''

In order to test a model of auditor decision making in the presence ofgoing-concern uncertainties I selected two samples of problem compa-nies—those that received a going-concern opinion (GCAR companies)and those that did not receive a going-concern opinion (NGCAR compa-nies) even though they exhibited similar kinds of problems. In order toreduce confounding effects and to ensure that sufficient data would beavailable, the samples were restricted to those companies with two-digitSIC codes from 20-39 (manufacturing companies) and with three yearsof data on the Disclosure II Database.'' The base year from which theaudit 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 duringthat time.

The initial set of GCAR companies was identified with reference to theaudit opinion field on the Disclosure II Database. Rather than rely onlyon this data base I examined the complete copy of the audit opinion inorder to make sure that the words "unable to continue in existence" or

•' All subjects interviewed were administrative partners, some at the regional but mostat the national level.

* This data base (June 1982 version) contains descriptive and financial statement dataon 8,443 SEC registrants including auditor name and current-year audit opinion type. Itdoes not include company records for management investment companies, real estatelimited 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 toobtain future financing" were used) I contacted the auditing firm todetermine if its opinion was meant to be a going-concern qualification.The final set of GCAR companies consisted of 119 manufacturing com-panies that received a going-concern opinion as defined and that met thesample restrictions described above.

Because 65% of the GCAR companies had received the going-concernopinion over two or more consecutive years, separate analyses wereconducted on both the full set of 119 GCAR companies (along with 119NGCAR companies) and on the set of 42 GCAR companies (along with42 NGCAR companies) that were initial recipients of the going-concernopinion. For ease of exposition the full sample of 238 companies will becalled Sample Set 1 and the set of 84 companies will be called SampleSet 2.

The selection of the NGCAR set of companies was based on the set ofproblem company criteria determined through the auditor interviews.These appear in table 1. In order to increase the potential set of firmswith problems but not with a going-concern qualification, the NGCARfirms were selected based only on criteria 5-11.® They were selected froma random sampling of 2,855 manufacturing companies from the Disclo-sure II Database, provided they met at least one of the criteria numbered5 to 11. Since equal-sized groups were used in the discriminant model,119 NGCAR companies were selected." They were also broken up intoSample 1 (all 119) and Sample 2 (a subset of 42). Table 2 describes thedistribution of the problem criteria across the entire set of companiesweighted to refiect the population. The probabilities are based on relativefrequencies.

2.2 MODELING PROCESS

The purpose of the modeling phase of the research was to construct amodel of the information cues used by auditors to determine whether aproblem company would receive a going-concern opinion. The cues usedwere also determined through the interview process using the same 16auditor subjects referred to earlier.

In the first stage, the model used the top six ratios as ranked by theauditor subjects. These were: (1) Cash Flow (working capital from oper-ations)/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. TheGCAR group in total represents 5% of the manufacturing population, with first-timerecipients representing 2%. A total of 684 companies was examined before 119 NGCARcompanies were identified.

672 JANE F. MUTCHLER

T A B L E 1Problem Company Criteria

1. Enter Receivership2. Enter Reorganization3. Inability to Meet Interest Payments4. Going-Concern Opinion in the

Previous Year5. In Liquidation6. Negative Net Worth7. Negative Cash Flow

8. Negative Income FromOperations

9. Negative Working Capital10. Current Year Loss, includ-

ing cases where therewere two and threestraight loss years

11. Current Year RetainedEarnings Deficit, includ-ing cases where therewere two and threestraight deficit years

TABLE 2Distribution of Problem Company Characteristics Weighted to Reflect Population

Proportions

C harac tcris t ic s

1. Three Straight Years ofLosses

2. Three Straight Years Deficit3. In Liquidation4. Two Straight Years of

Losses5. Two Straight Years of

Deficit6. Current Year Loss7. Current Year Deficit8. Negative Net Worth9. Negative Cash Flow

10. Negative Income FromOperations

11. Negative Working Capital12. Going-Concern Opinion in

Previous Year

P(x,)

5.6

13.3.3

11.2

14.3

19.715.62.1

13.813.2

3.04.4

P{xi\G)

50.4

73.92.5

68.1

84.0

84.994.138.777.365.5

37.066.7

Probabilities

P{x, 1 NG) 1

13.4

42.0.8

34.5

44.5

68.147.9

.843.743.7

5.04.6

45.5

28.240.130.6

29.6

21.730.591.128.325.1

62.076.2

PiNG 1 Xi)

54.5

71.859.969.4

70.4

78.369.5

8.971.774.9

38.023.8

(Xi) = Characteristic i; i — 1,12.P(_Xi) — The relative frequency of characteristic i in the population of problem companies.

P{Xi IG) = The relative frequency of characteristic i in the population of problem companies

that received a going-concern opinion.P(Xi I NG) = The relative frequency of characteristic i in the population of problem companies

that did not receive a going-concern opinion.P(G I Xi) = The relative frequency of companies that received a going-concern opinion in the

population of companies exhibiting characteristic i.P(NG I Xi) = 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 meanvalues of each ratio of the sample groups are shown in table 3, along withthe mean asset sizes of each group. Previous research has documented


TABLE 3Mean Ratio and Asset Values

Variable

CFTLCACLNWTLLTDTATLTANIBTSAssets

Sample Set 1N

GCAR

- .3451.666.515.347

1.352-.745

$8,766,394

= 238

NGCAR

- .3012.5581.334

.254

.590-.062

$299,083,193

Range of Asset Values

Sample Set 2N =

GCAR

-.3161.260.211.309.996

-.621$20,070,381

84

NGCAR

- .7353.0532.076

.205

.508-.230

$56,794,262

Sample Set 1GCAR $ 96,000-$ 6,270,000,000NGCAR $180,000-$23,021,400,000

Sample Set 2GCAR $369,000-$303,430,000NGCAR $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 corre-spondence between the going-concern opinion and bankruptcy (Altmanand McGough [1974]), the two events are related. As a result, I expectedthis ratio set to predict the opinion decision with a relatively high degreeof accuracy.

I also expected that the ratio model with Sample Set 2 data wouldproduce higher overall predictive accuracy than with Sample Set 1 datasince the latter included companies that had successive going-concernopinions, even though some of them were most likely improving theiroperations and performance. Auditors apparently find it easier not toremove going-concern opinions until companies are clearly out of trouble.Sample Set 2, on the other hand, consists only of initial recipients of thegoing-concern opinion, which in the auditor's mind clearly exhibitedsurvival difficulties. The predictive accuracy across the two samples offirms from the NGCAR group should be no different except for randomfiuctuations.

Altman and McGough [1974] observed cases where a bankrupt com-pany had not received a prior going-concern qualification, and caseswhere a going-concern qualification had been issued but the companysubsequently did not go bankrupt. Generally, the auditor does not know,at the time of the going-concern decision, whether a company willsubsequently file for bankruptcy. Going-concern states are assessed and,according to SAS No. 23, the auditor then decides whether there arefactors that may either mitigate any observed apparent problems, oractually make a declining situation even worse. As a result, we can expectto observe companies that look "bad" in terms of a ratio analysis but do


not receive qualifications, and companies that look relatively "good" butdo receive the qualification.

Figure 1 illustrates the nature of the inference problem facing theauditor. 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 representscompanies that have low scores but did not receive a qualification.Similarly, scores which fall to the right of the cutoff would warrantunqualified opinions, but the distribution of GCAR companies in area Bof figure 1 represents companies that received a going-concern opinioneven though they did not have composite scores as bad as the rest of theGCAR companies. In each case we would assume that other mitigatingfactors account for these inconsistent observations.

Such factors were identified in the following manner. The discriminantprocedure (to be described) was run using the ratio set above and anymisclassified companies falling in areas A and B of figure 1 were identi-fied. Using information gathered during the auditor interviews along withSAS No. 34 as guides, I examined the management discussion fields ofDisclosure II Database for these companies in order to identify factorsassociated with each. The factors were classified into sets. These sets ofitems constituted the good news (no qualification) and bad news (quali-fication) factors. These are shown in table 4.

Once this list was compiled, I then examined information for allcompanies in the sample, whether misclassified or not, to determinewhich of the companies exhibited any of the same factors. If theyexhibited any good news items, they were coded with a 1 on the goodnews variable; otherwise they were coded with a 0. A similar 0, 1 codingwas employed for the bad news variables for each firm exhibiting any ofthe bad news factors.

Table 5 presents the mean values of the good news and bad newsvariables for both groups and across both sample sets.

Distribution of Discriminant Scores

GCAR NGCAR

CutpointFIG, 1


TABLE 4Good News and Bad News Variables

Bad News

Default on debtInventory obsolescenceLoss of major customerAccounts receivable factoringPreferred dividend arrearagesEmployee strikeFederal tax lienProduct obsolescenceLost money on a fixed-price contractLoss of purchase discounts from suppliersIn reorganization

TABLE 5

Good News

Line of credit availableSuccessful new productIncrease in research and

development expendi-tures

Sale of common stockIssue of new debtForgiveness of debt in-

cluding preferred divi-dends

Restructuring of debtWaivers obtained for vi-

olation of debt cove-nants

Obtained employee andsupplier concessions

Mean Values for Good News and Bad News Variables

Sample Set 1JV = 238

GCAR NGCAR

Good News .376 .370Bad News .305 .140

Sample Set 2iV=84

GCAR NGCAR

.352 .357

.414 .213

The final phase of the modeling process included other variables thatwere suggested to be important by the auditor subjects. These subjectsindicated that although a company may look bad on the surface, itsperformance may have improved over the previous year and it may notreceive the qualification. To capture this feature I incorporated animprovement variable into the model which indicated whether a com-pany's performance had improved over the previous years. The measurewas calculated as follows:

IMPROVE =

NI denotes net income, EA ending assets, and i the current fiscalyear. A positive number indicated that the company's performance hadimproved over the previous year.

An analysis of the auditor interview responses also suggested that theprior-year opinion type might have an effect on the current year'sopinion. In particular, a company with a going-concern qualification inthe prior year was likely to receive the same qualification in the currentyear. For Sample Set 1, each company that had a going-concern opinionin the prior year was coded 1 on the PYAR variable and all others were


T A B L E 6Mean Values for PYAR and IMPROV Variables

Sample Set 1 Sample Set 2JV = 238 iV = 84

GCAH NGCAR GCAR NGCAR

PYAR .647 .042 NA NAIMPROV -.104 -.027 -.185 -.032

coded 0. Since companies in Sample Set 2 all had unqualified opinionsin the prior year, the models tested with that sample group did notcontain the PYAR variable. Table 6 presents the mean of the IMPROVand PYAR variables. Note that a higher percentage of the GCAR com-panies received a going-concern opinion in the previous year, and alsohad more negative values for the 7MPi?0y variable.

The model was estimated using ten separate discriminant runs. Foreach discriminant run, one-half of the total sample was used as aderivation (estimation) sample and one-half as a validation (holdout)sample. Each of the ten separate derivation and validation samples werechosen by using the SPSSX [1983] random sampling procedure. Theresults from these ten runs were then pooled to produce an average setof results.'

Two of the basic assumptions of the discriminant model are that theindependent variables are multivariate normal and that the covariancematrices are equal. In order to ensure that the assumptions of thediscriminant technique were met, I employed an outlier identificationand truncation process. Barnett and Lewis [1978], Frecka and Hopwood[1982], and Hopwood, McKeown, and Mutchler [1984] all provide infor-mation about the infiuence of outliers on the estimating and predictionprocesses. The outlier identification and truncation process described indetail in the last paper was used in this research. The process proceedsas follows. Descriptive statistics were calculated for all of the ratios inboth their raw and square root forms. The skewness of each form wasassessed, and the form with the lowest absolute skewness coefficient wasselected for the outlier identification process. Outlying observations weresequentially deleted until the skew was at or below a critical value. Ifskew was positive, the highest value was deleted; if negative, the lowestvalue was deleted. A critical value of .05 (see table XIVA in Barnett andLewis [1978]) was used and represents that level of skewness such that95% or less of normally distributed samples would have skew less thanthat value. Once the skewness had reached the critical level, the highest

'Each separate discriminant run produced a different accuracy level. I could simply havechosen the highest and reported that result. A more accurate representation of the predictivepower of the model, however, is obtained by pooling the results over several differentrandom samples. The range of the predictive accuracies across the ten runs is reported innotes 8 and 9.


TABLE 7Ratio Model Prediction

Sample Set 1iV=238

Sample Set 2

~ GCAR

"^ NGCAR

PredictedGCAR NGCAR

42472.4%

8615.0%

16227.6%

49085.0%

GCARCo

NGCAR

Overall = 82.8%

PredictedGCAR NGCAR

17479.1%

3516.7%

4620.9%

17483.3%

Overall = 83.0%

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

3. Research Results

The prediction accuracy results reported below are based on thevalidation (holdout) samples. The number of companies in each cellrepresents the total classifications across the ten separate discriminantruns, and the percentages the percent classified for each category ofcompany. The overall percentage is based on tbe overall correct classifi-cations and is restated to refiect what one would expect on a randomsample from the population.

Table 7 presents the prediction accuracy results for the auditor ratiomodel.® As expected the ratio model is able to predict the opinion decisionwith a relatively high degree of accuracy (approximately 83%). Theaccuracy on an overall basis for Sample Set 2 is only .2 percentage pointsdifferent than that of Sample Set 1, representing the combined effect ofthe 6.7 percentage point increase for the GCAR group and the 1.7percentage point decrease for the NGCAR group. The significant increasein the predictive accuracy for the GCAR group with Sample Set 2 datawas expected.

Table 8 presents the predictive accuracy results for the model contain-ing both ratios and the good news and bad news variables. Contrary toexpectations, the addition of the good news and bad news variablesdecreased the predictive accuracy for both sample sets. Although theseresults could indicate the fact that these variables are not used by auditorsin their opinion decisions, there are other possible explanations of the

* None of coefficients for the variables in the models is reported. Of interest here wasthe relative predictive power or increase in predictive power of given sets of variables. Theimportance of specific variables was immaterial. The ratios, for example, were testedindividually and in various combinations and none predicted better than the total set ofsix ratios chosen by the auditors.


decrease in predictive accuracy. Two in particular stand out. One is thefact that the good news variable means were about equal for the GCARand the NGCAR groups. This would contribute to lower predictive power.Second, no attempt was made to weight the relative importance of anyof the events included in the model. Nor would I know how to do this. Idid try to model multiple occurrences of these variables, but to no avail.Perhaps the only way to model these variables successfully in terms ofthe auditors' opinion decisions would be to examine the audit workpapers 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 betweenthe results with this model and the simple ratio model. This could indicatethat when making the going-concern opinion decision the auditor is moreinterested in what the improvement will be between the current year andthe next, not the current improvement. The auditor subjects interviewedstated that they carefully looked at cash fiow forecasts which would havean impact on their decisions. Cash fiow forecasts, of course, are notgenerally available and could be a major item of inside information thatis impounded in the opinion decisions.

Table 10 presents the results for the model with the ratios and thePYAR variable. A 9.1 percentage point increase in predictive accuracy

TABLE 8Model with Ratios and Good News and Bad News Variables

Sample Set 1AT =238

Sample Set 2

.- GCAR

NGCAR

IGCAR

NGCAR

PredictedGCAR NGCAR

PredictedGCAR NGCAR

42672.7%

10117.5%

16027.3%

47582.5%

GCAR

NGCAR

15771.4%

4019.1%

6328.6%

16980.9%

Overall = 80.7%

TABLE 9Model with Ratios and IMPROV

Sample Set 1A'=238

Variable

Overall = 80.2%

Sample Set 2N^84

PredictedGCAR NGCAR

PredictedGCAR NGCAR

43474.1%

8915.5%

15225.9%

48784.5%

GCAR

INGCAR

17479.0%

3818.2%

4621.0%

17181.8%

Overall = 82.6% Overall = 81.8


TABLE 10Model with Ratios and PYAR Variables

Sample Set 1AT = 2 3 8

41671,0%

345,9%

17029,0%

54294.1%

PredictedGCAR NGCAR

^ GCAR

^ NGCAR

Overall = 89.9%

occurred for the NGCAR companies, while a 1.7 percentage point decreaseoccurred for the GCAR group compared to the results in table 7. Thisresults in an overall increase in predictive accuracy of 7.1 percentagepoints. Clearly, knowledge about the ratio values and the prior-yearopinion type is useful in predicting the opinion decision.

Although the models tested in this research exhibit a relatively highdegree of predictive accuracy in each sample group and on an overallbasis, there are cases where it appears the opinion decision has infor-mation content beyond the items tested here. Moreover, there are casesin which companies were classified correctly with the ratio model, butthen misclassified with the model containing the ratios and the goodnews and bad news variables. This suggests that clues to informationcontent of the going-concern opinion may be linked to specific cases.

The auditor subjects interviewed suggested that there are cases inwhich inside information is embedded in the opinions. For example, ifmanagement is preparing to place in action a plan whereby certainoperations would be shut down and employees terminated, the auditorwould attempt to determine whether the completion of such a plan wouldimprove or reduce the chances that the company will continue in exist-ence. At the time the opinion is issued, however, these plans can only bepublicized in general terms. Thus, in this hypothetical example the factthat a "bad" company received an unqualified opinion would have infor-mation content for external parties. In contrast, it is difficult to imaginecases in which companies would look "good" and yet receive the going-concern opinion for the first time. If they did, we would expect disclosuresof whatever event caused the issuance of such an opinion.

Almost 80% of the GCAR companies in Sample Set 1 that weremisclassified had received a going-concern opinion in the previous year.This suggests that these companies were improving operations, andexcept for the fact that they already had received the qualification wouldprobably not have received one in the current year.

Of more interest to external parties, however, is the result that 79% ofcompanies that initially received going-concern opinions (Sample Set 2)


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

4. Summary and Conclusions

The purpose of this research was to determine the extent to whichauditors' going-concern opinion decisions could be predicted using pub-licly available information. Modeling of the opinion decision was con-ducted in three stages, with the first stage using only a set of auditor-chosen ratios, and the second and third stages adding SAS No. 34 typevariables (mitigating factors and contrary information) and a trendmeasure (IMPROV) and the prior-year opinion type (PYAR) to themodel, respectively. All variables used in the models were determinedwith reference to auditor subjects.

The sample of companies used in the research was composed of onlyproblem companies because the auditor must first identify a company ashaving a problem in some sense prior to making the going-concernopinion decision. The final sample consisted of 119 problem companiesthat had received a going-concern opinion (GCAR companies) and 119problem companies that did not receive a going-concern opinion (NGCARcompanies). The models were tested using two sample sets, the entiregroup of 119 GCAR companies (along with 119 NGCAR companies) anda subset of 42 GCAR companies (along with 42 NGCAR companies) thathad received the qualification for the first time.

The models were tested for violation of the assumptions of discriminantanalysis, and an outlier identification and truncation procedure wasfollowed to the extent appropriate. The accuracy of each model wasassessed by pooling the results of ten separate discriminant runs, eachof which was validated on a separate validation sample.

The model with the ratios and prior-year opinion variable had thehighest overall predictive accuracy. The rate for the entire sample (238companies)^ was 89.9% and for the smaller sample set (companies that

^ The range of predictive accuracy across the ten separate discriminant runs for eachgroup and on an overall basis is as follows: GCAR 64.2-79.2%; NGCAR 88.6-96.8%; overall86.7-93.6%.


had received the qualification for the first time) it was 83%.̂ ° While thegoing-concern opinion does not appear to have additional informationcontent for the majority of companies, there are specific cases in whichthe qualification has marginal information content. But each case ap-pears unique, which presents modeling difficulties.

This study was designed to test the extent to which the going-concernopinion could be predicted using only publicly available information. Theresults are limited in the sense that even with a 100% prediction accuracyrate, they could not resolve the question of whether the public informa-tion plus the qualification was important to financial statement users.The type of audit opinion could be used as a reinforcing signal, and casesin which the information and the opinions were consistent would justifymore reliance on their own decisions. Cases in which the two sources ofsignals were inconsistent might trigger the search for additional databefore decisions are made. This possibility only raises further questions,including whether the opinion is used as a signal of other more broaderissues in the evaluation of business risk. To test the overall function ofqualified opinions will require additional studies incorporating morecontrols.

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'" The range of predictive accuracy across the ten separate discriminant runs for eachgroup and on an overall basis is as follows: GCAR 68.2-90.5%; NGCAR 68.0-100%; overall75.0-89.0%.


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

HOPWOOD, W., J. MCKEOWN, AND J. MUTCHLER. "The Impact of the Cross-SectionalDistribution 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. NewYork: SPSS, Inc., 1983.

ZAVGREN, C. V. "The Prediction of Corporate Failure: The State of the Art." Journal ofAccounting Literature (Spring 1983): 1-38.

Documents

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