sharma & mahajan (1980) early warning indicators of business failure

8/10/2019 Sharma & Mahajan (1980) Early Warning Indicators of Business Failure

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University analyzed 45.000 and 100,000 vacuum tubes,respectively (Shooman 1966). The objectives of theanalysis were to determine causes of failure and developmathematical models for predicting failures. The resultsand the subsequent interest in failure analysis led toreliability engineering.

As mentioned earlier, very few studies related tosystematic analysis of business failures have been under-taken. The results of the few major attenipts to analyzebusiness failure (e.g., Altman, Haldeman, and Narayanan1977; Argenti 1976; Cooper 1979) have been encourag-ing. Thus, rather than accepting failures as they come,we need to develop a systematic study of failures thatwill enhance the body of knowledge and perhaps reducethe number of failures. Such study will eventually leadto ;

Identification of causes of failures. Identification ofthe indicators of failures. Development of mathematical models for predict-

ing failures.Therefore, the major purposes of our article are (1)

o develop a failure process model, (2) to review brieflyhe major research studies involving analysis of failures,

and (3) to illustrate a parsimonious financial corporateailure model to predict business failure.

What is a Failure?One of the most difficult tasks of researchers in analyz-ng failures is to define the term fa il u re . The word

has many meanings. The McG raw-Hill D ictionary Modern Economics (1973) defines business failures T h e cessation of operations by a business concbecause of involvement in court procedures or voluntactions which will result in loss to its cre di tor s. Acording to The Business Failure Record (1978), failureinclude concerns involved in court proceedings or vuntary actions involving loss to creditors. An entrepneur may discontinue operations for a variety of reasosuch as loss of capital, inadequate profits, ill health, retirement, but if the creditors are paid in full, tbusiness is not tallied as a failure. In contrast, Webster Third New International Dictionary (1961) defines faiure as th e fact of a certain action or process not havoccurred . . . the fact of nonoccurrence. ' In the first tdefinitions of failure a business is viewed essentiallya reservoir of cash (Walters 1957). The firm is consered to be bankrupt (failed) when the reservoir becomempty. In other words, an enterprise may be regardas a fa ilu re when it canno t meet its liabilities (VHome 1977). However, if one takes a broader viewfailure, as given in Webster's dictionary, a firm will considered a failure if it does not meet the objectives forth by management. On the basis of this defmitione may classify Sears as a failure (Business Wee1975; Time 1980) because it has failed to achieve toverall goal set by managem ent (e .g., ima ge, po sitioninIn most of the studies pertaining to corporate failurbankruptcy has been used as the definition of failuwhereas in studies on products the inability of the pr

FIGURE 1Failure Process

Ineffective orBad Management Leads to

Mistakes inStrategic Planand/or its Imple-mentation

CauseDeteriorationm PerformanceIndicators

In absence of no

corrective action,or an ineffectivecorrective action

a

Leads toFailure

Unanticipatedor

UnforseeableEvents


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TABLE 1 (continued)

Firm

JameswayGenescoA-Dry GoodsCaldorKingsOutletZayreGinosHecksWoolwor thCornwallFed-MartWinkleman

RichtonKrogerLuckyPenn-TrafficSupermarketLane-BryantMaysHouse-of-FabricsNew ProcessHost

Nonfailed

State YearIncorporated

NY (1966)TN (1968)VA (1916)DE {19611DE (1961)Rl (1925)DE (1962)MD (1960)VA (1959)NY (1911)DE (1935)CA (1954)Ml (1928)

DE (1969)OH (1902)CA (1931)PA (1903)DE (1966)DE (1920)NY (1927)CA (1946)DE (1924)DE (1914)

Firms

PrimaryBusiness

Discount storesFootwear, men s w earDepartment storesVariety storesVariety storesDepartment storesDiscount storesFast food restaurantDiscount storesVariety storesVariety storesLow-margin retail storesWom en s apparel

Variety and

jewelrySupermarket-grocerySupermarket-groceryDepartment storesSupermarket-groceryWo men s apparelDepartment storesFabrics and sewing notionsMail orderFast food restaurants

No ofStores

501500

3

5018721

25 1506160

37961935887a

12021317

4 4109188

856 0188

xecuting strategic market plans, the outcome of whichan be evaluated by the performance indicators.

The planning process described can be used to de-elop a failure process model. The model shown ingure 1 is adapted from Argenti 1976a). Ineffective oror management usually leads to mistakes in formulat-

g a strategic market plan and/or its implementation. Aor strategic plan will be ineffective no matter how

ell it is executed. In contrast, an excellent strategican can be ruined by improper execution. Such mis-kes affect the performance indicators. Thus, basicallyanagement mistakes are the major causes of failuresd the performance indicators are the symptoms ofssible failures. In fact, according to most studiesating to failure of firms and products, about 90 offailures can be traced to lack of adequate manage-

ent e.g ., Argenti 1975; Business Week 1971; Cooper75; Houston 1972; Kaye and Garter 1979; Lazo 1965;e Business Failure Record 1978; The CP Journal75; Worthing 1966; Wyant 1972).

ethods for Predicting Failurese failure process suggests that failures can be predictedher by analyzing the strategic plan and/or its imple

mentation i.e ., causes of failures) or by observing theperformance indicators i.e ., symptoms of failures).

auses o Failures

In almost all of the studies on the causes of failure a listof causes contributing to the failure has been developedThe list is obtained by analyzing case histories of thefailed firms or products (see, for example, Ross andKami 1973).

Although these studies have provided useful insighinto the causes of failures, they have certain limitation

First, the lists of causes may be, and are, differentacross studies. For instance, Smith 1966) lists six causesof failure, none of which are the same as those of Rossand Kami 1973). Second, one does not know whichcauses discriminate best between success and failureThird, there is no indication as to how many causemust occur before a firm or product will eventually faiFourth, one does not know how to use the list of causesto predict or avoid failures.

Some of the limitations have been overcome instudies by Miller 1977 ), Miller and Friesen 1977), and

Cooper 1979), who essentially used factor analysis toreduce a number of causes of failure to a few underlyin g causes Cooper 1979) extendedthe objective not


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only to measure the underlying causes of failures butalso to determine which causes discriminate best be-tween failures and successes. He first asked managersto choose two successful and two unsuccessful newproducts. The managers were then given a list of 77potential causes of success or failure and were asked to

indicate the degree (on a 0-10 scale) to which eachcause contributed to success or failure of each of thechosen products. The resulting data were factor ana-lyzed to obtain the underlying dimensions (causes) ofsuccess and failure. Discriminant analysis showed 11dimensions to be significant determinants of successand failure. The power of discrimination was strong,with about 84% of the products correctly classified.

Cooper's model has important implications for theproduct manager. First, the estimated discriminantfunction can be used to predict success or failure. Sec-ond, because the causes for failure are known, man-agement can take the necessary corrective action. How-ever, the usefulness of the model will depend on theaccuracy of management's rating of the product on the77 items. This rating process may be the major limita-tion, because it is basically judgmental. Another limita-tion is that the factor structure defining the underlyingdimensions may change. In spite of these limitations.Cooper's study shows how the causes of failures andsuccesses can be analyzed systematically to develop afailure prediction model.

Symptom s of ai luresThe second method which can be used to predict fail-ures is analysis of the performance indicators (i.e.,symptoms of failures). The usefulness and significanceof this approach can be effectively illustrated by aparsimonious financial corporate failure model. Unlikeearlier financial corporate failure models (e.g., Altman,Haldeman, and Narayanan 1977), this model is specifi-cally developed for retailing firms because the determi-nants of success and failure could be different acrossindustries Business Week 1980a, b, c). Furthermore, of

the businesses failing in 1977, 43% were retailing firms;they constituted the largest segment of all commercialand industrial business failures. Sixty percent of thefailed retailing concerns were within their first fiveyears of operation, 25% were within six to 10 years ofoperation, and 15% had operated more than 10 years The Business Failure Record 1978). The model isdeveloped to predict the business failure over the five-year period prior to failure. For the purpose of modeldevelopment, failed firms are defined as ones that filedunder Chapter XI of the bankruptcy act (Van Home

1977). Note that the n:iajor objective of the businessfailure model is no t to explain the success or failure ofbusiness performance but rather to predict success or

TABLE 2Description of Financial PerformanceIndicatorsProfitability

1. Return on Assets:Earnings Before Interest and Taxesn otal Assets

Leverage Ratios2. Debt Service:

Earnings Before Interest and Taxes/InterestCoverage

3. Cash Flow:Cash Flow/Total Debt

4. Capitalization:Market Value of Equity/Total Capital

Liquidity Ratios5. Current Ratio:

Current Assets/Current Liabilities6. Cash Turnover:

Net Sales/Cash7. Receivables Turnover:

Net Sales/Receivables8. Inventory Turnover:

Net Sales/Inventories9. Sales Per Dollar Working Capital:

Net Sales/(Current Assets - Current LiabilitiesMiscellaneous10. Retained Earnings/Total Assets11. Total Assets (in thousands of dollars)

failure from performance indicators.^The underlying rationale of the model develop

is that an enterprise is a reservoir of cash andobjective of the firm is to manage its cash feffectively (Day 1977; W alters 1957). The firm issidered to be bankrupt when this reservoir becem pty. The am ou nt ' of cash in the reservoir, ever, can be measured by certain financial performindicators reflecting the firm's profitability, growth,liquidity (see, for example. Table 2). Thereforeexamination of these indicators should provide siof possib le failure (B eave r 196 6; Fitzpa trick 1931)

Such a framework has been utilized in several ies to predict business failure. In an extensive resstudy. Beaver (1966) used financial performance to predict business failure. The study encompass

^In recent years some attempts have been made to study empiricrelationships among various strategic variables and to identify thminants of market and financial performance. The scope of these has ranged from the identification of determinants of profitabilitspecific industry (e.g.. Schendel and Patton 1978) to the formula prop ositio ns of corporate strategy (e.g .. PIMS). The most sub

attem pt yet in the field of business po licy and corpo rate strategongoing study being conducted by the Strategic Planning Institfened to as the PIMS progra m). See Wind and Mahajan (forthfor details of this program


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FIGURE 2Mean Current Ratio andfor Failed and N onfailed

3 -

.Q 2 -

u

e

0 -

0 .2 -

a

sp

eun

o

-0.1 -

0 2

Return on AssetsFirms

Figure 2

Mean current ratio and retum on assets tor tailed and non-tailed finns

-

4 3Year to failure

^- V5 4 3 ^

1 1

Year to failure

-

Non-failed

2 - 1

\ 2 1

\

\

sample of 79 relatively large firms that failed during the19541964 period. For each of these companies, an-other firm was selected that did not fail but was in thesame industry and was of approximately the same sizeas the firm that failed (referred to as the paired design)The data collected for the nonfailed companies were for

the same years as those for the failed firms. T hes esamples were used to test the predictive ability of 30fmancial ratios by univariate an alysis. B eav er s worhas been extended by Altman (1968) and others (e.g.,Blum 1974, Taffler and Tisshaw 1977). Whereas Bea-ver used univariate analysis to determine the predictiveability of individual financial ratios, researchers whoextended his work used discriminant analysis Joy andTollefson 1975). The discriminant scores were used todistinguish between failed and nonfailed firms. Althoughindividual financial performance indicators measucertain important aspects of the firm s performance,

discriminant analysis is a means of capturing the infor-mation provided by individual indicators into one com-posite score.

In our proposed model for retailing firms, we em-ploy a series of discriminant analyses to predict busi-ness failure. The runs are conducted separately foreach year over a five-year period prior to failure. Weuse a paired design approach to select the sample ofnonfailed firms. The validity of the model is establishedby the Lachenbruch holdout method.

The ModelTo develop the business failure model for retailing firms,we selected a sample of 46 firms. The sample was

FIGURE 3Scores of Failed and Nonfailed FirmsOne Year Prior to Failure

Arlans ^

Mange .^ ~--Hardart .- ~~-

Kent . - ^ ~ - -G r a n t - -, J ^ -F e d e r a l s - _ ^ ^Ancorp ~~ __

Bohack

F a i l e d n m i s

~ ^ ^ C r --^

^ _ ~

NahonaJ-BellasMjller-WohlHartfield-Zody

UnishopsBotanyBeckPenn FruitFishmanCoitBig Bear

2.0 2 5 3.0

3.5 -3.0 -2.5 -2-0 -1.5

New Process *^

GenescoKings

House c1 FabricsHecksLane BryantOutletHost


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TABLE 3Means and Standard Deviations of

indicator^

1. Return on assets

2. Debt service

3. Cash flow

4. Capitalization

5. Current ratio

6. Cash turnover

7. Receivables turnover

8. Inventory turnover

9. Sales per dollarworking capital

1 0. Retained earnings/total assets

1 1 . Total assets

^Numbers in the parentheses are stancJard

IndicatorsYear to

1

Failed

- . 1 3 0 .226)

-5 .092 12.587)

- 1 . 5 5 1 5.292)

.382 2.014)

1.628 .994)

43.667

28.240)44.436

63.201)18.630

35.883)17.894

48.442)- . 1 7 9 .532)

127.878 265.720)

deviations.

Failure

Nonfailed

.139 .050)

17.885 38.352)

1.342 3.419)1.364

1.113)

2.295 .967)

69.680

106.660)81.329

92.081)11.734

1 6.304)11.554 8.815)

.302 .129)

302.568 534.346)

Year

Failed

- . 0 2 0 .240)

- .567 10.243)

.162 .603)

1.125 1 .087)

1.658 .571)

52.562

50.735)35.527 56.001)

9.198 1 2.388)

9.997 38.708)

.159 .193)

126.977 262.901)

to Failure2

Nonfailed

.135 .047)

39.686 79.33)

1.207 2.806)1.909

2.806)2.549

1.184)49.732

50.385)65.914 72.181)10.138

11 .580)10.050 8.506)

.304 .132)

253.738 452.683)

limited to 46 firms because they were the only ones forwhich financial data were readily available from publishedsources such as Moody's Industrial Manual Half of thesample consisted of failed firms. All failures occurredduring 1970-1976. Table 1 is a description of the failedand nonfaiied firms.

The firms were paired on the basis of firm type as

classified by Moody's Industrial Manual

(e.g., discountstore, department store) and size (total assets). For ex-ample. Giant (discount store) was paired with Jamesway(also a discount store); Miller-Wohl (women's apparel)was paired with Winkleman (also a women's apparelstore).

Eleven fmancial performance indicators were selectedto discriminate the failed from nonfailed firms. Table 2is a description of the indicators. The selection of theseindicators was governed by the following considerations.

1. Results of past research which established theimportance of various financial indicators as de-terminants of success or failure (e g Altman

2. The development of a comprehensive set ofcators reflecting the fmancial performance in of profitability, leverage, and liquidity. Tindicators have been shown to measure thnancial health of firms (Van Home 1977).

3 . Data availability that permitted the calculaticertain indicators across firms and across y

Data on 11 performance indicators were taken Moody's Industrial Manual for five years prior to ure for failed firms and for a corresponding fiveperiod for each nonfailed firm. Table 3 gives the and standard deviation for each indicator across thyears for failed and nonfailed firms, respectively. values of two indicatorsreturn on assets and cratioare depicted in Figure 2 Visual examinatiomean values suggests the differences between failenonfailed firms on these indicators.

A two-group discriminant analysis was conducfind the bes t' ' linear discriminant functions ffive years prior to failure Table 4 is a summary


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TABLE 3 continued)

Year to Failure3

Failed

.058 .055)5.400

5.955).426

.868)

.946 .690)

1.751 .537)

78.848 98.628)

46.462 49.368)10.606

12.201)

16.220 21.779)

.203 .149)

123.734 246.50)

Nonfailed

.146 .058)16.529

17.774)1.159

2.355)

2.575 2.195)

2.508 1.213)

42.105 32.667)

89.948 136.478)

10.496 10.562)

10.923 9.939)

.311 .126)

223.794 400.900)

Year

Failed

.073 .054)6.445

9.202).352

.587)

1.203 1.0061

1.915 .781)

44.44 58.190)

44.710 51.559)

8.961 8.949)

13.000 16.074)

.232 .142)

114.848 216.691)

to Failure4

Nonfailed

.153 .060)

15.083 12.887)

.710 .677)

3.531 3.241)

2.273 .838)

35.884 19.962)

68.757 77.579)

10.555 12.121)

10.653 7.878)

.319 .129)

206.051 371.346)

Year

Failed

.0922 .050)

7.559 9.441)

.536 .679)1.338 .897)

1.923 .613)

47.939 32.942)

84.072 101.195)

11.359 10.794)

16.972 14.419)

.289 .122)

137.281 246.707)

to Failure5

Nonfailed

.155 .051)

13.455 10.829)

.742 .639)

3.856 4.119)

2.401 1.236)

53.563 73.590)

57.610 92.199)

9.522 5.494)

12.193 12.665)

.337 .127)

266.937 426.135)

nd the percentage correct classification of the sampleself indicate a high degree of separation between the

wo groups across the five years. The discriminantunctions for each of the five years include only twodicatorsreturn on assets and current ratio. Thescriminant scores for one year pnor to failure areotted in Figure 3.

The appearance of current ratio and return on assetstwo important indicators of failure is not surprising

both ratios indirectly or directly affect the cash flowa firm. Argenti (1976a), after analyzing a number of

siness failures, concluded that high leverage (meas-ed by current ratio) and the inability to make a profit

measured by return on assets) were among the majormptoms of failure.

The fact that ratios such as inventory and receiv-les turnover do not enter the discriminant function isrprising because the model was developed specificallyr retailing firms. A reason m ay be that these ratios affecte profitability of a firm and return on assets essen-

lly captures most of the information they provide.wever, the reason why some ratios did or did noter is not important because the objective of analyzing

different but to provide an e rly w rning sign l of possible failure.

Before applying the results to predict business faure, one must validate the model. Validation can bdone by means of a holdout sample. As our smsample size precluded such an analysis, we validatthe model by using the Lachenbruch holdout methoThis procedure involves holding out one group memat a time and predicting its membership by using t

discriminant function derived from the remainder of sample. The validated correct classifications, as shoin Table 4, for years one through five are 92 , 7 8 74 , 7 3 , and 77 >, respective ly. These results incate the strong separation power of the model and sugest that the model is at least partially validated.

The model and its concepts have several managerimplications. First, the discriminant weights of Tablecan be used along with the appropriate financial ratto calculate the discriminant score. If the discriminscore is less than the cutoff score (which happens to

zero) , the firm is a potential candidate for bankruptA negative score does not predict that the firm wbecome bankrupt. Rather, it is an e rly w rning sign


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TABLE 4Discriminant Analysis Results

Return on AssetsCurrent RatioConstantCanonical Correlation

Wilk's LambdaChi squareSignificance

Number of Companies AvailableCla ssifica tion Rate ( )Cla ssifica tion Rate ( )

(Lachenbruch Holdout Method)

1

3.6790.380

-0.8620.6510.576

18.230.000

3691.67

91.67

2

3.0310.630

-1.534

0.5310.718

14.259.001

4678.26

78.26

Year Before Failure

3

Weights

7.9050.443

-1.8750.5390.710

14.745.001

4673.91

73.91

4

10.8310.201

-1.7790.4910.759

11.286.004

4475.00

2 2

5

10.2980.417

-2.2770.5640.6828.790

.0122680.77

76.92

audit (Kotler, Gregor, and Rogers 1977).Second, the model can be incorporated in the man-

agement information system and used as a control modelfor monitoring a firm's performance over time. Thismonitoring can be accomplished by an analysis of thetrend of discriminant scores. A positive trend (i.e., thescores increase over time) is indicative of a healthyfirm. A negative trend is an early warning signal for anailing firm that may be heading for bankruptcy andneeding some sort of detailed performance evaluation bymanagement.

Third, the model is not limited to the prediction ofpotential bankruptcy of the firm as a whole. It can beused for decision making at a micro level, such asevaluating products, salespeople, and good and bad creditrisks. For example. Cheek (1979) analyzed various per-formance indicators of successful and failed products todevelop a lexicographic rule for predicting failures. Heclaims a classification rate of about 88%. The details ofhis study are not available to discuss or evaluate.

Obviously, an empirical model such as ours hascertain limitations. First, the choice of performance

indicators is not based on any th eo ry but on thefindings of previous research studies. Second, it may

t be possible to obtain a perfect pairing of failuresand successes. The effect of a paired design is notknown and needs to be investigated. However, theselimitations may not be severe given the objective of themodelto predict business failures by using perform-ance indicators rather than to explain it.

Conclusion

We stress the importance of analyzing failures and pro-pose a failure process model. The model suggests thatailures can be predicted by analysis of either the causes

of failure or the performance indicators. The mlimitation of the former is that the inputs to the mrely on managers' judgments and therefore are subto bias and error. Failure prediction models whichperformance indicators do not have this limitation. Tinputs are completely objective. These models, hower, do not tell the causes of failure. They only prethe possibility of failure. Because each model hasown advantages and disadvantages and their objecare different, management could use both as commentary models. Argenti (1977) describes a subjecmethod by which the two models might be combiWe believe that combining Cooper's method for anaing causes of failures with the method we suggestanalyzing symptoms of failures should be an interestopic for further research.

Finally, in spite of the limitations of analyzing ures, the identification of determinants of market financial performance of business firms is of intereboth practitioners and academicians. The recent emcal investigations, however, seem to have been limto going concerns (e.g., PIMS program). An impo

objective of our article is to suggest that the scopthese performance investigations and the value of results can be enhanced by including failures. Othrough the investigation of failures and successesdeterminants of business performance be identifiedpredicted.

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sharma & mahajan (1980) early warning indicators of business failure

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