cash flow in bankruptcy prediction

Cash Flow in Bankruptcy PredictionAuthor(s): Michael J. Gombola, Mark E. Haskins, J. Edward Ketz and David D. WilliamsSource: Financial Management, Vol. 16, No. 4 (Winter, 1987), pp. 55-65Published by: Wiley on behalf of the Financial Management Association InternationalStable URL: http://www.jstor.org/stable/3666109 .

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Cash Flow in Bankruptcy Prediction

Michael J. Gombola, Mark E. Haskins, J. Edward Ketz, and David D. Williams

Michael J. Gombola is on the faculty of Drexel University, Mark E. Haskins is with the University of Virginia, J. Edward Ketz is with Pennsylvania State University and David D. Williams is with Ohio State University.

0 Users of financial statements have been showing an increased interest in cash flow information [e.g., 13, 27]. In lieu of disclosures from companies that detail actual cash receipts and disbursements, users have had to derive their own estimates based upon the available financial disclosures [e.g., 7, 10, 18]. Empirically, Gombola and Ketz [15, 16, 17] have examined the similarity among several asset flow measures: income from operations, income plus depreciation, working capital from operations, and cash flow from operations (CFFO). CFFO was obtained by adjusting income from operations for all accruals. They found that income plus depreciation and working capital from operations, though frequently labeled "cash flow," were more similar to earnings. Via factor analysis, Gombola and Ketz found that there generally was a return factor on which income from operations, income plus depreciation, and working capital from operations all loaded heavily. They also generally obtained a separate cash

flow factor, thus providing empirical evidence that if one wanted CFFO one had to adjust for all of the accruals.

A second aspect of their work is that the presence of a cash flow factor depended on the time period. Using 1960s data, a cash flow factor did not always appear, and even when it did, the CFFO variables were dis- persed among the return factor, the debt factor, and the cash flow factor. From the mid-1970s on, the CFFO variables loaded unambiguously on a separate flow factor. Gombola and Ketz speculated that this effect may be due to the increased recognition of accruals on corporate balance sheets required by the accelerated issuance of accounting promulgations by the Account- ing Principles Board and the Financial Accounting Standards Board during this time (see also [20]). Ex- amples include deferred taxes, equity earnings, cap- italization of interest, recognition of foreign currency gains and losses, and more restrictive classifications of extraordinary items. The joint effect of these pro- nouncements is the decrease in the correlation between earnings and cash flow.

Professor Ketz would like to acknowledge research support provided Peat Marwick Main & Co.

55



56 FINANCIAL MANAGEMENT/WINTER 1987

Exhibit 1. Overview of Representative Studies Investigating Cash Flow Information for Bankruptcy Prediction Time Period Cash Flow

Study Number and Type of Firms of Data Variables Methods Findings

Panel A: Cash Flow Generally Defined as Income Plus Depreciation

Beaver [5] 79 failed and 79 nonfailed 1949-1963 CF/S Dichotomous classifica- CF/TD best single predictor firms from Moody's & CF/TA tion where sample was with 13% misclassifica- D&B. (failed = bank- CF/NW divided and the firms tion error 1 year prior to ruptcy, bond default, CF/TD were classified using cut- failure and 21%, 23%, overdrawn bank account, off points for each ratio 24%, and 22% for years or nonpayment of pre- derived from the other 2 to 5. ferred dividends.) subsample.

Paired sample design based on industry and asset size.

Deakin 32 failed and nonfailed 1959-1969 CF/TD Dichotomous classification Dichotomous test resulted [11] firms used for dichoto- test and single-year and in CF/TD having a

mous classification test. multiple-years discrimi- 28-16% misclassification A second sample of 32 non- 1957-1965 nant analysis: all for 1 to error rate.

failed firms used in dis- 5 years prior to failure. Single-years discriminant criminant analysis. Paired analysis by industry, analysis models with

Secondary validation sam- 1963-1964 asset size, and fiscal CF/TD were significant ple of 11 failed and 23 year. for 1, 2, and 3 years pri- nonfailed firms. No holdout sample, but or to failure.

used a secondary sample Multiple years test with and the multiple-year's CF/TD was better than discriminant functions. the dichotomous or sin-

gle-years test. Similar results for cross-

validation sample.

Altman 21 bankrupt railroads for 1937-1969 CF/Fixed Charges Performed univariate F-test The CF variable was sig- [2] original sample. on mean values for 14 ra- nificant as was the dis-

50 randomly selected rail- 1946-1969 tios between the bankrupt criminant model (97.7% roads for second (valida- firms and the industry classification accuracy I tion) sample. average. and 2 years prior to

Linear stepwise discrimi- bankruptcy). nant analysis. Good results with holdout

Holdout sample used as sample and second sam- well as second sample. ple.

Blum [6] 115 failed industrial firms 1951-1967 CF/TD Multivariate discriminant Predictive accuracy of the from D&B and Beaver analysis with 21 models, model is 97-70% from 1 [5] with minimum of 1 to 6 years prior to fail- to 5 years before failure. $1M in liabilities at time ure with various ranges CF/TD variable generally of failure. of data preceding the received high rankings.

115 nonfailed firms ran- failure date. Holdout sample produced domly chosen from the Paired analysis by industry, similar results.

January 1969 index to size (sales), number of Compustat. employees, and fiscal

year. Used holdout sample.

Altman, 53 bankrupt and 58 non- 1964-1974 CF/Fixed charges Performed six tests to iden- Out of 27 variables, the CF Halde- bankrupt firms from CF/TD tify most useful variables variables were not found man, manufacturing and retail- to include in the final to be a part of the best and ing. ZETA discriminant mod- model. Nar- el (forwards and back- 96% classification 1 year ayanan wards stepwise discrimi- prior to failure and 70% [3] nant analysis, scaled 5 years prior to failure

vector test, separation of without CF variables. means test, conditional deletion test, and univariate F-test).

Holdout sample. Paired sample based on in-

dustry and year.

Norton 30 bankrupt (per 8-Ks or 1967-1974 CF/S Linear multiple stepwise CF/TA and CF/TD were and O10-Ks) and 30 nonbank- CF/TA discriminant analysis - part of best discriminant Smith rupt publicly traded CF/NW models prepared for 1, 2, model 3 years prior to [22] firms. CF/TD 3, and 4 years prior to bankruptcy (83.3% clas-

bankruptcy using a po- sification accuracy). tential of 32 variables. CF/TD was identified by

Stepwise, linear multiple regressions for including regression was also used in second discriminant



GOMBOLA, HASKINS, KETZ, AND WILLIAMS/CASH FLOW AND BANKRUPTCY 57

to select a set of ratios analysis. Model achieved for including in another 87.7% to 66.7% classifi- discriminant analysis. cation accuracy 1 to 4

Holdout sample. years prior to bankrupt- Paired design based on in- cy.

dustry and asset size. Holdout results similar. Sharma 23 failed and 23 nonfailed 1965-1975 CF/TD Discriminant analysis. Best models did not include

and Ma- retail firms. Validation using Lachen- any cash flow variables. hajan bruch holdout method. Classification rates were [29] Paired analysis based on 92% to 74%.

firm type and assets. Validation results were similar.

Mensah For ex ante prediction pur- 1974-1978 CF/CL Used the means and standard CF/NW was most impor- [21] poses, 35 nonbankrupt CF/S deviations of the ratios tant ratio in discriminant

firms were randomly se- CF/TA instead of the ratios. (historical cost) model. lected and 11 bankrupt CF/NW Stepwise multiple discrimi- The CF/TD, CF/TA, and firms were used. CF/TD nant. CF/S variables loaded

For ex post discrimination 1970-1976 CF/Total Interest Performed factor analysis of highly on factors not purposes, 30 bankrupt ex post bankrupt and common to both the and 30 nonbankrupt firms nonbankrupt samples to bankrupt and nonbank- were selected. isolate the ratios not rupt ex post samples.

Bankrupt firms were identi- common to factors from Good holdout results. fled from Wall Street both groups. Journal Index. Ex post sample paired

based on industry and sales.

Lachenbruch's holdout method.

Panel B: Cash Flow Generally Defined as Income Adjusted For All Accruals

Largay One firm only, the W.T. 1966-1974 CFFO Comparisons of the levels CFFO provided a more ac- and Grant Co. and trends of CFFO with curate and timely signal Stickney other traditional financial of W.T. Grant's impend- [24] statement ratios and ing failure than the other

W.T. Grant's stock measures. price.

Takahashi, 36 firms that failed and 36 1958-1976 CFFO/TA Discriminant analysis Best model used all three Kuro- nonfailed firms. Cash Sales minus models. cash flow variables for kawa, Used 48 firms for verifica- cash purchases Used best discriminant periods 1, 2, and 3 years and Wa- tion sample prior to Increase in residual analysis model to predict prior to failure. tase bankruptcy. value to cash verification sample clas- Best model applied to the [30] sales sification. verification sample re-

Pair sampling based on in- suited in no Type I dustry fiscal year, and errors. asset size.

Casey and 60 failed firms listed by 1966-1981 CFFO Firms matched by industry. All MDA models sig nifi- Bartc- D&B and WSJ Index. CFFO/CL Eight multiple discriminant cant. CFFO/CL signifi- zak [9] 230 nonfailed firms chosen CFFO/TL analysis models using six cant for first three years,

from Compustat. accrual-based ratios alone CFFO/TD for first two and then adding one or years, and CFFO for more CFFO ratios. The years 1, 4, and 5 prior to models were constructed bankruptcy. The MDA from 1/2 of the sample model based on only ac- with the classification re- crual-based ratios and the sults determined from the best MDA model con- holdout sample. taining a CFFO variable,

Stepwise logistic regres- had similar classification sions for each year. accuracy.

Similar results in the regression tests.

Gentry, 33 "failed" and 33 non- 1967-1980 CFFO segmented Three separate techniques: Logit results similar to pro- New- failed firms; failed firms into seven com- linear discriminant, probit and MDA results (77 bold, located in Compustat An- ponents, each di- bit, and logit analysis to 83% correctly classi- and nual Industrial Research vided by total net considering all cash flow fled). Similar results for Whit- File. cash flow variables simultaneously. secondary sample. ford Paired analysis by asset Cash-flow-based funds flow [14] size, sales, and industry. components did not im-

No holdout sample, but a prove classifications. secondary sample of "weak" and "nonweak" companies.



58 FINANCIAL MANAGEMENTI/WINTER 1987

There are two implications of this previous research for the bankruptcy prediction literature.' First, those studies that reportedly examined the usefulness of CFFO but instead utilized income plus depreciation are suspect.2 Panel A of Exhibit 1 reviews the bankruptcy literature that proxied cash flow with income plus depreciation (some authors also added back deferred taxes). Generally, such a proxy for cash flow has been found to be useful in bankruptcy classification studies. The works cited in panel B of Exhibit 1 try to assess the efficacy of CFFO itself. To date, results pertaining to CFFO's usefulness in bankruptcy classification studies are mixed. The W. T. Grant bankruptcy studied by Largay and Stickney [24] demonstrated differences in behavior between income and CFFO measures. It provides anecdotal evidence of the potential information content of CFFO numbers for bankruptcy prediction. Casey and Bartczak [8, 9] provide a more general setting by looking at sixty firms that failed during the years 1971-1982 and 230 nonfailed firms. A discriminant model containing six accruals but no CFFO measures performs better than a model containing only CFFO measures in bankruptcy prediction. However, in their stepwise discriminant analysis, they find that CFFO to total debt is a significant predictor variable one year and two years prior to failure but not in later years. Gentry, Newbold, and Whitford [14] look at firms that failed3 over the 1970-1981 period. Their independent variables are the components of changes in cash. They find that CFFO is not a significant predictor of failure.

The second implication of the previous research conducted by Gombola and Ketz for the bankruptcy prediction literature is that if CFFO is a significant predictor it will be so in the mid-1970s and on, and probably not before. Casey and Bartczak use data from 1966-1981 and Gentry et al. use data from 1967- 1980. The commingling of data from the two time periods may bias their research and explain why they did not find CFFO to be a predictor of bankruptcy. In other words, if cash flow had been found to be significant in the earlier period, it probably would be due to the high collinearity between earnings and cash flow.

Exhibit 2. Bankruptcy Sample Firm-Year Observations by Year

Bankrupt Firms Prior to Failure

by Year of Year Prior Failure to Failure Observations

1970 13 1 49 1971 5 2 66 1972 6 3 65 1973 13 4 64 1974 7244 1975 11 1976 3 1977 5 1978 7 1979 1 1980 1 1981 4 1982 1

77

Only in the late period, as shown in section three, does the collinearity practically disappear. This fact allows us to test for the effects of cash flow "cleanly" by focusing on the latter period.

The central question in this study is whether CFFO is important in predicting corporate failure after the mid-1970s. In section one we describe the data set and the methodology. In section two we replicate the Gom- bola and Ketz factor analysis to verify the existence of early and late effects and to determine where the divid- ing line is. The results are shown in section three and the conclusions are presented in section four.

I. Data and Methodology The failed firms were identified by a list provided by

Dun and Bradstreet and by the Compustat Research tape. Seventy-seven industrial corporations were identified.4 Each company had to be in manufacturing or retailing and had to have complete data for at least one of the four years prior to failure.' Models are built for each of the four years prior to failure. There are 308 (77 x 4) possible firm-year observations. Sixty-four of these are eliminated because the annual reports were issued after bankruptcy was announced,6 not all of the

'A good review and critique of the bankruptcy literature is contained in Ball and Foster [4].

2Several bankruptcy studies did not include any cash flow variable, e.g. Altman [1].

3For unexplained reasons they classify 24 firms that underwent volun- tary liquidation as failed firms. In addition, they include as failed firms some that were merged or simply sold off some of their assets.

4A listing of all companies used in this study are available from the authors upon request.

SZmijewski [3 1] points out that requiring firms to have data for all of the years prior to failure under investigation might bias the results.

6See Ohlsson [26] and Lawrence [25] for a discussion of the reporting delays of bankrupt firms.




Exhibit 3. The Means of the Financial Ratios By Year Prior to Failure Ratio Means

Yr= 1 (n=46) Yr=2 (n=63) Yr= 3 (n= 62) Yr=4 (n=61) Financial Ratios F NF F NF F NF F NF

CASH/CURDEBT .127 .328* .131 .452* .185 .317* .227 .323 CASH/SALES .040 .049 .052 .069 .044 .049 .048 .055 CASH/ASSETS .051 .091* .049 .098* .058 .077 .060 .073 CASH/DEBTS .071 .207* .070 .241* .100 .170* .118 .169 CFFO/SALES .010 .064 .021 .061 .026 .076* .017 .056* CFFO/ASSETS .000 .099* .028 .071* .034 .084* .020 .065* CFFO/DEBTS .011 .218* .050 .188* .065 .195* .045 .151* COGS/INV 12.6 8.06 8.56 7.59 8.60 8.21 9.84 10.8 CURASS/CURDEBT 1.46 2.21* 1.40 2.51" 1.69 2.27* 1.85 2.24* CURASS/SALES .479 .350* .607 .390 .466 .373* .451 .370* CURASS/ASSETS .634 .619 .612 .611 .630 .594 .630 .587 CURDEBT/DEBTS .592 .624 .615 .598 .587 .590 .589 .581 INC/SALES - .069 .042* - .042 .045* - .156 .035 .017 .035* INC/ASSETS - .069 .066* - .033 .061* - .005 .052* .020 .049* INC/DEBTS - .065 .157* - .030 .204* .012 .137* .039 .131* INCDEP/SALES - .003 .087* .025 .097* .027 .086* .052 .085* INCDEP/ASSETS - .008 .132* .014 .128* .052 .114* .061 .115* INCDEP/DEBTS .008 .293* .034 .385* .098 .268* .110 .266* SALES/REC 15.1 31.14 13.2 17.7 15.5 21.6 17.4 20.6 SALES/ASSETS 1.80 2.15 1.61 1.91* 1.79 1.96 1.88 1.99 ASSETS/DEBTS 1.30 2.14* 1.40 2.38* 1.57 2.16* 1.68 2.19* WCFO/SALES - .019 .075* .004 .088* .022 .083* .044 .081* WCFO/ASSETS - .025 .116* .000 .116* .048 .110* .054 .110* WCFO/DEBTS - .010 .265* .019 .356* .093 .257* .098 .025*

*Significant difference at alpha = .05.

data were available to estimate cash flow from operations, or the firms did not produce annual reports in some years (i.e., either the firm was young or it recent- ly went public). The resulting file had 244 firm-year observations. Exhibit 2 shows the distribution of the 77 failed firms by year of failure as well as the 244 firm- year observations grouped by year prior to failure.

These firm-year observations are matched with nonfailed firms on the basis of industry and size. Industry grouping is based upon 3-digit SIC codes as reported by Standard and Poor Financial Services. Size is proxied by the book value of the firm's assets.

Twenty-four financial ratios are computed per observation. Data are gathered from the Compustat An- nual Industrial tape, the Compustat Research tape, and Moody's Industrial Manual. Cash/current debts (de- noted CURDEBT), cash/sales, cash/total assets (AS- SETS), and cash/total debts (DEBTS) represent measures of a firm's cash reservoir with that to pay debts. Current assets (CURASS)/current debts, current assets/sales, and current assets/total assets capture the firm's generation of current assets with that to pay debts. Current debts/total debts and total assets/total debts typify the firm's debt structure. Cost of goods sold (COGS)/inventory (INV) and sales/receivables

(REC) show the firm's ability to turn over its inventory and receivables. Sales/total assets reflects the firm's ability to generate sales. The twelve remaining ratios are asset flow measures: income (INC), income plus depreciation (INCDEP), working capital from operations (WCFO), and cash flow from operations (CFFO), each divided by sales, assets, and debts. Choice of the ratios is driven primarily by their use in previous bankruptcy research and ratios are typically used instead of the raw data because the ratios are interpreted frequently in terms of efficiency. The asset flow measures are chosen to provide a relative comparison of the effectiveness of these variables. All of the variables are contained in a firm's annual report except for CFFO, which is estimated in the same way as Largay and Stickney [24], Gombola and Ketz [15, 16, 17] and Casey and Bartczak [9].

The means of the financial ratios are recorded in Exhibit 3. The means are shown for the failed and the nonfailed companies by year prior to failure. Differ- ences between the means of failed and nonfailed firms were tested by a paired t-test. CFFO/ASSETS, CFFO/ DEBTS, CURASS/CURDEBT, INC/ASSETS, INC/ DEBTS, INCDEP/SALES, INCDEP/ASSETS, INCDEP/DEBTS, ASSETS/DEBTS, and all three




Exhibit 4. Factor Patterns of Early and Late Years Panel A: Similar Factors

Loading

Factor Variable Early Late

1. Cash Position CASH/CURDEBT .874 .872 CASH/SALES .883 .884 CASH/ASSETS .873 .897 CASH/DEBTS .733 .787

2. Current Asset to Sales or Assets CURASS/SALES .794 .642 CURASS/ASSETS .619 .855

3. Current Debts to Total Debts CURDEBT/DEBTS .656 .705 4. Debt INC/DEBTS .728 .623

INCDEP/DEBTS .774 .632 ASSETS/DEBTS .837 .817 WCFO/DEBTS .742 .614

Panel B: Dissimilar Factors Early Late

Factor Variable Loading Factor Variable Loading

1. Return on Assets CFFO/ASSETS .732 1. Return on Assets INC/SALES .826 CFFO/DEBTS .672 INC/ASSETS .817 INC/ASSETS .867 INC/DEBTS .558 INC/DEBTS .651 INCDEP/SALES .719 INCDEP/ASSET .922 INCDEP/ASSETS .773 INCDEP/DEBTS .682 INCDEP/DEBTS .515 WCFO/ASSETS .903 WCFO/SALES .682 WCFO/DEBTS .701 WCFO/ASSETS .772

WCFO/DEBTS .541 2. Return on Sales CFFO/SALES .820 2. Cash Flow CFFO/SALES .703

INC/SALES .673 CFFO/ASSETS .898 INCDEP/SALES .851 CFFO/DEBTS .610 SALES/ASSETS - .732 WCFO/SALES .874 3. Sales to Total Assets SALES/ASSETS .918

WCFO ratios are always significant in these tests. CASH/SALES, COGS/INV, CURASS/ASSETS, CURDEBT/DEBTS, and SALES/REC are never significant.

The methodology employed here is linear discriminant analysis. We also employed quadratic discriminant analysis and probit analysis and found that the results were approximately the same. Linear discriminant analysis takes a discrete variable as the dependent variable. In this study it is corporate failure (0 = failed; 1 = nonfailed). The independent variables are analyzed against the dependent variable in a manner analogous to linear regression when there are only two states for the dependent variable.7

We want to assess the predictive ability of the models. It is well known that a bias exists when the model

is tested with the same data as used to build the model. One suggestion is to have two samples, one used to build the model and one used to test it. This idea requires a lot of data and is not very efficient in utiliz- ing it. An improvement is to use the jackknife procedure of estimating a model with all but one observation and then use the model to test the one observation not included. The process is repeated with all observations held out once. The error rates obtained in this manner are asymptotically unbiased and the data are efficiently used. We assess the predictive ability of the models generated by means of this jackknife technique.8

II. Early/Late Replication Before carrying out the discriminant analysis tests,

there is a need to replicate prior research concerning the factor patterns of financial ratios. The chief pur- pose is to provide further evidence about the time peri- 7No adjustment is made for the prior probabilities of the failed and not

failed groups. It seems more consistent with the matched pairs design to have the odds 50-50. Furthermore, no adjustment is made for the costs of misclassification since these costs are user-specific.

8For further discussion of the methodology see Lachenbruch [23]. In fact, his book is an excellent study of discriminant analysis.




od in which a distinct cash flow factor is present. We apply factor analysis9 to all of the industrial

firms on the Compustat tape for each year from 1967 to 1981. The results, which cover 24 ratios for 442 companies, fall into two categories. One factoring exists for each year from 1967-1972, and we will refer to this time as the early period. A different factoring occurs for each year from 1973-1981. The results are summa- rized in Exhibit 4.

For the early years there are six factors: cash position, current assets to sales or assets, current debts to total debts, debt position, return on assets, and return on sales. For the late years there are seven factors: cash position, current assets to sales or assets, current debts to total debts, debt position, return on assets, cash flow, and sales to total assets. The loadings of the variables on the factors are shown in Exhibit 4.

The major cause of the differences between the early and the late years is the treatment of the CFFO. In the early years, CFFO/ASSETS and CFFO/DEBTS load onto the return on assets factor while CFFO/SALES load onto the return on sales factor. In the late years, the three CFFO variables load together on a separate and distinct factor, which we label cash flow.

The first impact of this replication is to help us in structuring our own research design. Given the ab- sence of a cash flow factor in the early years and the presence of it during the late years, one can postulate that if the cash flow effect is important, it should be visible in the late years and not necessarily in the early years. Thus, we partition the failed-nonfailed pairs according to the year of the data. The data structure by year prior to failure is depicted in Exhibit 5.

The second impact of the factor analysis pertains to the choice of independent variables used as inputs in the model. Putting all 24 financial ratios into the model would probably result in a high degree of multicollin- earity. We chose to use only the variables generally with the highest loading in each factor to be representative of the factors. Since the factors are theoretically independent, there should be little effect of multicol- linearity. CASH/ASSETS was used to represent the cash position factor, CURASS/SALES to stand for the current assets to sales or assets factor, CURDEBT/ DEBTS to represent itself, ASSETS/DEBTS to depict the debt position factor, and SALES/ASSETS to represent itself. INC/ASSETS is picked to proxy for the return on assets factor. SALES/ASSETS is selected for the return on sales factor because the other variables also load high on another factor. CFFO/ASSETS rep-

Exhibit 5. Early/Late Partitioning of Firm-Year Ob- servations

Year Prior Firm-Year Observations

to Failure Early Late

1 22 27 2 38 28 3 47 18 4 49 15

156 88 244 TOTAL

resents the cash flow factor. We also look at INCDEP/ASSETS and WCFO/ASSETS since previous studies have used these ratios (see Exhibit 1).10

The third impact of the factor analysis is to help in the interpretation of the linear discriminant analysis results. If the INCDEP or WCFO variables are significant, it may really be the income component that ac- counts for this significance. The product-moment correlation between INC and INCDEP is .93 in the early years and .81 in the late years. The correlation between INC and WCFO is .83 in the early years and .77 in the late years. Thus, there is a high degree of collinearity between these three variables.

Given that CFFO is related to earnings in the early years and that the correlation is .67 between them, the same can also be said about CFFO. That is, INC and CFFO are moderately collinear in the early years and any significance may reflect their combined effect. On the other hand, CFFO and INC load on different factors in the late years. This separation is also seen in their low correlation, only .36. Tests of significance of INC and of CFFO for that period may be viewed as distinct tests.

On the other hand, it should be made clear that factor analysis does not indicate importance of the variables. It does not pinpoint significance of variables in any decision context. Thus, the results of the factor analysis should not be interpreted as showing which variables are better than others. In particular, factor analysis does not tell us whether cash flow is a good predictor in the late period. It only says that earnings and cash flow are similar in the early period but dissimilar in the late period.

Ill. Results The results are displayed in Exhibits 6, 7 and 8.

Exhibit 6 gives the results over all years, Exhibit 7 for the early years, and Exhibit 8 for the late years. Each

9For an explanation of factor analysis see Harman [19]. 'oWe also tried using the factor scores in the discriminant models. The results are the same as those shown in section four.




Exhibit 6. Linear Discriminant Analysis Over all Years % Cor-

AS- rectly Year Prior CASH/ CURASS/ CURDEBT/ SALES/ SETS/ INC/ CFFO/ INCDEP/ WCFO/ Adjusted Pre- to Failure Intercept ASSETS SALES DEBTS ASSETS DEBTS ASSETS ASSETS ASSETS ASSETS R2 dicted

One .335* 1.462* -.271 -.199 .000 .171** 1.548** .403** 85** (n= 92) .359** 1.362* -.307 -.245 .001 .173** 1.365** .385 .412** 89**

.300* 1.489** -.488** -.095 -.009 .133** -.228 2.272** .481** 89**

.371** 1.521** -.434* -.164 -.013 .150** .272 1.457** .436** 86**

Two .344** 1.251** -.043 .462** .073* .115** 1.097** .285** 70** (n= 126) .350** 1.242** -.044 .459** .073* .114** 1.158** -.097 .279** 67**

.210 1.244** -.033 -.351* .080** .088** -.368 1.691** .305** 71**

.257** 1.261** -.038 -.394* .078** .090** -.637 1.804* .299** 70

Three .141 .462 -.029 -.340 .044 .237** .539 .148** 74** (n= 124) .026 -.007 .141 -.401"* .080 .227** .553 1.012** .171** 73**

.072 .315 -.094 -.207 .038 .206** -.174 1.357** .169** 78**

.022 .320 .004 -.238 .049 .192** -.166 1.829** .184** 77**

Four .463** .149 -.488** .262 -.010 .182** 1.320* .133** 71** (n= 122) .403** .063 -.383 -.274 .000 .176** 1.159 .655 .139** 70**

.203 .411 -.336 -.155 -.001 .165** -.906 2.378** .174** 76**

.267 .350 -.328 -.213 -.001 .162** -.420 2.082** .174** 72**

*Significant difference at alpha = .10. **Significant difference at alpha = .05.

exhibit shows sixteen models, four for each of the four years prior to bankruptcy. The base model for each of the years consists of the variables CASH/ASSETS, CURASS/SALES, CURDEBT/DEBTS, SALES/ ASSETS, ASSETS/DEBTS, and INC/ASSETS. The second model adds CFFO/ASSETS while the third model adds INCDEP/ASSETS, and the fourth one adds WCFO/ASSETS.

Exhibit 6 shows the linear discriminant analysis over all years. ASSETS/DEBTS is always significant, thus demonstrating the importance of debt position in predicting bankruptcy. CASH/SALES is significant in years one and two but not in the others. CURDEBT/ DEBTS and SALES/ASSETS are significant in year two but generally no other. CURASS/SALES is sel- dom significant. When INCDEP/ASSETS is in the model, it is always significant. The same can be said for WCFO/ASSETS. When they are not in the model, INC/ASSETS is generally significant. CFFO/ASSETS is significant only in year three.

Exhibit 7 gives the results for the early years. CASH/ASSETS, CURASS/SALES, CURDEBT/ DEBT, and SALES/ASSETS are generally insignificant. ASSETS/DEBTS again is always significant. INC/ASSETS is significant in years one and two when INCDEP/ASSETS and WCFO/ASSETS are not in the

model. The latter two variables are generally significant. CFFO/ASSETS is significant in years three and four but not in years one and two.

As stated earlier, the correlations among INC/ ASSETS, CFFO/ASSETS, INCDEP/ASSETS, and WCFO/ASSETS are rather high and all four ratios load on one factor in the early years. The correct interpretation seems to be that the return on assets factor is significant in predicting bankruptcy in the early years though it can be manifested in any one of the four variables.

If CFFO is an important predictor, it needs to dem- onstrate itself in the models of the late years. The results are tabulated in Exhibit 8. CASH/ASSETS is significant in the first two years only. CURASS/ SALES, CURDEBT/DEBT, and SALES/ASSETS are never statistically significant. INC/ASSETS is significant in years one and four. CFFO/ASSETS, INCDEP/ ASSETS, and WCFO/ASSETS are significant in year one only.

Notice that CFFO/ASSETS is insignificant in years two, three, and four. Indeed it has the wrong sign in the second and the fourth years. The only year it is significant is one year prior to failure. Perhaps CFFO is a predictor of failure but only in the very short run.

The models are significant when they are estimated



GOOMBOLA, HASKINS, KETZ, AND WILLIAMS/CASH FLOW AND BANKRUPTCY 63

Exhibit 7. Linear Discriminant Analysis Over Early Years % Cor-

AS- rectly Year Prior CASH/ CURASS/ CURDEBT/ SALES/ SETS/ INC/ CFFO/ INCDEP/ WCFO/ Adjusted Pre- to Failure Intercept ASSETS SALES DEBTS ASSETS DEBTS ASSETS ASSETS ASSETS ASSETS R2 dicted

One .674** .736 -.631 -.043 -.071 .100 2.714** .496** 84** (n= 42) .724** .448 -.617 -.095 -.050 .091 3.237** -.435 .498** 86**

.555* 1.182 -.725* .071 -.111 .081 .209 2.679* .533** 89**

.656** 1.014 -.738* -.007 -.109 .105 1.081 1.567 .502** 82**

Two .451 ** .763 -.047 -.293 .027 .069** 1.563** .306** 74** (n= 74) .437** .788 -.045 -.320 .029 .073** 1.405** .329 .299** 74**

.273 .726 -.035 -.129 .036 .033 -.097 2.076** .346** 72**

.302* .730 -.038 -.194 .044 .042 -.412 2.092* .334** 72**

Three .202 .752 -.055 -.393 .015 .253** .446 .179** 74** (n= 92) .093 .286 .135 -.489* .057 .243** .528 1.013* .202** 75**

.156 .667 -.127 -.251 .004 .210** -.260 1.478** .206** 80**

.091 .635 -.047 -.256 .016 .204** -.202 1.702** .217** 82**

Four .274 .548 -.378 -.221 .005 .231 ** .439 .142** 73** (n = 96) .177 .538 -.220 -.260 .021 .226** .166 .988* .165** 73**

.010 .905 -.192 -.117 .013 .209** - 1.800 2.527** .191** 78**

.057 .949 -.193 -.177 .014 .211** -1.308 2.070** .193** 77**


Exhibit 8. Linear Discriminant Analysis Over Late Years

Cor- CUR- AS- rectly

Year Prior CASH/ CURASS/ DEBT/ SALES/ SETS/ INC/ CFFO/ INCDEP/ WCFO/ Adjusted Pre- to Failure Intercept ASSETS SALES DEBTS ASSETS DEBTS ASSETS ASSETS ASSETS ASSETS R2 dicted

One .241 2.185** - .188 -.381 .016 .230** .989* .329** 87** (n = 50) .338 1.809* -.288 -.457 .015 .220** .824 .636* .361** 85**

.244 2.124** -.469 -.231 .003 .181* -.488 1.985** .409** 89**

.323 2.216** -.375 -.313 .001 .186* -.004 1.212* .354** 83**

Two .150 1.724* -.422 -.436 .049 .338** .349 .342** 82** (n= 52) .270 1.693* -.567 -.437 .026 .342** 1.353 -.975 .355** 80**

.023 1.636 -.373 -.356 .060 .315** -1.374 1.664 .342** 86**

.116 1.761" -.405 -.404 .050 .321** -.741 1.042 .330** 84**

Three .087 -.880 .125 - .499 .131 .168 3.358 .004 72** (n= 32) .048 - .984 .181 - .503 .138 .167 3.089 .360 - .028 67**

-.021 - 1.075 .141 -.442 .139 .185 2.556 .690 .028 69** -.211 -1.224 .372 -.436 .165 .168 .905 2.736 -.021 72**

Four .711 .461 -.713 -.389 -.005 .013 5.984** .148 70** (n= 26) .790 .703 -.863 -.384 -.023 .022 6.074** -.545 .119 70**

.561 .724 - .661 .363 .003 - .007 3.457 1.942 .126 60

.257 .080 -.378 -.267 .029 -.013 .940 5.412 .149 67**





over all years or over the early years. They are significant only for years one and two for the late years. Perhaps the samples are too small for those segments and thus there may be low power in the tests. Future research might be directed to remedy this.

The last column in Exhibits 6, 7, and 8 gives the percentage of firms correctly predicted. This is obtained using the quasi-validation jackknife procedure. The models are relatively good one year prior to failure and generally decay as the time before failure in- creases. Almost all models are statistically significant against the naive model of classifying the firms randomly.

Let us now assess the marginal predictive contribu- tion of CFFO/ASSETS. For the late years, adding CFFO/ASSETS to the model aids in its classificatory power only in the first year. Years two and three see a decline in the predictive power. Year four stays steady. It should be noted that the marginal increase in year one is not statistically significant (using a differences between proportions test). Thus, CFFO/ASSETS does not contribute to greater predictive ability. It is also interesting to note that the best classificatory models tend to be those with INCDEP/ASSETS."

IV. Conclusions Consistent with Casey and Bartczak [9], the evi-

dence in this paper is against cash flow from operations (calculated as working capital from operations plus/ minus changes in current liabilities and current assets other than cash). In the late years, where it should have manifested itself (if at all), CFFO/ASSETS is insignificant in predicting bankruptcy three years out of four as measured by the significance of the coefficient. The marginal predictive ability of CFFO is insignificant in all four years. It therefore appears that CFFO is not an important predictor of corporate failure. It may be that the information currently available in published financial statements is inadequate in providing CFFO information that is any more useful than just the sim- plistic estimates based on income plus depreciation [12, 28]. It remains to be seen whether actual, as opposed to estimated, cash flows are useful predictors of bankruptcy.

The cash flow measure employed in our study is an estimate. Since it is calculated with error, the results are limited by the effectiveness of the estimation procedure. Our study might be replicated at a later time

when firms report cash flow from operations. Besides the CFFO issue itself, this research provides

a methodological tool and a warning to those studying decision models over time. Relations among variables may change over time. Results over a long period of time may be inappropriate. By subdividing the period, one may examine the decision models over shorter periods in which the relationships among the variables are more stable. If instability is suspected, one can use factor analysis on a year-by-year basis to see whether any changes have occurred.

"The lack of significance of a refined measure of CFFO, beyond that of income plus depreciation in predicting bankruptcy, parallels Schaefer and Kennelly's [28] findings pertaining to changes in security returns.

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1980s," The Journal of Commercial Bank Lending (Sep- tember 1982), pp. 20-29.

28. T. Schaefer and M. Kennelley, "Alternative Cash Flow Measures and Risk-Adjusted Returns," Journal of Ac- counting, Auditing and Finance (Fall 1986), pp. 278-287.

29. S. Sharma and V. Mahajan, "Early Warning Indicators of Business Failure," Journal of Marketing (Fall 1980), pp. 80-89.

30. K. Takahashi, Y. Kurokawa, and K. Watase, "Corporate Bankruptcy Prediction in Japan," Journal of Banking and Finance (June 1984), pp. 229-247.

31. M. E. Zmijewski, "Methodological Issues Related to the Estimation of Financial Distress Prediction Models," Jour- nal of Accounting Research (Supplement, 1984), pp. 59-82.

CALL FOR PARTICIPATION:

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European equity markets, commodity and currency markets, futures and options. If you are interested in

participating, please write immediately to Dr. S. J. Taylor, School of Management, University of Lancaster,

England, LAl 4YX.



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