Since Beaver's seminal study in 1966, substantial research has
been published concerning the ability of accounting information to
predict financial distress (bankruptcy). This area of research uses
financial distress as a criterion for evaluating the usefulness of
accounting information. Although researchers have used this predictive
ability criterion to test many accounting variables, much of the
empirical research in this area has centered around the use of a
predictive ability criterion to compare the usefulness of accrual and
cash flow information.
The underlying theory justifying financial distress as a criterion
for testing accounting information is based on a future cash flows
emphasis. The Financial Accounting Standards Board (FASB) adopted a cash
flows concept approach in the development of the conceptual framework,
establishing the ability to help investors and creditors predict future
cash flows as the primary criterion for evaluating the usefulness of
accounting information. Since future cash flows are constructs,
researchers developed proxies for future cash flows; financial distress
was chosen by a number of researchers as an acceptable proxy. The
original studies in this area of research used a dichotomous failed and
nonfailed proxy for financial distress, while studies since the
mid-1970s used primarily a dichotomous bankrupt and nonbankrupt response
measure for financial distress.
This study provides a methodological review of prior financial
distress research. The purpose of this paper is to provide a framework
for improving the validity of future financial distress studies by
discussing the limitations found in prior financial distress research
and making recommendations concerning future accounting research. (1)
LANDMARK FINANCIAL DISTRESS STUDIES
The prediction of financial distress (bankruptcy) has been the
subject of a substantial amount of research. Most of the financial
distress studies fall into one of two categories based on the
study's primary objective. Either the primary purpose of the study
is to use financial distress as a criterion variable to test the
importance of accounting information or the primary purpose is to
develop the most accurate model possible to help users predict
bankruptcy. The two seminal works of Altman (1968) and Beaver (1966) are
typical of studies in each of these two categories.
Applied Bankruptcy Models
Altman attempted to improve conventional ratio analysis by
developing a multivariate model using multiple discriminant analysis
(MDA). His Z-score model is still used today to predict bankruptcy. The
primary objective of studies such as Altman's "landmark
study" is to develop the most accurate model or tool to enable
users to effectively, and economically, predict bankrupt firms. (2)
These researchers are primarily interested in the predictive accuracy of
the model and how the model compares to a naive model. The information
content of a specific variable is not their primary concern. (3)
Ohlson (1980) extended Altman's methodology three ways: (1) he
used multivariate logit analysis to determine probabilistic estimates of
failure instead of a simple deterministic classification of a firm as
bankrupt or nonbankrupt; (2) he collected a larger, and more
representative, sample of 105 bankrupt firms and 2,058 nonbankrupt
firms; and (3) he only included firms with published financial data
released prior to the announcement of bankruptcy to reduce the amount of
sampling bias. Ohlson found that firm size was a significant negative
predictor of bankruptcy; bankrupt firms tend to be smaller than
nonbankrupt firms. This result shows the need to control for firm size
in financial distress studies. Ohlson also showed that, for some firms,
the financial reports for the preceding year are issued after the
announcement of bankruptcy. Thus, the financial reports include
information about a firm's bankruptcy, resulting in the
overstatement of the predictive power of the accounting models. To
correct this problem, the researcher must either drop these firms from
the sample or substitute the financial statements from the previous year
for the most current year of interest.
Zavgren (1985) also used logistic analysis in a similar study of
forty-five bankrupt and forty-five nonbankrupt firms. Unlike Ohlson,
Zavgren theoretically justified the inclusion of seven variables found
to load highest on separate factors. Both Zavgren and Ohlson found that
their bankruptcy models could be used to predict a firm's
likelihood of failure for up to five years in advance with reasonable
Financial Distress--Naive Cash Flow Variable
The following studies analyzed the usefulness of cash flow
variables using a predictive ability criterion. However, each of these
studies used a crude approximation of cash flow from operations, net
income plus depreciation and amortization.
Beaver (1966) conducted a univariate analysis of the ability of
thirty ratios (four of which were cash flow ratios) to distinguish
between seventy-nine failed and seventy-nine nonfailed firms. Beaver
matched the failed firms with the nonfailed firms based on industry and
asset size. He defined a failed firm as one that had experienced one of
the following events: bankruptcy, bond default, overdrawn bank account,
or nonpayment of a preferred stock dividend. Beaver used simple
classification techniques based on various cutoff points to distinguish
between the failed and nonfailed firms. He found that: (1) accounting
information can be used to predict failure as early as five years before
failure; (2) different ratios predict with different degrees of
accuracy; (3) accounting information predicts nonfailure better than
failure; and (4) the ratio cash flow/total debt was the best performing
variable, with an eighty-seven percent classification rate for year one
and a classification rate of seventy-eight percent five years prior to
failure. However, Beaver's greatest contribution was his suggestion
that financial distress could be used as a framework for evaluating the
usefulness (predictive ability) of accounting information (Altman et
Deakin (1972) extended the studies by Altman (1968) and Beaver by
incorporating the fourteen "strongest" variables suggested by
Beaver and the multivariate methodology recommended by Altman. The
author obtained a sample of thirty-two failed and thirty-two nonfailed
firms. Deakin matched the firms based on industry, asset size, and year
of financial data. Like Beaver, Deakin sampled the firms based on a
failed and nonfailed classification scheme; a failed firm was one that
was bankrupt, insolvent, or liquidated. Using multiple discriminant
analysis (MDA), Deakin found that the ratio cash flow/total debt was
very important to the discriminant model. The fourteen variable model
predicted failure as far as three years in advance with fairly high
accuracy. Similar to Beaver and Deakin, Blum (1974) used a failed and
nonfailed classification to determine the predictive ability of
financial ratios. However, the primary purpose of Blum's study was
to "develop a Failing-Company Model to aid the Antitrust Division
of the Justice Department in assessing the probability of business
failure" (Altman et al. 1981, p. 227) using linear MDA. Blum
defined a failed firm as any firm meeting one of the following criteria:
(1) failure to pay debts when due, (2) debt accommodation agreement with
creditors to reduce debts, or (3) occurrence of bankruptcy. The only
cash flow variable in the model, cash flow/total debt, was generally
ranked high in the predictive models.
Criticism of Failed and Nonfailed Measure of Financial Distress
The prior studies by Beaver, Deakin, and Blum were criticized
because of the heterogeneous failed and nonfailed sample selection
scheme used. In a two-group failure classification, firms within a group
should be homogeneous and representative of the population of failed
enterprises (Altman et al., 1981). Thus, studies by Altman, Haldeman,
and Narayanan (1977), Norton and Smith (1979), and Mensah (1983) tested
the ability of financial ratios and cash flows to predict bankrupt and
nonbankrupt firms. The authors used various stepwise linear and
quadratic MDA models. Except for Altman, Haldeman, and Narayanan, the
results of these bankruptcy studies validated the results of prior
studies that used a broader definition of failure. Mensah found cash
flow/net worth to be the most important ratio, while cash flow/total
assets and cash flow/total debt were both found important by Norton and
FINANCIAL DISTRESS--REFINED OPERATING CASH FLOW VARIABLE
The following studies tested a more refined measure of operating
cash flow, with the most recent studies also testing additional cash
flow variables other than operating cash flow. In each of these studies,
the primary purpose was to test the usefulness of cash flow information
using a predictive ability criterion.
The study by Largay and Stickney (1980) of the 1975 bankruptcy of
W.T. Grant was the catalyst behind the explosion of cash flow studies
during the 1980s. In this study, Largay and Stickney observed the trends
of certain accrual ratios (profitability, turnover, liquidity, and
solvency ratios) and cash flow from operations (CFO) for ten years
preceding bankruptcy. The authors found that the company's CFO
provided a more accurate and timely signal of W.T. Grant's eventual
failure than traditional accrual ratios.
However, two studies by Casey and Bartczak (C&B) (1984, 1985)
appeared to contradict the study by Largay and Stickney. In the 1984
study, C&B tested whether CFO or accrual ratios could best predict
bankruptcy. The authors obtained a matched-pair sample (matched on
industry) of bankrupt and nonbankrupt firms for the period 1971-1982.
The sampling scheme resulted in a total sample of sixty bankrupt and 230
nonbankrupt firms. Half the firms (thirty bankrupt and 165 nonbankrupt
firms) were used to develop the classification models, while the other
firms were used as a holdout group. The authors generated two groups of
models with lag periods of one to five years before bankruptcy. The
first group of models was composed of univariate cash flow models for
each lag period. The cash flow variables tested were: CFO (working
capital provided by operations, plus or minus changes in the noncash
working capital accounts); CFO divided by current liabilities; and CFO
divided by total liabilities. The other group of models was composed of
multivariate accrual models generated from linear MDA. Each accrual
model included the following six accrual ratios: net income/total
assets, cash/total assets, current assets/current liabilities, net
sales/current assets, current assets/total assets, and total
liabilities/owners' equity. C&B found that neither cash flow
variable had higher classification rates than the combined six accrual
In their subsequent study, C&B (1985) attempted to determine
whether CFO could increase the accuracy of accrual-based ratios to
distinguish between bankrupt and nonbankrupt firms. The study was
essentially the same as the previous one except the approach was
different; the objective was to determine if CFO had incremental
predictive ability. The authors developed accrual, cash flow, and mixed
(cash flow variables added to the accrual models) models for lag periods
of one to five years. To obtain test statistics on each variable,
C&B also generated mixed models using conditional stepwise logit
analysis. (6) C&B concluded that operating cash flows "do not
provide incremental predictive power over accrual-based ratios" (p.
The research by C&B seems to validate the FASB's position
that accrual information better predicts future cash flows, but
contradicts the FASB position that cash flow information taken together
with accrual information should better predict future cash flows.
However, C&B only tested CFO, and they used working capital from
operations (WCFO) to calculate CFO. Drtina and Largay (1985) found that
using WCFO to calculate CFO might create confounded results because of
the inconsistencies among the way firms define WCFO and the diversity in
reporting practices. C&B also failed to control for firm size,
either by matching or by incorporating a size variable in the models.
In a study similar to the studies of C&B, Gentry, Newbold, and
Whitford (GN&W) (1985) compared thirty-three bankrupt and
thirty-three nonbankrupt firms to determine if funds flow information
could predict bankruptcy. GN&W matched the firms on size, industry,
and sales. The authors used a separate sample of twenty-three weak and
twenty-three nonweak firms to validate the models instead of a separate
holdout group of bankrupt and nonbankrupt firms. The weak firms were
identified by the "creditwatch list issued by the Wells Fargo
Bank" and "from various financial services who rate candidates
for financial failure" (p. 158). Instead of testing CFO, the
authors tested seven funds flows (each scaled by total net cash flows)
based on Helfert's (1982) "cash-based funds flow model"
and six accrual ratios. The seven funds flows tested were operations,
working capital, financial, fixed coverage expenses, capital
expenditures, dividends, and other asset and liability flows. The
authors used various statistical techniques to generate the models
(individual and mixed models) to test the funds flow and accrual ratios
(MDA, probit, and logit techniques); however, the logit models provided
the best results. Gentry, Newbold, and Whitford found that only the
dividend funds flow component was significant both one year and three
years before bankruptcy. They also found that the components of CFO
(funds from operations, working capital, and fixed coverage expenses)
failed to improve the classification of failed and non-failed firms.
However, the authors did not specifically test CFO.
In a subsequent study, GN&W (1987) extended the 1985 study by
comparing the predictive ability of accrual ratios and funds flow
components. The authors broke up the net working capital from operations
variable into five funds flow components (changes in accounts). The five
funds flow components were accounts receivable, inventory, other current
assets, accounts payable, and other current liabilities. The authors
also tested six accrual ratios common in prior financial distress
studies. GN&W found that outflows of certain components indicated a
healthy company. Inventories, dividends, and receivables were inversely
related to bankruptcy; nonfailed companies showed net outflows for
inventories, receivables, and dividends while failed companies showed
inflows leading up to the bankruptcy. Thus, this study offers evidence
that the particular inflows and outflows of funds may be more important
in predicting bankruptcies than a net cash flow such as CFO.
Aziz, Emanuel, and Lawson (AE&L) (1988) tested the predictive
ability of six cash flow variables on a sample of forty-nine bankrupt
and forty-nine nonbankrupt firms for the period 1971-82 (the models were
generated with data collected from 1966-1981). The cash flows tested
were operating cash flow, net capital investment, taxes paid, liquidity
change, stockholders' cash flow, and lender cash flows. AE&L
selected the cash flows based on a cash flow identity developed by
Lawson (1978, 1985) and scaled the cash flows by the book value of the
firm to avoid the problem of heteroscedasticity. Lawson's cash flow
identity differs from Helfert's (1982) cash-based model in that
Lawson's model is for "firm valuation," while
Helfert's model is to provide analysis by "area of management
attention" (AE&L, p. 419-420). MDA and logistic regression were
used to generate the models (lagged for 1-5 years) to test the cash flow
variables. AE&L found that the cash flows, taxes paid, operating
cash flow, and lender cash flow were significant predictors in two of
the five years before bankruptcy, with taxes paid significant in all
five years. A comparison of Lawson's model with the GN&W (1985)
and Altman, Haldeman, and Narayanan (1977) (ZETA) models indicated that
Lawson's model was superior to the GN&W model for the only year
compared and was superior to the ZETA model for three or more years
Aziz and Lawson (A&L) (1989) extended the research of AE&L
in two ways: (1) they tested the incremental usefulness of cash flow
information by combining the cash flow variables with the five accrual
ratios in Altman's Z-score model and (2) they used a holdout sample
of twenty-six bankrupt and sixty-seven nonbankrupt firms to test the
validity of the models generated. The authors also extended prior
research by incorporating formal hypothesis testing. Based on
classification accuracy, A&L confirmed the results of C&B and
GN&W, concluding that cash flow based models do not improve on the
existing model's (accrual) overall accuracy. However, concerning
the holdout sample, cash flow based and mixed models exhibited superior
predictive accuracy, thus suggesting cash flows may be more stable
The results of the above studies are surprising, since the main
stated benefit of cash flows is their incremental usefulness in helping
accrual information to predict insolvency (Staubus, 1989). The only
study showing cash flow based components have incremental predictive
content (Gentry, Newbold, and Whitford, 1987) found that certain changes
in accounts that comprise working capital have predictive content.
LIMITATIONS OF PRIOR FINANCIAL DISTRESS STUDIES
This section contains a discussion of the limitations of prior
financial distress studies that compared the predictive ability of
accrual and cash flow information. Where appropriate, methodology
studies addressing these limitations are also discussed.
Response Measure for Financial Distress
A limitation of prior financial distress studies is the use of a
dichotomous financial distress measure. The use of a dichotomous
classification of financial distress is an overly simple representation
of the financial distress process and is unlikely to capture the true
underlying construct. The financial distress of a firm is an
unobservable continuum. Firms are not simply bankrupt or nonbankrupt but
possess certain degrees of financial distress that vary from day to day
and period to period. However, since researchers do not have the
capability to observe this continuum, they select events to
operationalize this construct. The finance literature stresses the
belief that many events indicate different degrees of financial
distress, and companies may go through many of these events before
bankruptcy occurs (e.g., Dewing (1953), Donaldson (1969), and Newton
A truer test of the usefulness of a financial distress model would
be the model's ability to distinguish between marginally distressed
firms, not just between healthy firms and firms in very serious
financial distress (Jones, 1987). Events such as loan/interest default
and failure to pay dividends may be of more interest to investors and
creditors because the ability to predict these events one year in
advance would provide the users with an earlier warning signal than
predicting bankruptcy one year in advance.
Researchers have also questioned the use of bankruptcy as a proxy
for financial distress because bankruptcy is a legal event and not an
economic event (Dietrich, 1984). Financial distress results from
economic occurrences. Only economic events should truly capture the
level of financial distress of a firm. Legal recognition of bankruptcy
may occur sometime after the firm is economically insolvent, or occur
even though the company is not economically insolvent. Also, the
economic conditions of bankrupt firms are likely not similar to other
types of distressed firms. Thus, using a legal event as a proxy for
economic conditions may produce misleading results. Even the firms
selected by legal status may form a heterogeneous set because some firms
voluntarily choose bankruptcy, and others do not. Thus, the economic
conditions of these firms may be quite different (p. 84).
Gilbert, Menon, and Schwartz (GM&S) (1990) addressed the
appropriateness of using a bankrupt and nonbankrupt response scale to
test the importance of accounting information by replicating the studies
of C&B (1985) and Altman (1968) using two separate samples of firms.
The authors found that operating cash flow could significantly
distinguish between bankrupt and distressed firms. However, a healthy
and bankrupt model performed poorly when used to distinguish bankrupt
firms from distressed firms (defined as firms having consecutive losses)
for the holdout sample. This result suggests that cash flow information
may be more useful in distinguishing between events of financial
distress other than bankruptcy. However, GM&S failed to: (1) look at
other economic events of financial distress such as loan defaults and
failure to pay dividends; (2) develop multi-state models of distress to
better capture the predictive ability of cash flow and accrual
information; and (3) control for the size of the firms, either by
matching or by including size as an independent variable.
Lau (1987) corrected for many methodological limitations of the
prior dichotomous predictive models by using a five-state response scale
to approximate the continuum of corporate financial health instead of
the conventional bankrupt and nonbankrupt dichotomy. The states
included: (1) financial stability, (2) omitting or reducing dividend
payments, (3) default of loan interest or principal payments, (4)
protection under Chapter X or XI of the Bankruptcy Act, and (5)
bankruptcy and liquidation.
Lau considered these states of financial distress to be on an
ordinal scale. According to Lau, "states one to four are states of
increasing severity of financial distress" (1987, p. 128). However,
a limitation of Lau's study is that the statistical technique she
used did not incorporate the ordinal structure of her dependent variable
into her model; Lau used a nominal based logit model. For ordinally
scaled dependent variables, nominal logistic models are inferior to
ordinal logistic regression models (Agresti, 1984). According to Agresti
(1984), ordinal procedures have the following advantages over nominal
(1) Ordinal methods have greater power.
(2) Ordinal data description is based on measures similar to those
(e.g., correlations, slopes, means) used in ordinary regression for
(3) Ordinal analyses can be used in a greater variety of models,
most of which have simpler interpretations than the standard models for
(4) Ordinal models can be applied in settings where the standard
nominal models are trivial or else have too many parameters to be tested
for goodness of fit (p. 3).
Lau attempted to overcome these limitations by employing a
rank-scoring rule. However, the rank-scoring rule did not allow for
statistical tests of significance for each variable. An ordinal logistic
procedure is not limited by these problems. (7)
The sampling technique used by Lau to identify the loan default
firms also created additional sample selection bias. Lau used the SEC
10-Ks of firms that had filed for bankruptcy or had C-rated bonds during
1977 to 1980 to identify firms that defaulted on loans during 1976 or
1977. Thus, firms that recover from financial distress after
experiencing a loan default were excluded from her original (1976) and
holdout (1977) samples.
Ward (1994) extended Lau's multi-state methodology by using
ordinal logistic regression to generate the prediction models for a
four-state ordinal response scale. This study modeled the severity of a
firm's financial distress by using four ordinal states of financial
distress as the dependent (response) variable and by employing ordinal
logistic regression. The study did not include firms that declare
bankruptcy and then liquidate as a fifth state of financial distress
because the authors identified only four liquidation firms for the
developmental sample. The ordinal states of financial distress
(dependent variable) used in this study were:
DIST = 0 if firm was healthy (no event of financial distress during
1988 or 1989),
1 if firm experienced a greater than forty percent reduction in
cash dividend per share during 1988 or 1989 after a history of
successive cash dividends per share paid,
2 if firm experienced a loan principal/interest default or debt
accommodation (extension of cash payment schedules, reduction in
principal, or reduced interest rates) during 1988 or 1989, and
3 if firm filed, or was forced to file, for Chapter XI protection
during 1988 or 1989.
Firms used to generate the ordinal four-state regression models
were not pooled across many years but were all selected from one year,
1988 (developmental sample). The study used financial data for 1984/85
(year three models), 1985/86 (year two models), and 1986/87 (year one
models) to generate financial distress regression models from the
developmental sample of 1988 firms.
Ward and Foster (1997) subsequently tested whether or not using the
inability of a firm to pay debts when due, loan default/accommodation,
as a response measure produces different results than using legal
bankruptcy as the response measure. They developed two financial
distress prediction models from data using developmental samples of 1988
and 1989 firms. Each model was run three times, with data lagged one,
two, and three years before the event. The two models differed only in
the way the dependent variable was defined. The first model used a legal
definition of bankruptcy, healthy versus bankrupt, as the dependent
variable, while the second model used inability to pay debts when due,
healthy versus loan default/debt accommodation, as the dependent
The study's results suggested that a loan
default/accommodation response measure was more valid response measure
than bankruptcy for determining information useful to lenders in
predicting future insolvency (inability to pay obligations when due) of
a firm. The authors concluded that future researchers use either a
dichotomous loan default/accommodation dependent variable to generate
the prediction models, or sample both loan default/accommodation and
bankrupt firms to generate financial distress prediction models.
Violations of Statistical Assumptions
Most financial distress studies violated important assumptions of
the statistical techniques used. MDA assumes that the predictor
variables are randomly drawn and normally distributed, and linear MDA
also assumes equal variance/covariance matrices for each group (Altman
et al., 1981). Many researchers failed to test these assumptions. Those
researchers who did test for violations of MDA assumptions found that
these two assumptions were usually violated; financial ratios do not
tend to be normally distributed.
Logistic procedures are not restricted by assumptions concerning
the predictor variables. Thus, they are generally preferred over MDA
(Press and Wilson, 1978). However, logistic procedures assume that the
response variables are randomly drawn (Altman et al., 1981) and require
sample sizes of at least 10(S+1) to generate unbiased estimates, where S
is the number of predictor variables in the model (McFadden, 1974; and
Sufficient Sample Size
Noreen (1988) used simulations to compare the performance of probit
and ordinary least squares regression (OLS) models in predicting
bankruptcy. (8) Noreen found that, for a sample size of 10(S+1), probit
incorrectly rejected the null hypothesis of no effect at a rate twice
the normal level and greater than OLS. However, the results reversed
when the sample size was increased to 20(S+1) (OLS incorrectly rejected
the null hypothesis more than probit).
Stone and Rasp (1991) subsequently extended the research of Noreen
by using simulated data and actual data to compare the performance of
OLS regression models and logit regression models. Like Noreen, the
authors found that a sample size of 10(S+1) led to biased logistic
estimators; these logistic estimators were also more biased than the OLS
estimators. However, the authors found that the chi-square statistics
for the overall models incorrectly tended to reject the null hypothesis
of no effect while the individual parameter test statistics (t-test
statistics) tended to be conservatively biased against rejecting the
All of the early cash flow studies that used logistic procedures,
except the studies of Casey and Bartczak (1984, 1985), violated the
requirement of a sufficient sample size for some, if not all, of their
logistic models. The violations were especially severe for the study by
GN&W (1987) that found funds flows were significant predictors of
bankruptcy. GN&W needed a minimum sample size of 180 firms to meet
the sample size requirement of 10(S+1) for their mixed models. Since the
sample size used by GN&W is substantially less than the recommended
sample size, their results could be affected by the bias caused by using
small sample sizes to generate the logistic parameter estimates.
Choice-Base Sampling Bias
Financial distress studies may also suffer from stratification bias
because the researchers nonrandomly selected the bankrupt and
nonbankrupt firms. Instead of randomly selecting a sample of firms from
the population and then identifying the bankrupt and nonbankrupt firms,
researchers first identified the bankrupt firms to obtain a sufficient
sample size of these firms. Then, the researchers matched the bankrupt
firms with nonbankrupt firms, normally on a one to one basis.
Consequently, the samples were not representative of the population
(bankrupt firms were oversampled). This sample bias is often called
"choice-base sampling bias" (Manski and Lerman, 1977).
Zmijewski (1984) empirically demonstrated the existence of
choice-based sampling bias in a binary probit bankruptcy model. He also
showed that a weighted probit model eliminated most of the sampling bias
and that sampling a larger percentage of nonbankrupt firms lessened the
bias. Zmijewski also found that the bias did not change the statistical
results or overall accuracy of the model.
Manski and McFadden (1981) and Cosslett (1981) illustrate weighted
procedures to correct for choice-based sampling bias in binary logit
models. However, the impact of choice-base sampling bias in logit models
depends on the type of statistical model used. A weighted model is
needed to correct for the bias in the conditional logit models
illustrated by Manski and McFadden and Cosslett. However, Maddala (1991)
shows that a weighted model is not needed for the binary logit model
illustrated by Berkson (1944). For a logit model, choice-base bias is
not present in the parameter estimates, only the intercept is affected.
Thus, the bias simply affects classification accuracy and not
statistical tests of parameter estimates. Maddala recommends a simple
adjustment of the intercept to obtain accurate classifications.
Due to stratified sampling, all the bankruptcy studies using
logistic (probit) procedures violated the assumption of random selection
of the response variables. However, none of the probit studies used
weighted models to correct for the choice-based sampling bias, and only
Casey and Bartczak (1984, 1985) sampled a larger percentage of
Pooling of Firms Across Time and Selection of Holdout Sample
A third limitation of prior bankruptcy studies is that the
researchers pooled firms across a large period to obtain enough bankrupt
firms. Conditions probably are not stable across the different years
used to develop the samples. Thus, sampling across years tends to
increase the variation in the sample, resulting in a lower likelihood of
finding significant results. Also, studies that used holdout samples to
verify the predictive accuracy of their models selected the holdout
group of firms from the same period used to develop the models. These ex
post discriminations are true predictions only if stationarity exists.
Otherwise, one should draw the holdout sample from a future period
distinct from the original sample period. Evidence of ex ante predictive
power requires intertemporal validation and not just cross validation
(Altman et al., 1981).
Mensah (1984) found that financial distress prediction models were
not stationary across time, thus questioning the practice of pooling
bankrupt firms across wide periods of time. This result also indicates
that one must obtain the holdout sample from outside the period used to
develop the model to get an accurate picture of the predictive ability
of the model.
Period Used to Obtain Data
The period used to generate the predictor models is also very
important. Cash flows and accruals differ because various accrual
methods create a difference in the timing of the recognition of revenues
and expenses. This difference should have grown as the profession has
moved closer to the all-inclusive definition of income. Using factor
analysis, Gombola and Ketz (1983) found that cash flow variables scaled
by total debt, total assets, and equity loaded on a separate factor from
accrual variables after the mid 1970s, thus suggesting that cash flow
variables provided information not in other financial data. Gombola et
al. (1987) subsequently tested whether the failure of prior studies to
obtain sampling data points after the mid 1970s could be confounding the
results of these studies. The authors divided their sample into pre-1972
and post-1972 sampling groups. They concluded that cash flow variables
were not more relevant in the post-1972 model. However, their samples
may have been too small to detect a difference, and the authors failed
to develop models from the post-1981 period.
In a study concerning the intertemporal divergency among operating
cash flow, working capital, and income from operations, Franz and Thies
(1988) found that income and working capital from operations had
diminished as a percentage of operating cash flow over the period from
1967 to 1985. Their results also showed that most of the decrease in
income as a percentage of cash flow occurred from 1981 to 1985 (decrease
from .5099 to .3493 versus a decrease of .5519 to .5099 for the period
from 1967 to 1981, respectively) (1988, p. 24). This increasing gap
between income and cash flow suggests that cash flows may provide
additional information in predicting financial distress, and that models
developed from the post-1981 period are more likely to detect the
incremental usefulness of cash flows as predictors of financial
distress. However, all of the previous cash flow studies generated
models with at least half their data points falling before 1975, and all
of the studies' models were developed from pre-1982 data.
The above limitations of prior financial distress cash flow studies
indicate the need for additional research. However, the validity of
financial distress studies would be improved if researchers incorporate
the following recommendations in future studies:
(1) Obtain sample sizes of at least 10(S+1), and possibly 20(S+1)
for all logit (probit) models tested to lower the amount of sampling
(2) For binary studies, use logit regression to eliminate the
effects of choice-base sampling bias on parameter estimates. If probit
or conditional logit is used, the researcher should incorporate weighted
probit and weighted conditional logit models to limit the effects of
choice-base sampling bias in binary models.
(3) Sample so that firms used to generate the predictive models are
not pooled across a large period but are selected from one year (or as
short a period as possible).
(4) Holdout firms should be obtained from a year (period) other
than the year (period) used to generate the models.
(5) For cash flow research, either use actual cash flows as
reported on the statement of cash flows or estimate cash flows from
(6) For dichotomous financial distress studies, replicate research
using dichotomous measures of financial distress other than bankrupt and
nonbankrupt, especially loan default/ accommodation.
(7) Develop ordinal multi-state measures of financial distress
The development of an ordinal multi-state response variable is a
natural extension of prior financial distress research. Prior research
has failed to concentrate on obtaining a better measure of financial
distress; researchers assumed that bankruptcy was an appropriate
response measure for evaluating the usefulness of accounting
information. However, as stated earlier, the use of a dichotomous
measure, especially bankruptcy, as the proxy for financial distress
suffers from many weaknesses. The severity of financial distress of a
firm can be modeled using ordinal levels of financial distress. Whether
bankruptcy is the most appropriate proxy for financial distress is an
empirical question that needs to be addressed.
If a dichotomous response is used to evaluate accounting
information, a more appropriate dichotomous response may be loan
default/accommodation versus a healthy state. The use of loan
default/accommodation as the criterion variable for evaluating the
usefulness of accounting information also makes more sense from an
applied perspective. Lenders are primarily interested in whether a firm
defaults on a loan. Most firms that default on loans do not become
bankrupt. Even for those default/accommodation firms that do become
bankrupt, predicting the default or accommodation would be of more
interest to the lender because default normally occurs before
bankruptcy. Thus, models that predict loan default/accommodation should
provide lenders more time to react.
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(1.) The decision to limit the literature review to financial
distress studies that compared the predictive ability of accrual and
cash flow information was made to keep the task manageable.
However, the methodological limitations identified in the financial
distress studies discussed in this paper, and the recommendations for
correcting these limitations, apply to all studies using financial
distress as a criterion to evaluate the predictive usefulness of
(2.) Altman et al. (1981) provide a thorough discussion of what
they consider to be the "ten landmark studies."
(3.) The distinction between these two objectives was more obvious
in the earlier studies because of the limited capabilities of the
statistical techniques used (forms of discriminant analysis) to develop
tests of significance on particular variables. Later, the implementation
of logistic and probit techniques enabled researchers to address both
objectives. Still, the primary objective of a financial distress study
dictates the type of methodology used.
(4.) Predicting bankruptcy has been a long and fruitful area of
research. However, for brevity's sake, the author has only
described the earlier studies by Altman, Ohlson, and Zavgren because
these studies are typical of bankruptcy studies that did not incorporate
cash flow variables into their models. Interested readers should see
Ball and Foster (1982), which contains a complete listing of early
financial distress studies.
(5.) A more recent study by Holmen (1988) comparing the predictive
accuracy of the Altman Zscore model (Altman, 1968) and the cash
flow/total debt variable (.03 and .07 cutoff points) used by Beaver
(1966) on a sample of eighty-four bankrupt and eighty-four nonbankrupt
firms found that the simple cash flow/total debt univariate model
predicted bankruptcy with fewer errors than the multivariate Altman
(6.) Most statistical packages for MDA do not generate meaningful
test statistics for each variable tested (the slopes using MDA have no
intuitively practical meaning). MDA is also primarily a classification
technique instead of a predictive technique; thus, MDA is not the most
appropriate technique to use when the purpose is to test the ability of
independent variables to predict financial distress.
(7.) Since probit and logistic techniques are so similar, both
require similar statistical assumptions. Thus, the results from this
study for probit analysis should also hold true for logistic techniques.
(8.) Most current nominal logistic statistical packages provide
summary test statistics and are not as limited concerning the testing of
the incremental predictive ability of variables. However, nominal
logistic techniques still have many limitations when compared to ordinal
Terry J. Ward, Middle Tennessee State University