Knowing how to determine the credit-worthiness of your customers may help save your business.
In 2002, 191 public companies went bankrupt.
If one or more of your key customers were among them, how badly would
you be hurt? How do you weed them out of your accounts-receivable ledger
before it’s too late?
To put that number in perspective, 257 public companies, with total
assets of $256 billion, filed for bankruptcy in the U.S. in 2001. This
was the highest number of bankruptcy filings since 1980.
While the total number dropped to 191 companies filing in 2002, that was still well above the 1986-2000 average of 113.
Furthermore, these numbers are uncomfortably large compared to the
number of filings during the last recession (125 filings in 1991 and 91
filings in 1992).
It is not just the number of companies going bankrupt that is of
concern; it is their size. There has been a distinct trend of larger
companies filing for bankruptcy over the past several years. As one
dramatic example, total assets of the firms filing for bankruptcy in
2002 were $378.8 billion, compared to $258.5 billion in 2001.
While bankruptcy in firms of large asset size was quite rare prior to 1966, it became more common in the 1970s.
Since the enactment of the current U.S. Bankruptcy Code in 1978, there
have been at least 100 Chapter 11 bankruptcies of firms whose asset size
exceeded $1 billion.Of the 191 public companies that filed in 2002, 34 had more than $1 billion in assets.
Perhaps more ominously, as of this writing, of the five largest
bankruptcies since 1980, three have occurred within the last 15 months.
The bankruptcy of any company has potentially significant
consequences for those who do business with it. But the consequences of a
large company’s bankruptcy can be especially devastating, both because
it affects so many other businesses and individuals and because many of
its suppliers and other business associates depend disproportionately on
this one customer.
Consider the recent bankruptcy of WorldCom. As of the end of 2001,
the company had contractual obligations (including capital leases)
extending to 2006 and beyond that totaled just under $5 billion.
With WorldCom going through Chapter 11 restructuring, most of those
long-term commitments probably will never lead to revenues for vendors,
lessors, and landlords – who by and large had already incurred capital
expenditures necessary to service WorldCom’s now-defunct accounts.
In this environment, business executives and finance professionals
would be well advised to refresh their knowledge of bankruptcy
prediction models. Fortunately, those models have been around for a
while. One of the most popular of these is the Z-score model introduced
by Edward Altman in a pioneering paper in 1968.
This model is presented here and explained in enough detail that it can
be applied. However, even those who may sometimes feel intimidated by
quantitative research and formulas can understand the basic ideas.
The Z-score Model
For decades, considerable accounting and finance research was
directed at finding a ratio that would serve well as a predictor of
bankruptcy. One of the most comprehensive early studies was conducted by
William Beaver. He studied the performance of various ratios as
bankruptcy predictors and concluded that the cash flow to debt ratio was
the single best predictor.
The critical breakthrough in bankruptcy prediction, however, came in
1968 when Edward Altman decided to abandon the search for a single best
ratio and built a comprehensive, statistical model using a technique
called multiple discriminant analysis (MDA).
MDA allows a researcher to group observations into several
pre-determined categories. Needless to say, the two categories that
Altman was interested in were companies that did and did not go
bankrupt.
Altman selected a sample of 33 manufacturing companies that had filed
for bankruptcy between 1946 and 1965 and matched them with another 33
companies selected on a stratified (by both industry and asset size)
random basis. He then started with 22 ratios that seemed to be
intuitively plausible as bankruptcy predictors. After every trial run,
he excluded the ratio that contributed least to the explanatory power of
the model. Eventually, he came up with a model that contained only five
ratios.
When Altman added these ratios together in proportions determined by
the MDA procedure, he obtained a very convenient metric that he dubbed
the Z-score. If the Z-score was below the cutoff line – initially set at
2.675 – the firm was classified as bankrupt (i.e., insolvent, or headed
that way) and if above the cutoff line, as non-bankrupt. This model
allowed him to correctly classify 94 percent of the bankrupt firms and
97 percent of the non-bankrupt firms one year prior to the filing of
bankruptcy. An attempt to predict bankruptcy earlier, i.e., two years in
advance, yielded lower but still impressive accuracies of 72 percent
and 94 percent, respectively.
It is important to emphasize that the original Altman model is
intended for use in cases of publicly-traded manufacturing firms.
However, Altman has used the same approach to develop other models: Z’
for privately-held manufacturing firms and Z” for non-manufacturing
firms.
After conducting three subsequent tests (86 companies that had gone
bankrupt in 1969-75, 110 in 1976-95, and 120 in 1997-99), Altman
recommended a lower cutoff score of 1.81 and treating Z-scores between
1.81 and 2.675 as a “gray area” or “ignorance zone.” If a Z-score falls
into the “ignorance zone,” it means that the company in question has a
chance to go bankrupt, but it is not certain that it will.
Interestingly, Altman found that in 1999, 20 percent of U.S.
industrial firms referenced in Compustat data tapes had Z-scores below
1.81.
In other words, the unusually high incidence of bankruptcy in 2001-02 was to be expected!
Why Does It Work?
At this point, an interesting question to consider is why this
particular set of ratios appears to have so much predictive power. Let’s
take a look at each ratio separately.
X1 (Working Capital/Total Assets)
Working capital is simply the excess of current assets over current
liabilities. In accounting, assets are considered current if they are
expected to be converted into cash or used within one year or one
operating cycle of the company if it is longer than one year. Examples
of current assets include cash, accounts receivable, and inventories.
Similarly, current liabilities are obligations the firm expects to
settle within one year or one operating cycle. The most typical current
liabilities are short-term debt and accounts payable.
Therefore, a firm with a negative working capital is very likely to
experience problems meeting its short-term obligations. (There simply
aren’t enough current assets to cover them.) Conversely, a firm with a
significantly positive working capital rarely has problems paying its
bills.
X2 (Retained Earnings/Total Assets)
Retained earnings is the sum of past years’ profits the firm did not
pay back to its shareholders in dividends. Significant retained earnings
mean a history of profitable operation and ability to withstand periods
of losses. Low retained earnings, on the other hand, may signal that a
single bad year (or even quarter) can put the company out of business.
X3 (Earnings before Interest and Taxes/Total Assets)
This ratio indicates the firm’s ability to use its assets to generate
earnings before interest and taxes. We are particularly concerned about
earnings before interest and taxes because failing to meet interest
payments would technically put the company into default on its debt
obligations. EBIT is often used as an approximate measure of cash flow
generated by the firm’s operations. In other words, EBIT is an estimate
of the size of the cash pool available for distribution between three
major groups of claimants: creditors (interest and principal),
government (taxes), and shareholders (dividends).
X4 (Market Value of Equity/Book Value of Total Liabilities)
For a while, this ratio seemed somewhat puzzling. Market value of
equity (sometimes also called market capitalization) is simply the
market price of one common share multiplied times the number of shares
outstanding. In other words, this is the stock market’s estimate of what
the firm is worth. But what does market value of the firm’s equity have
to do with its ability to service its debt?
There are at least two ways to resolve this puzzle. First, if the
firm goes bankrupt, the value of its stock falls almost to zero very
quickly. Thus if a firm has significant market capitalization, it should
be perceived as an indication of the market’s belief in its solid
financial position. Second, if a firm has significant market
capitalization and begins to experience temporary financial
difficulties, it could resort to issuing more common stock at relatively
high prices. Although the resulting cash infusion dilutes the existing
shareholders’ interest, it would be beneficial to creditors because it
would improve the company’s chances to repay its outstanding
obligations.
X5 (Sales/Total Assets)
This ratio (commonly known as asset turnover and covered in much
detail in almost any accounting and finance textbook) shows how
efficiently the firm uses its assets to generate sales.
But What If the Books Are Cooked?
An interesting feature of the Z-score model is its ability to
withstand certain types of accounting irregularities. Consider the
recent high-profile bankruptcy of WorldCom, in which management
improperly recorded billions of dollars as capital expenditures instead
of as operating expenses. Such a treatment would have a twofold impact
on financial statements: (1) overstating earnings, and (2) overstating
assets. Overstated earnings would increase the X
3 ratio in the Z-score model, while overstated assets would actually decrease three ratios, X
1, X
2, and X
5
(all three are calculated with total assets in the denominator).
Therefore the overall impact of these accounting improprieties on the
company’s Z-score is likely to be downward.
A Test Using WorldCom
To examine the validity of this reasoning in a limited case-study
setting, we computed Z-scores for WorldCom for fiscal years ending
December 31, 1999, 2000, and 2001 based on its annual 10-K reports filed
with the U.S. Securities and Exchange Commission. We found that the company indeed experienced a rapid deterioration in
its Z-score. Obviously, this limited test has to be taken with a grain
of salt (especially given that WorldCom is not a manufacturing company),
but it does show how this particular type of accounting impropriety can
affect the Z-score.
Later Developments
As noted above, one innovative aspect of Altman’s original work was
its radical departure from the search for a single best ratio. Rather,
he sought a simple, yet comprehensive, multivariate model. Another
equally innovative and equally radical idea was to use a combination of
accounting and market-based indicators to forecast bankruptcy. At the
time, finance scholars often questioned the validity of accounting
measures, while accounting researchers thought that observing the equity
market had little to do with debt-related issues such as bankruptcy.
The significance of this synthesis was not fully understood until the
advent of option pricing models. First, in 1973, in a seemingly
unrelated development, Fischer Black and Myron Scholes,
and then Robert Merton,
discovered a mechanism for rational option pricing, which incidentally
depended on both the price of underlying shares and the volatility of
that price.
Then, around 1984, Oldrich Vasicek and Stephen Kealhofer proposed
viewing common stock as a call option on the firm’s assets with a strike
price equal to the book value of the firm’s liabilities.
This approach permits an estimate of the probability of default within a
specified period of time based on both accounting (the value of
liabilities) and market (the share price and volatility) data.
These and other related developments have led to the emergence of a new
school in credit analysis and fixed-income portfolio management. The
underlying mathematics of the Vasicek-Kealhofer model and other modern
credit risk models
is sometimes quite complex, but the general idea first proposed by
Altman – a comprehensive synthesis of accounting and market-based
measures – remains the cornerstone of contemporary credit analysis.
Conclusion
Companies that routinely grant trade credit to their key customers
should consider checking the publicly-traded customers’ Z-scores on a
quarterly basis. Rapid deterioration of a customer’s Z-score should be a
signal to consider lowering that customer’s credit limits and generally
reducing the company’s exposure to that customer.
Larger companies with sophisticated credit departments should
consider implementing a comprehensive credit risk model similar to those
used by commercial banks. Prudent use of credit risk modeling will help
companies avoid extreme losses related to a key customer’s bankruptcy.