Forecasting Financial Failure

There have been many academic studies on the use of financial ratios to forecast financial failure.  Basically, these studies try to isolate individual ratios or combinations of ratios that can be observed as trends that may forecast failure. A reliable model that can be used to forecast financial failure can also be used by management to take preventive measures. Such a model can aid investors in selecting and disposing of stocks. Banks can use it to aid in lending decisions and in monitoring loans. Firms can use it in making credit decisions and in monitoring accounts receivable. In general, many sources can use such a model to improve the allocation and control of resources. A model that forecasts financial failure can also be valuable to an auditor. It can aid in the determination of audit procedures and in making a decision as to whether the firm will remain as a going concern.

Financial failure can be described in many ways. It can mean liquidation, deferment of payments to short-term creditors, deferment of payments of interest on bonds, deferment of payments of principal on bonds, or the omission of a preferred dividend. One of the problems in examining the literature on forecasting financial failure is that different authors use different criteria to indicate failure. When reviewing the literature, always determine the criteria used to define financial failure.

UNIVARIATE MODEL

William Beaver reported his univariate model in a study published in The Accounting Review in January 1968.7 A univariate model uses a single variable. Such a model would use individual financial ratios to forecast financial failure. The Beaver study classified a firm as failed when any one of the following events occurred in the 1954–1964 period: bankruptcy, bond default, an overdrawn bank account, or nonpayment of a preferred stock dividend.

Beaver paired 79 failed firms with a similar number of successful firms drawn from Moody’s Industrial Manuals. For each failed firm in the sample, a successful one was selected from the same industry. The Beaver study indicated that the following ratios were the best for forecasting financial failure (in the order of their predictive power):

1. Cash flow/total debt

2. Net income/total assets (return on assets)

3. Total debt/total assets (debt ratio)

Beaver speculated as to the reason for these results:

My interpretation of the finding is that the cash flow, net income, and debt positions cannot be altered and represent permanent aspects of the firm. Because failure is too costly to all involved, the permanent, rather than the short-term, factors largely determine whether or not a firm will declare bankruptcy or default on a bond payment.8

Assuming that the ratios identified by Beaver are valid in forecasting financial failure, it would be wise to pay particular attention to trends in these ratios when following a firm. Beaver’s reasoning for seeing these ratios as valid in forecasting financial failure appears to be very sound.  These three ratios for Nike for 2007 have been computed earlier. Cash flow/total debt was 51.29%, which appears to be very good. Net income/total assets (return on assets) was 14.51%, which appears to be very good. The debt ratio was 34.27%, which is very good. Thus, Nike appears to have minimal risk of financial failure.

The Beaver study also computed the mean values of 13 financial statement items for each year before failure. Several important relationships were indicated among the liquid asset items

1. Failed firms have less cash but more accounts receivable.

2. When cash and receivables are added together, as they are in quick assets and current assets, the differences between failed and successful firms is obscured because the cash and receivables differences are working in opposite directions.

3. Failed firms tend to have less inventory.

These results indicate that particular attention should be paid to three current assets when forecasting financial failure: cash, accounts receivable, and inventory. The analyst should be alert for low cash and inventory and high accounts receivable.

MULTIVARIATE MODEL

Edward I. Altman developed a multivariate model to predict bankruptcy.10 His model uses five financial ratios weighted in order to maximize the predictive power of the model. The model produces an overall discriminant score, called a Z score. The Altman model is as follows:

Z _ .012 X1 _ .014 X2 _ .033 X3 _ .006 X4 _ .010 X5

X1 _ Working Capital/Total Assets

This computation is a measure of the net liquid assets of the firm relative to the total capitalization.

X2 _ Retained Earnings (balance sheet)/Total Assets

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This variable measures cumulative profitability over time.

X3 _ Earnings Before Interest and Taxes/Total Assets

X4 _ Market Value of Equity/Book Value of Total Debt

This variable measures how much the firm’s assets can decline in value before the liabilities

exceed the assets and the firm becomes insolvent. Equity is measured by the combined

market value of all shares of stock, preferred and common, while debt includes both current

and long-term debts.

X5 _ Sales/Total Assets

This variable measures the sales-generating ability of the firm’s assets.

When computing the Z score, the ratios are expressed in absolute percentage terms. Thus,

X1 (working capital/total assets) of 25% is noted as 25.

The Altman model was developed using manufacturing companies whose asset size was

between $1 million and $25 million. The original sample by Altman and the test samples used

the period 1946–1965. The model’s accuracy in predicting bankruptcies in more recent years

(1970–1973) was reported in a 1974 article.11 Not all of the companies included in the test

were manufacturing companies, although the model was initially developed by using only

manufacturing companies.

With the Altman model, the lower the Z score, the more likely that the firm will go bankrupt.

By computing the Z score for a firm over several years, it can be determined if the firm

is moving toward a more likely or less likely position in regard to bankruptcy. In a later study

that covered the period 1970–1973, a Z score of 2.675 was established as a practical cutoff

point. Firms that scored below 2.675 are assumed to have characteristics similar to those of

past failures.12 Current GAAP recognizes more liabilities than the GAAP used at the time of

this study. Thus, we would expect firms to score somewhat less than in the time period

1970–1973. The Altman model is substantially less significant if there is no firm market value

for the stock (preferred and common), because variable X4 in the model requires that the

market value of the stock be determined.

The Z score for Nike for 2007 follows:

Z _ .012 (working capital/total assets)

_ .014 (retained earnings [balance sheet]/total assets)

_ .033 (earnings before interest and taxes/total assets)

_ .006 (market value of equity/book value of total debt)

_ .010 (sales/total assets)

Z _ .012 ($5,492,500,000)/$10,688,300,000

_ .014 ($4,885,200,000)/$10,688,300,000

_ .033 ($2,199,900,000 _ $49,700,000)/$10,688,300,000

_ .006 ($501,700,000 _ $56.75)/$3,662,900,000

_ .010 ($16,325,900,000)/$10,688,300,000

Z _ .012 (51.39)

_ .014 (45.71)

_ .033 (21.05)

_ .006 (777.29)

_ .010 (152.75)

Z _ 8.14

The Z score for Nike for 2007 was 8.15. Considering that higher scores are better and that

companies with scores below 2.675 are assumed to have characteristics similar to those of

past failures, Nike is a very healthy company.

There are many academic studies on the use of ratios to forecast financial failure. These

studies help substantiate that firms with weak ratios are more likely to go bankrupt than firms

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with strong ratios. Since no conclusive model has yet been developed, the best approach is

probably an integrated one. As a supplemental measure, it may also be helpful to compute

some of the ratios that appear useful in forecasting financial failure.