INSOLVENCY PREDICTION
Interest in insolvency prediction has long been confined to academics,
with most of the published material restricted to business and accounting journals
specializing in esoteric and complicated subjects. A possible reason why insolvency
prediction models have not gained greater use in the business community is because
it has been difficult to calculate the results. With the wide spread use of personal
computers, the utilization of an insolvency prediction model is now practical and
available to all. Now may be the time when prediction models come into their own!
Four software programmes are reviewed here using five different prediction models.
All of the models reviewed here, but one, were developed using the statistical technique,
step-wise multiple discriminate analysis. This statistical technique gives weights
to financial ratios used to best differentiate or discriminate between failed and
successful companies. For example, 22 financial ratios were tested in developing
the Altman Model (1968). 66 companies were used - 33 failed and 33 successful. The
first result was a formula with 22 functions. The function that contributed the
least to discriminating between the failed and successful companies was dropped
and the statistical software was run again. This was repeated over and over each
time dropping the ratio which least contributed to discriminating between the failed
and successful companies. In the case of the Altman model, five functions remained.
The software we have reviewed here are easy to operate and give quick read outs.
We have not evaluated the models compared with each other because it is impossible
to say, in this kind of review, that one model is better or more accurate than another.
One of the great problems in developing and testing prediction models is that it
is very difficult to gather data on matched sets of failed and successful companies.
Some Words of Caution! All developers of prediction models warn that the technique
should be considered as just another tool of the analyst and that it is not intended
to replace experienced and informed personal evaluation. Perhaps the best use of
any of these models is as a "filter" to identify companies requiring further review
or to establish a trend for a company over a number of years. If, for example, the
trend for a company over a number of years is downward then that company has problems,
that if caught in time, could be corrected to allow the company to survive.
If bankers can identify companies in danger of failure sufficiently far in advance,
then corrective action can be taken. The banker can:
- decline to accept the company as a customer.
- encourage the company to identify its problems and take steps to rectify those
problems.
- encourage the principals of the company to inject more capital into the business.
- encourage the company to seek other financing.
ALTMAN MODEL (U.S. - 1968)
Edward I. Altman (1968) is the dean of insolvency predictors. He was the first person
to successfully use step-wise multiple discriminate analysis to develop a prediction
model with a high degree of accuracy. Using the sample of 66 companies, 33 failed
and 33 successful, Altman's model achieved an accuracy rate of 95.0%. Altman's model
takes the following form:
Z = 1.2A + 1.4B + 3.3C + 0.6D + .999E
Z < 2.675; then the firm is classified as "failed"
WHERE
A = Working Capital/Total Assets
B = Retained Earnings/Total Assets
C = Earnings before Interest and Taxes/Total Assets
D = Market Value of Equity/Book Value of Total Debt
E = Sales/Total Assets
SPRINGATE (CANADIAN - 1978)
This model was developed in 1978 at S.F.U. by Gordon L.V. Springate, following procedures
developed by Altman in the U.S. Springate used step-wise multiple discriminate analysis
to select four out of 19 popular financial ratios that best distinguished between
sound business and those that actually failed. The Springate model takes the following
form:
Z = 1.03A + 3.07B + 0.66C + 0.4D
Z < 0.862; then the firm is classified as "failed"
WHERE
A = Working Capital/Total Assets
B = Net Profit before Interest and Taxes/Total Assets
C = Net Profit before Taxes/Current Liabilities
D = Sales/Total Assets
This model achieved an accuracy rate of 92.5% using the 40 companies tested by Springate.
Botheras (1979) tested the Springate Model on 50 companies with an average asset
size of $2.5 million and found an 88.0% accuracy rate. Sands (1980) tested the Springate
Model on 24 companies with an average asset size of $63.4 million and found an accuracy
rate of 83.3%.
FULMER MODEL (U.S. - 1984)
Fulmer (1984) used step-wise multiple discriminate analysis to evaluate 40 financial
ratios applied to a sample of 60 companies -30 failed and 30 successful. The average
asset size of these firms was $455,000.
The model takes the following form:
H = 5.528 (V1) + 0.212 (V2) + 0.073 (V3)
+ 1.270 (V4) - 0.120 (V5) + 2.335 (V6)
+ 0.575 (V7) + 1.083 (V8) + 0.894 (V9)
- 6.075
H < 0; then the firm is classified as "failed"
WHERE
V1 = Retained Earning/Total Assets
V2 = Sales/Total Assets
V3 = EBT/Equity
V4 = Cash Flow/Total Debt
V5 = Debt/Total Assets
V6 = Current Liabilities/Total Assets
V7 = Log Tangible Total Assets
V8 = Working Capital/Total Debt
V9 = Log EBIT/Interest
Fulmer reported a 98% accuracy rate in classifying the test companies one year prior
to failure and an 81% accuracy rate more than one year prior to bankruptcy.
BLASZTK SYSTEM (CANADIAN 1984)
This is the only business failure prediction method outlined here that was not developed
using multiple discriminate analysis. This system was developed by William Blasztk
in 1984. The essence of the system is that the financial ratios for the company
to be evaluated are calculated, weighted and then compared with ratios for average
companies in that same industry as given by Dunn & Bradstreet. One of this method's
strengths is that it does compare the company being evaluated with companies in
the same industry.
CA-SCORE (CANADIAN 1987)
This model is recommended by the Ordre des compatables agrees des Quebec (Quebec
CA's) and according to its developer is used by over 1,000 CA's in Quebec.
This model was developed under the direction of Jean Legault of the University of
Quebec at Montreal, using step-wise multiple discriminate analysis. Thirty financial
ratios were analyzed in a sample of 173 Quebec manufacturing businesses having annual
sales ranging between $1-20 million.
The model takes the following form:
CA-Score = 4.5913 (*shareholders' investments(1)/total assets(1))
+ 4.5080 (earnings before taxes and extraordinary items + financial expenses(1)/total
assets(1))
+ 0.3936 (sales(2)/total assets(2))
- 2.7616
CA-Score < - 0.3; then the firm is classified as "failed"
*Shareholders' investments is calculated by adding to shareholders' equity the
net debt owing to directors This model, as reported in Bilanas (1987), has
an average reliability rate of 83% and is restricted to evaluating manufacturing
companies.
REFERENCES
- Altman, Edward I., "Financial Ratios, Discriminant Analysis and the Prediction
of Corporate Bankruptcy". Journal of Finance, (September 1968): pp. 589-609.
- Botheras, Donald A., "Use of a Business Failure Prediction Model for Evaluating
Potential and Existing Credit Risk". Unpublished M.B.A. Research Project, Simon
Fraser University, March, 1979.
- "C.A. - Score, A Warning System for Small Business Failures", Bilanas (June 1987):
pp. 29-31.
- Fulmer, John G. Jr., Moon, James E., Gavin, Thomas A., Erwin, Michael J., "A Bankruptcy
Classification Model For Small Firms". Journal of Commercial Bank Lending (July
1984): pp. 25-37.
- Sands, Earl Gordon, "Business Failure Prediction and the Efficient Market Hypothesis".
Unpublished M.B.A. Research Project, Simon Fraser University, November 1980.
- Sands, Earl G., Gordon L.V. Springate, and Turgut Var, "Predicting Business Failures".
CGA Magazine (May 1983): pp. 24-27.
- Springate, Gordon L.V., "Predicting the Possibility of Failure in a Canadian Firm".
Unpublished M.B.A. Research Project, Simon Fraser University, January 1978.
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