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 insolvency prediction 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 insolvency 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 prediction models. 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 insolvency prediction 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 insolvency prediction 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” 1) Figures from previous period 2) Figures from two previous periods * 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.
- 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.