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Insolvency PredictionSubmit your review. Questions? Click to Text. (M-F 9-5, Vancouver Time.)
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.