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The Z-score formula for predicting bankruptcy was published in 1968 by Edward I. Altman, who was, at the time, an Assistant Professor of Finance at New York University. The formula may be used to determine the probability that a firm will go into bankruptcy within two years.
In 1968, in the first formal multiple variable analysis, Edward I. Altman applied multiple discriminant analysis within a pair-matched sample. One of the most prominent early models of bankruptcy prediction is the Altman Z-score, which is still applied today.
Since probability tables cannot be printed for every normal distribution, as there are an infinite variety of normal distributions, it is common practice to convert a normal to a standard normal (known as a z-score) and then use the standard normal table to find probabilities.
The Altman Z-score is a multivariate formula for a measurement of the financial health of a company and a powerful diagnostic tool that forecasts the probability of a company entering bankruptcy. Studies measuring the effectiveness of the Z-Score have shown that the model has an 80%–90% reliability.
Predicting Financial Distress of Companies: Revisiting the Z-Score and Zeta Models by Edward Altman; Financial Distress, Bankruptcy Law, and the Business Cycle by Javier Suarez and Oren Sussman; Insolvency Service website; Probability of bankruptcy screener for public companies based on Altman Z Score
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Comparison of the various grading methods in a normal distribution, including: standard deviations, cumulative percentages, percentile equivalents, z-scores, T-scores. In statistics, the standard score is the number of standard deviations by which the value of a raw score (i.e., an observed value or data point) is above or below the mean value of what is being observed or measured.
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