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Altman Z-Score

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Written By
Dan Buckley
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Written By
Dan Buckley
Dan Buckley is an US-based trader, consultant, and part-time writer with a background in macroeconomics and mathematical finance. He trades and writes about a variety of asset classes, including equities, fixed income, commodities, currencies, and interest rates. As a writer, his goal is to explain trading and finance concepts in levels of detail that could appeal to a range of audiences, from novice traders to those with more experienced backgrounds.
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Updated
Jun 24, 2022

Devised in the 1960s by Edward Altman, the Altman Z-Score indicates the probability of a company entering bankruptcy within the next two years.

The Altman Z-Score is a basic credit robustness test that uses five financial ratios in various proportions to gauge a company’s health. These ratios can be calculated or found from various data terminals or sources online, and verified by the respective companies’ latest quarterly or annual reports.

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The metric is designed to take into account statistics related to business activity, liquidity, profitability, leverage, and sales. The formula for the Altman Z-Score is as follows:

Altman Z-Score = 1.2A + 1.4B + 3.3C + 0.6D + 1.0E

Where:

A = working capital / total assets

B = retained earnings / total assets

C = earnings before interest and tax / total assets

D = market value of equity / total liabilities

E = sales / total assets

Altman Z-Score Meaning

A score above 3.0 is interpreted as “not likely” to go bankrupt.

A score below 1.8 denotes a company “likely” to go bankrupt.

Scores between 1.8 and 3.0 are considered in a “grey zone” and neither particularly financially healthy or unhealthy. Converted to the ratings provided by rating agencies such as Moody’s, Fitch, and S&P, this would translate to BB/BBB credit, or the dividing point between investment grade and non-investment grade credit.

Altman Z-Score Use in Day Trading

For those who keep tabs on the Altman Z-Score, one could use the metric to bias the direction of their trading on a particular stock.

For example, if a company has an Z-Score of 1.2, one would be most inclined to think that such a high credit-risk company is likely to lose value over time. For a company with an Z-Score of 5, the bias might be to go long the stock.

Always note, however, that the market is forward looking and takes into account publicly known information. Generating alpha involves diverging from the consensus and being right.