Altman Z-Score

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Dan Buckley
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Developed by NYU professor Edward I. Altman and first published in 1968, the Altman Z-Score is a financial-distress model that uses accounting ratios to help classify a firm’s bankruptcy risk (commonly discussed over a roughly two-year horizon).

The original Z-Score was developed using publicly traded U.S. manufacturing firms, and Altman later proposed variants for private firms and non-manufacturing companies.

The test utilises five financial ratios in varying proportions to assess a company’s financial 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.

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 = EBIT (earnings before interest and taxes) / total assets

D = market value of equity / book value of total liabilities

E = sales / total assets

Altman Z-Score Meaning

A common interpretation of the original (1968) Z-Score is:

Important caveats: These cutoffs were derived from a specific historical sample (public U.S. manufacturers). For private firms and non-manufacturers, use the appropriate Z-Score variant and interpret results in context.

Also, Altman has argued that rigid reliance on older cutoffs (like 1.8) can misclassify many modern firms; in practice, users often calibrate scores to ‘bond rating equivalents‘ and then map those to probability-of-default estimates using current/default-history data rather than assuming a fixed ratings boundary.

Altman Z-Score Use in Day Trading

If you’re trading stocks, the Z-Score is better used as a risk filter than a short-horizon timing signal, because it’s derived from financial statements and tends to move slowly.

A very low score can flag elevated distress risk (which may matter for position sizing, avoiding highly levered firms, or assessing downside tail risk), but it does not mechanically predict near-term price direction; markets may price this information in well before it shows up in the ratios.

If you use it at all in your day trading strategy, consider combining it with other information (liquidity/volatility, earnings revisions, credit spreads where available, and industry-appropriate comparisons) and ensure you’re using the correct Z-Score variant for the company type/sector.