The Altman Z-Score Explained: Formula, Distress Zones, and How to Screen for It
7 min read·
A clear, practical guide to the Altman Z-Score: the formula, its five ratios, the safe/grey/distress zones, and how to use it as a bankruptcy-risk filter in your stock research.
What the Altman Z-Score measures
The Altman Z-Score, developed by NYU professor Edward Altman in 1968, is a single number that estimates how close a company is to bankruptcy. It blends five balance-sheet and income-statement ratios — covering liquidity, accumulated profitability, operating efficiency, leverage, and asset turnover — into one score using multiple discriminant analysis. The higher the score, the further a firm sits from financial distress. In Altman's original sample of manufacturing firms, the model correctly flagged roughly 72% of bankruptcies two years before they happened. It is not a forecast of stock returns; it is a structured, repeatable check on solvency and financial resilience.
The classic formula (manufacturing model)
The original Z-Score for public manufacturers is: Z = 1.2·X1 + 1.4·X2 + 3.3·X3 + 0.6·X4 + 1.0·X5. The five ratios are: X1 = Working Capital / Total Assets; X2 = Retained Earnings / Total Assets; X3 = EBIT / Total Assets; X4 = Market Value of Equity / Total Liabilities; X5 = Sales / Total Assets. Each coefficient is a weight estimated from real bankruptcy data, so the EBIT term (3.3) carries the most influence. StoqPulse computes exactly this model, deriving working capital as current assets minus current liabilities and using market capitalisation for X4.
The three distress zones
Interpretation hinges on three published thresholds. A score above 2.99 is the 'safe' zone — bankruptcy risk over the next two years is low. A score below 1.81 is the 'distress' zone — historically associated with a high probability of failure. Between 1.81 and 2.99 is the 'grey' zone, where the model can't classify confidently and the company warrants closer reading. These exact cut-offs power the colour coding in the StoqPulse screener: green for safe, amber for grey, red for distress. A falling Z-Score over several quarters can matter as much as the absolute level.
A worked example
Suppose a manufacturer reports: current assets $400M, current liabilities $150M (working capital $250M), total assets $1,000M, retained earnings $300M, EBIT $120M, market cap $900M, total liabilities $500M, and sales $1,100M. Then X1 = 250/1000 = 0.25, X2 = 300/1000 = 0.30, X3 = 120/1000 = 0.12, X4 = 900/500 = 1.80, X5 = 1100/1000 = 1.10. Z = 1.2(0.25) + 1.4(0.30) + 3.3(0.12) + 0.6(1.80) + 1.0(1.10) = 0.30 + 0.42 + 0.396 + 1.08 + 1.10 = 3.30. At 3.30 the firm sits comfortably in the safe zone.
Where the Z-Score breaks down
Honesty matters here: the original model was calibrated on public manufacturers, so it travels poorly to some sectors. Banks, insurers, and other financials carry naturally high leverage and asset structures the model never saw — their Z-Scores are not meaningful. Asset-light technology and service firms can also score oddly because X5 (asset turnover) and X3 lean on a heavy asset base. Altman later published variants (the Z'-Score for private firms and the Z''-Score that drops X5 for non-manufacturers) to address this. Treat a low score as a prompt to investigate debt maturities and cash flow, not as a verdict.
Using the Z-Score alongside other signals
The Z-Score answers one question — solvency — so it works best as one input among several. Pair it with the Piotroski F-Score (a 0-9 measure of fundamental momentum across profitability, leverage, and efficiency) to separate cheap-but-improving names from genuine value traps. Cross-check with margins, return on assets, and a leverage ceiling. A company that is both above the 2.99 safe line and scoring 7+ on the F-Score is a meaningfully sturdier candidate than one passing either test alone. No single metric replaces reading the filings, but stacking complementary checks reduces blind spots.
Screen for financial strength in StoqPulse
Instead of computing the formula by hand, the StoqPulse stock screener calculates the Altman Z-Score for every candidate from live fundamentals and lets you set a 'Min Z-Score' filter — for example, require 3.0 to keep only safe-zone names, or 2.0 to exclude obvious distress. Results are colour-coded by zone so you can scan a list at a glance, and you can combine the Z-Score with F-Score, P/E, margins, ROA, and a leverage cap in a single pass. US coverage is free for 14 days (no card), so you can pressure-test the idea before committing.
FAQ
What is a good Altman Z-Score?
A score above 2.99 is the 'safe' zone, indicating low near-term bankruptcy risk. Between 1.81 and 2.99 is the 'grey' zone where the model is inconclusive, and below 1.81 is the 'distress' zone associated with elevated failure risk. A stable or rising score is generally healthier than a high but declining one.
What are the five ratios in the Z-Score formula?
X1 = Working Capital / Total Assets, X2 = Retained Earnings / Total Assets, X3 = EBIT / Total Assets, X4 = Market Value of Equity / Total Liabilities, and X5 = Sales / Total Assets. They are combined as Z = 1.2·X1 + 1.4·X2 + 3.3·X3 + 0.6·X4 + 1.0·X5.
Does the Altman Z-Score work for banks and tech companies?
Not reliably. The original model was built for public manufacturers, so financials (banks, insurers) with naturally high leverage and asset-light tech or service firms can produce misleading scores. Altman published the Z''-Score variant, which drops the sales/assets term, for non-manufacturers and emerging-market firms.
Can the Z-Score predict stock returns?
No. The Z-Score estimates bankruptcy and solvency risk, not future price performance. It is best used as a financial-health filter — screening out fragile balance sheets — alongside valuation, profitability, and momentum measures rather than as a standalone buy or sell signal.
How does StoqPulse calculate the Z-Score?
StoqPulse applies the classic manufacturing model from live fundamentals: working capital as current assets minus current liabilities, retained earnings, EBIT (operating income), market capitalisation over total liabilities, and revenue over total assets. Each screener result is colour-coded by zone, and you can set a minimum Z-Score threshold to filter the universe.
Sources
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