Altman Z-Score Calculator
The formula
What the Z-Score actually measures
Edward Altman built the model in 1968 by running multiple discriminant analysis on 66 manufacturers — half of them bankrupt — and finding that five ratios together separated the two groups with roughly 95% accuracy one year before failure and 72% two years out. The genius of the model is that each ratio attacks a different failure mode: X1 catches a liquidity squeeze, X2 penalises young firms that have not accumulated earnings, X3 measures whether the asset base actually generates operating profit, X4 tests how much cushion equity provides before liabilities exceed value, and X5 checks whether assets are being used efficiently. No single ratio does the job alone.
Where the model misleads
Three limits matter in practice. First, the original coefficients were fitted on public manufacturing firms; for private companies Altman's Z′ substitutes book equity for market equity and changes the weights (0.716·X5, 3.25 excluded), and for non-manufacturers the Z″ variant drops X5 entirely. Second, the grey zone between 1.81 and 2.99 is genuinely ambiguous — a meaningful share of firms there neither fail nor thrive, so treat it as "read the statements" rather than a verdict. Third, a single-period Z-Score ignores direction; a company falling from 4.0 to 3.1 is more worrying than a stable 2.4. Always compute the trend across at least three years.
Frequently asked questions
What is a good Altman Z-Score?
Can I use it for a private company?
How accurate is it?
Does it work for banks or insurers?
Source & methodology
Altman, E. I. (1968), "Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy", Journal of Finance 23(4); Altman, Iwanicz-Drozdowska & Laitinen (2010) international validation study.
How Altman Z-Score works — and what your result really means
Scores feel like verdicts, but they are arithmetic — and arithmetic you understand is arithmetic you can plan toward. Altman Z-Score is the kind of number people argue about from memory. This page ends that argument with arithmetic you can watch happen: enter your figures, get Altman Z-Score in real time, and see every substitution printed below.
People rarely disagree about arithmetic; they disagree about assumptions that were never written down. Printing the substitution and the sensitivity ranking removes that category of disagreement for Altman Z-Score.
Most confusion about Altman Z-Score comes from mixing up the input you control with the input you merely observe. The formula treats them identically, but your decisions should not: one deserves measurement, the other deserves a defensible estimate.
Every figure in the prose is generated by the engine rather than written beside it — the worked example resolves to altman Z-Score = 3.74 on the default inputs, and the sensitivity section ranks the inputs that move the result — Total assets first, at ≈7.8 % of the headline for a ten-percent error either way in that input. Read the sections in order and you will be able to reproduce every figure by hand.
Inside the Altman Z-Score calculation: formula and every input
The engine implements a single stated model: Z = 1.2·(WC/TA) + 1.4·(RE/TA) + 3.3·(EBIT/TA) + 0.6·(MVE/TL) + 1.0·(Sales/TA). There is no hidden coefficient and no login-gated precision — the formula printed is the formula executed, which is also what makes the result citable.
Working capital (current assets − current liabilities) (example value $480,000.00) is a monetary input: one currency throughout, otherwise the result stops being comparable. The example value is there to demonstrate the mechanics, not to describe your situation — overwrite it.
Total assets (example value $3,200,000.00) is a money amount; keep it in the currency the rest of your inputs use, or the comparison steps lose meaning. If you are unsure what belongs in this field, look at the printed steps: the substitution shows exactly how the engine uses it.
Retained earnings (example value $950,000.00) is a money value, and money values are where rounding habits do the most damage — type the real figure, not a tidied one. This input feeds the formula directly; a defensible figure here is worth more than false precision anywhere else.
EBIT (earnings before interest and tax) (example value $420,000.00) is a money amount; keep it in the currency the rest of your inputs use, or the comparison steps lose meaning. Check the convention first: if the source says “6.5 % per year”, type 6.5 and let the formula do the rest.
Market value of equity (example value $2,600,000.00) is a currency figure — enter the total exactly as your document states it, fees included. Replace the example with the figure from your own records — the tool recomputes instantly and the printed steps follow your numbers.
Total liabilities (example value $1,400,000.00) is a currency figure — enter the total exactly as your document states it, fees included. The example value is there to demonstrate the mechanics, not to describe your situation — overwrite it.
Revenue (sales) (example value $5,100,000.00) is a money value, and money values are where rounding habits do the most damage — type the real figure, not a tidied one. Replace the example with the figure from your own records — the tool recomputes instantly and the printed steps follow your numbers.
Altman Z-Score with real numbers, step by step
The tool prints its own working; this is the same arithmetic on the example values, reproduced so you can follow it by hand. Starting figures: Working capital (current assets − current liabilities), Total assets, Retained earnings, EBIT (earnings before interest and tax). The endpoint of that chain is 3.74 of altman Z-Score, sitting in the “Safe zone — Low near-term bankruptcy probability” band.
- X1 = $480,000 ÷ $3,200,000 = 0.1500 → ×1.2 = 0.1800
- X2 = $950,000 ÷ $3,200,000 = 0.2969 → ×1.4 = 0.4156
- X3 = $420,000 ÷ $3,200,000 = 0.1313 → ×3.3 = 0.4331
- X4 = $2,600,000 ÷ $1,400,000 = 1.8571 → ×0.6 = 1.1143
- X5 = $5,100,000 ÷ $3,200,000 = 1.5938 → ×1.0 = 1.5938
- Z = 0.1800 + 0.4156 + 0.4331 + 1.1143 + 1.5938 = 3.74 → Safe zone.
Classification — Safe zone. This is the number experienced users quote when the headline alone would mislead.
Liquidity (X1) — 0.150. Read next to the headline, this is the figure that shows the shape of the trade-off.
Cumulative profitability (X2) — 0.297. Read next to the headline, this is the figure that shows the shape of the trade-off.
Operating productivity (X3) — 0.131. This is the number experienced users quote when the headline alone would mislead.
Market leverage (X4) — 1.857. If your inputs change, this is usually the first companion figure to move.
Keep this worked example open while you enter your own figures: matching the shape of the substitution is the fastest way to spot a unit or convention mistake.
How far Altman Z-Score can reasonably move
Read this table as the practical error bar on the headline figure. It answers the question people actually have after seeing a single number: how wrong could this be if the main input is off by ten or twenty percent?
| Total assets setting | Altman Z-Score |
|---|---|
| 80 % of the example value | 4.39 |
| 90 % of the example value | 4.03 |
| example inputs | 3.74 |
| 110 % of the example value | 3.50 |
| 120 % of the example value | 3.30 |
The corridor runs from 3.30 to 4.39 across that band — which is the range worth planning inside rather than the single 3.74 point estimate. Reproduce it in the form above by editing Total assets alone; the substitution steps will follow your figures exactly.
The levers behind Altman Z-Score, ranked by effect
This list was computed for this specific engine just now: each numeric input was nudged ±10 % while every other field held its example value, and the altman Z-Score was recalculated from scratch. Read it as a map of where Altman Z-Score is fragile — drawn for the example inputs, which is the map you want when deciding what to measure carefully.
- Nudge Total assets by ±10 % and Altman Z-Score lands between 3.50 and 4.03 (sensitivity ≈7.8 %)
- Revenue +10 % → Altman Z-Score = 3.90 · −10 % → 3.58 (the altman Z-Score shifts ≈4.3 %)
- Total liabilities: ten percent up gives 3.64, ten percent down gives 3.86 — ≈3.3 % of the altman Z-Score either way
- Market value of equity: ten percent up gives 3.85, ten percent down gives 3.63 — ≈3.0 % of the altman Z-Score either way
- Nudge EBIT (earnings before interest and tax) by ±10 % and Altman Z-Score lands between 3.69 and 3.78 (sensitivity ≈1.2 %)
- Nudge Retained earnings by ±10 % and Altman Z-Score lands between 3.70 and 3.78 (sensitivity ≈1.1 %)
Total assets is the dominant lever here: ten percent either way moves the result by about 7.8 %, from 3.50 to 4.03. That is where a careless figure does the most damage, so check it against a source, and treat Revenue as the second priority (≈4.3 % per ten percent). Everything below that can stay approximate, which is a licence to stop chasing data that would not change the decision.
Reading the result: bands, zones and thresholds
The value above only becomes useful the moment you know what it is being measured against. The verdict attached to the default calculation — “Safe zone — Low near-term bankruptcy probability” — is one band on a ladder your own figure may sit somewhere else on. How much of that band is luck? On Total assets alone ten percent either way covers 3.50 to 4.03. The practical reading rules: compare against your own history before comparing against other people; treat band boundaries as zones, not walls — one point on the wrong side of a threshold rarely changes a real outcome; and when a companion output contradicts the headline, believe the pair.
The comparison worth making is not with somebody else’s Altman Z-Score but with the version of your own situation measured last quarter — same formula, same conventions, different inputs.
How people get Altman Z-Score wrong
Feeding best-case scenarios and calling them plans. Run the number at least at expected and at pessimistic inputs; the gap between them is the actual risk you are accepting.
Feeding best-case scenarios and calling them plans. Run the number at least at expected and at pessimistic inputs; the gap between them is the actual risk you are accepting.
This model has its own particular trap: Total assets carries ≈7.8 % of the response per ten percent of error, so effort spent elsewhere is largely decorative.
A final trap is presentation: reporting Altman Z-Score to more decimal places than the inputs support. The tool prints what the arithmetic gives; you should quote only what your least precise field justifies.
Altman Z-Score verification checklist
- Confirm the example values are plausible for your own situation before replacing them.
- Compare the output against one published benchmark or a colleague’s independent estimate.
- Check Total assets before anything else: it is the measured dominant input, ≈7.8 % of Altman Z-Score per ten percent.
- Record the date and the inputs alongside the number so future-you knows what was assumed.
- Compare the result with one independent method or published benchmark.
- Confirm the answer against one independent method or published benchmark for Altman Z-Score.
How to use the Altman Z-Score tool properly
The tool does the arithmetic; the routine below is what makes the arithmetic trustworthy. Five steps, a minute total — and afterwards you will know not just what the number is, but which assumption produced it.
Step 1 — one document, one pass. Open the statement, label or record that holds working capital (current assets − current liabilities), total assets, retained earnings, ebit (earnings before interest and tax), market value of equity, total liabilities and the remaining fields listed above and transcribe straight into the form. Transcription from a single source removes the mismatch errors that appear when figures are assembled from three different places. Suspiciously tidy answers deserve the same scrutiny as untidy ones.
Step 2 — baseline before exploration. Get one defensible Altman Z-Score on screen before you start nudging numbers. Without a baseline, every variation looks interesting and none of them means anything. If a result feels obviously right, that is the moment to check the inputs hardest.
Step 3 — measure what deserves measuring. The ranked levers above tell you where a careful figure pays off. Everything else can stay approximate, which is a licence to stop hunting for data you do not need. A figure nobody can reproduce is a figure nobody will act on twice.
Step 4 — ask what would have to be true. Take the result you want and work backwards through the inputs until you find the assumption that produces it; then decide honestly whether that assumption is defensible. Suspiciously tidy answers deserve the same scrutiny as untidy ones.
Step 5 — record the context, not just the number. Note today’s date and the inputs alongside the 3.74 output. Six months from now the context is what turns this figure into a trend. If the answer changes your behaviour, it deserves a second run with different assumptions.
Who actually uses Altman Z-Score — and when
Numbers earn their keep in conversations, and Altman Z-Score is no exception. In Investing & Markets discussions around Stocks & Equities, this figure decides whether an option stays on the table: it converts an opinionated debate into an arithmetic one, and arithmetic is the only referee both sides accept. Numbers are cheap to produce and expensive to retract; the extra minute is the cheapest insurance available.
The companion outputs — Classification and Liquidity (X1) — exist for the second stage of that screen: the headline sizes the prize, while the companions describe the trade-off you are accepting to get it. Most surprises in numbers are surprises about inputs, not about formulas.
Re-running this page costs nothing and catches the slow drifts that never announce themselves. In Investing & Markets the expensive mistakes are rarely sudden; they are small changes in Working capital (current assets − current liabilities) that nobody recalculated for a year. If the answer changes your behaviour, it deserves a second run with different assumptions.
People also ask about Altman Z-Score
What affects Altman Z-Score the most?
The short answer: Total assets. The longer answer is the sensitivity table on this page, where every numeric input was probed by ten percent and the altman Z-Score recalculated — Total assets moved it furthest, by roughly 7.8 % (3.50 to 4.03).
Which matters more: revenue or total assets?
Total assets, by a factor of about 1.8. Concretely: ten percent on Total assets is worth ≈7.8 % of Altman Z-Score, the same nudge on Revenue only ≈4.3 %. That ranking is re-computed for each page from that page’s own defaults.
What is a typical Altman Z-Score?
On the worked example above Altman Z-Score comes out at 3.74. Treat that as one point on a distribution rather than a norm: the bands, the companion outputs and the sensitivity ranking tell you how wide the plausible range really is.
How do I calculate Altman Z-Score by hand?
Z = 1.2·(WC/TA) + 1.4·(RE/TA) + 3.3·(EBIT/TA) + 0.6·(MVE/TL) + 1.0·(Sales/TA) is the entire method. Work the inputs in that order — X1 = $480,000 ÷ $3,200,000 = 0.1500 → ×1.2 = 0.1800 — and you will reach 3.74 on the default figures, the same number this tool prints.
What happens to Altman Z-Score if total assets changes by 10 %?
On these inputs a ±10 % change in total assets takes Altman Z-Score to 3.50 against the 3.74 example — around 7.8 % of the headline. The same test on every other field is in the sensitivity section.
Is the Altman Z-Score result of 3.74 good or bad?
The tool answers “what”, not “whether”: 3.74 carries the verdict “Safe zone — Low near-term bankruptcy probability” for the default case, which is a starting interpretation rather than a verdict on your situation. Re-run with your own figures and compare the bands.
How do I calculate Altman Z-Score manually?
Write down the inputs, then follow the printed steps in order — Z = 1.2·(WC/TA) + 1.4·(RE/TA) + 3.3·(EBIT/TA) + 0.6·(MVE/TL) + 1.0·(Sales/TA). A spreadsheet replicates it with two or three cells. The manual route is worth doing once to build intuition; after that the calculator saves time and eliminates transcription slips.
Can I predict future returns with Altman Z-Score?
No tool predicts; Altman Z-Score computes implications. Use it to ask "if these inputs hold, what follows" — then stress the assumptions.
What is a good Altman Z-Score?
Above 2.99 is the safe zone in the original model. Between 1.81 and 2.99 is the grey zone where the evidence is mixed, and below 1.81 signals distress.
Can I use it for a private company?
Not with these weights. Use the Altman Z′ model, which replaces market value of equity with book value and re-weights the components (1.2, 1.4, 3.3, 0.6, 1.0 → 0.717, 0.847, 3.107, 0.428, 0.998 with thresholds 2.9 / 1.23).
Is my data private on this page?
Yes — computation happens locally in your browser, with no account, no tracking of your inputs and nothing sent to a server. The printed formula is the whole story of what happens to your figures.
Key terms on this page
- Classification
- Safe zone in the default Altman Z-Score calculation — one of the secondary figures an experienced reader checks before trusting the primary number.
- Liquidity (X1)
- 0.150 on the example inputs — a companion figure printed beside the headline of Altman Z-Score so that no result is read in isolation.
- Cumulative profitability (X2)
- 0.297 on the example inputs — a companion figure printed beside the headline of Altman Z-Score so that no result is read in isolation.
- Altman Z-Score
- The primary output of this tool: 3.74 for the default inputs, computed exactly from Z = 1.2·(WC/TA) + 1.4·(RE/TA) + 3.3·(EBIT/TA) + 0.6·(MVE/TL) + 1.0·(Sales/TA).
- Worked example
- Every substitution in order, from the example inputs to 3.74 — the audit trail for the headline figure.
- Verdict band
- The interpretation attached to a result (“Safe zone — Low near-term bankruptcy probability” on these inputs), which turns a bare number into a position on a scale.
- Sensitivity (±10 %)
- How far the Altman Z-Score moves when a single input is nudged ten percent either way; the ranked list on this page is computed live from this engine.
The provenance of this Altman Z-Score calculation
There is no black box on this page — the engine evaluates Z = 1.2·(WC/TA) + 1.4·(RE/TA) + 3.3·(EBIT/TA) + 0.6·(MVE/TL) + 1.0·(Sales/TA). Source of record: Altman, E. I. (1968), "Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy", Journal of Finance 23(4); Altman, Iwanicz-Drozdowska & Laitinen (2010) international validation study. Specialist software layers extra coefficients on top of the same algebra; none are hidden here, and that transparency is what makes a disagreement traceable to a specific input instead of a black box.
Treat the output as a precise answer to the question you typed: exact arithmetic, transparent formula, published source, no hidden rounding. The sensitivity table above is part of that promise rather than a bonus: it lets you see how much of the result depends on how carefully you measured.
After the number: what to do with it
Work backwards from your target: adjust the inputs until the result matches the score you need, and those inputs become the study plan.
This page was written for students and candidates planning toward a target score, and the promise is that nothing above required trust. Bookmark the tool, re-run it whenever an input moves by more than a few percent, and keep the date with the number.