Altman’s Z-Score blends five accounting ratios into a single index to flag a company’s near-term distress risk. First published in 1968 using multiple discriminant analysis on U.S. manufacturers, it remains one of the most cited early-warning tools in credit analysis, screening, and board/MD&A discussions. Used well, it complements cash-flow and liquidity work; used poorly, it’s a misleading shortcut. This guide explains the Z, Z′, and Z″ formulas, interprets “safe/grey/distress” zones, shows a step-by-step calculation, and highlights modern best practices and limitations.
Key Takeaways
- What it is: A weighted blend of profitability, liquidity, leverage/market confidence, cumulative profitability, and asset turnover that classifies firms into “safe,” “grey,” or “distress” zones.
- Variants matter: Use Z (public manufacturers), Z′ (private manufacturers), or Z″ (non-manufacturers/emerging markets). Thresholds differ by version.
- Interpret with context: Sector mix, accounting choices (e.g., leases), and business model affect signals — pair Z-scores with cash coverage, maturities, covenants, and qualitative developments.
- Not a statistical z-score: Altman’s Z-Score is not the standard-deviation “z-score” from hypothesis testing; don’t read normal-table probabilities from it.
- For banks/insurers: Z-Scores were not designed for depositories or insurers; supervisors use CAMELS/CELS and other sector-specific metrics.
Altman Z-Score Formula and Components (Public Manufacturers)
Altman’s original model combines five ratios into one index. The weighting reflects each ratio’s power (in the training sample) to separate future failures from non-failures.
Z = 1.2·X1 + 1.4·X2 + 3.3·X3 + 0.6·X4 + 1.0·X5
X1=Working Capital/Total Assets; X2=Retained Earnings/TA; X3=EBIT/TA; X4=Market Value of Equity/Total Liabilities; X5=Sales/TA.
| Component | Ratio | What It Measures |
|---|---|---|
| A (X1) | Working Capital ÷ Total Assets | Short-term liquidity and operating flexibility |
| B (X2) | Retained Earnings ÷ Total Assets | Cumulative profitability / firm maturity |
| C (X3) | EBIT ÷ Total Assets | Core operating performance and asset productivity |
| D (X4) | Market Value of Equity ÷ Total Liabilities | Market confidence and leverage cushion |
| E (X5) | Sales ÷ Total Assets | Asset utilization / turnover |
Z-Score Risk Zones (Original Z)
Analysts commonly group the Z-Score into three zones for quick triage. Treat these as decision aids, not verdicts, and always cross-check with cash-flow and liquidity analysis.
| Zone | Z-Score Range | Risk Level | Interpretation |
|---|---|---|---|
| Safe Zone | Z > 3.0 | Low risk | Financially healthy; low near-term bankruptcy probability |
| Grey Zone | 1.8–3.0 | Moderate risk | Mixed signals; monitor liquidity, coverage, and trends |
| Distress Zone | Z < 1.8 | High risk | Elevated distress probability within ~2 years |
Assume Total Assets (TA) = $2,000,000; Current Assets $800,000; Current Liabilities $300,000; Retained Earnings $600,000; EBIT $400,000; Market Value of Equity $2,200,000; Total Liabilities $900,000; Sales $1,500,000.
| Component | Calculation | Result | Weighted Value |
|---|---|---|---|
| A (X1) | (800,000 − 300,000) ÷ 2,000,000 | 0.25 | 1.2 × 0.25 = 0.30 |
| B (X2) | 600,000 ÷ 2,000,000 | 0.30 | 1.4 × 0.30 = 0.42 |
| C (X3) | 400,000 ÷ 2,000,000 | 0.20 | 3.3 × 0.20 = 0.66 |
| D (X4) | 2,200,000 ÷ 900,000 | 2.44 | 0.6 × 2.44 = 1.47 |
| E (X5) | 1,500,000 ÷ 2,000,000 | 0.75 | 1.0 × 0.75 = 0.75 |
| Total Z-Score | 3.60 → Safe Zone | ||
Even with a “safe” score, you still need to assess cash-flow trends, interest coverage, debt maturities, and qualitative risk factors before concluding.
Choosing the Right Variant (Z, Z′, Z″)
Z (Public manufacturers): Uses market-value equity in X4 and includes Sales/TA (X5). Thresholds commonly cited: <1.8 distress, 1.8–3.0 grey, >3.0 safe.
Z′ (Private manufacturers): Adjusts weights and substitutes book equity in X4. Typical cutoffs cited: distress <1.23; grey 1.23–2.90; safe >2.90.
Z″ (Non-manufacturers / EM): Drops Sales/TA; a four-variable model often applied to services and emerging-market firms. Common references cite cutoffs around ~1.1 (distress) and ~2.6 (safe), but studies vary by market/period.
Always compare within peer groups and verify that the variant matches the firm’s profile; otherwise, signals can skew.
How Professionals Use Z-Scores (and What to Pair Them With)
Credit teams use Z-Scores to screen portfolios, prioritize monitoring, and frame discussions. The score is most informative when combined with:
- Cash-flow analysis: CFO/operating cash-flow trends, free cash flow, and seasonality.
- Coverage & maturities: Interest coverage, Debt-Service Coverage Ratio, maturity walls, and refinancing risk.
- Covenants & liquidity: Headroom on leverage/coverage tests; revolver availability; working-capital lines.
- Qualitative events: Loss of a key customer, litigation, regulatory actions, management turnover.
- Disclosure discipline: In public filings, MD&A rules (Item 303) expect plain-English explanations of why metrics changed and known trends/uncertainties likely to affect future results.
Trend matters: a steady slide toward the grey zone often precedes covenant pressure and refinancing difficulty; an improving trajectory can validate turnaround progress.
Limitations and Modern Caveats
Accounting changes: Newer standards (e.g., lease capitalization) alter reported assets/liabilities and can affect components like EBIT/TA and leverage. Use care when comparing across regimes.
Sector fit: Asset-light services/software may look different from capital-intensive manufacturing; Z″ helps but peer benchmarks are essential.
One number ≠ a decision: The model is a screen, not a verdict. Over-reliance invites errors; combine with forward-looking analysis and scenario work.
Z-Score vs. the Statistical z-Score (Different Concepts)
Despite the name, Altman’s Z-Score is not the statistics textbook “z-score.” In statistics, a z-score standardizes a value relative to the mean and standard deviation and supports probability calculations under distributional assumptions. Altman’s index is a weighted linear combination of accounting ratios trained to separate distressed from healthy firms; you should not read normal-table probabilities from it.
Beyond Altman: Common Alternatives
Ohlson O-Score (1980): A nine-variable logit model that outputs an estimated failure probability. Useful as a cross-check when you want a probability framework.
Zmijewski X-Score (1984): A three-variable probit model emphasizing profitability, leverage, and liquidity. Often used for parsimony and robustness checks.
In practice, analysts triangulate: if Z-Score, O-Score, and X-Score all flash yellow while cash coverage falls and maturities loom, the signal is stronger than any single index.
Frequently Asked Questions
Which variant should I use?
Use Z for public manufacturers, Z′ for private manufacturers (book equity in X4), and Z″ for non-manufacturers/emerging markets (four-variable model). Apply peer-appropriate thresholds and always sanity-check with cash-flow and liquidity analysis.
Can I compare Z-Scores across industries?
With caution. Asset intensity and accounting regimes differ. Prefer within-sector comparisons and supplement with sector-specific diagnostics.
How do public companies use Z-Scores in filings?
Z-Scores can inform MD&A narrative, but SEC Item 303 emphasizes explaining causes of changes and known trends/uncertainties — not just publishing an index.
Does a “safe” score guarantee safety?
No. It indicates low risk conditional on the model and sample. Access to financing, market conditions, and off-balance-sheet risks still matter. Use the score to prioritize diligence, not to replace it.
Summary
Altman’s Z-Score remains a practical, evidence-based screen for corporate distress when you choose the right variant, interpret zones appropriately, and pair the index with cash-flow, liquidity, maturities, and qualitative analysis. Respect its limits — especially for banks/insurers and across changing accounting regimes — and treat the output as a starting point for rigorous MD&A-style explanations and decisions.
Sources
- Altman (1968). Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy (original Z-Score study).
- Investopedia — Altman Z-Score: Formula and Interpretation (variants & zones overview).
- Altman (2018). What Have We Learned After 50 Years from the Z-Score Models?
- Brattle Group (2022). Solvency Shortcuts: Use and Misuse (accounting changes caveat).
- SEC — 17 CFR §229.303 (Item 303 MD&A: trends/uncertainties disclosure).
- FDIC — Risk Management Manual (CAMELS overview for banks).
- Federal Reserve — Commercial Bank Examination Manual, CELS components.
- Wikipedia — Standard Score (definition of statistical z-score; for distinction only).
- Ohlson (1980). Financial Ratios and the Probabilistic Prediction of Bankruptcy (O-Score).
- Zmijewski (1984) overview — accuracy test / probit approach (X-Score).
- CFA Institute (2016). The Altman Z-Score after 50 Years: Use and Misuse.