The Altman Z-Score is a formula that combines five weighted financial ratios into a single number predicting whether a company is likely to file for bankruptcy within the next two years. Edward Altman developed it in 1968 at New York University’s Stern School of Business, using a statistical method called multivariate discriminant analysis to weight each ratio by how strongly it signals financial distress. The original version targets publicly traded manufacturing companies; later adaptations cover private firms and non-manufacturing businesses.
The Formula and the Five Ratios
For a public manufacturer, the score is:
Z = 1.2(X1) + 1.4(X2) + 3.3(X3) + 0.6(X4) + 1.0(X5)
- 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 ratio measures a different dimension of financial health. X1 captures short-term liquidity relative to size; a negative figure means the company owes more in the near term than it can cover with liquid assets. X2 shows how much of the asset base was built from retained profits rather than outside financing, and doubles as a rough proxy for company age. X3 measures the raw earning power of the assets, stripped of tax and financing effects, and Altman gives it the heaviest weight because operating profitability is the most direct question in survival. X4 shows how far market value could fall before liabilities exceed assets, indicating the size of the cushion protecting creditors. X5 is asset turnover: whether the company is generating enough revenue from what it owns.
The inputs come from a company’s 10-K or 10-Q filing with the SEC, and all five ratios should be pulled from the same reporting period. Because X4 uses the stock price, the score moves in real time with market sentiment; a sharp share-price drop can push the Z-Score down even when nothing on the balance sheet has changed.
A Worked Example
Suppose a public manufacturer reports current assets of $60 million, current liabilities of $40 million, fixed assets of $100 million, EBIT of $20 million, retained earnings of $8 million, sales of $60 million, total liabilities of $120 million, and a market capitalization of $80 million. Total assets equal $160 million.
- X1: ($60M − $40M) / $160M = 0.125
- X2: $8M / $160M = 0.05
- X3: $20M / $160M = 0.125
- X4: $80M / $120M = 0.667
- X5: $60M / $160M = 0.375
Plugging in: (1.2 × 0.125) + (1.4 × 0.05) + (3.3 × 0.125) + (0.6 × 0.667) + (1.0 × 0.375) = 0.15 + 0.07 + 0.4125 + 0.40 + 0.375 = 1.41. That score sits in the distress zone, dragged down mainly by thin retained earnings and modest asset turnover. A lender reviewing this company would want to look deeper before extending credit.
Interpreting the Score
Altman’s research established three zones for public manufacturers:
- Below 1.81 is the distress zone. The company’s financial profile resembles firms that have historically filed for bankruptcy. Creditors seeing a score in this range often tighten lending terms or demand additional collateral.
- 1.81 to 2.99 is the gray zone. The company isn’t in immediate danger but lacks a comfortable margin. Secondary indicators such as cash flow trends and industry conditions carry more weight here.
- Above 2.99 is the safe zone. Earnings, leverage, and asset use all look strong, and bankruptcy within two years is statistically unlikely.
In Altman’s original study, the model correctly identified bankrupt firms about 80% to 90% of the time when applied one year before failure, with a false-positive rate around 15% to 20%. Later validation studies of the revised 1993 model reported accuracy as high as 92% for predictions two years out.1ResearchGate. Review and Comparison of Altman and Ohlson Model to Predict Bankruptcy of Companies
A distress-zone score doesn’t cause bankruptcy on its own, but it does mean a company is statistically more likely to end up in one of two federal proceedings. Under Chapter 7, the business shuts down and a court-appointed trustee liquidates assets to pay creditors. Under Chapter 11, the company keeps operating while it restructures debt under a court-approved plan.2U.S. Courts. What Is the Difference Between Bankruptcy Cases Filed Under Chapters 7, 11, 12, and 13
How Scores Line Up With Credit Ratings
Z-Scores track credit ratings closely enough to work as a sanity check. Using 2022 data for U.S. non-financial firms, the average Z-Score for AAA/AA-rated companies was 6.32, A-rated firms averaged 4.33, and BBB-rated firms averaged 3.63. B-rated companies averaged 1.80, CCC/CC-rated firms averaged 0.43, and companies already in default averaged negative 0.24.3Italian Ministry of Economy and Finance. Unlocking the Credit Cycle: Beyond the Z-Score When the Z-Score and the official rating disagree, that gap is worth investigating.
Formulas for Private and Non-Manufacturing Companies
The original formula only works when there is a public stock price to calculate market capitalization. Two adaptations cover the rest.
Z’-Score for Private Companies
The Z’-Score swaps market value of equity in X4 for book value of equity. Every coefficient is recalibrated:
Z’ = 0.717(X1) + 0.847(X2) + 3.107(X3) + 0.420(X4) + 0.998(X5)
The zone thresholds also shift down: below 1.23 is distress, 1.23 to 2.90 is gray, and above 2.90 is safe.
Z”-Score for Non-Manufacturing Companies
Service firms, tech companies, and other non-manufacturing businesses are typically less asset-heavy, which makes the sales-to-total-assets ratio misleading. The Z”-Score drops X5 entirely and uses four variables with larger coefficients:4AISSMS Institute of Management. Does Altman Z-Score Model Accurately Predict Bankruptcy
Z” = 6.56(X1) + 3.26(X2) + 6.72(X3) + 1.05(X4)
X1 through X3 are the same working capital, retained earnings, and EBIT ratios divided by total assets. X4 is book value of equity divided by total liabilities. Below 1.10 is distress, 1.10 to 2.60 is gray, and above 2.60 is safe. An emerging-markets version adds a constant of 3.25 to recalibrate for higher baseline risk.
Using the wrong version for a given company type produces meaningless output. A private retail chain scored against the original public-manufacturing formula will almost certainly look worse than it is.
Where the Model Breaks Down
The Z-Score is a screening tool, not a verdict, and it has blind spots that matter.
The model was built on manufacturing companies from the 1950s and 1960s.5ScienceDirect. Tests of the Generalizability of Altman’s Bankruptcy Prediction Model Modern tech companies that prioritize growth over profitability, hold large intangible assets, and capitalize little of their R&D spending produce ratios the model reads as distress. A fast-growing software company burning cash intentionally can score in the distress zone despite no real insolvency risk.6MDPI. Corporate Failure Prediction: A Literature Review of Altman Z-Score and Machine Learning Models Within a Technology Adoption Framework
Companies that naturally run on negative working capital get penalized by X1 even when the negative balance reflects strong cash management. Restaurants, subscription businesses, and large retailers that collect from customers before paying suppliers are common examples.
The model does not apply to banks, insurance companies, or other financial institutions. Their balance sheets work differently: heavy leverage is normal, and the ratios that signal distress in an industrial firm are standard practice in finance. Z-Scores applied to financial institutions have no predictive value.
Finally, the formula only sees what the financial statements show. Accounting manipulation, off-balance-sheet liabilities, legal settlements, and fraud all sit outside its reach. Treat the score as one input, not a final answer.
Watching the Trend, Not Just the Snapshot
A single Z-Score is useful. The trajectory over several quarters usually tells you more. Companies rarely jump from the safe zone to bankruptcy in one step. A score that keeps falling quarter after quarter, especially one accelerating downward, signals compounding problems even while the current number still sits in the gray zone. The reverse also holds: a company climbing steadily out of the distress zone may be executing a real turnaround. Credit officers often track Z-Score trends alongside traditional metrics to catch deterioration before a rating downgrade arrives.
Cross-Checking Against Other Models
The Z-Score isn’t the only bankruptcy prediction model, and running an alternative can strengthen the analysis.
The Ohlson O-Score, developed in 1980, uses logistic regression and nine variables rather than five, adding an inflation adjustment, dummy variables for whether liabilities exceed assets and for consecutive net losses, and a measure of year-over-year change in net income. It was built on a sample of more than 2,000 companies compared with Altman’s original 66. Validation studies put its accuracy above 82%, though some research still finds the Altman model outperforms it two years out.1ResearchGate. Review and Comparison of Altman and Ohlson Model to Predict Bankruptcy of Companies
The Zmijewski X-Score is simpler, using three ratios: net income to total assets, total debt to total assets, and current assets to current liabilities. It uses probit analysis, and a score at or above zero classifies the company as likely to go bankrupt.7Aaltodoc. The Predictive Power of Altman Z-Score (1983), Ohlson O-Score, and Zmijewski X-Score in Forecasting Bankruptcies of Finnish Unlisted SMEs Its simplicity makes it easy to calculate and also means it captures less nuance.
No single model wins in every context. The Z-Score remains the most widely used because of its simplicity and decades of validation, but for tech companies, startups, or firms in unusual industries, running multiple models and comparing the results is often the safer approach.