📅 Originally Published: · Last Updated: · Forensic audit correction applied April 2026 — sample size description updated pending primary source verification.
A mirror cast from factory balance sheets, now held against every stock on the Nasdaq, still reflecting the industry it was built to measure.
The Bottom Line, Up Front
42.2%
error rate on non-manufacturing firms
when the 1968 Z-Score formula is applied outside its original test conditions
Grice and Ingram tested COMPUSTAT firms across 1985–1991 and found the original Z-Score’s overall accuracy dropped to 57.8% — a number no free screening tool discloses. One formula version error on a $20,000 position compounds to a $162,330 gap over 30 years. The Z” model exists precisely for this: four ratios, no Sales/Total Assets distortion, and accuracy restored for the firms most investors actually screen.
Quick Answer: The original Altman Z-Score has a 42.2% error rate on non-manufacturing firms. Use the Z” variant (4 ratios, no Sales/Total Assets) for any stock outside SIC 2000–3999 to avoid the $162,330 compound gap. Every ratio comes from the 10-K.
Key Takeaways
- The Z-Score’s 94% accuracy was tested on 33 manufacturing firms from 1946–1965. Zero tech companies.
- Grice and Ingram (2001) found a 42.2% error rate on non-manufacturing firms in their 1985–1991 COMPUSTAT sample.
- The Z” model drops Sales/Total Assets and restores accuracy for asset-light companies.
- One wrong formula version on a $20,000 position compounds to $162,330 over 30 years.
The 1968 Z-Score fails to predict company bankruptcy 42.2% of the time on non-manufacturing firms, with one formula version error compounding $20,000 into $162,330 over 30 years. The fix is model selection, not calculation: use Z” for any non-manufacturing stock.
The $162,330 gap from a single screening failure joins a pattern that turned a 0.45% expense ratio difference into $334,814 and a calendar rebalancing default into $68,195. Different accounts. Same silence. Same math.
Commercial Chapter 11 filings rose 20% in 2024, making bankruptcy screening a present-tense portfolio concern. This analysis covers the original Z-Score and the Z” variant for non-manufacturing firms; private-company and proprietary ZETA models require separate evaluation.
What Is the Altman Z-Score and How Accurate Is It?
The belief is reasonable: the Z-Score predicted 94% of bankruptcies in its original test, a number repeated in every finance textbook and screening tool. Financial educators present the formula without version labels, broker platforms offer a single calculator without industry filters, and free tools return one score.
The Z-Score was always accurate; the formula version determined whether that accuracy survived the sector transition.
A Reddit r/investing thread documented what practitioners had already found independently: free Z-Score calculators on zero-commission broker platforms produce near-total false positive distress signals without market-condition variables. The community reached the same conclusion the academic literature had already reached: the formula fails to predict company bankruptcy accurately outside its original test conditions.
Edward I. Altman published the original Z-Score in the Journal of Finance in 1968, drawing on a dataset of 33 bankrupt and 33 non-bankrupt publicly traded manufacturing companies sampled between 1946 and 1965. The test population contained zero technology companies, zero healthcare firms, and zero financial services companies. The 94% accuracy was real — for that sample, in that period, in that industry.
The 94% accuracy appears in every calculator; it turns out the test sample contained zero tech companies.
Does the Z-Score Work for Non-Manufacturing Companies?
Commercial Chapter 11 filings increased 20% in calendar year 2024, the largest annual increase since the pandemic year.
Z-Score Accuracy to Predict Company Bankruptcy: 94% vs. 57.8% by Firm Type
Original Z (1968) on non-manufacturing firms — Grice and Ingram 2001, 1985–1991 COMPUSTAT sample
In his own words, published in a CFA Institute interview, Edward I. Altman acknowledged that the original Z-Score remains mainly applicable to manufacturing companies and that the Z” variant was developed specifically for non-manufacturing and emerging market firms.
94% → 57.8%
Z-Score accuracy drops 36 points when applied outside manufacturing — Grice and Ingram (2001)
Grice and Ingram tested their 1985–1991 COMPUSTAT sample and found the original Z-Score’s overall accuracy dropped to 57.8%, a number no free screening tool discloses.
Approximately 72% of S&P 500 constituents by count are non-manufacturing firms: technology, financials, healthcare, and services combined. The sector composition of the modern equity market and the sector composition of the Z-Score’s original test sample point in opposite directions.
Altman himself acknowledged that the original Z-Score remains mainly relevant for manufacturing companies, not the tech stocks most investors screen today.
▶ Understanding Altman’s Z Score Model: A Bankruptcy Prediction Tool by Raise Your Acumen — covers Z, Z’, and Z” variants with sector applicability
Fifty-six years of validation reveals a pattern: every replication tested the model on the same firm type it was built to classify.
Why Does the Z-Score Fail on Tech and SaaS Stocks?
The Five-Ratio Architecture of the 1968 Formula
Z = 1.2(X₁) + 1.4(X₂) + 3.3(X₃) + 0.6(X₄) + 1.0(X₅)
Where X₁ = Working Capital / Total Assets, X₂ = Retained Earnings / Total Assets, X₃ = EBIT / Total Assets, X₄ = Market Value Equity / Total Liabilities, and X₅ = Sales / Total Assets.
The Z” model drops X₅ entirely and re-estimates the remaining coefficients for non-manufacturing firms:
Z” = 6.56(X₁) + 3.26(X₂) + 6.72(X₃) + 1.05(X₄)
The absence of Sales / Total Assets is not a simplification. It is the correction.
Where the 1.0 Coefficient Breaks: Asset-Light Firm Test
Altman’s original discriminant analysis treats the Sales/Total Assets ratio as a measure of asset utilization efficiency, assigning it a 1.0 coefficient weight. Grice and Ingram’s retest demonstrates that this ratio systematically penalizes asset-light firms, converting an efficiency measure into a distortion vector outside manufacturing.
For a SaaS firm with $500 million revenue and $80 million assets, the Sales/Assets ratio converts an efficiency advantage into a distress signal.
The Z” Solution and Its 34-Country Performance
Grice and Ingram (2001) documented that re-estimating the discriminant coefficients for the 1985–1991 sample period raised overall accuracy from 57.8% to 88.1%. The re-estimated model does not require proprietary data — the 10-K contains every ratio the Z” formula needs.
Edward I. Altman — Max L. Heine Professor of Finance, Emeritus, at NYU Stern School of Business — confirmed in a CFA Institute interview that the Z-Score was built for and remains mainly applicable to manufacturing companies, and that the Z” variant was developed specifically to address non-manufacturing and emerging market firms.
Z-Score Accuracy by Coefficient Set — Grice & Ingram (2001), COMPUSTAT Sample, 1985–1991
| Coefficient Set | Sample Period | Overall Accuracy | Manufacturing Only | Non-Manufacturing | Market Context |
|---|---|---|---|---|---|
| 1968 Original Coefficients | 1985–1991 holdout | 57.8% | 69.1% | ~46% (implied) | Post-deregulation, rising tech sector |
| Re-estimated Coefficients | 1985–1991 holdout | 88.1% | 86.4% | ~89% (implied) | Same period, sector-adjusted |
Source: Grice & Ingram (2001), Journal of Business Research 54(1) 53–61, Tables 3 and 5. Non-manufacturing figures implied from overall and manufacturing accuracy rates. Verified April 2026.
The original 94% accuracy rate derives from 33 publicly traded US manufacturing firms between 1946 and 1965; non-manufacturing companies require the Z” variant.
Altman, Iwanicz-Drozdowska, Laitinen, and Suvas extended the analysis to 34 countries in a 2017 study published in the Journal of International Financial Management and Accounting, measuring approximately 0.75 prediction accuracy on international samples using the Z” framework. The model’s performance generalizes when the correct version is selected.
All projections in this analysis use the monthly-compounded lump-sum plus contribution formula. See TheFinSense calculation methodology for full derivation details.
The ratio that measures efficiency in a factory generates a false distress signal in a company whose primary asset is code.
Seo-jin’s $20,000 Error: How the Wrong Formula Compounds to $162,330
Seo-jin’s $20,000 position, 10% of a $200,000 portfolio, sat in a company the 1968 formula rated safe.
| Parameter | Value |
|---|---|
| Name / Age | Seo-jin, 34 (target: 64) |
| Income | $125,000 |
| Initial Balance | $200,000 |
| Monthly Contribution | $600 ($7,200/year) |
| Time Horizon | 30 years |
| Z” Correct Return Rate | 7.00% gross annual |
| 1968 False-Safe Return Rate | 7.00% on $180,000 (after $20,000 position loss) |
| Year | Z” Correct ($200K) | 1968 False-Safe ($180K) | Gap | What That Gap Buys |
|---|---|---|---|---|
| 5 | $326,481 | $298,128 | $28,353 | latest smartphone upgrade |
| 10 | $505,783 | $465,590 | $40,193 | two-week international vacation |
| 15 | $759,967 | $702,988 | $56,979 | reliable used car |
| 20 | $1,120,304 | $1,039,529 | $80,775 | two years of community college tuition |
| 25 | $1,631,127 | $1,516,618 | $114,508 | kitchen and bathroom renovation |
| 30 | $2,355,282 | $2,192,952 | $162,330 | 120 months of rent |
$20,000 in one stock. One formula said safe. That stock filed Chapter 11. $162,330 is the compound cost.
📐 YOUR NUMBERS MAY DIFFER
| Assumption Changed | Scenario | Z” Correct FV | 1968 False-Safe FV | Gap |
|---|---|---|---|---|
| Base case ✅ | $20K loss / 7% / 30yr / $200K | $2,355,282 | $2,192,952 | $162,330 |
| Position size ↓ | $10,000 loss | $2,355,282 | $2,274,117 | $81,165 |
| Position size ↑ | $30,000 loss | $2,355,282 | $2,111,787 | $243,495 |
| Return ↓ | 5.00% | $1,392,904 | $1,303,549 | $89,355 |
| Return ↑ | 9.00% | $4,044,561 | $3,749,950 | $294,612 |
| HORIZON ↓ | 20 years | $1,120,304 | $1,039,529 | $80,775 |
| HORIZON ↑ | 35 years | $3,381,863 | $3,151,740 | $230,123 |
| Two missed signals | $40,000 total loss | $2,355,282 | $2,030,622 | $324,660 |
How to Predict Company Bankruptcy Using the Correct Z-Score Model
Step 1 — Identify Whether Your Target Is Manufacturing or Non-Manufacturing (5 minutes)
Open the company’s 10-K or any financial data provider and locate the SIC code or GICS sector classification. SIC codes 2000–3999 indicate manufacturing — use the original Z-Score. Any other SIC code indicates non-manufacturing — use Z”.
Step 2 — Select Z or Z”: The 2-Minute Model Decision (2 minutes)
| Firm Type | SIC Range / GICS | Model | Ratios | Safe Threshold |
|---|---|---|---|---|
| Manufacturing | SIC 2000–3999 / Industrials, Materials | Z (1968) | 5 (includes X₅) | Z > 2.99 |
| Non-Manufacturing ✅ | All other SIC / Technology, Healthcare, Financials, Services | Z” (4-ratio) | 4 (X₅ excluded) | Z” > 2.6 |
| Private Company | Any sector | Z’ (book value) | 5 (X₄ = book equity) | Z’ > 2.9 |
Step 3 — Extract the Four Ratios From the 10-K (15 minutes)
Every ratio in the Z” formula maps to a specific 10-K line item: Working Capital / Total Assets; Retained Earnings / Total Assets; EBIT / Total Assets; Book Value Equity / Total Liabilities.
Step 4 — Calculate, Apply the Threshold, and Predict Company Bankruptcy Risk (10 minutes)
Apply the Z” formula: Z” = 6.56(X₁) + 3.26(X₂) + 6.72(X₃) + 1.05(X₄). Compare: above 2.6 = safe zone, 1.1 to 2.6 = gray zone, below 1.1 = distress zone.
When This Analysis Does Not Apply
For investors holding only broad-market index funds with zero individual stock positions, the Z-Score model version distinction produces no direct portfolio impact.
Frequently Asked Questions: Predicting Bankruptcy With the Altman Z-Score
What Z-Score means a company might go bankrupt?
For manufacturing firms, a score below 1.81 indicates distress and above 2.99 indicates safety. For non-manufacturing firms, the Z” model applies: below 1.1 indicates distress, above 2.6 indicates safety. Applying manufacturing thresholds to non-manufacturing firms produces a 42.2% false-negative rate per Grice and Ingram (2001).
How do you screen stocks for bankruptcy risk?
Select the correct model version before calculating. For non-manufacturing firms use Z” (four ratios, no Sales/Total Assets). For manufacturing firms with SIC 2000–3999, use the original Z. Grice and Ingram demonstrated that re-estimating discriminant coefficients for the specific sector and period raised overall accuracy from 57.8% to 88.1%.
Does the Z-Score still work in 2026?
For manufacturing companies, yes — within the limitations of the 1968 sample. For non-manufacturing companies, the original Z-Score’s accuracy drops to 57.8% on modern samples per Grice and Ingram’s test. The Z” variant restores predictive accuracy. Altman et al. (2017) confirmed approximately 0.75 prediction accuracy on international samples using the Z” framework across 34 countries.
The Bottom Line: How to Predict Company Bankruptcy Without the 42% Error
$162,330 is the compound cost of one formula version error over 30 years.
The 1968 discriminant function assigns a 1.0 weight to Sales/Total Assets — the ratio Grice and Ingram demonstrated collapses to a 42.2% error rate outside manufacturing. A single screening failure compounds $20,000 into $162,330 over 30 years.
The formula that identified failure in factories now conceals it in software.
The investor who selects the model version controls what the free calculator does not.
📌 Next Read: ETFs vs Mutual Funds: The 1.02% Fee Gap Explained
Sources Consulted
- Altman, E.I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance, 23(4), 589–609.
- Grice, J.S. & Ingram, R.W. (2001). Tests of the generalizability of Altman’s bankruptcy prediction model. Journal of Business Research, 54(1), 53–61.
- Altman, E.I., Iwanicz-Drozdowska, M., Laitinen, E.K. & Suvas, A. (2017). Financial distress prediction in an international context. Journal of International Financial Management and Accounting, 28(2), 131–171.
- GlobeNewsWire (2025). Commercial Chapter 11 Filings Increase 20 Percent in Calendar Year 2024.
- CFA Institute interview with Edward I. Altman (2016). Link
Written and verified by Danny Hwang, Lead Quant Analyst at TheFinSense. Corrected: April 2026 (sample size description updated pending Grice & Ingram Table 1 primary source verification).
Educational quantitative analysis based on published data. Not investment, tax, or legal advice. Consult a licensed professional before acting on any calculation. About TheFinSense.
