Altman Z-Score Screener Excel: Bankruptcy Risk Dashboard (May 2026)

Altman Z-Score screener excel - if that is what you searched for, you are almost certainly trying to do one of three things. You want to spot balance-sheet landmines hiding inside a long portfolio. You want to size a speculative position in a turnaround story without lighting your capital on fire. Or you want a single, defensible number to put in front of an investment committee when somebody asks how much credit risk is sitting in the book.

The Altman Z-Score has been the academic and practitioner standard for that question since Edward Altman published it in 1968. Five ratios, one composite, and a three-zone classification (Safe, Grey, Distress) that has held up across more than fifty years of cycles. What it has never had is a great Excel front end.

This guide ships that front end. We walk through what a proper Altman Z-Score screener should measure, how to build it in Excel using live MarketXLS formulas (no scraping, no hand-typed balance-sheet numbers, no expired API tokens), and we hand you a downloadable premium template with KPI tiles, scenario stress tests, and conditional-formatted heatmaps. Free, dashboard-grade, and ready to plug into your portfolio.

Altman Z-Score Screener Excel: May 2026 Snapshot

Before we dig into the model, here is what the screener looks like across 20 large-cap names as of May 18, 2026. Values are illustrative composite ranges; the downloadable template pulls live numbers via MarketXLS so the figures update every time you open the file.

“

The values above are illustrative composites for educational purposes only. They are not the live numbers in the downloadable template, which uses real-time MarketXLS data. This article is analysis, not investment advice; see the disclaimer at the bottom.

Two patterns jump out. First, the cash-rich tech names (NVDA, MSFT, AAPL, GOOGL, META) sit comfortably in the Safe zone with Z-Scores well north of 5. The high market-value-to-liabilities ratio is doing most of the lifting; these are companies whose equity is worth a multiple of their entire book of debt. Second, large bank, airline, and capital-intensive industrial names land in the Distress zone. That is partly an artefact of the model (more on financials in a minute) and partly a real reflection of leverage and weak short-term liquidity.

Use that distribution as the starting point, not the conclusion. The point of the screener is to give you a quantitative anchor, then let qualitative judgement do the rest.

Altman Z-Score: A Quick Primer for the Screener

Edward Altman published the original Z-Score in 1968, using multivariate discriminant analysis on 66 manufacturing firms (33 bankrupt, 33 solvent) to find a linear combination of accounting ratios that separated the two groups. The result was a single composite score with a three-zone classification that, in the original sample, correctly classified 95% of cases on a one-year horizon.

The five components

The classic Z-Score combines five ratios:

1. Working Capital / Total Assets (WC/TA) - a short-term liquidity gauge.
2. Retained Earnings / Total Assets (RE/TA) - cumulative profitability and a proxy for the age of the business.
3. EBIT / Total Assets (EBIT/TA) - operating profitability without the noise of interest and taxes.
4. Market Value of Equity / Book Value of Total Liabilities (MV/TL) - the market's view of how much equity cushion sits above the debt stack.
5. Sales / Total Assets (Sales/TA) - asset turnover and operational efficiency.

The weighting

Each ratio is multiplied by a weight Altman derived from the discriminant analysis:

Z = 1.2 * (WC / TA)
  + 1.4 * (RE / TA)
  + 3.3 * (EBIT / TA)
  + 0.6 * (MV / TL)
  + 1.0 * (Sales / TA)

EBIT/TA carries the heaviest weight, which is intuitive: operating profitability is the strongest predictor of being able to service liabilities. MV/TL is the only market-based input, which is why the screener can pick up changes in credit risk faster than purely accounting-based models that wait for the next 10-Q.

The zones

The Z-Score buckets out into three risk zones:

• Safe zone: Z > 2.99 - statistically low bankruptcy risk over the next 12-24 months.
• Grey zone: 1.81 < Z < 2.99 - ambiguous; the model cannot confidently put the company in either bucket.
• Distress zone: Z < 1.81 - elevated bankruptcy risk; warrants deeper credit analysis.

These thresholds come from the original 1968 sample. Subsequent recalibrations have produced slightly different numbers, but the canonical thresholds are still the most widely used and are the ones the screener template uses.

Why an Altman Z-Score Screener Excel Matters in May 2026

The market backdrop matters here. Through 2024 and 2025, refinancing wave after refinancing wave pushed weak balance sheets to the brink. By May 2026, three patterns have crystallised:

1. High-yield default rates are normalising but still elevated versus the 2021 lows. The names that come up in the screener's Distress zone are exactly the cohort that has been driving those defaults.
2. Equity multiples on debt have compressed for the most leveraged names, while the cash-rich mega-caps have widened the gap further. The MV/TL term in the Z-Score amplifies this divergence, which is why the spread between Safe and Distress zones is wider in 2026 than it was in 2019.
3. Earnings revisions have been bifurcated. Operating margins at quality compounders have held up; margins at cyclicals and consumer discretionary have not. The EBIT/TA term picks this up directly.

A screener that rolls all of that into a single number, refreshes automatically, and prints a clean dashboard is more useful in this environment than at any point in the last decade.

What's Inside the Premium Template

The downloadable workbook is dashboard-style across 10 sheets. Here is what each sheet does and why it is there:

1. Cover - branded landing page with the workbook title, edition, data-as-of date, table of contents, and credits. No data. Tabs are color-coded so you can navigate the workbook without reading sheet names.
2. How To Use - step-by-step walkthrough of every other sheet, plus the full list of MarketXLS functions used in the workbook with one-line descriptions.
3. Dashboard - the headline sheet. A KPI tile row across the top (average Z-Score, percentage in Safe zone, count of Distress names, median Z, strongest name) sits above a 20-row screener with three-color conditional formatting on Z-Score, data bars on Sales/TA, and traffic-light zone fills. A horizontal bar chart of Z-Score by ticker and a pie chart of zone distribution are embedded on the same sheet.
4. Inputs - the single source of truth for everything downstream. Yellow input cells for portfolio size, minimum Z-Score floor, scenario selection (Conservative / Base / Aggressive via dropdown), recession haircuts on EBIT and market cap, per-zone allocation caps, and a custom-ticker mode for swapping in your own universe.
5. Scenario Analysis - stress-test matrix that re-runs the Z-Score under a mild recession (-25% EBIT, -35% market cap) and a severe stress (-50% EBIT, -55% market cap, -20% working capital, -25% retained earnings). Delta columns show how each name's score shifts; zone columns reclassify under each scenario.
6. Strategy - rules-based position-sizing table indexed by Z-Score band, plus a per-ticker recommended action column ("FULL SIZE", "WATCH", "AVOID OR HEDGE"). Pulls allocation caps directly from the Inputs sheet.
7. Portfolio - converts the portfolio size from Inputs into dollar allocations per ticker, ranked by zone. A donut chart of allocation by zone and a bar chart of allocation by sector sit alongside the table.
8. Comparison - balance-sheet metrics side by side: Z-Score, current ratio, debt-to-equity, ROE, and Sales/TA, with bespoke color scales on each column so you can spot whether a low Z-Score is a sector-wide phenomenon or a company-specific red flag. A sector-average summary sits below the main table.
9. Methodology - one-page explainer covering origin, the five ratios, the weighting, zone thresholds, data sources, known limitations, and use cases. The kind of page you want in front of a committee.
10. Glossary - definitions of every term used in the workbook plus a full educational disclaimer.

The template is designed to look presentation-ready on first open. Frozen panes are set on every sheet. Gridlines are hidden on the Cover and Dashboard sheets. The Dashboard has a print area set for clean landscape printing. Number formats are tuned per cell type. Tab colors are set to indicate sheet category (navy for cover, blue for dashboard, yellow for inputs, gray for methodology).

Building the Altman Z-Score in Excel With MarketXLS

The biggest reason a Z-Score template usually never gets built is the data. You need five ratios per ticker, each of which has to be pulled fresh, and most spreadsheets end up scraping financial-statement numbers off web pages or copy-pasting from 10-Q filings. That breaks the moment a filing changes format or a website blocks scrapers.

MarketXLS solves this by exposing financial-statement data as Excel functions. Every input ratio in the Z-Score is one function call away.

The five component formulas

WC / TA       = HF_WORKING_CAPITAL("AAPL") / HF_TOTALASSETS("AAPL")
RE / TA       = HF_ACCUMULATED_RETAINED_EARNINGS_DEFICIT("AAPL") / HF_TOTALASSETS("AAPL")
EBIT / TA     = HF_EBIT("AAPL") / HF_TOTALASSETS("AAPL")
MV / TL       = MarketCapitalization("AAPL") / HF_TOTALLIABILITIES("AAPL")
Sales / TA    = HF_REVENUE("AAPL") / HF_TOTALASSETS("AAPL")

Each formula auto-refreshes when the data updates. The screener template wraps every component in IFERROR(..., NA()) so a missing data point shows up as a clean NA rather than poisoning the row.

The composite

You can build the composite by hand:

=1.2*HF_WORKING_CAPITAL("AAPL")/HF_TOTALASSETS("AAPL")
 +1.4*HF_ACCUMULATED_RETAINED_EARNINGS_DEFICIT("AAPL")/HF_TOTALASSETS("AAPL")
 +3.3*HF_EBIT("AAPL")/HF_TOTALASSETS("AAPL")
 +0.6*MarketCapitalization("AAPL")/HF_TOTALLIABILITIES("AAPL")
 +1.0*HF_REVENUE("AAPL")/HF_TOTALASSETS("AAPL")

Or you can use the single-formula shortcut MarketXLS ships:

=AltmanZScore("AAPL")

The screener uses the single-formula version for the headline Z column and the component formulas for the breakdown columns. That lets you see at a glance which ratio is doing the heavy lifting for any given ticker.

Classifying the zone

The zone column is a simple nested IF:

=IF(D2>=2.99, "SAFE",
     IF(D2>=1.81, "GREY", "DISTRESS"))

Combined with conditional formatting that fills green, amber, or red, this turns a 20-row screener into a heatmap you can read in two seconds.

Reading the Components

Looking at the Z-Score alone tells you the bottom line. Looking at the five components tells you why the bottom line is what it is. Here is how to read each one in practice.

Working Capital / Total Assets

This is your short-term liquidity check. A negative value means current liabilities exceed current assets. That is not automatically catastrophic - airlines and large retailers routinely run negative working capital because customers pay before suppliers do - but it is something you want to be aware of, especially when paired with a weak EBIT/TA.

Names like AAL with a deeply negative WC/TA reflect both the airline working-capital pattern and a genuinely thin liquidity cushion.

Retained Earnings / Total Assets

This is the long-term profitability and survival history of the business. Positive RE/TA means the company has earned and retained more money than it has paid out; negative means accumulated losses. Boeing's negative RE/TA in 2026 reflects the 737-MAX and 787 cycle, not normalised operating performance, which is exactly the kind of nuance the screener flags but does not interpret for you.

A new growth company will have a low RE/TA simply because it is young. That does not mean it is distressed.

EBIT / Total Assets

The heaviest weight in the model. This is operating profitability normalised by the size of the asset base. Tech companies routinely score 0.20 to 0.40 here; capital-intensive industrials and airlines often hover near zero or go negative in cyclical troughs.

When you see a Z-Score deteriorate quarter on quarter, EBIT/TA is usually the component that moved.

Market Value of Equity / Book Value of Total Liabilities

The market-based check. A value of 1.0 means the market is pricing the equity at exactly the book value of total liabilities. A value of 10 or 20 means there is enormous equity cushion above the debt stack. A value below 0.5 is a real warning sign.

This ratio is what makes the Z-Score react faster than purely accounting-based models. If the market starts pricing in distress, MV/TL falls before the next earnings release.

Sales / Total Assets

Asset turnover. Mass retailers like Walmart and Costco score very high here because they push enormous volumes of inventory through their stores. Tech and capital-intensive industrials score low because the asset base is large relative to revenue.

This is usually the most stable component over time; it is mostly a reflection of business model rather than current performance.

The Scenario Analysis Sheet

A static Z-Score is useful. A Z-Score that you can stress-test under recession assumptions is more useful. The Scenario Analysis sheet in the template re-runs the model under two pre-built stress scenarios:

• Mild recession - EBIT haircut of 25%, market cap haircut of 35%. Working capital, retained earnings, and sales are held constant. This roughly approximates a typical mid-cycle correction.
• Severe stress - EBIT haircut of 50%, market cap haircut of 55%, working capital haircut of 20%, retained earnings haircut of 25%. Closer to a 2008-style shock.

Both haircut percentages are wired into the Inputs sheet (cells C8 and C9) so you can dial them up or down without touching the formulas.

The output is a 20-row matrix with three Z columns (Base, Mild, Severe), two delta columns showing how much each name moves under stress, and three zone columns reclassifying each ticker under each scenario. Conditional formatting colour-codes all three Z columns on the same red-amber-green scale, so you can scan the matrix and immediately see which names tip from Safe to Grey to Distress.

The most informative pattern to look for is not the names that start in Distress; you already knew those were risky. It is the names that start in Safe, stay in Safe under the mild scenario, but tip into Grey or Distress under severe stress. Those are the latent-risk holdings.

Position Sizing by Zone

The Strategy sheet turns the screener into a position-sizing rulebook. The default configuration:

• Safe zone - up to 6% per name. Full sizing. Entry filter: positive RE/TA and EBIT/TA above 10%. Exit trigger: Z drops below 2.50.
• Grey zone - up to 3% per name. Watchlist allocation. Entry filter: stable WC/TA, no recent rating downgrade. Exit trigger: Z below 1.81 for two consecutive quarters.
• Distress zone - up to 1% per name. Speculative. Entry filter: documented restructuring or turnaround catalyst. Exit trigger: Z drops below 1.10.

All three caps are stored on the Inputs sheet so you can tighten them for a more conservative configuration or loosen them if you are running a more aggressive book. The per-ticker action column ("FULL SIZE", "WATCH", "AVOID OR HEDGE") is a direct lookup against the zone column, so it updates automatically when you swap your universe or change the scenario.

This is risk budgeting, not a recommendation. The Z-Score is one input into position sizing, not the only one.

Limitations You Should Know About

The screener is a sharp tool, but it has known limitations. Three of them matter for day-to-day use:

Financial-sector firms

Banks, insurers, and asset managers do not fit the original 1968 manufacturing-firm calibration well. Their balance sheets carry large amounts of investment securities classified differently from operating assets, and many of their "liabilities" are customer deposits, not creditor claims. JPMorgan's Z-Score of ~0.4 in our screener is mechanical, not a credit-quality verdict.

For financial-sector names, treat the Z-Score as a relative ranking inside the sector (JPM vs WFC vs BAC vs C) rather than an absolute distress probability. Better still, use the Z'' variant (which drops the Sales/TA term) or a specialised credit model for those names.

Negative-equity edge cases

Companies in restructuring sometimes carry deeply negative retained earnings or even negative book equity. The Z-Score will go very negative as well. That is mathematically correct but you should not over-interpret the magnitude; a Z of -1 is not "twice as bad" as a Z of -0.5. Below the Distress threshold, the Z-Score is best read as a binary signal rather than a continuous one.

Survivorship and look-back bias

The Altman thresholds were calibrated on a small mid-20th-century sample. Modern firms have very different capital structures (much higher market-cap-to-book ratios in tech, much higher leverage in private-equity-backed industrials). The Safe-zone threshold of 2.99 catches most of the bad names but also lets some risky asset-light businesses through, and the Distress threshold catches plenty of names that go on to do just fine.

Use the Z-Score as a screening tool that surfaces names for further work, not as a final verdict.

Download the Templates

Download the templates:

• - pre-filled with illustrative values; every data cell has a comment showing the MarketXLS formula that would produce it live.
• - dashboard-style workbook with live formulas. Open in Excel with the MarketXLS add-in and every metric refreshes from real data.

Both files include all 10 sheets described above. The premium version has zero static data; the sample version is useful if you want to study the layout without the add-in installed.

How the Template Compares to Manual Screening

If you tried to build this by hand using free data sources, you would need to:

1. Pull total assets, total liabilities, working capital, retained earnings, EBIT, and revenue from every ticker's most recent 10-Q.
2. Cross-reference market cap from a separate price source.
3. Repeat for every ticker every time you wanted a refresh.
4. Maintain that workflow as filings change format and data sources change their APIs.

The template collapses all of that into one set of MarketXLS function calls. The Z-Score recalculates on every Excel refresh. The screener works for any ticker MarketXLS covers, which is essentially the full US listed universe and substantial international coverage.

For the Excel-native financial advisor or self-directed investor, the time saving compounds: any time you want to add a new ticker, you just type it into the Inputs sheet, and every downstream sheet recalculates.

FAQ: Altman Z-Score Screener Excel

What is the Altman Z-Score and why does it matter?

The Altman Z-Score is a five-factor composite credit-risk indicator developed by NYU finance professor Edward Altman in 1968. It combines working capital, retained earnings, operating profit, market value of equity, and revenue, each scaled by total assets or book liabilities, into a single number that estimates the probability of corporate bankruptcy within 12 to 24 months. It matters because it gives a quantitative, balance-sheet-driven anchor that you can apply consistently across an entire universe, rather than relying on rating-agency labels or narrative-driven sentiment.

Is the Altman Z-Score still accurate in 2026?

The original Z-Score was calibrated on a small sample of 1960s US manufacturing firms, so a literal reading of the zone thresholds is dated. The directional signal, though, has held up remarkably well across cycles: names that score deep into the Distress zone really do default more often than Safe-zone names. Modern variants (Z' for private firms, Z'' for non-manufacturers) refine the calibration for specific use cases. The screener uses the original Z-Score and flags financial-sector firms as a known limitation.

How is the Altman Z-Score calculated in Excel?

You can build it from the five component ratios:

Z = 1.2*HF_WORKING_CAPITAL/HF_TOTALASSETS
  + 1.4*HF_ACCUMULATED_RETAINED_EARNINGS_DEFICIT/HF_TOTALASSETS
  + 3.3*HF_EBIT/HF_TOTALASSETS
  + 0.6*MarketCapitalization/HF_TOTALLIABILITIES
  + 1.0*HF_REVENUE/HF_TOTALASSETS

Or you can use the single-formula shortcut: =AltmanZScore("AAPL"). Both approaches refresh automatically when you open the workbook with the MarketXLS add-in installed.

What is the difference between Safe, Grey, and Distress zones?

The Safe zone (Z > 2.99) covers companies that, in the original Altman calibration, almost never went bankrupt over a 12 to 24 month horizon. The Grey zone (1.81 < Z < 2.99) is the ambiguous band where the model cannot confidently classify the name; further analysis is required. The Distress zone (Z < 1.81) covers names with elevated bankruptcy risk historically. The thresholds are statistical, not deterministic; plenty of Distress-zone names recover and plenty of Safe-zone names go through difficult periods.

Does the Altman Z-Score work for banks and financial firms?

Not really. Banks have large investment securities portfolios and deposit liabilities that the original manufacturing-firm calibration does not handle well. The screener template flags this in the Methodology sheet and recommends using the Z'' variant or a specialised credit model for those names. For financial-sector tickers, treat the Z output as a relative ranking inside the sector rather than an absolute distress probability.

How often should I refresh the Altman Z-Score Screener?

The market-value-of-equity component (MV/TL) updates with every price tick, so the Z-Score will show some movement throughout the trading day. The accounting components (working capital, retained earnings, EBIT, revenue, total assets, total liabilities) update with each quarterly filing. A useful cadence for most users is to refresh the workbook once a week and do a fuller review after each earnings season. The template recalculates automatically when you press F9 or reopen the file.

Can I customise the ticker universe?

Yes. On the Inputs sheet, switch the Universe dropdown to "Custom" and enter a comma-separated list of tickers in the Custom Tickers cell. The screener, scenario analysis, strategy, portfolio, and comparison sheets all recalculate against the new universe. The default 20-ticker universe spans Safe, Grey, and Distress zones so you can see the full range of outputs without configuring anything.

The Bottom Line

Bankruptcy risk does not announce itself. It builds quietly over multiple quarters as working capital tightens, EBIT compresses, and the market starts to mark down the equity multiple on the debt stack. The Altman Z-Score collapses all of that into one number that you can monitor across an entire watchlist, refresh on demand, and stress-test under recession assumptions.

The downloadable template puts that number on a dashboard. KPI tiles for the universe-wide stats. Conditional-formatted screener with traffic-light zone classification. A scenario sheet that re-runs the model under recession haircuts. A position-sizing rulebook indexed by zone. All wired together by the Inputs sheet so you can change one number and the whole workbook updates.

Two design choices are worth flagging. First, every component ratio is built from a MarketXLS function, so there is no scraping, no hand-typed data, and no maintenance overhead when filings change format. Second, the workbook is dashboard-style across 10 sheets rather than a single grid, because the Z-Score is most useful when you can see the components, the scenarios, and the position-sizing implications side by side.

If you want to learn more about the Excel-native financial-data stack the template is built on, visit MarketXLS or book a demo to see the full add-in in action. For the latest dashboards and screeners that ship with the MarketXLS workflow, browse the blog index.

This article is educational and informational. Nothing here is investment, legal, tax, or accounting advice. The Altman Z-Score is one of many credit-risk indicators and is not a substitute for full financial analysis or professional advice. Past performance does not guarantee future results.