Altman Z-score screener: choose the model before the cutoff

Three formulas, three sets of cutoffs

Altman re-estimated the model when replacing market equity with book equity; changing only that input in the original formula does not produce Z-prime. The four-factor Z-double-prime removes sales/assets to reduce industry effects. The zone ranges below are those reported by Cındık and Armutlulu (2021) for the conventional versions. The formulas use decimal ratios: 20% enters as 0.20. The original Z convention shown retains the 0.999 sales coefficient; some implementations round it to 1.0.

• The upper zone is often called the safe zone. That historical label does not establish that a company, bond or stock is safe.
• This guide puts exact cutoff values in the grey zone. Compare unrounded scores, and keep the convention consistent across the screen.
• The emerging-market bond-rating adaptation adds 3.25 to Z-double-prime. Do not compare that shifted score with the unshifted 1.10 and 2.60 cutoffs.
• Use one model within a comparison group. Differences between Z, Z-prime and Z-double-prime are not changes in a company over time.

Inputs to reconcile before calculating

Use a consistent consolidated reporting scope and currency. For an annual screen, pair full-year EBIT and sales with the corresponding fiscal year-end balance sheet. Record the market equity date separately. A trailing-12-month implementation must disclose that choice; a single quarter of sales or EBIT is not an annual substitute.

A reproducible comparison using hypothetical figures

Assume all amounts are USD millions: current assets 400, current liabilities 200, total assets 1,000, retained earnings 100, annual EBIT 80, total liabilities 600, annual sales 1,200, market equity 1,800 and book equity 400. These assumptions describe an illustrative balance sheet, not a live company. A = 0.20, B = 0.10, C = 0.08, E = 1.20; D is 3.00 with market equity or 0.666667 with book equity.

• Only the market equity assumption changes in the last row. The 1.2-point decline comes entirely from 0.6 × (3.0 - 1.0); the financial statements are unchanged.
• If the business is a listed manufacturer, the original Z is the relevant comparison here. Running the other models explains their mechanics; it is not a way to select the most favorable result.
• Rounding the sales coefficient to 1.0 gives original Z = 3.644, a difference of 0.0012. Near a cutoff, record the coefficient and avoid classifying a rounded display value.

Exclusions, missing values and misleading comparisons

Separate out-of-scope companies from companies with weak scores. Financial institutions need sector-specific measures because deposits, lending assets and regulatory capital have different economic roles. An unavailable score also needs its own status; it does not mean distress or financial strength.

Map the research to Pineify and a chart watchlist

This page explains a screening workflow; it does not run a live Altman scan. Pineify Market Screener works with supported symbols and technical indicators. AI Finance Agent has separate financial-score, income-statement and balance-sheet tools. Verify the returned data and chosen model before adding a company to the technical watchlist.

• Build a candidate sheet with ticker, sector, model, period, filing date, input values, source links, score and exclusion reason. Keep unsupported or incomplete companies outside the ranked set.
• Ask AI Finance Agent to retrieve the inputs for each ticker and identify missing fields. Open the issuer filing to resolve discrepancies; do not accept a returned score as proof of a particular formula.
• Save supported candidates in a Market Screener collection. Open each matching exchange and ticker in TradingView, then review liquidity, trend and the timeframe you intend to trade.
• Recheck the accounting after a new filing or a material financing event. Use original-Z market-value changes to distinguish price movements from accounting changes.