Altman Z-Score Calculator

Calculate the Altman Z-Score to predict the probability of corporate bankruptcy. This model uses multiple financial ratios to assess a company's financial health and bankruptcy risk.

Financial Statement Data

Z-Score Results

Altman Z-Score: 0.000
Bankruptcy Risk: N/A
Financial Health: N/A

Component Analysis

Liquidity Ratio (X1): 0.000
Profitability (X2): 0.000
Efficiency (X3): 0.000
Leverage (X4): 0.000
Activity (X5): 0.000

Risk Assessment

Confidence Level: N/A
Time Horizon: N/A
Investment Action: N/A

Understanding the Altman Z-Score

The Altman Z-Score is a financial model developed by Edward Altman in 1968 to predict the probability of corporate bankruptcy. It combines five financial ratios into a single score that indicates a company's financial health and likelihood of default.

Altman Z-Score Formula

Original Z-Score Model

  • Z = 1.2X1 + 1.4X2 + 3.3X3 + 0.6X4 + 1.0X5
  • X1 = Working Capital / Total Assets
  • X2 = Retained Earnings / Total Assets
  • X3 = EBIT / Total Assets
  • X4 = Market Value Equity / Book Value Debt
  • X5 = Sales / Total Assets

Z-Score Interpretation

  • Z > 3.0: Safe zone (low bankruptcy risk)
  • 1.8 < Z < 3.0: Grey zone (moderate risk)
  • Z < 1.8: Distress zone (high bankruptcy risk)
  • Higher scores indicate better financial health

Component Analysis

Z-Score Components Explained

Understanding each ratio's contribution to bankruptcy prediction

Liquidity (X1)

  • Measures short-term financial health
  • Working capital relative to total assets
  • Weight: 1.2 (importance in model)
  • Higher values indicate better liquidity

Profitability (X2)

  • Cumulative profitability over time
  • Retained earnings to total assets
  • Weight: 1.4 (highest weight)
  • Reflects ability to generate profits

Efficiency (X3)

  • Operating efficiency
  • EBIT relative to total assets
  • Weight: 3.3 (most important)
  • Highest weighted component

Leverage (X4)

  • Financial leverage and solvency
  • Market value equity to book debt
  • Weight: 0.6 (lowest weight)
  • Market perception of financial health

Activity (X5)

  • Asset utilization efficiency
  • Sales relative to total assets
  • Weight: 1.0 (moderate weight)
  • Revenue generation capability

Model Accuracy and Limitations

Aspect Strength Limitation
Predictive Accuracy 72-80% accuracy in original study Accuracy varies by industry and time
Time Horizon Predicts bankruptcy 1-2 years ahead Not useful for very short-term predictions
Industry Application Works well for manufacturing firms Less accurate for financial and service firms

Applications in Finance

Credit Analysis

  • Bank lending decisions
  • Bond rating assessments
  • Credit risk evaluation
  • Loan pricing

Investment Decisions

  • Stock selection criteria
  • Portfolio risk assessment
  • Distressed securities investing
  • Risk management

Industry-Specific Considerations

Manufacturing Sector

  • Original model works well
  • High predictive accuracy
  • Asset-intensive operations
  • Traditional capital structure

Service Industries

  • Lower predictive power
  • Different capital structures
  • Intangible assets
  • Adjusted models needed

Z-Score vs Other Models

Advantages

  • Multiple financial ratios
  • Empirically tested
  • Easy to calculate
  • Industry standard

Alternatives

  • Ohlson O-Score
  • Zmijewski Score
  • Neural network models
  • Machine learning approaches

Key Takeaways for Altman Z-Score Calculator

  • Altman Z-Score = 1.2X1 + 1.4X2 + 3.3X3 + 0.6X4 + 1.0X5 predicts corporate bankruptcy probability
  • Z > 3.0 indicates low bankruptcy risk, Z < 1.8 indicates high risk, and 1.8-3.0 is the grey zone
  • The model combines liquidity, profitability, efficiency, leverage, and activity ratios
  • EBIT/Total Assets (X3) has the highest weight (3.3) in the model
  • Z-Score is most accurate for manufacturing companies and has 72-80% predictive accuracy
  • The model predicts bankruptcy 1-2 years in advance
  • Use Z-Score for credit analysis, investment decisions, and risk assessment
  • Consider industry differences when interpreting Z-Score results

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