Skip to content

Altman Z-Score

The Altman Z-Score is a financial metric used to predict the probability of a company going bankrupt within the next two years. Developed by Edward I. Altman in 1968, it combines five financial ratios with weighted coefficients to produce a single score that indicates financial health.

Usage in Pypulate

from pypulate.credit import altman_z_score


# Calculate Altman Z-Score
result = altman_z_score(
    working_capital=120000000,        # Working capital
    retained_earnings=200000000,      # Retained earnings
    ebit=80000000,                    # Earnings before interest and taxes
    market_value_equity=500000000,    # Market value of equity
    sales=350000000,                  # Sales
    total_assets=400000000,           # Total assets
    total_liabilities=150000000       # Total liabilities
)

# Access the results
z_score = result['z_score']
risk_assessment = result['risk_assessment']
zone = result['zone']
components = result['components']

Parameters

Parameter Type Description Default
working_capital float Working capital (current assets - current liabilities) Required
retained_earnings float Retained earnings Required
ebit float Earnings before interest and taxes Required
market_value_equity float Market value of equity Required
sales float Annual sales Required
total_assets float Total assets Required
total_liabilities float Total liabilities Required

Return Value

The function returns a dictionary with the following keys:

Key Type Description
z_score float The calculated Z-Score value
risk_assessment str Text description of the bankruptcy risk
zone str Classification zone ("Safe", "Grey", or "Distress")
interpretation str Same as risk_assessment (for compatibility)
components dict Dictionary containing the individual ratio components

The components dictionary includes:

  • 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

Risk Level Classification

The Z-Score is categorized into risk zones:

Z-Score Range Risk Zone
> 2.99 Safe Zone
1.81 - 2.99 Grey Zone
< 1.81 Distress Zone

Comprehensive Example

Here's a complete example analyzing three companies with different financial profiles:

from pypulate.credit import altman_z_score
import matplotlib.pyplot as plt
import numpy as np


# Example 1: Financially healthy manufacturing company
healthy_company = altman_z_score(
    working_capital=120000000,        # $120M working capital
    retained_earnings=200000000,      # $200M retained earnings
    ebit=80000000,                    # $80M earnings before interest and taxes
    market_value_equity=500000000,    # $500M market value of equity
    sales=350000000,                  # $350M annual sales
    total_assets=400000000,           # $400M total assets
    total_liabilities=150000000       # $150M total liabilities
)

# Example 2: Company in the "grey zone"
grey_zone_company = altman_z_score(
    working_capital=30000000,         # $30M working capital
    retained_earnings=40000000,       # $40M retained earnings
    ebit=25000000,                    # $25M earnings before interest and taxes
    market_value_equity=120000000,    # $120M market value of equity
    sales=200000000,                  # $200M annual sales
    total_assets=250000000,           # $250M total assets
    total_liabilities=150000000       # $150M total liabilities
)

# Example 3: Financially distressed company
distressed_company = altman_z_score(
    working_capital=5000000,          # $5M working capital
    retained_earnings=-20000000,      # -$20M retained earnings (accumulated losses)
    ebit=-8000000,                    # -$8M earnings before interest and taxes (operating loss)
    market_value_equity=30000000,     # $30M market value of equity
    sales=100000000,                  # $100M annual sales
    total_assets=150000000,           # $150M total assets
    total_liabilities=120000000       # $120M total liabilities
)

# Print the results
print("Altman Z-Score Analysis")
print("======================")

print("\nExample 1: Financially Healthy Company")
print(f"Z-Score: {healthy_company['z_score']:.2f}")
print(f"Risk Assessment: {healthy_company['risk_assessment']}")
print(f"Zone: {healthy_company['zone']}")
print("Component Values:")
for component, value in healthy_company['components'].items():
    print(f"  {component}: {value:.4f}")

print("\nExample 2: Grey Zone Company")
print(f"Z-Score: {grey_zone_company['z_score']:.2f}")
print(f"Risk Assessment: {grey_zone_company['risk_assessment']}")
print(f"Zone: {grey_zone_company['zone']}")
print("Component Values:")
for component, value in grey_zone_company['components'].items():
    print(f"  {component}: {value:.4f}")

print("\nExample 3: Financially Distressed Company")
print(f"Z-Score: {distressed_company['z_score']:.2f}")
print(f"Risk Assessment: {distressed_company['risk_assessment']}")
print(f"Zone: {distressed_company['zone']}")
print("Component Values:")
for component, value in distressed_company['components'].items():
    print(f"  {component}: {value:.4f}")

# Visualize the results
companies = ['Healthy', 'Grey Zone', 'Distressed']
z_scores = [
    healthy_company['z_score'],
    grey_zone_company['z_score'],
    distressed_company['z_score']
]

# Create a bar chart
plt.figure(figsize=(10, 6))
bars = plt.bar(companies, z_scores, color=['green', 'orange', 'red'])

# Add horizontal lines for the Z-score thresholds
plt.axhline(y=1.81, color='r', linestyle='--', label='Distress Zone (Z < 1.81)')
plt.axhline(y=2.99, color='g', linestyle='--', label='Safe Zone (Z > 2.99)')
plt.axhspan(1.81, 2.99, alpha=0.2, color='orange', label='Grey Zone (1.81 < Z < 2.99)')

# Add labels and title
plt.ylabel('Altman Z-Score')
plt.title('Altman Z-Score Comparison')
plt.ylim(bottom=0)  # Start y-axis at 0

# Add the Z-score values on top of the bars
for bar in bars:
    height = bar.get_height()
    plt.text(bar.get_x() + bar.get_width()/2., height + 0.1,
             f'{height:.2f}', ha='center', va='bottom')

plt.legend()
plt.tight_layout()
plt.show()

Example Output

Altman Z-Score Analysis
======================
Example 1: Financially Healthy Company
Z-Score: 4.59
Risk Assessment: Low risk of bankruptcy
Zone: Safe
Component Values:
  x1: 0.3000
  x2: 0.5000
  x3: 0.2000
  x4: 3.3333
  x5: 0.8750
Example 2: Grey Zone Company
Z-Score: 1.98
Risk Assessment: Grey area, moderate risk
Zone: Grey
Component Values:
  x1: 0.1200
  x2: 0.1600
  x3: 0.1000
  x4: 0.8000
  x5: 0.8000
Example 3: Financially Distressed Company
Z-Score: 0.49
Risk Assessment: High risk of bankruptcy
Zone: Distress
Component Values:
  x1: 0.0333
  x2: -0.1333
  x3: -0.0533
  x4: 0.2500
  x5: 0.6667

Visualization

Altman Z-Score Comparison

The visualization shows the Z-scores for three example companies, with horizontal lines indicating the threshold values that separate the Safe, Grey, and Distress zones.

Component Analysis

Each component of the Z-Score provides insight into different aspects of a company's financial health:

  1. X₁ (Working Capital / Total Assets)

    • Measures liquidity relative to company size
    • Higher values indicate better short-term financial health
    • Weight: 1.2

  2. X₂ (Retained Earnings / Total Assets)

    • Measures cumulative profitability and company age
    • Higher values indicate stronger historical performance
    • Weight: 1.4

  3. X₃ (EBIT / Total Assets)

    • Measures operating efficiency independent of tax and leverage
    • Has the highest weight (3.3), indicating its importance
    • Higher values indicate better operational performance

  4. X₄ (Market Value of Equity / Total Liabilities)

    • Measures financial leverage and market confidence
    • Higher values indicate lower financial risk
    • Weight: 0.6

  5. X₅ (Sales / Total Assets)

    • Measures asset turnover and management efficiency
    • Higher values indicate better utilization of assets
    • Weight: 0.999

Practical Applications

The Altman Z-Score can be used for:

  1. Credit Risk Assessment: Evaluating potential borrowers' bankruptcy risk
  2. Investment Screening: Identifying financially stable companies
  3. Portfolio Risk Management: Monitoring existing investments
  4. Supplier Evaluation: Assessing the financial stability of key suppliers
  5. Merger & Acquisition Analysis: Evaluating target companies

Limitations

While the Altman Z-Score is a powerful tool, it has some limitations:

  1. Originally developed for manufacturing companies
  2. May need industry-specific adjustments
  3. Works best for public companies with market value data
  4. Should be used alongside other financial metrics
  5. Historical performance may not predict future outcomes