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Financial Ratios

The financial_ratios function calculates key financial ratios used in credit assessment and provides an overall assessment of a company's financial health. These ratios are grouped into categories including liquidity, solvency, profitability, coverage, and efficiency.

Usage in Pypulate

from pypulate.credit import financial_ratios

# Calculate financial ratios
result = financial_ratios(
    current_assets=250000,         # $250,000 current assets
    current_liabilities=100000,    # $100,000 current liabilities
    total_assets=1000000,          # $1,000,000 total assets
    total_liabilities=400000,      # $400,000 total liabilities
    ebit=150000,                   # $150,000 earnings before interest and taxes
    interest_expense=30000,        # $30,000 interest expense
    net_income=100000,             # $100,000 net income
    total_equity=600000,           # $600,000 total equity
    sales=800000                   # $800,000 sales
)

# Access the results
liquidity = result["liquidity"]
solvency = result["solvency"]
profitability = result["profitability"]
coverage = result["coverage"]
efficiency = result["efficiency"]
overall = result["overall_assessment"]

Parameters

Parameter Type Description Default
current_assets float The company's current assets Required
current_liabilities float The company's current liabilities Required
total_assets float The company's total assets Required
total_liabilities float The company's total liabilities Required
ebit float Earnings before interest and taxes Required
interest_expense float Interest expense Required
net_income float Net income Required
total_equity float Total equity Required
sales float Total sales Required

Return Value

The function returns a dictionary with the following keys:

Key Type Description
liquidity dict Dictionary containing liquidity ratios and assessment
solvency dict Dictionary containing solvency ratios and assessment
profitability dict Dictionary containing profitability ratios and assessment
coverage dict Dictionary containing coverage ratios and assessment
efficiency dict Dictionary containing efficiency ratios
overall_assessment str Overall assessment of financial health

The liquidity dictionary includes: - current_ratio: Current assets divided by current liabilities - assessment: Assessment of liquidity ("Strong", "Adequate", or "Weak")

The solvency dictionary includes: - debt_ratio: Total liabilities divided by total assets - debt_to_equity: Total liabilities divided by total equity - assessment: Assessment of solvency ("Strong", "Adequate", or "Weak")

The profitability dictionary includes: - return_on_assets: Net income divided by total assets - return_on_equity: Net income divided by total equity - assessment: Assessment of profitability ("Strong", "Adequate", or "Weak")

The coverage dictionary includes: - interest_coverage: EBIT divided by interest expense - assessment: Assessment of coverage ("Strong", "Adequate", or "Weak")

The efficiency dictionary includes: - asset_turnover: Sales divided by total assets - assessment: Assessment of efficiency ("Strong", "Adequate", or "Weak")

Risk Level Classification

The financial ratios are categorized into assessment levels:

Ratio Range Assessment
Current Ratio < 1.0 Weak
1.0 - 2.0 Adequate
> 2.0 Strong
Debt Ratio > 0.6 Weak
0.4 - 0.6 Adequate
< 0.4 Strong
Debt-to-Equity > 1.5 Weak
0.5 - 1.5 Adequate
< 0.5 Strong
Return on Assets < 0.02 Weak
0.02 - 0.05 Adequate
> 0.05 Strong
Return on Equity < 0.05 Weak
0.05 - 0.15 Adequate
> 0.15 Strong
Interest Coverage < 1.5 Weak
1.5 - 3.0 Adequate
> 3.0 Strong
Asset Turnover < 0.5 Weak
0.5 - 1.0 Adequate
> 1.0 Strong

Comprehensive Example

Here's a complete example demonstrating how to calculate and analyze financial ratios for different companies:

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

# Example 1: Financially strong company
strong_company = financial_ratios(
    current_assets=500000,         # $500,000 current assets
    current_liabilities=200000,    # $200,000 current liabilities
    total_assets=2000000,          # $2,000,000 total assets
    total_liabilities=600000,      # $600,000 total liabilities
    ebit=400000,                   # $400,000 earnings before interest and taxes
    interest_expense=50000,        # $50,000 interest expense
    net_income=300000,             # $300,000 net income
    total_equity=1400000,          # $1,400,000 total equity
    sales=2500000                  # $2,500,000 sales
)

# Example 2: Company with adequate financial health
adequate_company = financial_ratios(
    current_assets=300000,         # $300,000 current assets
    current_liabilities=200000,    # $200,000 current liabilities
    total_assets=1500000,          # $1,500,000 total assets
    total_liabilities=750000,      # $750,000 total liabilities
    ebit=200000,                   # $200,000 earnings before interest and taxes
    interest_expense=80000,        # $80,000 interest expense
    net_income=100000,             # $100,000 net income
    total_equity=750000,           # $750,000 total equity
    sales=1200000                  # $1,200,000 sales
)

# Example 3: Financially weak company
weak_company = financial_ratios(
    current_assets=150000,         # $150,000 current assets
    current_liabilities=200000,    # $200,000 current liabilities
    total_assets=1000000,          # $1,000,000 total assets
    total_liabilities=700000,      # $700,000 total liabilities
    ebit=50000,                    # $50,000 earnings before interest and taxes
    interest_expense=60000,        # $60,000 interest expense
    net_income=20000,              # $20,000 net income
    total_equity=300000,           # $300,000 total equity
    sales=600000                   # $600,000 sales
)

# Print the results
print("Financial Ratios Analysis")
print("========================")

print("\nExample 1: Strong Company")
print(f"Current Ratio: {strong_company['liquidity']['current_ratio']:.2f} ({strong_company['liquidity']['assessment']})")
print(f"Debt Ratio: {strong_company['solvency']['debt_ratio']:.2f} ({strong_company['solvency']['assessment']})")
print(f"Return on Equity: {strong_company['profitability']['return_on_equity']:.2f} ({strong_company['profitability']['assessment']})")
print(f"Interest Coverage: {strong_company['coverage']['interest_coverage']:.2f} ({strong_company['coverage']['assessment']})")
print(f"Asset Turnover: {strong_company['efficiency']['asset_turnover']:.2f}")
print(f"Overall Assessment: {strong_company['overall_assessment']}")

print("\nExample 2: Adequate Company")
print(f"Current Ratio: {adequate_company['liquidity']['current_ratio']:.2f} ({adequate_company['liquidity']['assessment']})")
print(f"Debt Ratio: {adequate_company['solvency']['debt_ratio']:.2f} ({adequate_company['solvency']['assessment']})")
print(f"Return on Equity: {adequate_company['profitability']['return_on_equity']:.2f} ({adequate_company['profitability']['assessment']})")
print(f"Interest Coverage: {adequate_company['coverage']['interest_coverage']:.2f} ({adequate_company['coverage']['assessment']})")
print(f"Asset Turnover: {adequate_company['efficiency']['asset_turnover']:.2f}")
print(f"Overall Assessment: {adequate_company['overall_assessment']}")

print("\nExample 3: Weak Company")
print(f"Current Ratio: {weak_company['liquidity']['current_ratio']:.2f} ({weak_company['liquidity']['assessment']})")
print(f"Debt Ratio: {weak_company['solvency']['debt_ratio']:.2f} ({weak_company['solvency']['assessment']})")
print(f"Return on Equity: {weak_company['profitability']['return_on_equity']:.2f} ({weak_company['profitability']['assessment']})")
print(f"Interest Coverage: {weak_company['coverage']['interest_coverage']:.2f} ({weak_company['coverage']['assessment']})")
print(f"Asset Turnover: {weak_company['efficiency']['asset_turnover']:.2f}")
print(f"Overall Assessment: {weak_company['overall_assessment']}")

# Visualize the results
companies = ['Strong', 'Adequate', 'Weak']
current_ratios = [
    strong_company['liquidity']['current_ratio'],
    adequate_company['liquidity']['current_ratio'],
    weak_company['liquidity']['current_ratio']
]
debt_ratios = [
    strong_company['solvency']['debt_ratio'],
    adequate_company['solvency']['debt_ratio'],
    weak_company['solvency']['debt_ratio']
]
interest_coverages = [
    strong_company['coverage']['interest_coverage'],
    adequate_company['coverage']['interest_coverage'],
    weak_company['coverage']['interest_coverage']
]
return_on_equities = [
    strong_company['profitability']['return_on_equity'],
    adequate_company['profitability']['return_on_equity'],
    weak_company['profitability']['return_on_equity']
]

# Create a comparison chart
fig, axs = plt.subplots(2, 2, figsize=(14, 10))
fig.suptitle('Financial Ratios Comparison', fontsize=16)

# Current Ratio
axs[0, 0].bar(companies, current_ratios, color=['green', 'orange', 'red'])
axs[0, 0].axhline(y=2, color='g', linestyle='--', label='Strong (≥ 2)')
axs[0, 0].axhline(y=1, color='r', linestyle='--', label='Weak (< 1)')
axs[0, 0].set_title('Current Ratio')
axs[0, 0].set_ylabel('Ratio')
axs[0, 0].legend()

# Debt Ratio
axs[0, 1].bar(companies, debt_ratios, color=['green', 'orange', 'red'])
axs[0, 1].axhline(y=0.4, color='g', linestyle='--', label='Strong (≤ 0.4)')
axs[0, 1].axhline(y=0.6, color='r', linestyle='--', label='Weak (> 0.6)')
axs[0, 1].set_title('Debt Ratio')
axs[0, 1].set_ylabel('Ratio')
axs[0, 1].legend()

# Interest Coverage
axs[1, 0].bar(companies, interest_coverages, color=['green', 'orange', 'red'])
axs[1, 0].axhline(y=3, color='g', linestyle='--', label='Strong (≥ 3)')
axs[1, 0].axhline(y=1.5, color='r', linestyle='--', label='Weak (< 1.5)')
axs[1, 0].set_title('Interest Coverage Ratio')
axs[1, 0].set_ylabel('Ratio')
axs[1, 0].legend()

# Return on Equity
axs[1, 1].bar(companies, return_on_equities, color=['green', 'orange', 'red'])
axs[1, 1].axhline(y=0.15, color='g', linestyle='--', label='Strong (≥ 0.15)')
axs[1, 1].axhline(y=0.08, color='r', linestyle='--', label='Weak (< 0.08)')
axs[1, 1].set_title('Return on Equity')
axs[1, 1].set_ylabel('Ratio')
axs[1, 1].legend()

plt.tight_layout()
plt.subplots_adjust(top=0.9)
plt.show()

# Create a sensitivity analysis for current ratio
current_assets_values = np.linspace(100000, 500000, 100)  # Range of current assets values
current_liabilities = 200000  # Fixed current liabilities

current_ratios = [ca / current_liabilities for ca in current_assets_values]
assessments = []

for ratio in current_ratios:
    if ratio >= 2:
        assessments.append("Strong")
    elif ratio >= 1:
        assessments.append("Adequate")
    else:
        assessments.append("Weak")

# Create a plot showing how current ratio changes with current assets
plt.figure(figsize=(12, 6))

# Plot current ratio curve
plt.plot(current_assets_values, current_ratios, 'b-', linewidth=2)

# Add colored regions for different assessments
weak_indices = [i for i, r in enumerate(assessments) if r == "Weak"]
adequate_indices = [i for i, r in enumerate(assessments) if r == "Adequate"]
strong_indices = [i for i, r in enumerate(assessments) if r == "Strong"]

if weak_indices:
    plt.fill_between(current_assets_values[min(weak_indices):max(weak_indices)+1], 
                     0, current_ratios[min(weak_indices):max(weak_indices)+1], 
                     color='red', alpha=0.3, label='Weak')
if adequate_indices:
    plt.fill_between(current_assets_values[min(adequate_indices):max(adequate_indices)+1], 
                     0, current_ratios[min(adequate_indices):max(adequate_indices)+1], 
                     color='orange', alpha=0.3, label='Adequate')
if strong_indices:
    plt.fill_between(current_assets_values[min(strong_indices):max(strong_indices)+1], 
                     0, current_ratios[min(strong_indices):max(strong_indices)+1], 
                     color='green', alpha=0.3, label='Strong')

# Add horizontal lines for the ratio thresholds
plt.axhline(y=1, color='r', linestyle='--')
plt.axhline(y=2, color='g', linestyle='--')

# Add labels and title
plt.xlabel('Current Assets ($)')
plt.ylabel('Current Ratio')
plt.title('Current Ratio Sensitivity to Current Assets (Fixed Current Liabilities: $200,000)')
plt.grid(True, linestyle='--', alpha=0.7)
plt.legend()
plt.tight_layout()
plt.show()

Example Output

Financial Ratios Analysis
========================

Example 1: Strong Company
Current Ratio: 2.50 (Strong)
Debt Ratio: 0.30 (Strong)
Return on Equity: 0.21 (Strong)
Interest Coverage: 8.00 (Strong)
Asset Turnover: 1.25
Overall Assessment: Strong financial position

Example 2: Adequate Company
Current Ratio: 1.50 (Adequate)
Debt Ratio: 0.50 (Adequate)
Return on Equity: 0.13 (Adequate)
Interest Coverage: 2.50 (Adequate)
Asset Turnover: 0.80
Overall Assessment: Adequate financial position

Example 3: Weak Company
Current Ratio: 0.75 (Weak)
Debt Ratio: 0.70 (Weak)
Return on Equity: 0.07 (Weak)
Interest Coverage: 0.83 (Weak)
Asset Turnover: 0.60
Overall Assessment: Weak financial position

Visualizations

Financial Ratios Comparison

The following visualization shows a comparison of key financial ratios across three different companies (strong, adequate, and weak):

Financial Ratios Comparison

This chart displays four key financial ratios: - Current Ratio: Shows liquidity with thresholds at 2.0 (strong) and 1.0 (weak) - Debt Ratio: Shows solvency with thresholds at 0.4 (strong) and 0.6 (weak) - Interest Coverage Ratio: Shows debt service ability with thresholds at 3.0 (strong) and 1.5 (weak) - Return on Equity: Shows profitability with thresholds at 0.15 (strong) and 0.08 (weak)

Ratio Sensitivity Analysis

The following visualization demonstrates how the current ratio changes as current assets increase, while keeping current liabilities constant:

Ratio Sensitivity Analysis

This sensitivity analysis shows: - The blue line represents the current ratio as current assets increase - The red region represents the "Weak" assessment zone (ratio < 1.0) - The orange region represents the "Adequate" assessment zone (1.0 ≤ ratio < 2.0) - The green region represents the "Strong" assessment zone (ratio ≥ 2.0) - The horizontal dashed lines mark the threshold values at 1.0 and 2.0

Ratio Categories and Thresholds

1. Liquidity Ratios

Measure a company's ability to pay short-term obligations.

  • Current Ratio = Current Assets / Current Liabilities
  • Strong: ≥ 2.0
  • Adequate: 1.0 - 2.0
  • Weak: < 1.0

2. Solvency Ratios

Measure a company's ability to meet long-term obligations.

  • Debt Ratio = Total Liabilities / Total Assets
  • Strong: ≤ 0.4
  • Adequate: 0.4 - 0.6

  • Weak: > 0.6

  • Debt-to-Equity Ratio = Total Liabilities / Total Equity

  • Lower values indicate better solvency

3. Profitability Ratios

Measure a company's ability to generate earnings relative to its assets and equity.

  • Return on Assets (ROA) = Net Income / Total Assets

  • Higher values indicate better profitability

  • Return on Equity (ROE) = Net Income / Total Equity

  • Strong: ≥ 0.15 (15%)
  • Adequate: 0.08 - 0.15 (8% - 15%)
  • Weak: < 0.08 (8%)

4. Coverage Ratios

Measure a company's ability to service its debt.

  • Interest Coverage Ratio = EBIT / Interest Expense
  • Strong: ≥ 3.0
  • Adequate: 1.5 - 3.0
  • Weak: < 1.5

5. Efficiency Ratios

Measure how effectively a company uses its assets.

  • Asset Turnover Ratio = Sales / Total Assets
  • Higher values indicate better efficiency

Practical Applications

Financial ratios can be used for:

  1. Credit Risk Assessment: Evaluating a borrower's financial health
  2. Investment Analysis: Identifying financially stable companies
  3. Benchmarking: Comparing a company's performance against industry peers
  4. Trend Analysis: Monitoring changes in a company's financial health over time
  5. Covenant Compliance: Ensuring borrowers maintain acceptable financial metrics

Limitations

When using financial ratios, consider these limitations:

  1. Industry differences may affect appropriate ratio values
  2. Seasonal variations can impact short-term ratios
  3. Accounting methods can affect ratio calculations
  4. Historical ratios may not predict future performance
  5. Ratios should be used alongside other financial metrics for comprehensive analysis