1
Introduction to Credit Analysis
Credit analysis is the process of evaluating the ability and willingness of a borrower to meet debt obligations. It involves assessing the probability of default and estimating potential losses in the event of default.
Objectives of Credit Analysis
- Default Risk Assessment: Estimate the probability that the borrower will fail to meet obligations
- Loss Severity Estimation: Determine potential losses if default occurs
- Relative Value Analysis: Compare credit spreads across different securities
- Portfolio Risk Management: Assess concentration and correlation risks
Key Credit Risk Components
Expected Loss = Probability of Default × Loss Given Default × Exposure at Default
Understanding each component is crucial for comprehensive credit risk assessment.
Understanding each component is crucial for comprehensive credit risk assessment.
2
Traditional Credit Analysis: The Four Cs
The traditional framework for credit analysis focuses on four fundamental factors that determine creditworthiness.
| Factor | Description | Key Metrics |
|---|---|---|
| Capacity | Ability to repay debt obligations | Cash flow, earnings, debt ratios |
| Collateral | Assets that secure the debt | Asset quality, coverage ratios |
| Covenants | Terms and conditions of the debt agreement | Financial maintenance, restrictions |
| Character | Integrity and commitment to repay | Management quality, track record |
Capacity Analysis
Capacity analysis focuses on the borrower's financial ability to generate sufficient cash flows to service debt obligations.
Key Capacity Ratios
- EBITDA Coverage: EBITDA / Interest Expense
- Debt-to-EBITDA: Total Debt / EBITDA
- Free Cash Flow to Debt: FCF / Total Debt
- Debt Service Coverage: EBITDA / (Interest + Principal Payments)
Collateral and Covenants
Collateral provides security in case of default, while covenants protect lenders by imposing restrictions or requiring certain financial performance levels.
Covenant Types
- Affirmative Covenants: Actions the borrower must take
- Negative Covenants: Actions the borrower cannot take
- Financial Covenants: Specific financial ratios that must be maintained
- Incurrence Covenants: Tests only when taking certain actions
3
Credit Ratings and Rating Agencies
Credit rating agencies provide independent assessments of credit risk through standardized rating scales that facilitate comparison across issuers and securities.
Major Rating Agencies
| Agency | Investment Grade | Speculative Grade | Default |
|---|---|---|---|
| Moody's | Aaa to Baa3 | Ba1 to C | Ca, C |
| S&P | AAA to BBB- | BB+ to C | D |
| Fitch | AAA to BBB- | BB+ to C | RD, D |
Rating Process and Methodology
Rating agencies follow systematic processes that combine quantitative analysis with qualitative factors:
- Business Risk Assessment: Industry dynamics, competitive position, management quality
- Financial Risk Assessment: Leverage, coverage ratios, cash flow quality
- Comparative Analysis: Peer comparison and industry benchmarking
- Forward-Looking Perspective: Expected future performance and scenarios
Rating Outlooks and Watches
- Positive Outlook: Rating likely to be upgraded within 1-2 years
- Negative Outlook: Rating likely to be downgraded within 1-2 years
- Stable Outlook: Rating unlikely to change
- CreditWatch: Rating under review for potential change within 90 days
4
Structural and Reduced-Form Models
Structural Models
Structural models, pioneered by Merton (1974), treat a company's equity as a call option on the firm's assets. Default occurs when asset value falls below debt obligations.
Merton Model
Default occurs when V_T < D
where: V_T = Asset value at maturity, D = Debt obligation
Distance to Default = (ln(V₀/D) + (μ - σ²/2)T) / (σ√T)
where: V_T = Asset value at maturity, D = Debt obligation
Distance to Default = (ln(V₀/D) + (μ - σ²/2)T) / (σ√T)
Structural Model Advantages and Limitations
Advantages:
- Economic intuition based on firm fundamentals
- Links credit risk to asset volatility and leverage
- Provides default probabilities and recovery rates
- Assumes asset values are observable
- Simplified capital structure assumptions
- May not predict short-term defaults well
Reduced-Form Models
Reduced-form models treat default as an unpredictable event governed by a stochastic process. They focus on modeling default intensity or hazard rates directly from market prices.
| Model Type | Approach | Key Inputs | Primary Use |
|---|---|---|---|
| Structural | Firm value based | Asset volatility, leverage | Fundamental analysis |
| Reduced-Form | Default intensity based | Credit spreads, recovery rates | Market-based pricing |
5
Credit Scoring and Machine Learning Models
Modern credit analysis increasingly incorporates quantitative models and machine learning techniques to improve prediction accuracy and efficiency.
Traditional Credit Scoring Models
Altman Z-Score Model
One of the most famous credit scoring models for predicting bankruptcy:
Z = 1.2A + 1.4B + 3.3C + 0.6D + 1.0E
Where:
1.81 < Z < 2.99: Grey zone
Z < 1.81: Distress zone
Z = 1.2A + 1.4B + 3.3C + 0.6D + 1.0E
Where:
- A = Working Capital / Total Assets
- B = Retained Earnings / Total Assets
- C = EBIT / Total Assets
- D = Market Value of Equity / Total Liabilities
- E = Sales / Total Assets
1.81 < Z < 2.99: Grey zone
Z < 1.81: Distress zone
Machine Learning Applications
Advanced analytics and machine learning are revolutionizing credit analysis by processing vast amounts of data and identifying complex patterns.
| Technique | Application | Advantages | Challenges |
|---|---|---|---|
| Random Forest | Default prediction | Handles non-linear relationships | Black box nature |
| Neural Networks | Complex pattern recognition | High accuracy potential | Requires large datasets |
| SVM | Classification problems | Works with high dimensions | Parameter selection complexity |
Model Risk Considerations
- Overfitting: Models may perform poorly on new data
- Data Quality: Garbage in, garbage out principle applies
- Model Stability: Performance may degrade over time
- Regulatory Acceptance: Explainability requirements for some applications
6
Sovereign Credit Analysis
Sovereign credit analysis evaluates the creditworthiness of national governments, considering both the ability and willingness to repay debt obligations.
Sovereign Risk Factors
| Category | Key Factors | Indicators |
|---|---|---|
| Economic | Growth, inflation, competitiveness | GDP per capita, productivity growth |
| Fiscal | Budget balance, debt levels | Debt-to-GDP, deficit ratios |
| External | Current account, reserves | External debt, reserve coverage |
| Political | Stability, governance quality | Political risk indices |
Currency Considerations
Local Currency vs. Foreign Currency Debt:
Sovereigns generally have lower default risk on local currency debt because they can print money, though this may lead to inflation. Foreign currency debt poses higher risk as countries cannot print foreign currencies.
Sovereigns generally have lower default risk on local currency debt because they can print money, though this may lead to inflation. Foreign currency debt poses higher risk as countries cannot print foreign currencies.
Sovereign Rating Methodology
Rating agencies typically assess sovereigns across multiple dimensions:
- Economic Assessment: GDP per capita, growth prospects, economic diversity
- Institutional Assessment: Governance, rule of law, policy credibility
- Fiscal Assessment: Debt burden, fiscal flexibility, contingent liabilities
- External Assessment: Current account dynamics, external liquidity