162 lines
4.0 KiB
Markdown
162 lines
4.0 KiB
Markdown
# Dividend Trend Analysis Framework
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## 1. Theoretical Foundations
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### 1.1 Gordon Growth Model
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- Basic formula: P = D/(r-g)
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- Where:
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- P = Price
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- D = Dividend
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- r = Required rate of return
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- g = Growth rate
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- Application: Use to validate dividend sustainability
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### 1.2 Dividend Discount Model (DDM)
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- Multi-stage DDM for ETFs with varying growth phases
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- Terminal value calculation using industry averages
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- Sensitivity analysis for different growth scenarios
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### 1.3 Modern Portfolio Theory (MPT)
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- Dividend yield as a risk factor
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- Correlation with market returns
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- Beta calculation specific to dividend-paying securities
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## 2. Empirical Analysis Framework
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### 2.1 Historical Data Analysis
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- Rolling 12-month dividend growth rates
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- Year-over-year comparisons
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- Seasonality analysis
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- Maximum drawdown during dividend cuts
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### 2.2 Statistical Measures
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- Mean reversion analysis
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- Volatility clustering
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- Autocorrelation of dividend payments
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- Skewness and kurtosis of dividend distributions
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### 2.3 Machine Learning Components
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- Time series forecasting (ARIMA/SARIMA)
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- Random Forest for feature importance
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- Gradient Boosting for non-linear relationships
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- Clustering for similar ETF behavior
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## 3. Risk Assessment Framework
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### 3.1 Quantitative Risk Metrics
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- Dividend Coverage Ratio
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- Payout Ratio
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- Free Cash Flow to Dividend Ratio
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- Interest Coverage Ratio
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### 3.2 Market Risk Factors
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- Interest Rate Sensitivity
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- Credit Spread Impact
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- Market Volatility Correlation
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- Sector-Specific Risks
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### 3.3 Structural Risk Analysis
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- ETF Structure (Physical vs Synthetic)
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- Tracking Error
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- Liquidity Risk
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- Counterparty Risk
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## 4. Implementation Guidelines
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### 4.1 Data Requirements
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- Minimum 5 years of historical data
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- Monthly dividend payments
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- NAV/Price history
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- Trading volume
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- AUM (Assets Under Management)
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### 4.2 Calculation Methodology
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```python
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def calculate_dividend_trend(etf_data: Dict) -> Dict:
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"""
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Calculate comprehensive dividend trend analysis
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Returns:
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{
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'gordon_growth': float, # Growth rate from Gordon model
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'ddm_value': float, # Value from DDM
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'empirical_metrics': {
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'rolling_growth': float,
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'volatility': float,
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'autocorrelation': float
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},
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'risk_metrics': {
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'coverage_ratio': float,
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'payout_ratio': float,
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'market_correlation': float
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},
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'ml_predictions': {
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'next_year_growth': float,
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'confidence_interval': Tuple[float, float]
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}
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}
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"""
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pass
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```
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### 4.3 Validation Framework
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- Backtesting against historical data
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- Cross-validation with similar ETFs
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- Stress testing under market conditions
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- Sensitivity analysis of parameters
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## 5. Practical Considerations
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### 5.1 ETF-Specific Adjustments
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- New ETFs (< 2 years): Use peer comparison
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- Established ETFs: Focus on historical patterns
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- Sector ETFs: Consider industry cycles
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- Global ETFs: Account for currency effects
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### 5.2 Market Conditions
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- Interest rate environment
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- Economic cycle position
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- Sector rotation impact
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- Market sentiment indicators
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### 5.3 Reporting Standards
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- Clear confidence intervals
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- Multiple scenario analysis
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- Risk factor decomposition
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- Historical comparison benchmarks
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## 6. Continuous Improvement
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### 6.1 Performance Monitoring
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- Track prediction accuracy
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- Monitor model drift
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- Update parameters quarterly
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- Validate against new data
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### 6.2 Model Updates
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- Incorporate new market data
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- Adjust for structural changes
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- Update peer comparisons
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- Refine risk parameters
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## 7. Implementation Roadmap
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1. Phase 1: Basic Implementation
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- Gordon Growth Model
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- Historical trend analysis
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- Basic risk metrics
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2. Phase 2: Advanced Features
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- Machine Learning components
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- Market risk factors
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- Structural analysis
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3. Phase 3: Optimization
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- Parameter tuning
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- Performance validation
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- Reporting improvements
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4. Phase 4: Maintenance
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- Regular updates
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- Performance monitoring
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- Model refinement |