SEC MCP

# PhD-Level Financial MCPs Documentation ## 🎓 Advanced Features Overview ### 1. XBRL Financial Data Parsing - **Capability**: Extract structured financial data from SEC XBRL filings - **Metrics**: 50+ financial metrics automatically extracted - **Time Series**: Historical data tracking with version control ### 2. Advanced DCF Valuation - **Monte Carlo Simulation**: 10,000 iterations for uncertainty analysis - **WACC Calculation**: Dynamic cost of capital based on market conditions - **Sensitivity Analysis**: Multi-variable sensitivity tables ### 3. PhD-Level NLP Sentiment Analysis - **Earnings Call Analysis**: Separate management tone from analyst questions - **Context-Aware Sentiment**: Understands financial context and negation - **Entity-Level Sentiment**: Track sentiment for specific financial metrics ### 4. Institutional Research Reports - **Format**: 25+ page comprehensive equity research reports - **Sections**: Executive summary, thesis, financials, valuation, risks - **Quality**: Institutional-grade analysis with supporting data ### 5. Peer Comparison & Relative Valuation - **Peer Identification**: Automatic peer group selection - **Metrics**: 20+ comparative metrics - **Ranking**: Percentile rankings across peer group ### 6. Technical Analysis Suite - **Indicators**: RSI, MACD, Bollinger Bands, Support/Resistance - **Signals**: Buy/sell signals with confidence levels - **Regime Detection**: Bull/bear market regime identification ### 7. Risk Assessment Framework - **Financial Risk**: Altman Z-Score, Piotroski F-Score - **Market Risk**: Beta, volatility, correlation analysis - **Operational Risk**: Customer concentration, key person risk ### 8. Data Intelligence - **Caching**: Intelligent caching with TTL management - **Versioning**: Track changes in financial data over time - **Quality Scoring**: Assess data quality and completeness ## 📊 Usage Examples ### Comprehensive Analysis ``` Use SEC to perform comprehensive analysis for ticker "AAPL" ``` ### DCF Valuation with Monte Carlo ``` Use SEC to perform DCF valuation for ticker "MSFT" ``` ### Generate Research Report ``` Use RESEARCH-ADMINISTRATOR to generate research report for ticker "GOOGL" ``` ### Sentiment Analysis ``` Use NEWS-SENTIMENT to analyze comprehensive sentiment for ticker "TSLA" ``` ## 🔧 Configuration ### Analysis Parameters Located in each MCP's `analysis_config`: - `dcf_years`: Number of years for DCF projection (default: 5) - `peer_count`: Number of peers for comparison (default: 10) - `monte_carlo_simulations`: Number of simulations (default: 10,000) ### Cache Settings - Price data: 5 minutes - Financial statements: 90 days - News: 1 hour - Research reports: 30 days ## 📈 Quality Metrics Each analysis includes quality scoring: - **Data Completeness**: % of required data available - **Data Freshness**: How recent the data is - **Analysis Depth**: Number of metrics calculated - **Confidence Level**: Statistical confidence in results ## 🚀 Advanced Workflows ### 1. Investment Decision Workflow 1. Comprehensive analysis → Overall assessment 2. DCF valuation → Intrinsic value calculation 3. Peer comparison → Relative positioning 4. Risk assessment → Risk-adjusted returns 5. Generate report → Investment recommendation ### 2. Earnings Analysis Workflow 1. Parse latest 10-Q XBRL data 2. Compare with previous quarters 3. Analyze earnings call sentiment 4. Update financial model 5. Generate earnings report ### 3. Sector Analysis Workflow 1. Identify sector peers 2. Comparative analysis across sector 3. Sector rotation signals 4. Relative value opportunities 5. Sector report generation ## ⚡ Performance Optimization - **Parallel Processing**: Multiple analyses run concurrently - **Smart Caching**: Reduces redundant API calls - **Batch Operations**: Process multiple tickers efficiently - **Async Architecture**: Non-blocking operations ## 🛡️ Data Quality Assurance - **Validation**: All financial data validated for reasonableness - **Cross-Verification**: Multiple sources cross-checked - **Outlier Detection**: Automatic flagging of suspicious data - **Audit Trail**: Complete tracking of data sources and transformations