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User Guide Overview

The Systematic Investment Analysis Framework provides a comprehensive, objective approach to stock screening and analysis. This guide will help you master all aspects of the system.

What This Framework Does

Core Functionality

The framework performs systematic analysis of stocks through a multi-stage pipeline:

  1. Universe Selection - Choose stocks to analyze (S&P 500, custom lists, sectors)
  2. Data Collection - Fetch financial metrics from Yahoo Finance
  3. Quality Screening - Evaluate financial strength and stability
  4. Value Assessment - Analyze valuation metrics and ratios
  5. Growth Analysis - Assess business growth and prospects
  6. Risk Evaluation - Identify financial and business risks
  7. Valuation Modeling - Apply DCF and RIM models
  8. Results Generation - Produce reports and data exports

Key Benefits

Eliminates Bias: Every stock goes through identical analysis steps

Configurable: Define your investment criteria without coding

Comprehensive: Analyzes quality, value, growth, and risk systematically

Reproducible: Same inputs always produce same outputs

Scalable: Analyze individual stocks or entire market indices

Analysis Pipeline

Stage 1: Universe Building

The framework starts by building a universe of stocks to analyze:

graph LR
    A[Stock Universe] --> B[Market Cap Filter]
    B --> C[Sector Filter]
    C --> D[Custom Filters]
    D --> E[Final Universe]

Sources: - S&P 500 constituents - Custom ticker lists - Sector-based selection - Market cap ranges

Stage 2: Data Collection

For each stock, the framework fetches:

  • Basic Data: Price, market cap, sector, industry
  • Financial Ratios: P/E, P/B, EV/EBITDA, etc.
  • Profitability: ROE, ROIC, profit margins
  • Growth Metrics: Revenue/earnings growth rates
  • Financial Health: Debt ratios, liquidity measures

Stage 3: Multi-Dimensional Screening

Each stock receives scores across four dimensions:

Quality Assessment (0-100 points)

  • Return on Invested Capital (ROIC)
  • Return on Equity (ROE)
  • Current ratio (liquidity)
  • Debt-to-equity ratio
  • Interest coverage

Value Analysis (0-100 points)

  • Price-to-Earnings (P/E) ratio
  • Price-to-Book (P/B) ratio
  • Enterprise Value to EBITDA
  • Price-to-Free Cash Flow
  • Dividend yield considerations

Growth Evaluation (0-100 points)

  • Revenue growth rate
  • Earnings growth rate
  • Free cash flow growth
  • Book value growth
  • Growth sustainability metrics

Risk Assessment (0-100 points, lower is better)

  • Beta (market risk)
  • Financial leverage
  • Business model stability
  • Sector-specific risks
  • Cyclical adjustments

Stage 4: Composite Scoring

Scores are combined using weighted averages:

Composite Score = (Quality × 30%) + (Value × 30%) + (Growth × 25%) + (Risk × 15%)

Risk scores are inverted (lower risk = higher contribution to composite score).

Stage 5: Filtering and Ranking

Filtering: Stocks must meet minimum thresholds: - Quality Score ≥ 40 - Value Score ≥ 30
- Growth Score ≥ 20 - Risk Score ≤ 80 - Composite Score ≥ 50

Ranking: Stocks are sorted by composite score (or user-specified criterion).

Stage 6: Valuation Models

Top candidates undergo detailed valuation:

Discounted Cash Flow (DCF): - Projects future cash flows - Applies appropriate discount rate - Calculates intrinsic value per share

Residual Income Model (RIM) (planned): - Book value-based approach - Focuses on economic profits - Alternative valuation perspective

Interactive Dashboard

The Interactive Dashboard provides a live web interface for real-time investment analysis:

poetry run python scripts/dashboard_server.py
# Opens automatically at http://localhost:8080

Key Features: - Live Valuations: Compare DCF, Enhanced DCF, and Simple Ratios side-by-side - One-Click Updates: Refresh all data with button click - Real-Time Prices: Current market data with upside/downside calculations - Interactive Tooltips: Hover explanations for all metrics - Professional Interface: Clean, responsive design with visual feedback

See Dashboard Guide for detailed usage instructions.

Output Formats

1. Executive Summary Report

High-level overview with: - Total stocks analyzed - Number passing filters - Top recommendations - Sector breakdown - Key insights

2. Detailed Stock Reports

For each stock: - Company overview - Quality assessment details - Value analysis breakdown
- Growth evaluation - Risk assessment - Valuation results (if applicable) - Investment thesis summary

3. CSV Data Export

Structured data including: - All stocks analyzed - Pass/fail filter status - Detailed scores and metrics - Financial ratios - Ready for spreadsheet analysis

4. JSON Data Export

Raw data in JSON format: - Complete analysis results - Configuration used - Intermediate calculations - API integration ready

Understanding Filter Results

Passes_Filters = Y (Yes)

Stock meets all screening criteria: - Quality: Strong financial metrics - Value: Attractive valuation - Growth: Positive business momentum
- Risk: Acceptable risk profile - Overall: Recommended for further analysis

Passes_Filters = N (No)

Stock fails one or more criteria. Common reasons:

Quality Issues: - Low ROE or ROIC - High debt levels - Poor liquidity ratios

Value Concerns: - High P/E ratio (overvalued) - Excessive P/B ratio - Premium EV/EBITDA multiples

Growth Problems: - Declining revenues - Negative earnings growth - Deteriorating fundamentals

Risk Factors: - High volatility (beta) - Financial leverage - Sector-specific concerns

Sector-Specific Considerations

The framework automatically adjusts expectations by sector:

Technology Sector

  • Higher P/E acceptable: Growth companies command premiums
  • R&D investments: May temporarily depress margins
  • Scalability: High incremental margins expected

Utilities Sector

  • Lower growth expected: Regulated, stable businesses
  • Higher debt tolerance: Infrastructure requires capital
  • Dividend focus: Steady cash flow distribution

Financial Sector

  • Different debt metrics: Debt is part of business model
  • Interest rate sensitivity: Performance tied to rate environment
  • Regulatory considerations: Capital requirements and compliance

Energy Sector

  • Cyclical adjustments: Commodity price volatility
  • Capital intensity: High fixed costs and depreciation
  • Environmental factors: Transition and sustainability risks

Best Practices

Starting Your Analysis

  1. Define Investment Thesis: What type of stocks are you seeking?
  2. Choose Appropriate Config: Conservative, growth, value, or custom
  3. Set Realistic Expectations: Understand typical sector characteristics
  4. Review Results Critically: Numbers don't tell the whole story

Interpreting Results

  1. Focus on Pass/Fail First: Understand why stocks are filtered out
  2. Compare Within Sectors: Tech vs tech, utilities vs utilities
  3. Consider Market Context: Bull vs bear market conditions
  4. Look for Red Flags: Unusual metrics or deteriorating trends

Common Pitfalls to Avoid

Over-Optimization: Don't adjust criteria to fit predetermined conclusions

Ignoring Sector Context: A 40 P/E might be reasonable for growth tech

Mechanical Application: Use quantitative results to guide, not replace, qualitative analysis

Timing Ignorance: Great companies at wrong prices are poor investments

Getting Help

Documentation Structure

  • Getting Started: Installation, quick start, configuration basics
  • User Guide: Comprehensive usage (this section)
  • Developer Guide: Extending and customizing the framework
  • API Reference: Technical documentation
  • Tutorials: Step-by-step examples

Support Resources

Next Steps