Overview
Risk Runners employs a multi-faceted approach to corporate risk assessment, combining quantitative analysis with qualitative insights derived from regulatory filings. Our methodology is designed to provide comprehensive, comparable, and actionable risk intelligence across thousands of publicly traded companies.
The foundation of our analysis rests on SEC filings, particularly the "Risk Factors" sections of 10-K annual reports, supplemented by industry analysis, market data, and proprietary risk scoring algorithms.
📊 Data Sources
SEC Filings (Primary)
10-K Reports • Risk Factor Sections • ~2019 Base Year
Comprehensive analysis of risk factor disclosures from annual reports, focusing on forward-looking risk statements and management assessments.
Industry Classifications
GICS Sectors • SIC Codes • Custom Categories
Multi-level industry classification system enabling sector-specific risk analysis and peer group comparisons.
Market Data
Market Cap • Trading Volume • Volatility Metrics
Quantitative market indicators providing context for risk assessment and company size categorization.
🔄 Data Processing Pipeline
Document Extraction
Automated extraction of risk factor sections from SEC EDGAR filings using pattern recognition and NLP techniques
Text Analysis
Natural language processing to identify key risk themes, sentiment analysis, and risk factor categorization
Risk Categorization
Classification of risks into standardized categories: regulatory, operational, financial, market, and strategic
Relationship Mapping
Identification of risk interconnections and creation of associative networks between risk factors
Risk Category Framework
Risk Scoring Methodology
Our risk scoring system combines multiple quantitative and qualitative factors to provide a comprehensive risk assessment for each company. The scoring methodology is designed to be transparent, consistent, and comparable across companies and industries.
Quantitative Factors (40%)
Financial metrics, volatility measures, debt ratios, and market-based risk indicators
Qualitative Analysis (35%)
Risk factor disclosure analysis, management commentary, and industry-specific considerations
Industry Context (15%)
Sector-specific risk patterns, regulatory environment, and competitive dynamics
Market Indicators (10%)
Market capitalization, liquidity metrics, and analyst coverage considerations
🛠️ Technical Implementation
Natural Language Processing
Python • NLTK • spaCy • Custom Models
Advanced NLP techniques for text extraction, sentiment analysis, and risk factor identification from unstructured SEC filings.
Data Storage & Indexing
JSON • Elasticsearch • Full-text Search
Optimized data structures and search indices enabling fast, flexible querying across large datasets of risk information.
Visualization Engine
D3.js • Network Analysis • Interactive Charts
Dynamic visualization system for displaying risk relationships, industry networks, and associative risk webs.
⚠️ Methodology Limitations
While our methodology provides valuable insights into corporate risk profiles, users should be aware of several important limitations:
Temporal Constraints
Analysis based primarily on 2019 SEC filings; risk profiles may have evolved significantly since then
Disclosure Dependency
Risk assessment limited to what companies choose to disclose in their regulatory filings
Subjective Elements
Some risk categorization and scoring involves subjective judgment and interpretation
Static Analysis
Point-in-time analysis that doesn't capture real-time changes in risk profiles
🚀 Future Enhancements
Machine Learning
Advanced ML models for risk pattern recognition, predictive analytics, and automated risk scoring
Real-time Updates
Continuous monitoring of new filings and real-time risk profile updates as companies report
Alternative Data
Integration of news sentiment, social media indicators, and other alternative data sources
Quantitative Models
Enhanced quantitative risk models incorporating market data and financial metrics
Validation & Quality Assurance
Our methodology undergoes continuous validation and refinement to ensure accuracy and reliability:
- Cross-validation: Risk scores validated against known market events and company performance
- Industry expertise: Methodology reviewed by financial professionals and risk management experts
- Peer comparison: Risk assessments benchmarked against industry standards and peer analysis
- Backtesting: Historical validation of risk predictions against actual company outcomes
- Continuous improvement: Regular updates based on user feedback and methodological advances