S&P 500 Historical Performance Analysis System
A comprehensive research framework for analyzing 100+ years of stock market performance and macro-economic correlations.
Research Objectives
Identify Long-Term Winners
Which stocks delivered exceptional returns over multiple decades?
Business Model Resilience
What business models proved most resilient across economic cycles?
Impact of Economic Events
How did recessions, wars, and policy changes affect sectors?
Early Indicators
What early signals predicted long-term winners vs. losers?
Survivorship Analysis
Why did some companies thrive while others failed?
Project Overview
This system analyzes the maximum available historical data (up to 100 years) for S&P 500 companies to identify patterns of long-term outperformance and their correlation with macro-economic factors. The focus is on monthly data to reduce noise and emphasize trends across economic cycles.
The framework is built with Python scripts and Jupyter notebooks, automating data collection, performance analysis, and macro-economic correlation studies. Outputs include raw data, comprehensive metrics, sector analysis, and human-readable reports.
The project is designed for researchers, academics, and investors seeking to understand the drivers of long-term business success in the stock market.
Project Details
Technologies Used
System Architecture
Historical Data Collector
Downloads and analyzes maximum historical data for all S&P 500 stocks. Outputs raw data, rankings, and survivorship analysis. (historical_sp500_analyzer.py)
Macro-Economic Correlation Analyzer
Correlates stock performance with economic cycles and major events. Outputs era-based and event-based analysis. (macro_correlation_analyzer.py)
Structured Output
Organized output: raw data, analysis, reports, and charts for further research.
Key Metrics Calculated
Methodology & Limitations
Survivorship Bias Awareness
Focus on current S&P 500 members. Excludes failed companies, creating upward bias.
Data Quality
Stock splits/dividends adjusted, missing data handled, corporate actions considered.
Statistical Approach
Monthly log returns, risk-adjusted metrics, rolling analysis for trends.
Limitations
Data availability, rate limiting, memory usage, correlation ≠ causation.
Getting Started
- Clone or download the project files from GitHub.
- Install Python dependencies:
pip install yfinance pandas numpy requests beautifulsoup4 matplotlib seaborn scipy
- Collect historical data:
python historical_sp500_analyzer.py
- Run macro-economic analysis:
python macro_correlation_analyzer.py
- Explore the generated reports and charts in the output folders.
Expected Results & Insights
- Long-term performance patterns by sector and era
- Identification of "Hall of Fame" stocks (50+ years, 12%+ CAGR)
- Sector rotation and macro-economic correlation insights
- Recession-resilient and high-risk stocks
- Comprehensive data and reports for further research
Ready to Discover the Secrets of Long-Term Market Outperformance?
Start with the historical analyzer and see what patterns emerge from decades of market data!