Core Resources:

1. Traditional Statistical Models

• Introduction to OLS
• OLS with Python
• Python Documentation

2. Machine Learning Regression

• Introduction to ML Linear Regression
• Gradient Descent

3. Financial Applications

• Fama-French Models:
  • Common Risk Factors in Stock Returns (1993)
  • A Five-Factor Asset Pricing Model (2015)
• Implementation Resources:
  • WRDS Fama-French Factors Documentation
  • WRDS Factor Construction Guide
  • WRDS Python Factor Construction
• Data Sources:
  • WRDS Database
  • Kenneth French Data Library

Weekly Assignment

Tasks:

1. Data Collection
   • Download Fama-French factors from Kenneth French's website
   • Gather stock return data for analysis
   • Prepare risk-free rate data
2. Factor Construction (3 factors required, 5 optional)
   • Replicate the Fama-French factor construction following the WRDS Python implementation guide
   • Compare your constructed factors with the official Fama-French factors
   • Document any discrepancies and potential causes
3. Model Implementation (3 factors required, 5 optional)
   • Code the three-factor model regression
   • Extend to five-factor model
   • Calculate factor loadings
4. Analysis
   • Compare model performances
   • Analyze factor contributions
   • Create summary visualizations

Resources and Implementation Guides:

• Official Documentation:
  • WRDS Fama-French Factors Documentation
  • WRDS Factor Construction Guide
  • WRDS Python Factor Construction
• Data Sources:
  • WRDS Database Access
  • Kenneth French Data Library
• Additional Reading:
  • Fama & French (1993) - Common Risk Factors in Stock Returns
  • Fama & French (2015) - A Five-Factor Asset Pricing Model