Data and Code

Data and Code for Working Papers:

• Shrinking the Term Structure (with D. Filipovic and Y. Ye)
  Discount bond returns and term structure factors:
  • Discount Bond Database
  • GitHub code
• Stripping the Discount Curve—a Robust Machine Learning Approach (with D. Filipovic and Y. Ye)
  Yield of US Treasury zero coupon bonds:
  • Treasury Yield Database
  • GitHub code
• Deep Learning Statistical Arbitrage (with J. Guijarro-Ordonez and G. Zanotti)
  • Code
  • Data for Code
• Selective Multiple Testing: Inference for Large Panels with Many Covariates  (with J. Zhou)
  • GitHub Code

Data and Code for Published Papers:

• Missing Financial Data (with S. Bryzgalova, S. Lerner and M. Lettau)
  Characteristic data with and without imputation:
  • Characteristic data
  • GitHub code
• Forest Through the Trees: Building Cross-Sections of Stock Returns (with S. Bryzgalova and J. Zhu)
  • Code
  • Data for Code
  • AP-Tree Portfolios and Characteristic Data
• Machine-Learning the Skill of Mutual Fund Managers (with R. Kaniel, Z. Lin and S. Van Nieuwerburgh)
  • Data and Code
• Deep Learning in Asset Pricing (with L. Chen and J. Zhu)
  Management Science (2022)
  • SDF, portfolio and economic states time-series
  • Individual stock returns, asset specific characteristics and macroeconomic time-series
  • GitHub code
• Factors that Fit the Time-Series and Cross-Section of Stock Returns (with M. Lettau)
  Review of Financial Studies (2020)
  • RP-PCA Factors
  • Matlab Code for RP-PCA Factors
• Understanding Systematic Risk: A High-Frequency Approach
  Journal of Finance (2020)
  • High-Frequency Stock Returns
  • Intraday and Overnight Stock Returns
  • High-Frequency Factor Returns
  • Intraday and Overnight Factor Returns
  • Matlab Code for High-Frequency Factor Analysis