Portfolio construction using Black-Litterman Model

This project explores portfolio optimization and factor investing strategies using quantitative methods. The project focuses on constructing an optimal portfolio by selecting assets based on risk factors, utilizing statistical techniques for covariance matrix estimation, and implementing portfolio optimization models.

Key Findings

1. Covariance Matrix Estimation:
   • Implemented the shrinkage estimator proposed by Ledoit-Wolf (2003) to address the instability of the sample covariance matrix.
2. Portfolio Optimization Models:
   • Explored Mean-Variance Optimization, Max Sharpe Ratio Optimization, and the Black-Litterman model to derive optimal portfolio weight allocations.
3. Factor Exposure Analysis:
   • Investigated factor exposures of securities to target factors, providing insights into the diversification and risk management strategies.
4. Future Enhancements:
   • Recommendations for future improvements include incorporating international markets, refining optimization methods, and exploring advanced topics like individual investor uncertainty.