This repository contains an analysis and implementation of portfolio allocation strategies within the S&P 500 investment universe.
- The project analyzes sector and factor indices of the S&P 500, focusing on portfolio construction and quantitative evaluation of strategies.
- Historical data includes prices and market capitalizations of 11 sector indices and 5 factor indices.
- Key metrics include Sharpe Ratio, volatility, maximum drawdown, and diversification ratio.
- Markowitz Efficient Frontier:
- Computed portfolios with Minimum Variance and Maximum Sharpe Ratio.
- Enhanced Constraints:
- Custom constraints for sector weights to manage risk and diversification.
- Resampled Frontiers:
- Used Monte Carlo simulations to improve robustness under dynamic market conditions.
- Black-Litterman Model:
- Incorporated investor views into the Markowitz framework for stable allocations.
- Alternative Optimization:
- Portfolios optimized for diversification (entropy, diversification ratio) and VaR-modified Sharpe Ratio.
- Portfolios maximizing the Sharpe Ratio showed high returns but increased concentration in sectors like Information Technology and Momentum.
- Minimum Variance portfolios offered more stability, ideal for risk-averse investors.
- Resampling and advanced models, such as Black-Litterman, improved adaptability to real-world data.
This project was completed as part of the Computational Finance course.