Portfolio Management using Machine Learning: Hierarchical Risk Parity

Portfolio Management using Machine Learning will teach you the implementation of the hierarchical risk parity (HRP) strategy on a set of sixteen stocks and the evaluation of its performance in comparison to the inverse volatility weighted portfolios (IVP), equal-weighted portfolios (EWP), and critical line algorithm (CLA) techniques. Also included are concepts such as risk management, hierarchical clustering, and dendrograms.

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Portfolio Management using Machine Learning with Quantra Are you in search of a dependable method to distribute your capital among various assets within your portfolio? This particular course is worth enrolling in.

1. Implement a hierarchical risk parity approach to allocate weights to a portfolio.
2. Develop a stock screener.
3. Explain inverse volatility weighted portfolios (IVP) and the critical line algorithm (CLA).
4. Conduct backtesting for different portfolio management techniques.
5. Elaborate on the limitations of IVPs, CLA, and equal-weighted portfolios.
6. Calculate and graphically depict portfolio performance statistics, including returns, volatility, and drawdowns.
7. Execute a hierarchical clustering algorithm, elucidating the underlying mathematical principles.
8. Describe dendrograms and provide interpretations of the linkage matrix.

Other course: Risk Parity