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About this paper
Jaydip Sen, Sidra Mehtab, Abhishek Dutta, Saikat Mondal
Researchers at Praxis Business School systematically compared the Hierarchical Risk Parity (HRP) and Critical Line Algorithm (CLA) for designing investment portfolios on NIFTY 50 stocks and seven Indian sectors. The study found that HRP consistently yielded higher out-of-sample Sharpe Ratios for 7 out of 8 sectors while providing greater diversification, despite CLA sometimes performing better on in-sample data.
Key insights
- The Hierarchical Risk Parity (HRP) algorithm, by incorporating techniques like hierarchical clustering and recursive bisection, inherently produces more diversified portfolios, which contributes to its superior out-of-sample stability.
- Traditional mean-variance optimization, represented by CLA, struggles with out-of-sample performance due to its sensitivity to input errors, leading to unstable and concentrated asset allocations.
- The effectiveness of portfolio optimization algorithms can vary by market sector, with HRP demonstrating broad applicability but specific sectors, like pharmaceuticals, showing different optimal performance trends.
Summary by alphaXiv.
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