Risk budget portfolios with convex Non-negative Matrix Factorization
Abstract
We propose a portfolio allocation method based on risk factor budgeting using convex Nonnegative Matrix Factorization (NMF). Unlike classical factor analysis, PCA, or ICA, NMF ensures positive factor loadings to obtain interpretable long-only portfolios. As the NMF factors represent separate sources of risk, they have a quasi-diagonal correlation matrix, promoting diversified portfolio allocations. We evaluate our method in the context of volatility targeting on two long-only global portfolios of cryptocurrencies and traditional assets. Our method outperforms classical portfolio allocations regarding diversification and presents a better risk profile than hierarchical risk parity (HRP). We assess the robustness of our findings using Monte Carlo simulation.
- Publication:
-
arXiv e-prints
- Pub Date:
- April 2022
- DOI:
- 10.48550/arXiv.2204.02757
- arXiv:
- arXiv:2204.02757
- Bibcode:
- 2022arXiv220402757S
- Keywords:
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- Quantitative Finance - Portfolio Management;
- Economics - Econometrics;
- Statistics - Applications;
- Statistics - Machine Learning