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.
Keywords
Cite
@article{arxiv.2204.02757,
title = {Risk budget portfolios with convex Non-negative Matrix Factorization},
author = {Bruno Spilak and Wolfgang Karl Härdle},
journal= {arXiv preprint arXiv:2204.02757},
year = {2023}
}