  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.
