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.

翻译：我们提出了一种基于凸非负矩阵分解（NMF）的风险因子预算投资组合配置方法。与经典因子分析、主成分分析（PCA）或独立成分分析（ICA）不同，NMF确保因子载荷为正，从而获得可解释的纯多头投资组合。由于NMF因子代表独立的风险来源，其相关矩阵呈准对角结构，有助于实现多样化的投资组合配置。我们以波动率目标策略为背景，对两个包含加密货币和传统资产的全球纯多头投资组合进行了方法评估。实验表明，该方法在分散化程度上优于传统投资组合配置，且相比层次风险平价（HRP）具有更优的风险特征。我们通过蒙特卡洛模拟评估了结果稳健性。