2022/04/06 by Bruno Spilak, Wolfgang Karl Härdle, Spilak, Bruno +1
Computer Science · Economics, Econometrics and Finance · #Applications (stat.AP) #Distributed and Parallel Computing Systems #Econometrics (econ.EM) #FOS: Computer and information sciences #FOS: Economics and business #Financial Markets and Investment Strategies #Machine Learning (stat.ML) #Portfolio Management (q-fin.PM)
paper · pdf · doi:10.48550/arxiv.2204.02757
openalex publication_date 2022/04/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.