Bruno Spilak, Wolfgang Karl Härdle
arXiv 6 Apr 2022 · Finance — Portfolio Management · 1 citations (OpenAlex)
arXiv:2204.02757 · PDF · DOI · OpenAlex · Extracted main text
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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Raffinot (2017) Hierarchical clustering-based asset allocation, The Journal of Portfolio Management 44(2): 89–99 | 1.000 | 7 | 3 | 100% |
| 2 | López de Prado (2016) Building diversified portfolios that outperform out of sample, The Journal of Portfolio Management 42: 59–69 | 1.000 | 6 | 4 | 100% |
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| 5 | Ding, Li \ Jordan (2010) Convex and semi-nonnegative matrix factorizations, IEEE Transactions on Pattern Analysis and Machine Intelligence 32(1): 45–55 | 0.874 | 7 | 2 | 100% |
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| 8 | Ding, He \ Simon (2005) On the equivalence of nonnegative matrix factorization and spectral clustering, Proceedings of the 2005 SIAM International Confe… | 0.737 | 3 | 2 | 100% |
| 9 | Petukhina, Klochkov, Härdle \ Zhivotovskiy (2023) Robustifying Markowitz, Journal of Econometrics | 0.644 | 2 | 2 | 100% |
| 10 | Jaeger, Krügel, Marinelli, Papenbrock \ Schwendner (2021) Interpretable machine learning for diversified portfolio construction, The Journal of Financial Data Science | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 52 scored citations.