Applying separative non-negative matrix factorization to extra-financial data
| Autor: | Fogel, P, Geissler, C, Cotte, P, Luta, G |
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| Přispěvatelé: | Advestis, Georgetown University [Washington] (GU), Morizet, Nicolas |
| Rok vydání: | 2022 |
| Předmět: |
[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI]
FOS: Computer and information sciences [QFIN.CP] Quantitative Finance [q-fin]/Computational Finance [q-fin.CP] Computational Finance (q-fin.CP) Machine Learning (stat.ML) [STAT.ML] Statistics [stat]/Machine Learning [stat.ML] Clustering [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] Machine Learning FOS: Economics and business Quantitative Finance - Computational Finance [STAT.ML]Statistics [stat]/Machine Learning [stat.ML] Statistics - Machine Learning Dimension reduction Interpretability ESG data Features Engineering |
| Zdroj: | Bankers Markets & Investors : an academic & professional review Bankers Markets & Investors : an academic & professional review, Groupe Banque, In press |
| ISSN: | 2101-9304 |
| DOI: | 10.48550/arxiv.2206.04350 |
| Popis: | International audience; We present here an original application of the non-negative matrix factorization (NMF) method, for the case of extra-financial data. These data are subject to high correlations between co-variables, as well as between observations. NMF provides a much more relevant clustering of co-variables and observations than a simple principal component analysis (PCA). In addition, we show that an initial data separation step before applying NMF further improves the quality of the clustering. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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