Applying separative non-negative matrix factorization to extra-financial data

P Fogel (), C Geissler (), P Cotte () and G Luta
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P Fogel: Advestis
C Geissler: Advestis
P Cotte: Advestis
G Luta: GU - Georgetown University [Washington]

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Abstract: 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.

Keywords: Machine Learning; Dimension reduction; Clustering; Interpretability; Features Engineering; ESG data (search for similar items in EconPapers)
Date: 2021
Note: View the original document on HAL open archive server: https://hal.science/hal-03141876v2
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Published in Bankers Markets & Investors : an academic & professional review, inPress

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