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: GU
C Geissler: GU
P Cotte: GU
G Luta: GU

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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.

Date: 2022-06
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