Applying separative non-negative matrix factorization to extra-financial data
Mis à jour : mars 4
P. Fogel, C. Geissler, P. Cotte and G. Luta. Bankers Markets & Investors: an academic & professional review, Groupe Banque, In press. February 2021.
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.