Applying separative non-negative matrix factorization to extra-financial data
Computational Finance
2022-06-10 v1 Machine Learning
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.
Cite
@article{arxiv.2206.04350,
title = {Applying separative non-negative matrix factorization to extra-financial data},
author = {P Fogel and C Geissler and P Cotte and G Luta},
journal= {arXiv preprint arXiv:2206.04350},
year = {2022}
}