000			02232cam a2200349 4500500
005			20250112004332.0
041			_afre
042			_adc
100	1	0	_aFogel, Paul _eauthor
700	1	0	_a Geissler, Christophe _eauthor
700	1	0	_a Von Mettenheim, Hans-Jörg _eauthor
700	1	0	_a Luta, George _eauthor
245	0	0	_aApplying non-negative tensor factorization to centered data
260			_c2023.
500			_a44
520			_aWe present here an original application of the non-negative matrix factorization (NMF) method, applied to the case of extra-financial data. NMF allows to reduce the useful dimension of a dataset by simultaneously creating new meta-features linked to the original variables through non-negative loadings, and nonnegative scores linking the observations to the meta-features. Thanks to the non-negativity constraints, meta-features can be easily interpreted by looking at the features with the highest loadings in the NMF representation. However, the lowest loadings are generally ignored. We show that this asymmetrical treatment can be problematic in some instances of data sets. The innovation introduced in this paper is to apply a tensorized version of NMF to centered data, which we call Semi Non-Negative Tensor Factorization (semi-NTF). The method is illustrated on a set of ESG scores of European equity issuers, resulting in a fully interpretable reduced set of meta-features. In particular, we show that the scores associated with these meta-features are significantly less correlated with each other than the ready-to-use ESG scores, leading to improved discriminatory power of the meta-features. JEL Classification: C02, C14, C65, C81.
690			_aPCA
690			_asemi-NTF
690			_aPrincipal Components
690			_aESG data
690			_aInterpretability
690			_aFactor Analysis
690			_aSemi-NMF
690			_aPosNegNMF
690			_aNTF
690			_aClassification Methods
690			_aCluster Analysis
690			_aDimension Reduction
690			_aNMF
786	0		_nBankers, Markets & Investors | 174 | 3 | 2023-11-02 | p. 2-13
856	4	1	_uhttps://shs.cairn.info/revue-bankers-markets-et-investors-2023-3-page-2?lang=en
999			_c99726 _d99726