Extracting Discriminative Features Using Non-negative Matrix Factorization in Financial Distress Data
ABSTRACT In the recent financial crisis the incidence of important cases of bankruptcy led to a growing interest in corporate bankruptcy
prediction models. In addition to building appropriate financial distress prediction models, it is also of extreme importance
to devise dimensionality reduction methods able to extract the most discriminative features. Here we show that Non-Negative
Matrix Factorization (NMF) is a powerful technique for successful extraction of features in this financial setting. NMF is
a technique that decomposes financial multivariate data into a few basis functions and encodings using non-negative constraints.
We propose an approach that first performs proper initialization of NMF taking into account original data using K-means clustering.
Second, builds a bankruptcy prediction model using the discriminative financial ratios extracted by NMF decomposition. Model
predictive accuracies evaluated in real database of French companies with statuses belonging to two classes (healthy and distressed)
are illustrated showing the effectiveness of our approach.
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ABSTRACT: We apply Non-negative Matrix Factorization (NMF) to the problem of identifying underlying trends in stock market data. NMF is a recent and very successful tool for data analysis including image and audio processing; we use it here to decompose a mixture a data, the daily closing prices of the 30 stocks which make up the Dow Jones Industrial Average, into its constitute parts, the underlying trends which govern the financial marketplace. We demonstrate how to impose appropriate sparsity and smoothness constraints on the components of the decomposition. Also, we describe how the method clusters stocks together in performance-based groupings which can be used for portfolio diversification.
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ABSTRACT: Is perception of the whole based on perception of its parts? There is psychological and physiological evidence for parts-based representations in the brain, and certain computational theories of object recognition rely on such representations. But little is known about how brains or computers might learn the parts of objects. Here we demonstrate an algorithm for non-negative matrix factorization that is able to learn parts of faces and semantic features of text. This is in contrast to other methods, such as principal components analysis and vector quantization, that learn holistic, not parts-based, representations. Non-negative matrix factorization is distinguished from the other methods by its use of non-negativity constraints. These constraints lead to a parts-based representation because they allow only additive, not subtractive, combinations. When non-negative matrix factorization is implemented as a neural network, parts-based representations emerge by virtue of two properties: the firing rates of neurons are never negative and synaptic strengths do not change sign.Nature 11/1999; 401(6755):788-91. · 38.60 Impact Factor
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ABSTRACT: The development and use of low-rank approximate nonnegative matrix factorization (NMF) algorithms for feature extraction and identification in the fields of text mining and spectral data analysis are presented. The evolution and convergence properties of hybrid methods based on both sparsity and smoothness constraints for the resulting nonnegative matrix factors are discussed. The interpretability of NMF outputs in specific contexts are provided along with opportunities for future work in the modification of NMF algorithms for large-scale and time-varying data sets.Computational Statistics & Data Analysis. 01/2007;