Analysis of Financial Data Using Non-Negative Matrix Factorization

de Fréin, Ruairí and Drakakis, Konstantinos and Rickard, Scott and Cichocki, Andrzej (2008) Analysis of Financial Data Using Non-Negative Matrix Factorization. International Mathematical Forum, 3 (38). 1853 -1870.

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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.

Item Type: Article
Departments or Groups: Walton Institute for Information and Communications Systems Science
Divisions: School of Science > Department of Computing, Maths and Physics
Depositing User: Ruairi De Frein
Date Deposited: 02 Dec 2013 11:44
Last Modified: 22 Aug 2016 10:27

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