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Analysis of financial data using non-negative matrix factorisation
University College Dublin.
University College Dublin.
University College Dublin.ORCID iD: 0000-0002-3912-1470
Laboratory for Advanced Brain Signal Processing Brain Science Institute RIKEN.
2008 (English)In: International Mathematical Forum, Vol. 3, no 38, 1853-70 p.Article in journal (Refereed) Published
Abstract [en]

We apply Non-negative Matrix Factorization (NMF) to the prob- lem 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 In- dustrial Average, into its constitute parts, the underlying trends which govern the financial marketplace. We demonstrate how to impose ap- propriate sparsity and smoothness constraints on the components of the decomposition. Also, we describe how the method clusters stocks to- gether in performance-based groupings which can be used for portfolio diversification.

Place, publisher, year, edition, pages
2008. Vol. 3, no 38, 1853-70 p.
Keyword [en]
Computational finance
National Category
Signal Processing
Research subject
Applied and Computational Mathematics
URN: urn:nbn:se:kth:diva-174166OAI: diva2:858330

QC 20151005

Available from: 2015-10-01 Created: 2015-10-01 Last updated: 2015-10-05Bibliographically approved

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de Fréin, Ruairí
Signal Processing

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