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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, ISSN 1312-7594, E-ISSN 1314-7536, 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
Identifiers
URN: urn:nbn:se:kth:diva-174166OAI: oai:DiVA.org:kth-174166DiVA: diva2:858330
Note

QC 20151005

Available from: 2015-10-01 Created: 2015-10-01 Last updated: 2017-12-01Bibliographically approved

Open Access in DiVA

No full text

Other links

http://www.m-hikari.com/imf-password2008/37-40-2008/drakakisIMF37-40-2008.pdf

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de Fréin, Ruairí

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  • apa
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  • de-DE
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  • fi-FI
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  • Other locale
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Output format
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