Analysis of financial data using non-negative matrix factorisation
2008 (English)In: International Mathematical Forum, Vol. 3, no 38, 1853-70 p.Article in journal (Refereed) Published
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.
Research subject Applied and Computational Mathematics
IdentifiersURN: urn:nbn:se:kth:diva-174166OAI: oai:DiVA.org:kth-174166DiVA: diva2:858330
QC 201510052015-10-012015-10-012015-10-05Bibliographically approved