Analysis of Financial Data Using Non-Negative Matrix Factorization

Ruairí de Fréin, Konstantinos Drakakis, Scott Rickard, Andrzej Cichocki

    Research output: Contribution to journalArticlepeer-review


    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.
    Original languageEnglish
    Pages (from-to)1853 -1870
    JournalInternational Mathematical Forum
    Issue number38
    Publication statusPublished - 2008


    Dive into the research topics of 'Analysis of Financial Data Using Non-Negative Matrix Factorization'. Together they form a unique fingerprint.

    Cite this