This item is available under a Creative Commons License for non-commercial use only
Applied mathematics, Electrical and electronic engineering
We apply Non-negative Matrix Factorization (NMF) to the prob-lem of identifying underlying trends in stock market data. NMF is arecent and very successful tool for data analysis including image andaudio processing; we use it here to decompose a mixture a data, thedaily 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 thedecomposition. Also, we describe how the method clusters stocks to-gether in performance-based groupings which can be used for portfoliodiversification.
Drakakis, K. et al. (2008) Analysis of Financial Data Using Non-Negative Matrix Factorization. International Mathematical Forum, 3 (38). 1853 -1870. DOI: 10.12988/imf