Non-Negative Matrix Factorization for Stock Market Pricing

Author

Liu, Tang

Author_Institution

Dept. of Math., Tianjin Univ. of Finance & Econ., Tianjin, China

fYear

2009

fDate

17-19 Oct. 2009

Firstpage

1

Lastpage

5

Abstract

In this paper, we use non-negative matrix factorization (NMF) to analyze the data from stock market. By using the multiplicative update rules algorithm, we decompose the data matrix V of the daily closing prices of the 40 stocks, of which the Shenzhen component index is made up, into two matrices W and H, in which the columns of W correlate to the underlying trends. In addition, the Euclidean distance between the 40 stocks and the underlying forces is constructed. By means of the K-means routine in MATLAB, the 40 stocks are classified into different clusters with the center of the underlying forces, which can be finished automatically by MATLAB. Finally, the properties of these clusters are studied.

Keywords

mathematics computing; matrix decomposition; pricing; stock markets; Euclidean distance; K-means routine; MATLAB; Shenzhen component index; multiplicative update rules algorithm; nonnegative matrix factorization; stock market pricing; Clustering algorithms; Data analysis; Euclidean distance; Finance; MATLAB; Mathematics; Matrix decomposition; Portfolios; Pricing; Stock markets;

fLanguage

English

Publisher

ieee

Conference_Titel

Biomedical Engineering and Informatics, 2009. BMEI '09. 2nd International Conference on

Conference_Location

Tianjin

Print_ISBN

978-1-4244-4132-7

Electronic_ISBN

978-1-4244-4134-1

Type

conf

DOI

10.1109/BMEI.2009.5304773

Filename

5304773