Detecting correlation in stock market

Jörg D. Wichard, Christian Merkwirth and Maciej Ogorzałek

Physica A: Statistical Mechanics and its Applications, 2004, vol. 344, issue 1, 308-311

Abstract: We present a new method for detecting dependencies in the stock market. In order to find hidden correlations in the daily returns, we build cross prediction models and use the normalized modeling error as a generalized correlation measure that extends the concept of the classical correlation matrix.

Keywords: Econophysics; Multivariate analysis; Time series analysis (search for similar items in EconPapers)
Date: 2004
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0378437104009550
Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:344:y:2004:i:1:p:308-311

DOI: 10.1016/j.physa.2004.06.140

Access Statistics for this article

Physica A: Statistical Mechanics and its Applications is currently edited by K. A. Dawson, J. O. Indekeu, H.E. Stanley and C. Tsallis

More articles in Physica A: Statistical Mechanics and its Applications from Elsevier
Bibliographic data for series maintained by Catherine Liu ().