Clustering stock market companies via chaotic map synchronization

N. Basalto, R. Bellotti, F. De Carlo, P. Facchi and S. Pascazio

Papers from arXiv.org

Abstract: A pairwise clustering approach is applied to the analysis of the Dow Jones index companies, in order to identify similar temporal behavior of the traded stock prices. To this end, the chaotic map clustering algorithm is used, where a map is associated to each company and the correlation coefficients of the financial time series are associated to the coupling strengths between maps. The simulation of a chaotic map dynamics gives rise to a natural partition of the data, as companies belonging to the same industrial branch are often grouped together. The identification of clusters of companies of a given stock market index can be exploited in the portfolio optimization strategies.

Date: 2004-04
References: Add references at CitEc
Citations:

Published in Physica A 345 (2005) 196

Downloads: (external link)
http://arxiv.org/pdf/cond-mat/0404497 Latest version (application/pdf)

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:arx:papers:cond-mat/0404497

Access Statistics for this paper

More papers in Papers from arXiv.org
Bibliographic data for series maintained by arXiv administrators ().