Clustering financial time series

Nicolas Basalto () and Francesco Carlo ()
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Nicolas Basalto: University of Pavia
Francesco Carlo: University of Bari

A chapter in Practical Fruits of Econophysics, 2006, pp 252-256 from Springer

Abstract: Summary We analyze the shares aggregated into the Dow Jones Industrial Average (DJIA) index in order to recognize groups of stocks sharing synchronous time evolutions. To this purpose, a pairwise version of the Chaotic Map Clustering algorithm is applied: a map is associated to each share and the correlation coefficients of the daily price series provide the coupling strengths among maps. A natural partition of the data arises by simulating a chaotic map dynamics. The detection of clusters of similar stocks can be exploited in portfolio optimization.

Keywords: Clustering algorithms; Chaotic maps; Portfolio optimization (search for similar items in EconPapers)
Date: 2006
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-4-431-28915-9_46

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DOI: 10.1007/4-431-28915-1_46

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