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.
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© 2006 Springer-Verlag Tokyo
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Basalto, N., De Carlo, F. (2006). Clustering financial time series. In: Takayasu, H. (eds) Practical Fruits of Econophysics. Springer, Tokyo. https://doi.org/10.1007/4-431-28915-1_46
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DOI: https://doi.org/10.1007/4-431-28915-1_46
Publisher Name: Springer, Tokyo
Print ISBN: 978-4-431-28914-2
Online ISBN: 978-4-431-28915-9
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