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Clustering financial time series

In: Practical Fruits of Econophysics

Author

Listed:
  • Nicolas Basalto

    (University of Pavia)

  • Francesco Carlo

    (University of Bari)

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.

Suggested Citation

  • Nicolas Basalto & Francesco Carlo, 2006. "Clustering financial time series," Springer Books, in: Hideki Takayasu (ed.), Practical Fruits of Econophysics, pages 252-256, Springer.
    • Handle: RePEc:spr:sprchp:978-4-431-28915-9_46
    DOI: 10.1007/4-431-28915-1_46
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    Citations

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    Cited by:

    1. Gautier Marti & Frank Nielsen & Miko{l}aj Bi'nkowski & Philippe Donnat, 2017. "A review of two decades of correlations, hierarchies, networks and clustering in financial markets," Papers 1703.00485, arXiv.org, revised Nov 2020.
    2. Bracewell Paul J & Farhadieh Farinaz & Jowett Clint A & Forbes Don G. R. & Meyer Denny H, 2009. "Was Bradman Denied His Prime?," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 5(4), pages 1-26, October.

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