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Tracing the temporal evolution of clusters in a financial stock market

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  • Argimiro Arratia
  • Alejandra Caba\~na
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    Abstract

    We propose a methodology for clustering financial time series of stocks' returns, and a graphical set-up to quantify and visualise the evolution of these clusters through time. The proposed graphical representation allows for the application of well known algorithms for solving classical combinatorial graph problems, which can be interpreted as problems relevant to portfolio design and investment strategies. We illustrate this graph representation of the evolution of clusters in time and its use on real data from the Madrid Stock Exchange market.

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    File URL: http://arxiv.org/pdf/1111.3127
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    Bibliographic Info

    Paper provided by arXiv.org in its series Papers with number 1111.3127.

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    Date of creation: Nov 2011
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    Handle: RePEc:arx:papers:1111.3127

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    Web page: http://arxiv.org/

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    1. Otranto, Edoardo, 2008. "Clustering heteroskedastic time series by model-based procedures," Computational Statistics & Data Analysis, Elsevier, Elsevier, vol. 52(10), pages 4685-4698, June.
    2. John Davis, 2005. "Introduction," Journal of Economic Methodology, Taylor & Francis Journals, Taylor & Francis Journals, vol. 12(3), pages 361-361.
    3. Robert Tibshirani & Guenther Walther & Trevor Hastie, 2001. "Estimating the number of clusters in a data set via the gap statistic," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(2), pages 411-423.
    4. S. Illeris & G. Akehurst, 2001. "Introduction," The Service Industries Journal, Taylor & Francis Journals, Taylor & Francis Journals, vol. 21(1), pages 1-4, January.
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