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A Graphical Tool for Describing the Temporal Evolution of Clusters in Financial Stock Markets

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  • Argimiro Arratia
  • Alejandra Cabaña

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. Copyright Springer Science+Business Media New York 2013

Suggested Citation

  • Argimiro Arratia & Alejandra Cabaña, 2013. "A Graphical Tool for Describing the Temporal Evolution of Clusters in Financial Stock Markets," Computational Economics, Springer;Society for Computational Economics, vol. 41(2), pages 213-231, February.
    • Handle: RePEc:kap:compec:v:41:y:2013:i:2:p:213-231
    DOI: 10.1007/s10614-012-9327-x
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    References listed on IDEAS

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    1. Otranto, Edoardo, 2008. "Clustering heteroskedastic time series by model-based procedures," Computational Statistics & Data Analysis, Elsevier, vol. 52(10), pages 4685-4698, June.
    2. 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.
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