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Clustering Financial Time Series: How Long is Enough?

Author

Listed:
  • Gautier Marti
  • S'ebastien Andler
  • Frank Nielsen
  • Philippe Donnat

Abstract

Researchers have used from 30 days to several years of daily returns as source data for clustering financial time series based on their correlations. This paper sets up a statistical framework to study the validity of such practices. We first show that clustering correlated random variables from their observed values is statistically consistent. Then, we also give a first empirical answer to the much debated question: How long should the time series be? If too short, the clusters found can be spurious; if too long, dynamics can be smoothed out.

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  • Gautier Marti & S'ebastien Andler & Frank Nielsen & Philippe Donnat, 2016. "Clustering Financial Time Series: How Long is Enough?," Papers 1603.04017, arXiv.org, revised Apr 2016.
    • Handle: RePEc:arx:papers:1603.04017
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    References listed on IDEAS

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    8. Gautier Marti & Philippe Very & Philippe Donnat & Frank Nielsen, 2015. "A proposal of a methodological framework with experimental guidelines to investigate clustering stability on financial time series," Papers 1509.05475, arXiv.org.
    9. Tola, Vincenzo & Lillo, Fabrizio & Gallegati, Mauro & Mantegna, Rosario N., 2008. "Cluster analysis for portfolio optimization," Journal of Economic Dynamics and Control, Elsevier, vol. 32(1), pages 235-258, January.
    10. Wenbo Hu & Alec Kercheval, 2010. "Portfolio optimization for student t and skewed t returns," Quantitative Finance, Taylor & Francis Journals, vol. 10(1), pages 91-105.
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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. Gautier Marti & Frank Nielsen & Philippe Donnat & S'ebastien Andler, 2016. "On clustering financial time series: a need for distances between dependent random variables," Papers 1603.07822, arXiv.org.
    3. Philipp Wirth & Francesca Medda & Thomas Schroder, 2024. "Longitudinal market structure detection using a dynamic modularity-spectral algorithm," Papers 2407.04500, arXiv.org.
    4. Deborah Miori & Mihai Cucuringu, 2022. "Returns-Driven Macro Regimes and Characteristic Lead-Lag Behaviour between Asset Classes," Papers 2209.00268, arXiv.org, revised Sep 2022.
    5. Jos'e Vin'icius de Miranda Cardoso & Jiaxi Ying & Daniel Perez Palomar, 2020. "Algorithms for Learning Graphs in Financial Markets," Papers 2012.15410, arXiv.org.
    6. Xiaoyue Chen & Bin Li & Andrew C. Worthington, 2022. "Economic uncertainty and Australian stock returns," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 62(3), pages 3441-3474, September.
    7. Alejandro Rodriguez Dominguez, 2022. "Portfolio Optimization based on Neural Networks Sensitivities from Assets Dynamics respect Common Drivers," Papers 2202.08921, arXiv.org, revised Dec 2022.

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