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Trimmed fuzzy clustering of financial time series based on dynamic time war**

  • S.I.: Recent Developments in Financial Modeling and Risk Management
  • Published:
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Abstract

In finance, cluster analysis is a tool particularly useful for classifying stock market multivariate time series data related to daily returns, volatility daily stocks returns, commodity prices, volume trading, index, enhanced index tracking portfolio, and so on. In the literature, following different methodological approaches, several clustering methods have been proposed for clustering multivariate time series. In this paper by adopting a fuzzy approach and using the Partitioning Around Medoids strategy, we suggest to cluster multivariate financial time series by considering the dynamic time war** distance. In particular, we proposed a robust clustering method capable to neutralize the negative effects of possible outliers in the clustering process. The clustering method achieves its robustness by adopting a suitable trimming procedure to identify multivariate financial time series more distant from the bulk of data. The proposed clustering method is applied to the stocks composing the FTSE MIB index to identify common time patterns and possible outliers.

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Notes

  1. http://finance.yahoo.com.

  2. Results are available upon request.

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Acknowledgements

The authors thank Editor-in-Chief, and the referees for their useful comments and suggestions which helped to improve the quality and presentation of this manuscript.

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Authors and Affiliations

  1. Department of Social Sciences and Economics, Sapienza - University of Rome, P.za Aldo Moro, 5, 00185, Rome, Italy

    Pierpaolo D’Urso & Riccardo Massari

  2. Department of Political Sciences, LUISS Guido Carli, Rome, Italy

    Livia De Giovanni

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  1. Pierpaolo D’Urso
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  3. Riccardo Massari
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Correspondence to Riccardo Massari.

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D’Urso, P., De Giovanni, L. & Massari, R. Trimmed fuzzy clustering of financial time series based on dynamic time war**. Ann Oper Res 299, 1379–1395 (2021). https://doi.org/10.1007/s10479-019-03284-1

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