Trimmed fuzzy clustering of financial time series based on dynamic time warping

Pierpaolo D’Urso (), Livia Giovanni () and Riccardo Massari ()
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Pierpaolo D’Urso: Sapienza - University of Rome
Livia Giovanni: LUISS Guido Carli
Riccardo Massari: Sapienza - University of Rome

Annals of Operations Research, 2021, vol. 299, issue 1, No 54, 1379-1395

Abstract: 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 warping 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.

Keywords: Robust fuzzy C-medoids clustering; Trimming; Dynamic time warping; Multivariate financial time series; FTSE MIB index (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s10479-019-03284-1

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