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

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.

Automated summary

This paper proposes a robust clustering method for multivariate financial time series, adopting a fuzzy approach and using the Partitioning Around Medoids strategy to consider dynamic time warping distance for neutralizing the negative effects of outliers. The method utilizes a suitable trimming procedure to identify financial time series distant from the data bulk, and is applied to stocks in the FTSE MIB index to identify common time patterns and outliers.