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 differe...

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Bibliographic Details
Published in:Annals of operations research Vol. 299; no. 1-2; pp. 1379 - 1395
Main Authors: D’Urso, Pierpaolo, De Giovanni, Livia, Massari, Riccardo
Format: Journal Article
Language:English
Published: New York Springer US 01.04.2021
Springer
Springer Nature B.V
Subjects:
Analysis
Business and Management
Cluster analysis
Clustering
Combinatorics
Data analysis
Fuzzy algorithms
Fuzzy logic
Fuzzy systems
Identification and classification
Management
Monte Carlo simulation
Multivariate analysis
Operations research
Operations Research/Decision Theory
Outliers (statistics)
Pricing
Regression analysis
S.I.: Recent Developments in Financial Modeling and Risk Management
Stock exchanges
Stock markets
Stocks
Technology application
Theory of Computation
Time dilatation
Time series
Time-series analysis
Volatility
Warping
Italy
Multivariate financial time series
Robust fuzzy C-medoids clustering
FTSE MIB index
Dynamic time warping
Trimming
ISSN:0254-5330, 1572-9338
Online Access:Get full text
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Summary: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.
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ISSN:0254-5330
1572-9338
DOI:10.1007/s10479-019-03284-1