Index Tracking Based on Dynamic Time Warping and Constrained k-medoids Clustering

Ran Zhang, Hongzong Li, Jun Wang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Index tracking is a passive investment strategy by replicating a financial market index using its constituents. In this paper, index tracking is addressed based on k-medoids clustering. k-medoids clustering is formulated as a valuation-constrained k-median problem to cluster index constituents. The dissimilarity coefficients among stocks are measured by using dynamic time warping. Experimental results of index tracking on four major indices are elaborated to demonstrate that the tracking performance of the proposed method with dynamic time warping is superior to that with Pearson correlation coefficients.

Original languageEnglish
Title of host publication11th International Conference on Intelligent Control and Information Processing, ICICIP 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages352-359
Number of pages8
ISBN (Electronic)9781665425155
DOIs
Publication statusPublished - 2021
Externally publishedYes
Event11th International Conference on Intelligent Control and Information Processing, ICICIP 2021 - Dali, China
Duration: 3 Dec 20217 Dec 2021

Publication series

Name11th International Conference on Intelligent Control and Information Processing, ICICIP 2021

Conference

Conference11th International Conference on Intelligent Control and Information Processing, ICICIP 2021
Country/TerritoryChina
CityDali
Period3/12/217/12/21

Keywords

  • dynamic time warping
  • Index tracking
  • k-medoids clustering

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