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

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review

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Author(s)

Detail(s)

Original languageEnglish
Title of host publication2021 11th International Conference on Intelligent Control and Information Processing (ICICIP)
PublisherIEEE
Pages352-359
ISBN (Electronic)978-1-6654-2515-5
Publication statusPublished - Dec 2021

Publication series

NameInternational Conference on Intelligent Control and Information Processing, ICICIP

Conference

Title11th International Conference on Intelligent Control and Information Processing (ICICIP 2021)
PlaceChina
CityDali
Period3 - 7 December 2021

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.

Research Area(s)

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

Citation Format(s)

Index Tracking Based on Dynamic Time Warping and Constrained k-medoids Clustering. / Zhang, Ran; Li, Hongzong; Wang, Jun.

2021 11th International Conference on Intelligent Control and Information Processing (ICICIP). IEEE, 2021. p. 352-359 (International Conference on Intelligent Control and Information Processing, ICICIP).

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review