A Revamped Sparse Index Tracker Leveraging K–Sparsity and Reduced Portfolio Reshuffling

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

View graph of relations

Related Research Unit(s)

Detail(s)

Original languageEnglish
Title of host publicationNeural Information Processing
Subtitle of host publication30th International Conference, ICONIP 2023, Changsha, China, November 20–23, 2023, Proceedings, Part XII
EditorsBiao Luo, Long Cheng, Zheng-Guang Wu, Hongyi Li, Chaojie Li
Place of PublicationSingapore
PublisherSpringer 
Pages499-512
ISBN (electronic)978-981-99-8148-9
ISBN (print)9789819981472
Publication statusPublished - 2024

Publication series

NameCommunications in Computer and Information Science
Volume1966
ISSN (Print)1865-0929
ISSN (electronic)1865-0937

Conference

Title30th International Conference on Neural Information Processing (ICONIP 2023)
PlaceChina
CityChangsha
Period20 - 23 November 2023

Abstract

In financial engineering, sparse index tracking (SIT) serves as a specialized and cost-effective passive strategy that seeks to replicate a financial index using a representative subset of its constituents. However, many existing SIT algorithms have two imperfections: (1) they do not allow investors to explicitly control the number of assets held in the portfolio, and (2) these algorithms often result in excess purchasing and selling activities during the rebalancing process. To address these deficiencies, this paper first proposes a practical constrained optimization problem. Afterwards, the paper develops the corresponding algorithm, termed the index tracker with four portfolio constraints via projected gradient descent (IT4-PGD). With IT4-PGD, investors can freely define the settings of a portfolio, including the number of holding assets, the maximum holding position, and the maximum turnover ratio of each constituent. Simulation results using real-world data demonstrate that IT4-PGD outperforms existing methods by its lower magnitude of daily tracking error (MDTE) and lower accumulative turnover ratio (ATR). © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Research Area(s)

  • portfolio optimization, projected gradient descent (PGD), Sparse index tracking (SIT), turnover constraint, ℓ0 -norm constraint

Citation Format(s)

A Revamped Sparse Index Tracker Leveraging K–Sparsity and Reduced Portfolio Reshuffling. / Chan, Yiu Yu; Leung, Chi-Sing.
Neural Information Processing: 30th International Conference, ICONIP 2023, Changsha, China, November 20–23, 2023, Proceedings, Part XII. ed. / Biao Luo; Long Cheng; Zheng-Guang Wu; Hongyi Li; Chaojie Li. Singapore: Springer , 2024. p. 499-512 (Communications in Computer and Information Science; Vol. 1966).

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review