HCGM-NET: A deep unfolding network for financial index tracking

Ruben Alan J Pauwels, Evangelia Tsiligianni, Nikos Deligiannis

Research output: Chapter in Book/Report/Conference proceedingConference paper

Abstract

Tracking the performance of a financial index by selecting asubset of assets composing the index is a problem that raisesseveral difficulties due to the large size of the stock market.Typically, optimisation algorithms with high complexity areemployed to address such problems. In this paper, we focus on sparse index tracking and employ a Frank-Wolfe-based algorithm which we translate into a deep neural network. This strategy, known as deep unfolding, leads to a learned model with high accuracy at a low computational cost. To the bestof our knowledge, this is the first deep unfolding design pro-posed for financial data processing. Numerical experimentsdemonstrate the superior performance of our approach.
Original languageEnglish
Title of host publicationIEEE International Conference on Acoustics, Speech and Signal Processing
Subtitle of host publicationICASSP
PublisherIEEE
Pages1-5
Number of pages5
Publication statusAccepted/In press - 2021
Event2021 IEEE International Conference on Acoustics, Speech and Signal Processing - Metro Toronto Convention Centre, Toronto, Canada
Duration: 6 Jun 202111 Jun 2021
https://2021.ieeeicassp.org

Conference

Conference2021 IEEE International Conference on Acoustics, Speech and Signal Processing
Abbreviated titleICASSP 2021
CountryCanada
CityToronto
Period6/06/2111/06/21
Internet address

Keywords

  • financial index tracking
  • sparse portfolio selection
  • conditional gradient method
  • deep unfolding

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