Another Two-Timescale Duplex Neurodynamic Approach to Portfolio Selection

Man Fai Leung, Jun Wang, Hangjun Che

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

Abstract

This paper is concerned with portfolio selection based on the Markowitz mean-variance framework using neurodynamic optimization. The portfolio optimization problem is formulated as a biconvex optimization problem. A two-timescale duplex neurodynamic approach is then applied for solving the profolio selection problem. The approach makes use of two recurrent neural networks (RNNs) which operate at different timescales for local search. A particle swarm optimization algorithm is employed to update the neuronal states of the two RNNs for global optima. Experimental results on four stock market datasets show the superior performance of the neurodynamic approach in terms of long-term expected returns.

Original languageEnglish
Title of host publication11th International Conference on Intelligent Control and Information Processing, ICICIP 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages387-391
Number of pages5
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

  • local search
  • neural networks
  • portfolio optimization
  • Two-timescale

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