A neurodynamic approach for solving portfolio optimisation problem in high-frequency trading based on charnes-chooper transformation

This study addresses real-time portfolio optimisation in high-frequency trading by formulating it as a single-ratio fractional programming problem. By utilising the Charnes-Cooper transformation, we convert fractional programming into non-fractional programming. Subsequently, we introduce a projecti...

Full description

Saved in:
Bibliographic Details
Published in:International journal of systems science Vol. 56; no. 7; pp. 1561 - 1576
Main Authors: Zhu, Wenli, Chen, Jia, Hu, Jin
Format: Journal Article
Language:English
Published: London Taylor & Francis 19.05.2025
Taylor & Francis Ltd
Subjects:
Charnes-Cooper transformation
Mathematical programming
Neural networks
neurodynamic optimisation
Optimization
Portfolio optimisation
Real time
Systems science
ISSN:0020-7721, 1464-5319
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This study addresses real-time portfolio optimisation in high-frequency trading by formulating it as a single-ratio fractional programming problem. By utilising the Charnes-Cooper transformation, we convert fractional programming into non-fractional programming. Subsequently, we introduce a projection neural network to solve this non-fractional programming efficiently. Theoretical analysis substantiates that our neural network model exhibits exponential convergence to the optimal solution of the problem, underpinning its efficacy. The numerical simulations validate the reliability and efficiency of the obtained results.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0020-7721
1464-5319
DOI:10.1080/00207721.2024.2428844