Neural Networks for Portfolio Analysis in High-Frequency Trading

Xinwei Cao, Chen Peng*, Yuhua Zheng*, Shuai Li, Tran Thu Ha, Victor Shutyaev, Vasilios Katsikis, Predrag Stanimirovic

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

10 Citations (Scopus)

Abstract

High-frequency trading proposes new challenges to classical portfolio selection problems. Especially, the timely and accurate solution of portfolios is highly demanded in financial market nowadays. This article makes progress along this direction by proposing novel neural networks with softmax equalization to address the problem. To the best of our knowledge, this is the first time that softmax technique is used to deal with equation constraints in portfolio selections. Theoretical analysis shows that the proposed method is globally convergent to the optimum of the optimization formulation of portfolio selection. Experiments based on real stock data verify the effectiveness of the proposed solution. It is worth mentioning that the two proposed models achieve 5.50% and 5.47% less cost, respectively, than the solution obtained by using MATLAB dedicated solvers, which demonstrates the superiority of the proposed strategies.

Original languageEnglish
Pages (from-to)18052-18061
Number of pages10
JournalIEEE Transactions on Neural Networks and Learning Systems
Volume35
Issue number12
DOIs
Publication statusPublished - 2024
MoE publication typeA1 Journal article-refereed

Funding

The work of Chen Peng was supported in part by the Natural Science Foundation of China under Grant 62006095 and in part by the Natural Science Foundation of Hunan Province, China, under Grant 2021JJ40441. The work of Victor Shutyaev was supported by the Russian Science Foundation under Project 20-11-20057.

Keywords

  • Global convergence
  • Markowitz model
  • neural dynamics
  • Pareto frontier
  • portfolio optimization

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