Exploiting Mean-Variance Portfolio Optimization Problems through Zeroing Neural Networks

In this research, three different time-varying mean-variance portfolio optimization (MVPO) problems are addressed using the zeroing neural network (ZNN) approach. The first two MVPO problems are defined as time-varying quadratic programming (TVQP) problems, while the third MVPO problem is defined as...

Full description

Saved in:
Bibliographic Details
Published in:Mathematics (Basel) 2022-08, Vol.10 (17), p.3079
Main Authors: Mourtas, Spyridon D., Kasimis, Chrysostomos
Format: Article
Language:English
Subjects:
Genetic algorithms
Markowitz framework
Mathematical models
Matlab
mean-variance portfolio optimization (MVPO)
Methods
Neural networks
Nonlinear programming
Optimization
Portfolio management
Quadratic programming
Solvers
time-varying nonlinear programming
time-varying quadratic programming
zeroing neural network (ZNN)
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In this research, three different time-varying mean-variance portfolio optimization (MVPO) problems are addressed using the zeroing neural network (ZNN) approach. The first two MVPO problems are defined as time-varying quadratic programming (TVQP) problems, while the third MVPO problem is defined as a time-varying nonlinear programming (TVNLP) problem. Then, utilizing real-world datasets, the time-varying MVPO problems are addressed by this alternative neural network (NN) solver and conventional MATLAB solvers, and their performances are compared in three various portfolio configurations. The results of the experiments show that the ZNN approach is a magnificent alternative to the conventional methods. To publicize and explore the findings of this study, a MATLAB repository has been established and is freely available on GitHub for any user who is interested.
ISSN:2227-7390
2227-7390
DOI:10.3390/math10173079