Exploiting Mean-Variance Portfolio Optimization Problems through Zeroing Neural Networks

Spyridon D. Mourtas and Chrysostomos Kasimis ()
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Spyridon D. Mourtas: Department of Economics, Division of Mathematics and Informatics, National and Kapodistrian University of Athens, Sofokleous 1 Street, 10559 Athens, Greece
Chrysostomos Kasimis: Department of Physics, Electronics Laboratory, University of Patras, 26504 Patras, Greece

Mathematics, 2022, vol. 10, issue 17, 1-20

Abstract: 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.

Keywords: Markowitz framework; mean-variance portfolio optimization (MVPO); zeroing neural network (ZNN); time-varying quadratic programming; time-varying nonlinear programming (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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