Minimax and Biobjective Portfolio Selection Based on Collaborative Neurodynamic Optimization.
IEEE Trans Neural Netw Learn Syst
; 32(7): 2825-2836, 2021 07.
Article
in En
| MEDLINE
| ID: mdl-31902773
ABSTRACT
Portfolio selection is one of the important issues in financial investments. This article is concerned with portfolio selection based on collaborative neurodynamic optimization. The classic Markowitz mean-variance (MV) framework and its variant mean conditional value-at-risk (CVaR) are formulated as minimax and biobjective portfolio selection problems. Neurodynamic approaches are then applied for solving these optimization problems. For each of the problems, multiple neural networks work collaboratively to characterize the efficient frontier by means of particle swarm optimization (PSO)-based weight optimization. Experimental results with stock data from four major markets show the performance and characteristics of the collaborative neurodynamic approaches to the portfolio optimization problems.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Neural Networks, Computer
/
Models, Economic
/
Models, Neurological
Type of study:
Health_economic_evaluation
/
Prognostic_studies
Language:
En
Journal:
IEEE Trans Neural Netw Learn Syst
Year:
2021
Document type:
Article