TY - JOUR
T1 - Minimax and Biobjective Portfolio Selection Based on Collaborative Neurodynamic Optimization
AU - Leung, Man Fai
AU - Wang, Jun
N1 - Funding Information:
Manuscript received December 20, 2018; revised September 6, 2019 and November 28, 2019; accepted November 28, 2019. Date of publication January 1, 2020; date of current version July 7, 2021. This work was supported in part by the Research Grants Council of the Hong Kong Special Administrative Region of China under Grant 11208517 and Grant 11202019, in part by the National Natural Science Foundation of China under Grant 61673330, and in part by the Open University of Hong Kong Research Grant R5083/2019/20 S&T. (Corresponding author: Jun Wang.) M.-F. Leung is with the School of Science and Technology, The Open University of Hong Kong, Hong Kong (e-mail: [email protected]).
Publisher Copyright:
© 2012 IEEE.
PY - 2021/7
Y1 - 2021/7
N2 - 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.
AB - 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.
KW - Conditional value-at-risk
KW - mean-variance (MV)
KW - minimax optimization
KW - multiobjective optimization
KW - neurodynamics
KW - portfolio selection
UR - http://www.scopus.com/inward/record.url?scp=85111949710&partnerID=8YFLogxK
U2 - 10.1109/TNNLS.2019.2957105
DO - 10.1109/TNNLS.2019.2957105
M3 - Article
C2 - 31902773
AN - SCOPUS:85111949710
SN - 2162-237X
VL - 32
SP - 2825
EP - 2836
JO - IEEE Transactions on Neural Networks and Learning Systems
JF - IEEE Transactions on Neural Networks and Learning Systems
IS - 7
M1 - 8948344
ER -