A Collaborative Neurodynamic Optimization Approach to Bicriteria Portfolio Selection

被引：3
|
作者：
Leung, Man-Fai [1 ,3 ]
Wang, Jun [1 ,2 ,3 ]
机构：
[1] City Univ Hong Kong, Dept Comp Sci, Kowloon, Hong Kong, Peoples R China
[2] City Univ Hong Kong, Sch Data Sci, Kowloon, Hong Kong, Peoples R China
[3] City Univ Hong Kong, Shenzhen Res Inst, Shenzhen, Peoples R China
来源：
ADVANCES IN NEURAL NETWORKS - ISNN 2019, PT I | 2019年 / 11554卷
基金：
中国国家自然科学基金;
关键词：
Portfolio selection; Multiobjective optimization; Neurodynamic optimization approach; RECURRENT NEURAL-NETWORK; LIMITING ACTIVATION FUNCTION; VARIATIONAL-INEQUALITIES; ALGORITHM;
D O I：
10.1007/978-3-030-22796-8_34
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, a collaborative neurodynamic optimization approach is applied for bicriteria portfolio selection in the Markowitz mean-variance framework. The bicriteria portfolio selection problem consists of two objectives (risk and return) which are scalarized using a weighted Chebyshev function. Multiple neurodynamic optimization models are used to generate a set of Pareto-optimal solutions. Particle swarm optimization is used to diversify the Pareto-optimal solutions by optimizing the weights of the scalarized objective functions. Experimental results show the superiority of the applied approach.
引用
收藏
页码：318 / 327
页数：10
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