Liquidity Risk Portfolio Optimization Using Swarm Intelligence

被引：0
|
作者：
Niu, Ben [1 ,2 ]
Xiao, Han [1 ]
Tan, Lijing [3 ]
Fan, Yan [1 ]
Rao, Junjun [1 ]
机构：
[1] Shenzhen Univ, Coll Management, Shenzhen 518060, Peoples R China
[2] Chinese Acad Sci, Hefei Inst Intelligent Mach, Hefei 230031, Peoples R China
[3] Measurement Specialties Inc, Shenzhen 508107, Peoples R China
来源：
ADVANCED INTELLIGENT COMPUTING THEORIES AND APPLICATIONS | 2010年 / 93卷
关键词：
Liquidity risk; risk appetite; genetic algorithm; bacterial foraging optimization; particle swarm optimization; SELECTION;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The liquidity risk is one of the most important adjustable parameters of the portfolio selection. This paper proposes an improved model considering the liquidity risk and market risk, which makes it more suitable for the actual situation. In the improved model we take into account the risk appetite of investors and other psychological factors. To solve the improved portfolio optimization model with complex constraints, we present a comparative study for three swarm intelligence methods namely genetic algorithm (GA), bacterial foraging optimization (BFO) and particle swarm optimization (PS0). The primary results demonstrate their effectiveness and efficiency.
引用
收藏
页码：551 / +
页数：2
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