Using investment satisfaction capability index based particle swarm optimization to construct a stock portfolio

被引:43
作者
Chang, Jui-Fang [1 ]
Shi, Peng [2 ,3 ]
机构
[1] Natl Kaohsiung Univ Appl Sci, Dept Int Business, Kaohsiung 807, Taiwan
[2] Univ Glamorgan, Dept Comp & Math Sci, Pontypridd CF37 1DL, M Glam, Wales
[3] Univ S Australia, Div Informat Technol Engn & Environm, Adelaide, SA 5001, Australia
关键词
Artificial intelligence; Particle swarm algorithms; Investment satisfaction capability index; Particle swarm optimization; GENETIC ALGORITHMS;
D O I
10.1016/j.ins.2010.05.008
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The goal of this study is to construct an enhanced process based on the investment satisfied capability index (ISCI). The process is divided into two stages. The first stage is to apply the Process Capability Indices (PCI) for quality management so as to develop a new performance appreciation method. Investors can utilize the ISCI index to rapidly evaluate individual stock performance and then select those stocks which can lead to achieve investment satisfaction. In the second stage, a particle swarm optimization (PSO) algorithm with moving interval windows is applied to find the optimal investment allocation of the stocks in this portfolio. Based on those algorithms we can ensure investment risk control and obtain a more profitable stock investment portfolio. (C) 2010 Elsevier Inc. All rights reserved.
引用
收藏
页码:2989 / 2999
页数:11
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