The YTM-based Stock Portfolio Mining Approach by Genetic Algorithm

被引：0
|
作者：
Chen, Chun-Hao [1 ]
Hsieh, Ching-Yu [1 ]
Lee, Yeong-Chyi [2 ]
机构：
[1] Tamkang Univ, Dept Comp Sci & Informat Engn, Taipei, Taiwan
[2] Cheng Shiu Univ, Dept Management Informat, Kaohsiung, Taiwan
来源：
2013 IEEE INTERNATIONAL CONFERENCE ON GRANULAR COMPUTING (GRC) | 2013年
关键词：
portfolio selection; genetic algorithm; yield to maturity; transaction lots; M-V model; MINIMUM TRANSACTION LOTS; SELECTION; COSTS; OPTIMIZATION; CONSTRAINTS;
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：
This study proposes a yield-to-maturity (YTM)-based genetic portfolio selection model with user defined constraints, namely YTMGPSM. A set of real numbers are encoded into a chromosome to form a possible portfolio, which presents whether buy or not buy and purchased units of assets. The fitness value of a chromosome is evaluated by return on investment, value at risk and suitability of the respective portfolio. The suitability of a chromosome consists of portfolio penalty and investment capital penalty that are used to reflect the satisfactions of user predefined maximum investment and maximum number of companies, respectively. Experiments on real dataset are made to show the merits of the proposed approach.
引用
收藏
页码：38 / 42
页数：5
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