Mining group stock portfolio by using grouping genetic algorithms

機構典藏 > College of Engineering > Graduate Institute & Department of Computer Science and Information Engineering > Proceeding > Item 987654321/106403

Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/106403

Title:	Mining group stock portfolio by using grouping genetic algorithms
Authors:	Chen, C. H.;Lin, C. B.;Chen, C. C.
Keywords:	data mining;genetic algorithms;grouping genetic algorithms;grouping problems;stock portfolio optimization
Date:	2015-05-25
Issue Date:	2016-04-27 11:11:59 (UTC+8)
Publisher:	IEEE
Abstract:	In this paper, a grouping genetic algorithm based approach is proposed for dividing stocks into groups and mining a set of stock portfolios, namely group stock portfolio. Each chromosome consists of three parts. Grouping and stock parts are used to indicate how to divide stocks into groups. Stock portfolio part is used to represent the purchased stocks and their purchased units. The fitness of each chromosome is evaluated by the group balance and the portfolio satisfaction. The group balance is utilized to make the groups represented by the chromosome have as similar number of stocks as possible. The portfolio satisfaction is used to evaluate the goodness of profits and satisfaction of investor's requests of all possible portfolio combinations that can generate from a chromosome. Experiments on a real data were also made to show the effectiveness of the proposed approach.
Relation:	Evolutionary Computation (CEC), 2015 IEEE, pp.738-743
DOI:	10.1109/CEC.2015.7256964
Appears in Collections:	[Graduate Institute & Department of Computer Science and Information Engineering] Proceeding

Files in This Item:

File	Description	Size	Format
DetailProgram-ver19.pdf		651Kb	Adobe PDF	65	View/Open
index.html		0Kb	HTML	232	View/Open