TY - GEN
T1 - Mining group stock portfolio by using grouping genetic algorithms
AU - Chen, Chun Hao
AU - Lin, Cheng Bon
AU - Chen, Chao Chun
N1 - Funding Information:
This research was supported by the Ministry of Science and Technology of the Republic of China under grant MOST 103-2221-E-032-029 and MOST 103-2221-E-006-155.
Publisher Copyright:
© 2015 IEEE.
PY - 2015/9/10
Y1 - 2015/9/10
N2 - 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.
AB - 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.
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U2 - 10.1109/CEC.2015.7256964
DO - 10.1109/CEC.2015.7256964
M3 - Conference contribution
AN - SCOPUS:84963538273
T3 - 2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings
SP - 738
EP - 743
BT - 2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - IEEE Congress on Evolutionary Computation, CEC 2015
Y2 - 25 May 2015 through 28 May 2015
ER -