Using genetic algorithm to support clustering-based portfolio optimization by investor information

Donghyun Cheong; Young Min Kim; Hyun Woo Byun; Kyong Joo Oh; Tae Yoon Kim

doi:10.1016/j.asoc.2017.08.042

Using genetic algorithm to support clustering-based portfolio optimization by investor information

Donghyun Cheong, Young Min Kim, Hyun Woo Byun, Kyong Joo Oh, Tae Yoon Kim

Department of Industrial Engineering

Research output: Contribution to journal › Article › peer-review

35 Citations (Scopus)

Abstract

A clustering-based portfolio optimization scheme that employs a genetic algorithm (GA) based on investor information for active portfolio management is presented. Whereas numerous studies have investigated trading behaviors, investor performance, and portfolio investment strategies, few works have developed investment strategies based on investor information. This study is conducted in two phases. First, a basket of portfolio (i.e., a collection of stocks held in individual portfolios) is developed through a cluster analysis of investor information. A GA is then employed to optimize the weights of the selected stocks. And the optimized portfolio is rebalanced to get excess return. It is concluded that the proposed multistage portfolio optimization scheme for active portfolio management generates superior results than previously proposed methods for the Korean stock market.

Original language	English
Pages (from-to)	593-602
Number of pages	10
Journal	Applied Soft Computing
Volume	61
DOIs	https://doi.org/10.1016/j.asoc.2017.08.042
Publication status	Published - 2017 Dec

Bibliographical note

Funding Information:
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning ( NRF-2014R1A2A1A11052670 ).

Publisher Copyright:
© 2017 Elsevier B.V.

All Science Journal Classification (ASJC) codes

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10.1016/j.asoc.2017.08.042

Cite this

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title = "Using genetic algorithm to support clustering-based portfolio optimization by investor information",

abstract = "A clustering-based portfolio optimization scheme that employs a genetic algorithm (GA) based on investor information for active portfolio management is presented. Whereas numerous studies have investigated trading behaviors, investor performance, and portfolio investment strategies, few works have developed investment strategies based on investor information. This study is conducted in two phases. First, a basket of portfolio (i.e., a collection of stocks held in individual portfolios) is developed through a cluster analysis of investor information. A GA is then employed to optimize the weights of the selected stocks. And the optimized portfolio is rebalanced to get excess return. It is concluded that the proposed multistage portfolio optimization scheme for active portfolio management generates superior results than previously proposed methods for the Korean stock market.",

author = "Donghyun Cheong and Kim, {Young Min} and Byun, {Hyun Woo} and Oh, {Kyong Joo} and Kim, {Tae Yoon}",

note = "Funding Information: This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning ( NRF-2014R1A2A1A11052670 ). Publisher Copyright: {\textcopyright} 2017 Elsevier B.V.",

year = "2017",

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doi = "10.1016/j.asoc.2017.08.042",

language = "English",

volume = "61",

pages = "593--602",

journal = "Applied Soft Computing",

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AU - Cheong, Donghyun

AU - Kim, Young Min

AU - Byun, Hyun Woo

AU - Oh, Kyong Joo

AU - Kim, Tae Yoon

N1 - Funding Information: This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning ( NRF-2014R1A2A1A11052670 ). Publisher Copyright: © 2017 Elsevier B.V.

PY - 2017/12

Y1 - 2017/12

N2 - A clustering-based portfolio optimization scheme that employs a genetic algorithm (GA) based on investor information for active portfolio management is presented. Whereas numerous studies have investigated trading behaviors, investor performance, and portfolio investment strategies, few works have developed investment strategies based on investor information. This study is conducted in two phases. First, a basket of portfolio (i.e., a collection of stocks held in individual portfolios) is developed through a cluster analysis of investor information. A GA is then employed to optimize the weights of the selected stocks. And the optimized portfolio is rebalanced to get excess return. It is concluded that the proposed multistage portfolio optimization scheme for active portfolio management generates superior results than previously proposed methods for the Korean stock market.

AB - A clustering-based portfolio optimization scheme that employs a genetic algorithm (GA) based on investor information for active portfolio management is presented. Whereas numerous studies have investigated trading behaviors, investor performance, and portfolio investment strategies, few works have developed investment strategies based on investor information. This study is conducted in two phases. First, a basket of portfolio (i.e., a collection of stocks held in individual portfolios) is developed through a cluster analysis of investor information. A GA is then employed to optimize the weights of the selected stocks. And the optimized portfolio is rebalanced to get excess return. It is concluded that the proposed multistage portfolio optimization scheme for active portfolio management generates superior results than previously proposed methods for the Korean stock market.

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Using genetic algorithm to support clustering-based portfolio optimization by investor information

Abstract

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