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 Journal |
| Volume | 61 |
| DOIs | |
| Publication status | Published - 2017 Dec 1 |
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All Science Journal Classification (ASJC) codes
- Software
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Using genetic algorithm to support clustering-based portfolio optimization by investor information. / Cheong, Donghyun; Kim, Young Min; Byun, Hyun Woo; Oh, Kyong Joo; Kim, Tae Yoon.
In: Applied Soft Computing Journal, Vol. 61, 01.12.2017, p. 593-602.Research output: Contribution to journal › Article
TY - JOUR
T1 - Using genetic algorithm to support clustering-based portfolio optimization by investor information
AU - Cheong, Donghyun
AU - Kim, Young Min
AU - Byun, Hyun Woo
AU - Oh, Kyong Joo
AU - Kim, Tae Yoon
PY - 2017/12/1
Y1 - 2017/12/1
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.
UR - http://www.scopus.com/inward/record.url?scp=85028936428&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85028936428&partnerID=8YFLogxK
U2 - 10.1016/j.asoc.2017.08.042
DO - 10.1016/j.asoc.2017.08.042
M3 - Article
AN - SCOPUS:85028936428
VL - 61
SP - 593
EP - 602
JO - Applied Soft Computing
JF - Applied Soft Computing
SN - 1568-4946
ER -