Using genetic algorithms to develop volatility index-assisted hierarchical portfolio optimization

변동성 지수기반 유전자 알고리즘을 활용한 계층구조 포트폴리오 최적화에 관한 연구

  • Byun, Hyun-Woo (Department of Information and Industial Engineering, Yonsei University) ;
  • Song, Chi-Woo (Department of Information and Industial Engineering, Yonsei University) ;
  • Han, Sung-Kwon (Department of Information and Industial Engineering, Yonsei University) ;
  • Lee, Tae-Kyu (Department of Information and Industial Engineering, Yonsei University) ;
  • Oh, Kyong-Joo (Department of Information and Industial Engineering, Yonsei University)
  • 변현우 (연세대학교 정보산업공학과) ;
  • 송치우 (연세대학교 정보산업공학과) ;
  • 한성권 (연세대학교 정보산업공학과) ;
  • 이태규 (연세대학교 정보산업공학과) ;
  • 오경주 (연세대학교 정보산업공학과)
  • Published : 2009.11.30

Abstract

The expansion of volatility in Korean Stock Market made it more difficult for the individual to invest directly and increased the weight of indirect investment through a fund. The purpose of this study is to construct the EIF(enhanced index fund) model achieves an excessive return among several types of fund. For this purpose, this paper propose portfolio optimization model to manage an index fund by using GA(genetic algorithm), and apply the trading amount and the closing price of standard index to earn an excessive return add to index fund return. The result of the empirical analysis of this study suggested that the proposed model is well represented the trend of KOSPI 200 and the new investment strategies using this can make higher returns than Buy-and-Hold strategy by an index fund, if an appropriate number of stocks included.

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