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.

국내 금융시장의 변동성의 확대는 개인투자자들의 직접투자를 어렵게 만들면서 펀드를 통한 간접 투자 비중을 증가시켰다. 본 연구의 목적은 여러 가지 형태의 펀드 중에서도 인덱스펀드를 바탕으로 초과수익을 추구하는 인핸스드 인덱스 펀드 모델을 구축하는데 있다. 유전자알고리즘을 활용하여 인덱스펀드 관리를 위한 포트폴리오 최적화 모델을 제안하고, 이렇게 얻은 인덱스펀드의 수익에 초과수익을 얻을 수 있도록 기준지수의 일별 거래대금과 종가를 활용하였다. 실증분석 결과 본 연구의 제안모델은 코스피 200의 움직임을 잘 반영하고 있으며, 이를 활용한 전략은 순수 인덱스펀드에 의한 단순매수 후 보유 전략보다 적절한 개수의 종목을 편입시킨다면 높은 수익률을 가져다줌을 알 수 있었다.

Keywords

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