Using genetic algorithm to support portfolio optimization for index fund management

Kyong Joo Oh, Tae Yoon Kim, Sungky Min

Research output: Contribution to journalArticlepeer-review

120 Citations (Scopus)

Abstract

Using genetic algorithm (GA), this study proposes a portfolio optimization scheme for index fund management. Index fund is one of popular strategies in portfolio management that aims at matching the performance of the benchmark index such as the S&P 500 in New York and the FTSE 100 in London as closely as possible. This strategy is taken by fund managers particularly when they are not sure about outperforming the market and adjust themselves to average performance. Recently, it is noticed that the performances of index funds are better than those of many other actively managed mutual funds [Elton, E., Gruber, G., & Blake, C. (1996). Survivorship bias and mutual fund performance. Review of Financial Studies, 9, 1097-1120; Gruber, M. J. (1996). Another puzzle: the growth in actively managed mutual funds. Journal of Finance, 51(3), 783-810; Malkiel, B. (1995). Returns from investing in equity mutual funds 1971 to 1991. Journal of Finance, 50, 549-572]. The main objective of this paper is to report that index fund could improve its performance greatly with the proposed GA portfolio scheme, which will be demonstrated for index fund designed to track Korea Stock Price Index (KOSPI) 200.

Original languageEnglish
Pages (from-to)371-379
Number of pages9
JournalExpert Systems with Applications
Volume28
Issue number2
DOIs
Publication statusPublished - 2005 Feb

Bibliographical note

Funding Information:
This research was financially supported by Hansung University in the year of 2003. We are very grateful to Chun Soo Park and Jung Soon Lee, who work for Hyundai Securities Co., for their careful comments. In addition, we would like to express our thanks to Yang Jin Kim, a devoted librarian of Hansung University.

All Science Journal Classification (ASJC) codes

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence

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