NDLI: Index fund optimization using a genetic algorithm and a heuristic local search algorithm on scatter diagrams

Content Provider	IEEE Xplore Digital Library
Author	Orito, Y. Yamamoto, H.
Copyright Year	2007
Description	Author affiliation: Ashikaga Inst. of Technol., Tochigi (Orito, Y.)
Abstract	It is well known that index funds are popular passively managed portfolios and have been used very extensively for investment. Index funds consist of a certain number of stocks of listed companies on a stock market such that the fund's return rates follow a similar path to the changing rates of the market indices. However it is hard to make a perfect index fund consisting of all companies included in the market. Thus, the index fund optimization can be viewed as a combinatorial optimization for portfolio managements. In this paper, we propose a method that consists of a genetic algorithm and a heuristic local search algorithm to maximize the correlation between the fund's return rates and the changing rates of the market index. We then apply the method to the Tokyo Stock Exchange and compare it with a GA method and a hybrid GA method. The results show that our proposed method is effective for the index fund optimization.
Starting Page	2562
Ending Page	2568
File Size	278853
Page Count	7
File Format	PDF
ISBN	9781424413393
DOI	10.1109/CEC.2007.4424793
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-09-25
Publisher Place	Singapore
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Genetic algorithms Heuristic algorithms Scattering Evolutionary computation
Content Type	Text
Resource Type	Article

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