Index Fund Optimization Using a Genetic Algorithm and a Heuristic Local Search

摘要

It is well known that index funds are popular passively managed portfolios and have been used very extensively in hedge trading. 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. Thus, index fund optimization can be viewed as a combinatorial optimization problem for portfolio management. In this paper, we propose an optimization method that consists of a genetic algorithm and a heuristic local search algorithm to make strong linear association between the fund's return rates and the changing rates of the market index. We apply our method to the Tokyo Stock Exchange and create index funds whose return rates follow a similar path to the changing rates of the Tokyo Stock Price Index (TOPIX). The results show that our proposed method creates index funds with a strong linear association to the market index with minimal computing time.