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

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.