Index Fund Optimization Using Genetic Algorithm and Scatter Diagram Based on Coefficients of Determination

Abstract

Index fund optimization is one of portfolio optimizations and can be viewed as a combinatorial optimization for portfolio managements. It is well known that an index fund consisting of stocks of listed companies on a stock market is very useful for hedge trading if the total return rate of a fund follows a similar path to the rate of change of a market index. In this paper, we propose a method that consists of a genetic algorithm and a heuristic local search on scatter diagrams to make linear association between the return rates and the rates of change strong. A coefficient of determination is adopted as a linear association measure of how the return rates follow the rates of change. We then apply the method to the Tokyo Stock Exchange. The results show that the method is effective for the index fund optimization.