Index Tracking : A Stock Selection Model using Particle Swarm Optimization

Index tracking has long been of interest for both industry of fund management andacademia. Various methods have been proposed and tested and various issues arediscussed throughout the past 30 years. Yet one issue remains unresolved is how toperform stock selection optimally. In this paper, I propose to use an artificial intelligentmethod – particle swarm optimization (or PSO) to select the most effective stocks totrack a target index most closely.I track the S&P 500 index using a small number of its constituents from 1990 till 2019.Practical constraints such as liquidity (in a form of bid-ask spread), transaction costs(commission), capital requirement are considered. The overall out-of-sample error isconsistent with the literature and shown to be greatly reduced if the rebalancing horizonis shorter and the number of stocks is increased. Also turnovers are lower if rebalancingis more frequent and if more stocks are chosen. Hence, there is a clear tradeoff betweenrebalancing cost and tracking accuracy