An enhanced model for the index tracking problem with transaction costs

Portfolio optimization is one of the most important issues in financial sciences. Various strategies have been used to manage stock portfolios, which can be categorized into types: active and passive strategies. One of the most important passive portfolio management approaches is to form an index tracking portfolio. The purpose of index tracking portfolio is to its performance replicate market index as benchmark as closely as possible with a limited stocks in portfolio which will result in lower transaction costs for investor. In this research, a model for index tracking problem is proposed which aims to minimize undesirable deviations and maximize desirable deviations. Finally, a genetic algorithm is used to solve the leading model. To evaluate the performance of the model, data from four major industries of Tehran Stock Exchange has been used. The results show that the proposed model has a suitable performance in tracking the relevant index and achieving excess return over the benchmark.