introducing a model for selecting a lower-cost optimal portfolio using smart beta

In recent years, selecting optimal portfolios with higher returns and lower costs compared to a benchmark portfolio has drawn the attention of researchers to examine smart beta in choosing the investment portfolio. due to the significance of the issue, the current research aims to provide a model to select the optimal portfolio at a lower cost using smart beta and compare its performance with the benchmark portfolio in the tehran stock exchange. the present applied research is quantitative regarding its data type. the statistical population includes all active companies on the tehran stock exchange. regarding the many companies accepted on the stock exchange, the asset records were examined from 2014 to 2019; a statistical sample of 148 companies with 15 shares was obtained from systematic elimination sampling. to optimize the portfolio and get the model weights based on the defined models, the genetic algorithm was used, and to solve the algorithm, matlab and spss22 software were used. the results obtained from the algorithm iteration and the optimization of the objective function are equal to 1.23. in consecutive iterations for various rates of mutation and intersection, the obtained values of the objective function are very close to each other, which indicates that the genetic algorithm is suitable to solve this model and that no scattered or outlier solutions exist. the smart beta model provides a better risk-return tradeoff and better performance than the benchmark portfolio in the iranian stock market.