Using genetic algorithm to construct a momentum-based stock fund

Abstract

Portfolio optimization is an important research field in modern finance. An important goal in portfolio optimization is to maximize risk-adjusted returns. In addition, momentum investing has gained a wide acceptance by asset managers. Besides, genetic algorithms (GA), which are based on the ideas of evolution and the concepts of Darwin’s natural selection, have been widely used to generate high-quality solutions to optimization problems. In this paper, we propose an approach using genetic algorithms to construct a momentum-based 130-30 stock fund. We use the Sharpe Ratio as the fitness function for portfolio evaluation and a Mean-Variance Model with Monte Carlo simulation to optimize the portfolio’s long and short positions. Using 2020 market data for the S&P500, our fund outperforms a variety of stock portfolios as well as the S&P500 ETF Fund SPY measured by Total Return, Sharpe Ratio, and Information Ratio.