Implementation of particle swarm optimization in construction of optimal risky portfolios

Since Markowitz's substantial work, the mean-variance model has revolutionized the way people think about portfolio of assets. According to the modern portfolio theory, the fundamental principle of financial investments is a diversification where investors diversify their investments into different types of assets. Constructing an optimal risky portfolio is a high-dimensional constrained optimization problem where financial investors look for an optimal combination of their investments among different financial assets with the aim of achieving a maximum reward-to-variability ratio. Among the various methodologies suggested, the most popular one is based on maximizing the well-known Sharpe ratio. In this study, we apply particle swarm optimization (PSO) for constructing optimal risky portfolios based on Sharpe ratio for financial investments. A particle swarm solver is developed and tested on a risky investment portfolio. The method is applied to a sample of stocks in Tehran Stock Exchange. Experimental results reveal that the proposed PSO algorithm provides a very feasible and useful tool to assist the investors in planning their investment strategy and constructing their portfolio.