Artificial Intelligence Based Method For Portfolio Selection Using Particle Swarm Optimization And Gentic Algorithms

The construction of an investment portfolio with tradable assets is one of the most studied problems in Finance and is done correctly through an optimal asset allocation. This research attempts to construct an efficient portfolio using an artificial intelligence approach using Particle Swarm Optimization (PSO) technique and the use of the genetic algorithm (GA) to find the best parameters setting for the PSO model. Historical price quotes for companies listed in the Dow Jones Industrial Average (DJIA) at different times frames were used to build a portfolio with the best Sharpe Ratio (SR) by maximizing the rate of return and minimizing risk.