Advances in Portfolio Optimization by Genetic Algorithms

Abstract

Porfolio Optimization is an important problem for financial engineering. It consists of finding out the best investment weights for a large group of assets, so that the Expected Return of those assets is maximized and the specific risk of the portfolio is minimized. This problem can be modeled as a Parameter Optimization problem, and Genetic Algorithms have shown better results every year. In this paper we review recently proposed techniques to optimize Financial Portfolio using Historical Price values, compare them, and draw up proposals about how to improve these results even further.