Investment portfolio optimization using genetic algorithm

Abstract

In investment, it is highly desirable to maximize return or profit within a given risk level. Constructing a portfolio of investments to optimize the outcome is among the most significant financial decisions facing individuals and institutions. Essentially the standard portfolio optimization problem is to identify the optimal allocation of limited resources among a limited set of investments. Optimality is measured using a tradeoff between perceived risk and expected return. Expected future returns are based on historical data. Risk is measured by the variance of those historical returns. In this project, Genetic Algorithm is explored to tackle the multi-objective portfolio problem. GA is inspired from evolution process in which species evolve to improve themselves. This technique has received much attention in the past few years due to its powerful optimization and structure determining capabilities.