Local Search Algorithms for Portfolio Selection: Search Space and Correlation Analysis

Modern Portfolio Theory dates back from the fifties, and quantitative approaches to solve optimization problems stemming from this field have been proposed ever since. We propose a metaheuristic approach for the Portfolio Selection Problem that combines local search and Quadratic Programming, and we compare our approach with an exact solver. Search space and correlation analysis are performed to analyse the algorithm's performance, showing that metaheuristics can be efficiently used to determine optimal portfolio allocation.