Using MOEAs to Outperform Stock Benchmarks in the Presence of Typical Investment Constraints
21 Pages Posted: 24 Jul 2011 Last revised: 3 Jan 2012
Date Written: July 17, 2011
Abstract
Portfolio managers are typically constrained by turnover limits, minimum and maximum stock positions, cardinality, a target market capitalization and sometimes the need to hew to a style (such as growth or value). In addition, portfolio managers often use multifactor stock models to choose stocks based upon their respective fundamental data.
We use multiobjective evolutionary algorithms (MOEAs) to satisfy the above real-world constraints. The portfolios generated consistently outperform typical performance benchmarks and have statistically significant asset selection.
Keywords: Multiobjective Optimization, Evolutionary Algorithms, Portfolio Optimization, Linear and Nonlinear Constraints
JEL Classification: C60,C61,C63
Suggested Citation: Suggested Citation