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

Clark, Andrew and Kenyon, Jeff, Using MOEAs to Outperform Stock Benchmarks in the Presence of Typical Investment Constraints (July 17, 2011). Journal of Investing, February 2012, Available at SSRN: https://ssrn.com/abstract=1893644 or http://dx.doi.org/10.2139/ssrn.1893644

Jeff Kenyon

Thomson Reuters ( email )

3 Times Square
New York, NY 10036
United States

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