Using MOEAs to Outperform Stock Benchmarks in the Presence of Typical Investment Constraints

Chapter
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 170)

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 multi-objective 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

Asset selection Financial constraints Multi-objective evolutionary algorithms (MOEA) Multi-period MOEAs Mean-variance optimization (MVO) Portfolio construction 

References

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Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  1. 1.Thomson ReutersDenverUSA

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