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Using MOEAs To Outperform Stock Benchmarks In The Presence of Typical Investment Constraints

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  • Andrew Clark
  • Jeff Kenyon

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.

Suggested Citation

  • Andrew Clark & Jeff Kenyon, 2011. "Using MOEAs To Outperform Stock Benchmarks In The Presence of Typical Investment Constraints," Papers 1109.3488, arXiv.org, revised Jan 2012.
  • Handle: RePEc:arx:papers:1109.3488
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    References listed on IDEAS

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