Using MOEAs To Outperform Stock Benchmarks In The Presence of Typical Investment Constraints
AbstractPortfolio 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.
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Bibliographic InfoPaper provided by arXiv.org in its series Papers with number 1109.3488.
Date of creation: Sep 2011
Date of revision: Jan 2012
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Web page: http://arxiv.org/
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