Using MOEAs to Outperform Stock Benchmarks in the Presence of Typical Investment Constraints
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.
KeywordsAsset selection Financial constraints Multi-objective evolutionary algorithms (MOEA) Multi-period MOEAs Mean-variance optimization (MVO) Portfolio construction
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