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

TL;DR

This paper demonstrates how multiobjective evolutionary algorithms can effectively optimize stock portfolios, outperform benchmarks, and satisfy real-world investment constraints such as turnover limits and style adherence.

Contribution

The study introduces a novel application of MOEAs to handle complex investment constraints and improve portfolio performance over traditional methods.

Findings

Portfolios optimized with MOEAs outperform benchmarks.

Generated portfolios meet typical investment constraints.

Asset selection is statistically significant.

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.