Generative AI: Crafting Portfolios Tailored to Investor Preferences

Abstract

Investors often face challenges aligning portfolios with specific preferences or regulatory constraints, such as avoiding commodities.Can machine learning offer a solution? Using Graphical Models, we frame this as an inference problem to replicate a desired fund within set constraints. Our approach achieves a high correlation with the target portfolio, allowing cost-effective replication. By harnessing generative AI, we can even enhance the original portfolio’s performance. Applied to hedge funds, our method outperforms benchmarks while maintaining a strong correlation with the original strategy.

Keywords: Machine Learning

JEL Classification: G13