AI shrinkage: a data-driven approach for risk-optimized portfolios

Gianluca De Nard and Damjan Kostovic

No 470, ECON - Working Papers from Department of Economics - University of Zurich

Abstract: The paper introduces a new type of shrinkage estimation that is not based on asymptotic optimality but uses artificial intelligence (AI) techniques to shrink the sample eigenvalues. The proposed AI Shrinkage estimator applies to both linear and nonlinear shrinkage, demonstrating improved performance compared to the classic shrinkage estimators. Our results demonstrate that reinforcement learning solutions identify a downward bias in classic shrinkage intensity estimates derived under the i.i.d. assumption and automatically correct for it in response to prevailing market conditions. Additionally, our data-driven approach enables more efficient implementation of risk-optimized portfolios and is well-suited for real-world investment applications including various optimization constraints.

Keywords: Covariance matrix estimation; linear and nonlinear shrinkage; portfolio management reinforcement learning; risk optimization (search for similar items in EconPapers)
JEL-codes: C13 C58 G11 (search for similar items in EconPapers)
Date: 2025-05
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Persistent link: https://EconPapers.repec.org/RePEc:zur:econwp:470

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