Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/322286 
Year of Publication: 
2025
Series/Report no.: 
Working Paper No. 470
Publisher: 
University of Zurich, Department of Economics, 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.
Subjects: 
Covariance matrix estimation
linear and nonlinear shrinkage
portfolio management reinforcement learning
risk optimization
JEL: 
C13
C58
G11
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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