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

Author

Listed:
  • Gianluca De Nard
  • Damjan Kostovic

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.

Suggested Citation

  • Gianluca De Nard & Damjan Kostovic, 2025. "AI shrinkage: a data-driven approach for risk-optimized portfolios," ECON - Working Papers 470, Department of Economics - University of Zurich.
    • Handle: RePEc:zur:econwp:470
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    File URL: https://www.zora.uzh.ch/id/eprint/277803/1/econwp470.pdf
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    More about this item

    Keywords

    Covariance matrix estimation; linear and nonlinear shrinkage; portfolio management reinforcement learning; risk optimization;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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