Learning the Shrinkage Intensity: A Data-Driven Approach for Risk-Optimized Portfolios.

We introduce a new type of shrinkage estimator that is not based on asymptotic optimality, but instead learns a state-dependent shrinkage policy via supervised learning in a contextual bandit setup. The proposed estimator applies to both linear and nonlinear shrinkage and shows improved performance...

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Detalles Bibliográficos

Publicado en:	Journal of Financial Econometrics Vol. 24; no. 2; pp. 1 - 31
Autores principales:	Nard, Gianluca De, Kostovic, Damjan
Formato:	Artículo
Publicado:	Oxford University Press / USA 2026
Materias:	Portfolio management (Investments) Supervised learning Algorithms Financial markets Multi-armed bandit problem (Probability theory) Investment policy
Acceso en línea:	Ver este registro en EBSCOhost