A dimension-agnostic neural network jointly learns lag transforms and eigenvalue regularization to produce minimum-variance equity portfolios that outperform non-linear shrinkage estimators in 2000-2024 out-of-sample tests.
Optnet: Differentiable optimization as a layer in neural networks
2 Pith papers cite this work. Polarity classification is still indexing.
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RAYEN enforces hard convex constraints (linear, quadratic, SOC, LMI) on neural networks with negligible overhead while guaranteeing satisfaction at all times.
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End-to-End Large Portfolio Optimization for Variance Minimization with Neural Networks through Covariance Cleaning
A dimension-agnostic neural network jointly learns lag transforms and eigenvalue regularization to produce minimum-variance equity portfolios that outperform non-linear shrinkage estimators in 2000-2024 out-of-sample tests.
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RAYEN: Imposition of Hard Convex Constraints on Neural Networks
RAYEN enforces hard convex constraints (linear, quadratic, SOC, LMI) on neural networks with negligible overhead while guaranteeing satisfaction at all times.