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.
A well-conditioned estimator for large-dimensional covariance matrices.Journal of Multivariate Analysis, 88(2):365–411, 2004
2 Pith papers cite this work. Polarity classification is still indexing.
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A quantum framework introduces C-Estimator and E-Estimator for classical covariance matrices using variational circuits, with regularization to ensure positive definiteness and mitigate barren plateaus, validated via simulations.
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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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Quantum Learning of Classical Correlations with continuous-domain Pauli Correlation Encoding
A quantum framework introduces C-Estimator and E-Estimator for classical covariance matrices using variational circuits, with regularization to ensure positive definiteness and mitigate barren plateaus, validated via simulations.