Bagged Pretested Portfolio Selection

DOI10.1080/07350015.2022.2110880OpenAlexW4290649034WikidataQ114639486 ScholiaQ114639486MaRDI QIDQ6190724

Winfried Pohlmeier, Ekaterina Kazak

Publication date: 6 March 2024

Published in: Journal of Business & Economic Statistics (Search for Journal in Brave)

Full work available at URL: https://pure.manchester.ac.uk/ws/files/226829078/Bagged_Pretested_Portfolio_Selection.pdf

zbMATH Keywords

adaptive learningbaggingportfolio allocationpretest estimation

Mathematics Subject Classification ID

Applications of statistics to economics (62P20)

Cites Work

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• A well-conditioned estimator for large-dimensional covariance matrices
• Distributional properties of portfolio weights
• Dominating estimators for minimum-variance portfolios
• Robust forecast combinations
• Developing new portfolio strategies by aggregation
• Breakthroughs in statistics. Volume I: Foundations and basic theory
• Analyzing bagging
• Sparse and stable Markowitz portfolios
• Sparse Weighted-Norm Minimum Variance Portfolios
• Vast Portfolio Selection With Gross-Exposure Constraints

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