Black-Litterman, Bayesian Shrinkage, and Factor Models in Portfolio Selection: You Can Have It All
Chong (2023) studies portfolio optimization in equities using Bayesian methods. The abstract mentions excess returns and a benchmark but not an out-of-sample test, transaction costs, multiple testing or the size of the universe.
“While shrinkage estimators and factor models have been introduced to improve estimation accuracy through bias-variance trade-offs, and the Black-Litterman model has been developed to integrate investor opinions, a unified framework combining three approaches has been lacking.”
AssetEquities
StrategyPortfolio optimization
TechniqueBayesianlinear factor model
GeographyUSEurope
Also stated: Excess return, benchmark
Missing from abstract: out-of-sample test, costs, multiple testing, universe
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The paper's neighbourhoodPortfolio optimization by year
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