Black-Litterman End-to-End
Duraj and Yu (2023) study portfolio optimization in bonds and credit using a feedforward neural network. The abstract mentions excess returns and the size of the universe but not a benchmark, an out-of-sample test, transaction costs or multiple testing.
“BLEnd2End significantly outperforms the mean-variance benchmarks based on PCA factor models on test set.”
AssetBonds and creditETFs, bonds
StrategyPortfolio optimizationmomentum
TechniqueFeedforward networklinear factor model
ClaimExcess return
Also stated: Macro, universe of 14 ETFs
Missing from abstract: benchmark, out-of-sample test, costs, multiple testing
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The paper's neighbourhoodPortfolio optimization by year
The family is counted across the whole universe. Asset, method and authors are counted inside the family.
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