Bond Risk Premia with Machine Learning
Bianchi, Büchner and Tamoni (2019) study machine learning return prediction using a feedforward neural network. The abstract mentions excess returns and an out-of-sample test but not a benchmark, transaction costs, multiple testing or the size of the universe.
“Neural networks outperform both linear penalized regressions and non-linear shallow learners like random forests.”
StrategyMachine learningmacro signals
TechniqueFeedforward networktree ensemble
FrequencyMonthly
ClaimExcess return
Also stated: out-of-sample test
Missing from abstract: benchmark, costs, multiple testing, universe
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The paper's neighbourhoodMachine learning return prediction by year
The family is counted across the whole universe. Asset, method and authors are counted inside the family.
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