2022
DOI: 10.1007/s10479-022-04880-4
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Forecast combinations for benchmarks of long-term stock returns using machine learning methods

Abstract: Forecast combinations are a popular way of reducing the mean squared forecast error when multiple candidate models for a target variable are available. We apply different approaches to finding (optimal) weights for forecasts of stock returns in excess of different benchmarks. Our focus lies thereby on nonlinear predictive functions estimated by a fully nonparametric smoother with the covariates and the smoothing parameters chosen by cross-validation. Based on an out-of-sample study, we find that individual non… Show more

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