Abstract
AbstractForecast 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 nonparametric models outperform their forecast combinations. The latter are prone to in-sample over-fitting and in consequence, perform poorly out-of-sample especially when the set of possible candidates for combinations is large. A reduction to one-dimensional models balances in-sample and out-of-sample performance.
Publisher
Springer Science and Business Media LLC
Subject
Management Science and Operations Research,General Decision Sciences
Cited by
1 articles.
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