Forecast combinations for benchmarks of long-term stock returns using machine learning methods
研究了多种方法寻找股票超额收益预测的最优权重组合，发现单个非参数模型优于预测组合，后者易过拟合且样本外表现差。
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 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.