Forecasting stock returns with model uncertainty and parameter instability
比较多种模型平均和变量选择技术预测股票收益，发现结合历史平均并考虑参数不稳定性后，复杂模型表现显著提升，月度样本外R²达1.10%，年化效用增益2.34%。
Summary We compare several representative sophisticated model averaging and variable selection techniques of forecasting stock returns. When estimated traditionally, our results confirm that the simple combination of individual predictors is superior. However, sophisticated models improve dramatically once we combine them with the historical average and take parameter instability into account. An equal weighted combination of the historical average with the standard multivariate predictive regression estimated using the average windows method, for example, achieves a statistically significant monthly out‐of‐sample of 1.10% and annual utility gains of 2.34%. We obtain similar gains for predicting future macroeconomic conditions.