Shrinking return forecasts
提出一种将给定模型预测向基准模型预测收缩的新方法，模拟显示优于预测组合等流行方法，应用于标普500指数回报预测时发现显著可预测性。
Abstract We develop a new approach that shrinks a given model forecast to the benchmark model forecast in order to improve forecasting performance. Simulation results show the superior performance of our approach, relative to popular methods such as forecast combination and the robustness to model misspecification. We apply our method to forecasting the returns on the S&P 500 index and find significant predictability when shrinking the principal component (PC) regression forecasts based on statistical and economic evaluation criteria. The forecast improvement from our shrinkage approach can be explained by the ability of its hyperparameters to be better predict real economic changes.