Shrinking return forecasts

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.