Penetrating sporadic return predictability

Return predictability has been one of the central research questions in finance for many decades. This paper proposes a predictive regression with multiple structural changes to capture the sporadic predictive ability of potential predictors for the return series. An adaptive group Lasso procedure, augmented with a forward regression for break screening, is adopted to efficiently and consistently identify the structural breaks in the predictive regression, with predictors exhibiting low signal strength and heterogeneous degrees of persistence. To enhance the prediction accuracy, adaptive Lasso is further used to eliminate the irrelevant predictors and is shown to achieve the oracle property. Simulation studies demonstrate the effectiveness of the proposed methods in break detection and predictor selection, and further show that ignoring structural breaks could abate predictability. The application to predicting U.S. equity premium illustrates the practical merits of our methodology in revealing return predictability that changes over time.