Data Snooping in Equity Premium Prediction

Abstract

We study the performance of a comprehensive set of equity premium forecasting strategies. All of these strategies have been found to outperform the mean in previous academic publications. However, using a multiple testing framework to account for data snooping, our findings support Goyal and Welch (2008) – almost all equity premium forecasts fail to beat the mean out-of-sample. Only few forecasting strategies that are based on Ferreira and Santa-Clara’s (2011) “sum-of-the-parts” approach generate robust and statistically significant economic gains relative to the historical mean even after controlling for data snooping and accounting for transaction costs.

Keywords: Equity risk premium prediction, data snooping bias

JEL Classification: G11, G12, G14