Data Snooping in Equity Premium Prediction

Abstract

We study the performance of a comprehensive set of equity premium forecasting strategies that have been shown to outperform the historical mean out-of-sample when tested in isolation. Using a multiple testing framework, we find that previous evidence on out-of-sample predictability is primarily due to data snooping. We are not able to identify any forecasting strategy that produces robust and statistically significant economic gains after controlling for data snooping biases and transaction costs. By focusing on the application of equity premium prediction, our findings support Harvey’s (2017) more general concern that many of the published results in financial economics will fail to hold up.

Keywords: Equity risk premium prediction, data snooping bias

JEL Classification: G11, G12, G14