Abstract Forecasting equity risk premium is notoriously difficult due to a mass of data noise in the raw series and the absence of clear tendency. Using the monthly S&P 500 excess returns from 1927:01 to 2018:12, we first de‐noise the in‐sample original returns series via wavelet method to capture the basic trend of equity risk premium, and then propose forecasting models to obtain one‐step forward out‐of‐sample predicted values based on the de‐noised returns. Our new models can provide substantially superior out‐of‐sample performance c...
