Chapter 6. Forecasting Stock Returns We survey the literature on stock return forecasting highlighting the challenges faced by forecasters as well as strategies for improving return forecasts. We focus on U.S. equity premium forecastability and illustrate key issues via an empirical application based on updated data. Some studies argue that despite extensive in sample evidence of equity premium predictability popular predictors from the literature fail to outperform the simple historical average benchmark forecast in out of sample tests. Recent studies however provide improved forecasting strategies that deliver statistically and economically significant out of sample gains relative to the historical average benchmark. These strategies – including economically motivated model restrictions forecast combination diffusion indices and regime shifts – improve forecasting performance by addressing the substantial model uncertainty and parameter instability surrounding the data generating process for stock returns. In addition to the U.S. equity premium we succinctly survey out of sample evidence supporting U.S. cross sectional and international stock return forecastability. The significant evidence of stock return forecastability worldwide has important implications for the development of both asset pricing models and investment management strategies.