Forecasting US Stock Returns

Abstract

We forecast quarterly US stock returns using eighteen predictor variables both individually and in multivariate regressions, with the former also used in forecast combinations. Using rolling and recursive approaches, we consider a range of statistical and economic evaluation measures. We consider linear and non-linear regressions as well as forecast evaluations over both market and economic regimes and calculated on a rolling and recursive basis. The results reveal that the term structure of interest rates consistently provides the preferred forecast performance, especially when evaluated using the Sharpe ratio. Additionally, the purchasing managers index consistently provides a strong forecast performance. A broad view over the full set of predictive variables tends to indicate that such models are unable to beat the historical mean model. However, nuances to these results reveals forecast success varies according to how the forecasts are evaluated and over time. The success of the term structure (and the purchasing managers index) reveals that investor (and firm) expectations of future economic performance provide valuable stock return forecasts and is consistent with asset pricing models that indicate movements in returns are conditioned by such expectations.