Forecasting U.S. stock returns

Abstract

We forecast quarterly US stock returns using a breadth of forecast variables, methods and metrics, including linear and non-linear regressions, rolling and recursive techniques, forecast combinations and statistical and economic evaluation. Thus, extending research in terms of the range of predictor series and the scope of analysis. Consistent with much of literature, a broad view over the full set of predictor variables indicates that such models are unable to beat the historical mean model. However, nuances reveal forecast success varies according to how the forecasts are evaluated and over time. Results reveal that the term structure of interest rates consistently provides the preferred forecast performance, especially when evaluated using the Sharpe ratio. The purchasing managers index also consistently provides a strong forecast performance. Further results reveal that forecast combinations over the full set of variables do not outperform the preferred single variable forecasts, while an interest rate forecast combination subset does perform well. The success of the term structure and the purchasing managers index highlights the importance of, respectively, investor and firm expectations of future economic performance in providing valuable stock return forecasts and is consistent with asset pricing models that indicate movements in returns are conditioned by such expectations.