Forecasting U.S. Stock Returns

Abstract

We forecast quarterly US stock returns using 25 predictor variables. We consider a breadth of forecast methods and metrics, including bi- and multi-variate regressions, linear and non-linear models, rolling and recursive techniques, forecast combinations and statistical and economic evaluation. In doing so, we extend existing research both in terms of the range of predictor series and the scope of the analysis. In common with much of literature, a broad view over the full set of predictor 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. Notably, the 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 also reveal that forecast combinations over the full set of variables do not outperform the preferred single variable forecasts, while forecast combinations using an interest rate subset group do 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. This is also consistent with asset pricing models that indicate movements in returns are conditioned by such expectations.