This paper forecasts earnings per share four - and eight - quarters ahead for 30 Dow firms using out-of-sample combination forecast methods. We show that many financial/economic variables, such as price-earnings ratio, dividend yield and Treasury bill rate, fail to predict out-of-sample EPS...

We develop metrics based on Shapley values for interpreting time-series forecasting models, including “black-box” models from machine learning. Our metrics are model agnostic, so that they are applicable to any model (linear or nonlinear, parametric or nonparametric). Two of the metrics,...