Forecasting equity returns with oil prices: addressing model choice uncertainty

We introduce a method to adapt Sala-i-Martin’s (1997) Extreme Bounds Analysis (EBA) to out-of-sample forecasting. This method allows us to robustly detect if the information in a particular variable improves forecasting performance. As an application of our modified EBA, we test whether the information in oil prices can be used to forecast stock returns. Using multiple models with different combinations of predictor variables, we find moderate evidence to suggest that oil prices provide incremental information about out-of-sample stock returns at the aggregate level. However, after combining oil prices with other oil-related variables, we find strong evidence of out-of-sample predictability for returns at the aggregate and sectoral levels. A Monte Carlo simulation exercise confirms that the methodology has the power to identify predictability.