Break Risk

We propose a new approach to forecasting stock returns in the presence of structural breaks that simultaneously affect the parameters of multiple portfolios. Exploiting information in the cross-section increases our ability to identify breaks in return prediction models and enables us to detect breaks more rapidly in real time, thereby allowing the parameters of the predictive return regression to be updated with little delay. Empirically, we find that accounting for breaks in panel return models allows us to generate out-of-sample return forecasts that are significantly more accurate than existing forecasts along both statistical and economical measures of performance. Moreover, we find that firms whose equity risk premium processes are most affected by breaks earn significantly higher average returns than firms with lower break exposure, suggesting that "breaks'' is a risk factor that is priced in the cross-section. Finally, we find that the majority of breaks in equity premiums can be closely tied to breaks in the dividend growth process