Is Imperfection Better? Evidence from Predicting Stock and Bond Returns*

Abstract

The standard predictive regression assumes expected returns to be perfectly correlated with predictors. In the recently introduced predictive system, imperfect predictors account only for a partial variance in expected returns. However, the out-of-sample benefits of relaxing the assumption of perfect correlation are unclear. We compare the performance of the two models from an investor’s perspective. In the Bayesian setup, we allow for various distributions of R2 to account for different degrees of optimism about predictability. We find that relaxing the assumption of perfect predictors does not pay off out-of-sample. Furthermore, extreme optimism or pessimism reduces the performance of both models.