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 not clear. We compare the performance of the two models from an investor's perspective. In the Bayesian setup, we allow for various distributions of the coefficient of determination 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 performance of both models.
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