Predictive Systems: Living with Imperfect Predictors
提出一个估计预期收益的框架（预测系统），允许预测因子与条件预期收益不完全相关。实证发现，关于意外收益与预期收益创新之间相关性的先验信念（很可能为负）显著影响预期收益估计和预测有用性评估。相比标准预测回归，该系统能给出不同且精度更高的预期收益估计。
ABSTRACT We develop a framework for estimating expected returns—a predictive system —that allows predictors to be imperfectly correlated with the conditional expected return. When predictors are imperfect, the estimated expected return depends on past returns in a manner that hinges on the correlation between unexpected returns and innovations in expected returns. We find empirically that prior beliefs about this correlation, which is most likely negative, substantially affect estimates of expected returns as well as various inferences about predictability, including assessments of a predictor's usefulness. Compared to standard predictive regressions, predictive systems deliver different expected returns with higher estimated precision.