The Poor Predictive Performance of Asset Pricing Models
研究条件性和无条件性资产定价模型对投资组合和个股预期收益的时间序列预测误差，发现条件模型通常产生更高的均方预测误差，且预测方差过大导致其预测能力不如常数基准。
Abstract This paper examines time-series forecast errors of expected returns from conditional and unconditional asset pricing models for portfolio and individual firm equity returns. A new result that increases predictive precision concerning model specification and forecasting is introduced. Conditional versions of the models generally produce higher mean squared errors than unconditional versions for step ahead prediction. This holds for individual firm data when the instruments are firm specific. Mean square forecast error decompositions indicate that the asset pricing models produce relatively unbiased predictions, but the variance is severe enough to ruin the step ahead predictive ability beyond that of a constant benchmark.