Modeling the Cross Section of Stock Returns: A Model Pooling Approach
提出模型池化方法替代传统模型选择，通过结合多个资产定价模型来预测股票收益横截面，在样本外表现优于任何单一模型，尤其在经济压力时期效果显著，对资产配置决策有实用价值。
Abstract Model selection (i.e., the choice of an asset pricing model to the exclusion of competing models) is an inherently misguided strategy when the true model is unavailable to the researcher. This paper illustrates the advantages of a model pooling approach in characterizing the cross section of stock returns. The optimal pool combines models using the log predictive score criterion, a measure of the out-of-sample performance of each model, and consistently outperforms the best individual model. The benefits to model pooling are most pronounced during periods of economic stress, and it is a valuable tool for asset allocation decisions.