Integrating Factor Models
开发了一个综合框架来处理因子模型的不确定性，通过整合多个竞争模型并加权后验概率，发现无条件模型概率极低，而条件资产定价中某些因子表现强劲，模型不确定性使股票看起来风险更高。
ABSTRACT This paper develops a comprehensive framework to address uncertainty about the correct factor model. Asset pricing inferences draw on a composite model that integrates over competing factor models weighted by posterior probabilities. Evidence shows that unconditional models record near‐zero probabilities, while postearnings announcement drift, quality‐minus‐junk, and intermediary capital are potent factors in conditional asset pricing. Out‐of‐sample, the integrated model performs well, tilting away from subsequently underperforming factors. Model uncertainty makes equities appear considerably riskier, while model disagreement about expected returns spikes during crash episodes. Disagreement spans all return components involving mispricing, factor loadings, and risk premia.