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.

中文翻译：

---

整合因子模型

本文开发了一个综合框架来解决有关正确因子模型的不确定性。资产定价推断利用了一个复合模型，该模型集成了由后验概率加权的竞争因素模型。证据表明，无条件模型记录的概率接近于零，而盈余公告漂移、质量减去垃圾和中介资本是有条件资产定价的有力因素。在样本外，集成模型表现良好，远离随后表现不佳的因素。模型的不确定性使股票看起来风险更高，而模型对预期回报率的分歧在崩盘期间激增。分歧涵盖所有涉及错误定价、因子负荷和风险溢价的回报成分。