Factor Model Comparisons with Conditioning Information
开发了在因子和测试资产权重可随滞后信息变化时检验因子模型的方法，推导并评估了一致标准误和有限样本偏差调整，发现多数流行因子模型被拒绝。
Abstract We develop methods for testing factor models when the weights in portfolios of factors and test assets can vary with lagged information. We derive and evaluate consistent standard errors and finite sample bias adjustments for unconditional maximum squared Sharpe ratios and their differences. Bias adjustment using a second-order approximation performs well. We derive optimal zero-beta rates for models with dynamically trading portfolios. Factor models’ Sharpe ratios are larger but standard test asset portfolios’ maximum Sharpe ratios are larger still when there is dynamic trading. As a result, most of the popular factor models are rejected.