Alpha Signals, Smart Betas, and Factor Model Alignment
研究了在投资组合构建中，是否应将经理的阿尔法信号纳入风险模型。发现机械添加风险因子可能扭曲因子结构，而遗漏隐藏因子比误加虚假因子损失更大。
The authors consider the case for augmenting risk models to be used in portfolio construction to reflect information embedded in the portfolio manager’s alphas. They consider both smart beta models and cases in which alpha signals are partly factor driven but incorrectly perceived to be stock specific. In smart beta cases, the authors argue that mechanically augmenting the risk model can cause losses by distorting an otherwise-correct factor structure. The authors show that for cases in which asset-specific alpha signals might unexpectedly be related to hidden systematic factors, errors of <i>omission</i>—missing these hidden factors—generally result in larger expected losses in portfolio efficiency than do errors of <i>commission</i>—unintentionally including nonexistent “phantom” factors. When the alpha signals are very noisy, the practice of mechanically augmenting the risk model with a custom risk factor to offset that noise can improve portfolio efficiency. However, in those cases, the custom risk factor has nothing to do with underlying sources of true risk that all investors face, but instead serves as a penalty that in a back-door way tends to adjust for weak quality of the manager’s alphas. <b>TOPICS:</b>Factor-based models, portfolio construction