Asset Pricing Models: Implications for Expected Returns and Portfolio Selection
当资产定价模型遗漏风险因子时，错误定价会嵌入残差协方差矩阵。利用这一现象可得到比标准方法更稳定精确的预期收益估计，并改进投资组合选择。模拟和样本外比较表明，无因子时最优组合权重与预期收益成比例且表现良好。
When a risk factor is missing from an asset pricing model, the resulting mispricing is embedded within the residual covariance matrix. Exploiting this phenomenon leads to expected return estimates that are more stable and precise than estimates delivered by standard methods. Portfolio selection can also be improved. At an extreme, optimal portfolio weights are proportional to expected returns when no factors are observable. We find that such portfolios perform well in simulations and in out-of-sample comparisons.