Asset Pricing Models: Implications for Expected Returns and Portfolio Selection

Abstract

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.