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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
