Asset Pricing Models: Implications for Expected Returns and Portfolio Selection

43 Pages Posted: 27 Aug 1998  

A. Craig Mackinlay

University of Pennsylvania - Finance Department; National Bureau of Economic Research (NBER)

Lubos Pastor

University of Chicago - Booth School of Business; Centre for Economic Policy Research (CEPR); National Bureau of Economic Research (NBER)

Multiple version iconThere are 2 versions of this paper

Date Written: December 1999

Abstract

Implications of factor-based asset pricing models for estimation of expected returns and for portfolio selection are investigated. In the presence of model mispricing due to a missing factor, the mispricing and the residual covariance matrix are linked together. Imposing a strong form of this link leads to expected return estimates that are more precise and more stable over time than unrestricted estimates. Optimal portfolio weights that incorporate the link when no factors are observable are proportional to expected return estimates, effectively using an identity matrix as a covariance matrix. The resulting portfolios perform well both in simulations and in out-of-sample comparisons.

JEL Classification: G11, G12

Suggested Citation

Mackinlay, A. Craig and Pastor, Lubos, Asset Pricing Models: Implications for Expected Returns and Portfolio Selection (December 1999). CRSP Working Paper No. 510; Rodney L. White Center for Financial Research Working Paper No. 019-98. Available at SSRN: https://ssrn.com/abstract=112748 or http://dx.doi.org/10.2139/ssrn.112748

Archie Craig MacKinlay (Contact Author)

University of Pennsylvania - Finance Department ( email )

The Wharton School
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215-898-5309 (Phone)

National Bureau of Economic Research (NBER)

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Lubos Pastor

University of Chicago - Booth School of Business ( email )

5807 S. Woodlawn Avenue
Chicago, IL 60637
United States
773-834-4080 (Phone)
773-702-0458 (Fax)

HOME PAGE: http://www.ChicagoGSB.edu/fac/lubos.pastor/

Centre for Economic Policy Research (CEPR)

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London, EC1V 3PZ
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National Bureau of Economic Research (NBER)

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