A shrinkage approach to model uncertainty and asset allocation

This article takes a shrinkage approach to examine the empirical implications of aversion to model uncertainty. The shrinkage approach explicitly shows how predictive distributions incorporate data and prior beliefs. It enables us to solve the optimal portfolios for uncertainty-averse investors. Aversion to uncertainty about the capital asset pricing model leads investors to hold a portfolio that is not mean-variance efficient for any predictive distribution. However, mean-variance efficient portfolios corresponding to extremely strong beliefs in the Fama-French model are approximately optimal for uncertainty-averse investors. The empirical Bayes approach does not result in optimal portfolios for investors who are averse to model uncertainty.