Mean Variance Optimization Using Forward Looking Return Estimates
Despite its theoretical appeal, Markowitz mean variance portfolio optimization is plagued by practical issues. It is especially difficult to obtain reliable estimates of a stock's expected return. Recent research has therefore focused on minimum volatility portfolio optimization, which implicitly assumes that expected returns for all assets are equal. We argue that investors are better off using the implied cost of capital based on analysts' earnings forecasts as a forward looking return estimate. Correcting for predictable analyst forecast errors, we demonstrate that mean variance optimized portfolios based on these estimates outperform on both an absolute and a risk adjusted basis the minimum volatility portfolio as well as naive benchmarks, such as the value weighted and equally weighted market portfolio. The results continue to hold when extending the sample to international markets, using different methods for estimating the forward looking return, including transaction costs, and using different optimization constraints.