Mean-Variance Optimization Using Forward-Looking Return Estimates

43 Pages Posted: 1 Oct 2017 Last revised: 5 Oct 2017

See all articles by Patrick Bielstein

Patrick Bielstein

Barclays PLC

Matthias X. Hanauer

Technische Universität München (TUM); Robeco Asset Management

Date Written: October 1, 2017


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.

Keywords: Portfolio Optimization, Expected Returns, Implied Cost of Capital, Momentum, Maximum Sharpe Ratio

JEL Classification: G11, G12, G17

Suggested Citation

Bielstein, Patrick and Hanauer, Matthias Xaver, Mean-Variance Optimization Using Forward-Looking Return Estimates (October 1, 2017). Available at SSRN: or

Patrick Bielstein (Contact Author)

Barclays PLC ( email )

1 Churchill Place
London, E14 5HP
United Kingdom

Matthias Xaver Hanauer

Technische Universität München (TUM) ( email )

Arcisstr. 21
Munich, D-80290


Robeco Asset Management ( email )

Weena 850
Rotterdam, 3014 DA


Do you have negative results from your research you’d like to share?

Paper statistics

Abstract Views
PlumX Metrics