Patrick Bielstein and Matthias Hanauer won the ACATIS Value Prize 2018 (3rd place) with their paper Mean-variance optimization using forward-looking return estimates. The paper is published in the Review of Quantitative Finance and Accounting.

About the authors

Patrick is a Senior Quantitative Equity Analyst at ERI Scientific Beta in London and an alumni of the Chair of Financial Management and Capital Markets.

Matthias is postdoc at the Chair of Financial Management and Capital Markets and also works for Robeco, an investment management firm.

Paper abstract 

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.

About the ACATIS Value Prize

Every year ACATIS honours academic research on the subject of value investing with the ACATIS Value Prize. For more information about the "ACATIS Value Prize", please click here.

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