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Review of Quantitative Finance and Accounting

, Volume 52, Issue 3, pp 815–840 | Cite as

Mean-variance optimization using forward-looking return estimates

  • Patrick BielsteinEmail author
  • Matthias X. HanauerEmail author
Original Research
  • 154 Downloads

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.

Keywords

Portfolio optimization Expected returns Implied cost of capital Momentum Maximum sharpe ratio 

JEL Classification

G11 G12 G17 

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Financial Management and Capital Markets, TUM School of ManagementTechnical University of MunichMunichGermany

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