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Review of Quantitative Finance and Accounting

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19.05.2018 | Original Research

Mean-variance optimization using forward-looking return estimates

verfasst von: Patrick Bielstein, Matthias X. Hanauer

Erschienen in: Review of Quantitative Finance and Accounting | Ausgabe 3/2019

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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.

Vorheriger Artikel Managerial risk incentives and accounting conservatism

Nächster Artikel Managerial overconfidence, ability, firm-governance and audit fees

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We use minimum variance portfolio and minimum volatility portfolio interchangeably throughout this study.

Optimization is only one approach to construct a low-volatility portfolio. The alternative of ranking stocks by their beta or volatility and then forming decile portfolios, as in Blitz and van Vliet (2007), results in similar risk-return profiles.

We re-run the main analyses using an investment universe that is not constrained by the availability of ICC estimates for methods not based on the ICC. The findings are similar and available upon request.

Section A.2 in the Appendix provides details on the data requirements and methodology of the ICC computation.

Stock return of the past 12 months lagged by 1 month, see Carhart (1997).

For companies with other financial year-ends, we compute synthetic book values using the latest available book value, earnings forecasts, and clean-surplus accounting, as in Gebhardt et al. (2001).

To illustrate this point, consider the following example: positive cash flow news for a company leads to an increase in its share price. Assume that analysts are slow to incorporate this positive news into their earnings forecasts. Then the ICC would decline in order to equate the (now higher) share price with the (unchanged) earnings forecasts. The result would be a negative correlation between the momentum variable and the ICC.

Chow et al. (2014) compare different methods to estimate a robust covariance matrix and find that, in the U.S., the shrinkage according to Ledoit and Wolf (2004) performs similarly to shrinkage according to Clarke et al. (2006), a principal component factor model, and a four factor model.

For the optimization, we use the Rsolnp package (Ghalanos and Theussl 2015) in R, which employs a general non-linear augmented Lagrange multiplier method solver (Ye 1987).

http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html.

We download all factor returns from Kenneth French’s website: http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html.

We download the BAB factor returns from https://www.aqr.com/library/data-sets/betting-against-beta-equity-factors-monthly.

We download these factor returns from Kenneth French’s website: http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html.

If the return (r) in t or \(t-1\) is larger than 300% and \((1 + r_t) (1 + r_{t-1}) - 1\) is less than 50% then \(r_t\) and \(r_{t-1}\) are set to NA.

We use German government bonds as the risk-free instrument for the European Monetary Union.

We only include firms for which ICC estimates for all methods are available.

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Titel: Mean-variance optimization using forward-looking return estimates
verfasst von: Patrick Bielstein
Matthias X. Hanauer
Publikationsdatum: 19.05.2018
Verlag: Springer US
Erschienen in: Review of Quantitative Finance and Accounting / Ausgabe 3/2019
Print ISSN: 0924-865X
Elektronische ISSN: 1573-7179
DOI: https://doi.org/10.1007/s11156-018-0727-4

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