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Estimation Methods in Portfolio Selection and the Effectiveness of Short Sales Restrictions: UK Evidence

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  • John L. G. Board

    (Department of Accounting and Finance, London School of Economics and Political Science, London, United Kingdom)

  • Charles M. S. Sutcliffe

    (Department of Accounting and Management Science, University of Southampton, Southampton, United Kingdom)

Abstract

Forecasting the mean returns vector and the covariance matrix is a key feature in implementing portfolio theory. The performance of the Bayes-Stein method for forecasting these parameters for use in the Markowitz model (with and without short sales) was compared with that of seven other estimation methods, and three alternative portfolio selection techniques. This paper represents the first large scale empirical investigation of the usefulness of the Bayes-Stein approach using historical data. This data was drawn from the London Stock Exchange. In contrast to earlier studies, the relative performance of Bayes-Stein was mixed. While it produced reasonable estimates of the mean returns vector, there were superior methods, e.g., overall mean, for estimating the covariance matrix when short sales were permitted. When short sales were prohibited, actual portfolio performance was clearly improved, although there was little to choose between the various estimation methods.

Suggested Citation

  • John L. G. Board & Charles M. S. Sutcliffe, 1994. "Estimation Methods in Portfolio Selection and the Effectiveness of Short Sales Restrictions: UK Evidence," Management Science, INFORMS, vol. 40(4), pages 516-534, April.
    • Handle: RePEc:inm:ormnsc:v:40:y:1994:i:4:p:516-534
    DOI: 10.1287/mnsc.40.4.516
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    Citations

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    Cited by:

    1. Platanakis, Emmanouil & Sutcliffe, Charles & Ye, Xiaoxia, 2021. "Horses for courses: Mean-variance for asset allocation and 1/N for stock selection," European Journal of Operational Research, Elsevier, vol. 288(1), pages 302-317.
    2. Ghaemi Asl, Mahdi & Rashidi, Muhammad Mahdi & Tavakkoli, Hamid Raza & Rezgui, Hichem, 2024. "Does Islamic investing modify portfolio performance? Time-varying optimization strategies for conventional and Shariah energy-ESG-utilities portfolio," The Quarterly Review of Economics and Finance, Elsevier, vol. 94(C), pages 37-57.
    3. Hsiao-Fen Hsiao & Jiang-Chuan Huang & Zheng-Wei Lin, 2020. "Portfolio construction using bootstrapping neural networks: evidence from global stock market," Review of Derivatives Research, Springer, vol. 23(3), pages 227-247, October.
    4. Greyserman, Alex & Jones, Douglas H. & Strawderman, William E., 2006. "Portfolio selection using hierarchical Bayesian analysis and MCMC methods," Journal of Banking & Finance, Elsevier, vol. 30(2), pages 669-678, February.
    5. Platanakis, Emmanouil & Urquhart, Andrew, 2020. "Should investors include Bitcoin in their portfolios? A portfolio theory approach," The British Accounting Review, Elsevier, vol. 52(4).
    6. James DiLellio, 2015. "A Kalman filter control technique in mean-variance portfolio management," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 39(2), pages 235-261, April.
    7. Panos K. Pouliasis & Nikos C. Papapostolou, 2018. "Volatility and correlation timing: The role of commodities," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 38(11), pages 1407-1439, November.
    8. Xinyu Huang & Weihao Han & David Newton & Emmanouil Platanakis & Dimitrios Stafylas & Charles Sutcliffe, 2023. "The diversification benefits of cryptocurrency asset categories and estimation risk: pre and post Covid-19," The European Journal of Finance, Taylor & Francis Journals, vol. 29(7), pages 800-825, May.
    9. Hasan, Iftekhar & Simaan, Yusif, 2000. "A rational explanation for home country bias," Journal of International Money and Finance, Elsevier, vol. 19(3), pages 331-361, June.
    10. Frank Schuhmacher & Hendrik Kohrs & Benjamin R. Auer, 2021. "Justifying Mean-Variance Portfolio Selection when Asset Returns Are Skewed," Management Science, INFORMS, vol. 67(12), pages 7812-7824, December.
    11. Li, Xiaoyue & Uysal, A. Sinem & Mulvey, John M., 2022. "Multi-period portfolio optimization using model predictive control with mean-variance and risk parity frameworks," European Journal of Operational Research, Elsevier, vol. 299(3), pages 1158-1176.
    12. John Board & Charles Sutcliffe, 2007. "Joined-Up Pensions Policy in the UK: An Asset-Liability Model for Simultaneously Determining the Asset Allocation and Contribution Rate," Economic Analysis, Institute of Economic Sciences, vol. 40(3-4), pages 87-118.
    13. Platanakis, Emmanouil & Urquhart, Andrew, 2019. "Portfolio management with cryptocurrencies: The role of estimation risk," Economics Letters, Elsevier, vol. 177(C), pages 76-80.
    14. Khashanah, Khaldoun & Simaan, Majeed & Simaan, Yusif, 2022. "Do we need higher-order comoments to enhance mean-variance portfolios? Evidence from a simplified jump process," International Review of Financial Analysis, Elsevier, vol. 81(C).
    15. Iwanicz-Drozdowska Małgorzata & Rogowicz Karol & Smaga Paweł, 2023. "Market-moving events and their role in portfolio optimization of generations X, Y, and Z," International Journal of Management and Economics, Warsaw School of Economics, Collegium of World Economy, vol. 59(4), pages 371-397, December.
    16. Nigel Meade & Gerry Salkin, 2000. "The selection of multinational equity portfolios: forecasting models and estimation risk," The European Journal of Finance, Taylor & Francis Journals, vol. 6(3), pages 259-279.
    17. Post, Thierry & Karabatı, Selçuk & Arvanitis, Stelios, 2018. "Portfolio optimization based on stochastic dominance and empirical likelihood," Journal of Econometrics, Elsevier, vol. 206(1), pages 167-186.
    18. Walsh, David M. & Walsh, Kathleen D. & Evans, John P., 1998. "Assessing estimation error in a tracking error variance minimisation framework," Pacific-Basin Finance Journal, Elsevier, vol. 6(1-2), pages 175-192, May.
    19. Manfred Gilli & Enrico Schumann & Giacomo di Tollo & Gerda Cabej, 2011. "Constructing 130/30-portfolios with the Omega ratio," Journal of Asset Management, Palgrave Macmillan, vol. 12(2), pages 94-108, June.

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