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Constructing Long/Short Portfolios with the Omega ratio

Author

Listed:
  • Manfred GILLI

    (University of Geneva and Swiss Finance Institute)

  • Enrico SCHUMANN

    (University of Geneva)

  • Giacomo DI TOLLO

    (University of Pescara)

  • Gerda CABEJ

    (University of Geneva)

Abstract

We construct portfolios with an alternative selection criterion, the Omega function, which can be expressed as the ratio of two partial moments of the returns distribution. Finding Omega-optimal portfolios, in particular under realistic constraints like cardinality restrictions, requires to solve non-convex optimisation problems. Since standard (gradient-based) optimisation methods fail here, we suggest to use a heuristic technique (Threshold Accepting). The main purpose of the paper is to investigate the empirical performance of the selected portfolios, especially the effects of allowing short positions. Many studies on portfolio optimisation assume that short sales are not allowed. This is despite the fact that theoretically, short positions can improve the risk-return characteristics of a portfolio, and practically, institutional investors can and do sell stocks short.We investigate whether removing the non-negativity constraint really improves out-of-sample portfolio performance under realistic assumptions, that is when optimal weights need to be estimated from the data, different transaction costs apply to long and short positions or short selling is restricted to specific assets.

Suggested Citation

  • Manfred GILLI & Enrico SCHUMANN & Giacomo DI TOLLO & Gerda CABEJ, 2008. "Constructing Long/Short Portfolios with the Omega ratio," Swiss Finance Institute Research Paper Series 08-34, Swiss Finance Institute.
  • Handle: RePEc:chf:rpseri:rp0834
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    Citations

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    Cited by:

    1. Caporin, Massimiliano & Costola, Michele & Jannin, Gregory & Maillet, Bertrand, 2018. "“On the (Ab)use of Omega?”," Journal of Empirical Finance, Elsevier, vol. 46(C), pages 11-33.
    2. Manfred Gilli & Enrico Schumann, 2012. "Heuristic optimisation in financial modelling," Annals of Operations Research, Springer, vol. 193(1), pages 129-158, March.
    3. Gilles Boevi Koumou, 2020. "Diversification and portfolio theory: a review," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 34(3), pages 267-312, September.
    4. Yu, Jing-Rung & Paul Chiou, Wan-Jiun & Hsin, Yi-Ting & Sheu, Her-Jiun, 2022. "Omega portfolio models with floating return threshold," International Review of Economics & Finance, Elsevier, vol. 82(C), pages 743-758.
    5. Marie Briere & Ariane Szafarz, 2017. "Factor Investing: The Rocky Road from Long-Only to Long-Short," Working Papers CEB 17-013, ULB -- Universite Libre de Bruxelles.
    6. Eduardo Acosta-Gonz�lez & Reinaldo Armas-Herrera & Fernando Fern�ndez-Rodr�guez, 2015. "On the index tracking and the statistical arbitrage choosing the stocks by means of cointegration: the role of stock picking," Quantitative Finance, Taylor & Francis Journals, vol. 15(6), pages 1075-1091, June.
    7. Guastaroba, G. & Mansini, R. & Ogryczak, W. & Speranza, M.G., 2016. "Linear programming models based on Omega ratio for the Enhanced Index Tracking Problem," European Journal of Operational Research, Elsevier, vol. 251(3), pages 938-956.
    8. Amita Sharma & Sebastian Utz & Aparna Mehra, 2017. "Omega-CVaR portfolio optimization and its worst case analysis," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 39(2), pages 505-539, March.
    9. Kapsos, Michalis & Christofides, Nicos & Rustem, Berç, 2014. "Worst-case robust Omega ratio," European Journal of Operational Research, Elsevier, vol. 234(2), pages 499-507.
    10. Eric Benhamou & Beatrice Guez & Nicolas Paris1, 2019. "Omega and Sharpe ratio," Papers 1911.10254, arXiv.org.

    More about this item

    Keywords

    Optimisation heuristics; Threshold Accepting; Portfolio Optimisation;
    All these keywords.

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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