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Constructing 130/30-portfolios with the Omega ratio

Author

Listed:
  • Manfred Gilli

    (University of Geneva and Swiss Finance Institute)

  • Enrico Schumann
  • Giacomo di Tollo
  • Gerda Cabej

Abstract

We construct portfolios with an alternative selection criterion, the Omega function, which can be expressed as the ratio of two partial moments of a portfolio's return distribution. The main purpose of the article 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 and different transaction costs apply to long and short positions.

Suggested Citation

  • 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.
    • Handle: RePEc:pal:assmgt:v:12:y:2011:i:2:d:10.1057_jam.2010.25
    DOI: 10.1057/jam.2010.25
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    References listed on IDEAS

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    Cited by:

    1. 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.
    2. Carole Bernard & Massimiliano Caporin & Bertrand Maillet & Xiang Zhang, 2023. "Omega Compatibility: A Meta-analysis," Computational Economics, Springer;Society for Computational Economics, vol. 62(2), pages 493-526, August.
    3. Gianni Filograsso & Giacomo Tollo, 2023. "Adaptive evolutionary algorithms for portfolio selection problems," Computational Management Science, Springer, vol. 20(1), pages 1-38, December.

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