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Mean-Semivariance Efficient Frontier: A Downside Risk Model for Portfolio Selection


  • Enrique Ballestero


An ongoing stream in financial analysis proposes mean-semivariance in place of mean-variance as an alternative approach to portfolio selection, since segments of investors are more averse to returns below the mean value than to deviations above and below the mean value. Accordingly, this paper searches for a stochastic programming model in which the portfolio semivariance is the objective function to be minimized subject to standard parametric constraints, which leads to the mean-semivariance efficient frontier. The proposed model relies on an empirically tested basis, say, portfolio diversification and the empirical validity of Sharpe's beta regression equation relating each asset return to the market. From this basis, the portfolio semivariance matrix form is strictly mathematically derived, thus an operational quadratic objective function is obtained without resorting to heuristics. Ease of computation is highlighted by a numerical example, which allows one to compare the results from the proposed mean-semivariance approach with those derived from the traditional mean-variance model.

Suggested Citation

  • Enrique Ballestero, 2005. "Mean-Semivariance Efficient Frontier: A Downside Risk Model for Portfolio Selection," Applied Mathematical Finance, Taylor & Francis Journals, vol. 12(1), pages 1-15.
  • Handle: RePEc:taf:apmtfi:v:12:y:2005:i:1:p:1-15 DOI: 10.1080/1350486042000254015

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    References listed on IDEAS

    1. J. G. Kallberg & W. T. Ziemba, 1983. "Comparison of Alternative Utility Functions in Portfolio Selection Problems," Management Science, INFORMS, vol. 29(11), pages 1257-1276, November.
    2. Dybvig, Philip H, 1984. " Short Sales Restrictions and Kinks on the Mean Variance Frontier," Journal of Finance, American Finance Association, vol. 39(1), pages 239-244, March.
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    Cited by:

    1. Marco Corazza & Giovanni Fasano & Riccardo Gusso, 2011. "Particle Swarm Optimization with non-smooth penalty reformulation for a complex portfolio selection problem," Working Papers 2011_10, Department of Economics, University of Venice "Ca' Foscari".
    2. Reus, Lorenzo & Mulvey, John M., 2016. "Dynamic allocations for currency futures under switching regimes signals," European Journal of Operational Research, Elsevier, vol. 253(1), pages 85-93.
    3. Ballestero, E. & Gunther, M. & Pla-Santamaria, D. & Stummer, C., 2007. "Portfolio selection under strict uncertainty: A multi-criteria methodology and its application to the Frankfurt and Vienna Stock Exchanges," European Journal of Operational Research, Elsevier, vol. 181(3), pages 1476-1487, September.
    4. Guo, Sini & Yu, Lean & Li, Xiang & Kar, Samarjit, 2016. "Fuzzy multi-period portfolio selection with different investment horizons," European Journal of Operational Research, Elsevier, vol. 254(3), pages 1026-1035.
    5. repec:spr:annopr:v:251:y:2017:i:1:d:10.1007_s10479-015-1829-1 is not listed on IDEAS
    6. Ballestero, Enrique & Bravo, Mila & Pérez-Gladish, Blanca & Arenas-Parra, Mar & Plà-Santamaria, David, 2012. "Socially Responsible Investment: A multicriteria approach to portfolio selection combining ethical and financial objectives," European Journal of Operational Research, Elsevier, vol. 216(2), pages 487-494.
    7. Cumova, Denisa & Nawrocki, David, 2011. "A symmetric LPM model for heuristic mean-semivariance analysis," Journal of Economics and Business, Elsevier, vol. 63(3), pages 217-236, May.
    8. Beach, Steven L., 2011. "Semivariance decomposition of country-level returns," International Review of Economics & Finance, Elsevier, vol. 20(4), pages 607-623, October.


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