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Estimating the Optimal Stochastic Dominance Efficient Set with a Mean-Semivariance Algorithm

Published online by Cambridge University Press:  06 April 2009

Roger P. Bey
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Extract

The theoretical desirability of stochastic dominance (SD) as a decision rule is well established [1, 3, 4, 7, and 11]. However, implementation of SD as a decision rule has been hindered seriously by the lack of an optimal search algorithm [8]. An optimal search algorithm is desirable since it takes the distribution of returns for a group of assets and determines the optimal proportion of each asset which should be combined to provide efficient combinations. For example, for a given expected value (variance) the mean-variance (EV) algorithm builds the portfolio with the smallest (largest) variance (expected value). The EV algorithm determines which assets should be combined and the proportion of the total investment that should be invested in each asset. An analogous algorithm does not exist for SD.

Type
Research Article
Information
Journal of Financial and Quantitative Analysis , Volume 14 , Issue 5 , December 1979 , pp. 1059 - 1070
Copyright
Copyright © School of Business Administration, University of Washington 1979

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References

REFERENCES

[I]Ali, M. M. “Stochastic Dominance and Portfolio Analysis.” Journal of Financial Economics, Vol. 2 (06 1975) pp. 205–229.CrossRefGoogle Scholar
[2]Ang, James S. “A Note on the E, SL Portfolio Selection Model.” Journal of Financial and Quantitative Analysis, Vol. 10 (12 1975), pp. 849–858.CrossRefGoogle Scholar
[3]Bawa, V. S. “Optimal Rules for Ordering Uncertain Prospects.” Journal of Financial Economics, Vol. 2 (03 1975), pp. 95–121.CrossRefGoogle Scholar
[4]Hadar, J., and Russell, W. R.. “Rules for Ordering Uncertain Prospects.” American Economic Review, Vol. 59 (03 1969), pp. 25–34.Google Scholar
[5]Hogan, W. W., and Warren, J. M.. “Computation of the Efficient Boundary in the E-S Portfolio Selection Model.” Journal of Financial and Quantitative Analysis, Vol. 8 (09 1972), pp. 1881–1896.CrossRefGoogle Scholar
[6]Markowitz, H. M.Portfolio Selection: Efficient Diversification of Investments. New Haven: Yale University Press (1959).Google Scholar
[7]Porter, R. B. “Semivariance and Stochastic Dominance: A Comparison.” American Economic Review, Vol. 53 (09 1973), pp. 665–672.Google Scholar
[8]Porter, R. B., and Bey, R. P.. “An Evaluation of the Empirical Significance of Optimal Seeking Algorithms in Portfolio Selection.” Journal of Finance, Vol. 29 (12 1974), pp. 1479–1490.Google Scholar
[9]Porter, R. B., and Gaumnitz, J.. “Stochastic Dominance Versus Mean-Variance Portfolio Analysis.” American Economic Review, Vol. 52 (06 1972), pp. 438–446.Google Scholar
[10]Porter, R. B.; Wart, J. R.; and Ferguson, D. L.. “Efficient Algorithms for Conducting Stochastic Dominance Tests on Large Numbers of Portfolios.” Journal of Financial and Quantitative Analysis, Vol. 8 (01 1973), pp. 71–81.CrossRefGoogle Scholar
[11]Quirk, J., and Saposnik, R.. “Admissibility and Measurable Utility Functions.” Review of Economic Studies, Vol. 19 (02 1962), pp. 104–146.Google Scholar
[12]Vickson, R. G. and Altaian, M. “On the Relative Effectiveness of Stochastic Dominance Rules: Extension to Decreasing Risk-Averse Utility Functions.” Journal of Financial and Quantitative Analysis, Vol. 12 (03 1977), pp. 73–84.CrossRefGoogle Scholar