Methods of finding the effective portfolio for semi-variance

In the classic Markowitz model, risk is measured by the return rates variance. However, equal treatment of negative and positive deviations from the expected return rate is a slight shortcoming of variance as the risk measure. Markowitz defined semi-variance to measure the negative deviations only. However, finding the portfolio with minimum semi-variance is much more difficult than finding a portfolio with minimum variance. The critical line method proposed by Markowitz in 1959 was the oldest method for finding optimum portfolios for semi-variances. That method was highly complicated and as a consequence the search for methods of finding a quasi-optimum solution continued. Quasi-optimum solutions are based on the colower partial moments. Until today they find application in practice. Their advantage is that it is possible to use one of many available software packages for square or non-linear optimization. Unfortunately the solution obtained is quasi optimal and it is not known how far it deviates from the optimum solution. As a consequence, the need to formulate a new method that could offer optimum solution and at the same time would be simple and easy for software design as a means to select optimum portfolios with the minimum semi-variance from the assumer return rate appeared.