Semivariance, as a measure of risk, has the advantage relative to variance of focusing on reduction of losses. When this measure is used in capital budgeting problems, a full knowledge of the joint probability distribution about the projects’ investment returns is required to calculate the semivariance of alternatives. Unfortunately, when the decision maker is faced with a set of alternatives and each alternative has a large number of projects, the development of such a distribution is usually impractical. Therefore, many analysts resort to computer simulation to approximate the semivariance of project portfolios with little knowledge about the accuracy of the simulation