This paper presents the Mean-Gini (MG) approach to analyze risky prospects and construct optimum portfolios. The method possesses the simplicity of the mean-variance model with the efficiency of stochastic dominance. Hence, Gini's mean difference is superior to the variance for evaluating the variability Of a prospect. The analysis is further extended with the concentration ratio that permits to classify different securities with respect to their relative riskiness. The MG approach is then applied to capital markets and the security valuation theorem is derived as a general relationship between average return and risk. This is further extended to include a degree of risk aversion that can be estimated from capital market data.