It has been a generation since Harry Markowitz laid the foundations and built much of the structure of what we now know as Modern Portfolio Theory ("MPT"). The greatest contribution of MPT is the establishment of a formal risk/return framework for investment decision-making. By defining investment risk in quantitative terms, Markowitz gave investors a mathematical approach to asset selection and portfolio management. But as Markowitz himself and William Sharpe, the other giant of MPT,

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... e, there are important limitations to the original MPT formulation. Under certain conditions, the mean-variance approach can be shown to lead to unsatisfactory predictions of behavior. Markowitz suggests that a model based on the semi-variance would be preferable; in light of the formidable computational problems, however, he bases his analysis on the variance and standard deviation.' The causes of these "unsatisfactory" aspects of MPT are the assumptions that 1) variance of portfolio returns is the correct measure of investment risk, and 2) that the investment returns of all securities and assets can be adequately represented by the normal distribution. Stated another way, MPT is limited by measures of risk and return that do not always represent the realities of the investment markets. Fortunately, recent advances in portfolio and financial theory, coupled with today's increased computing power, have overcome these limitations. The resulting expanded risk/return paradigm is known as Post-Modern Portfolio Theory ("PMPT"). MPT thus becomes nothing more than a special (symmetrical) case of the PMPT formulation. This article discusses return and risk under MPT and PMPT and demonstrates an approach to asset allocation based on the more general rules permitted by PMPT.