An important issue in the financial literature concerns the conflict between the stochastic dominance (SD) and the mean-variance (EV) methods of choosing optimal portfolios of risky assets. Much of the recent theoretical and empirical work in portfolio analysis has been devoted to the extension and testing of the Markowitz two-moment model, in which it is assumed that either (a) decision makers have quadratic utility functions with negative second derivatives or (b) the probability functions are from some appropriate two-parameter family and the investor is risk averse.

The weighted average cost of capital (Ko) is presented in virtually all textbooks in financial management and capital budgeting as a practical concept fundamental to the actual selection of optimal financial and investment alternatives. As often employed Ko can be defined aswhereKo = the weighted average cost of capital, Ks = the cost of equity capital, Kb = the cost of debt capital, S = the market value of the firm's equity, B = the market value of the firm's debt, andV = S + B, the total market value of the firm.

R. Burr Porter, Roger C. Pfaffenberger, Efficient Algorithms for Conducting Stochastic Dominance Tests on Large Numbers of Portfolios: Reply, The Journal of Financial and Quantitative Analysis, Vol. 10, No. 1 (Mar., 1975), pp. 181-185

Reviews the book "Risk, Return, and Equilibrium: A General Single-Period Theory of Asset Selection and Capital-Market Equilibrium," by Bernell Kenneth Stone.

Most of the work in portfolio theory in the past decade has been based on the principle of utility maximization where either the investor's utility function is assumed to be a second degree polynomial with a positive first derivative and a negative second derivative, or the probability functions are assumed to be normal. If at least one of these conditions holds, it can be shown that choosing among risky assets on the basis of their mean and variance only is consistent with the von Neumann-Morgenstern utility maximization model.' Thus, given the above assumptions, the incorporation of higher moments of a distribution and the adoption of alternative approaches to portfolio selection have largely been ignored in favor of the more familiar mean-variance approaches.