Roger P. Bey

Market Model Stationarity of Individual Public Utilities

The search for an economically sound procedure for estimating an appropriate rate of return on equity consistent with the Supreme Courts ruling in the Hope case [13] has led many economists, financial experts, and public service commissions to estimate the rate of return on equity with the capital asset pricing model (CAPM) (see [30], [19], and [21]). The popularity of the CAPM in regulatory proceedings was reported by Harrington [15] who, in a survey of public service commissions, found that 38 states were considering or had seen the CAPM used, two jurisdictions preferred the CAPM, Oregon required the CAPM, and South Carolina would require the CAPM in all future cases. Hence, given the popularity of the CAPM and the tremendous economic impact that outcomes of regulatory proceedings have on the financial well-being of both the regulated firm and the consumer, it is critical that if the CAPM is used in regulatory proceedings that it be applied in the best manner possible and that any limitations associated with the CAPM be recognized fully.

Additional Evidence of Heteroscedasticity in the Market Model

Sharpes market model [29] is widely used both by academic researchers and practitioners in finance, but it cannot be accepted with complete confidence until some of its basic assumptions are tested more thoroughly. The applicability, usefulness, and reliability of the model are functions of its conformity to real data, which in turn depends partly on the unresolved question of heteroscedasticity.

Gini's Mean Difference and Portfolio Selection: An Empirical Evaluation

Yitzhaki [19] recently developed two portfolio selection criteria (EG and ET) based on the mean and Ginis mean difference. Similar to mean-variance (EV), the EG criterion uses two summary statistics to describe the probability distribution of a risky prospect, the mean and one-half Ginis mean difference. Ginis mean difference is defined as the average of the absolute differences be? tween all possible pairs of observations of a random variable. Yitzhakis de? velopment concentrated on the theoretical aspects of EG and ET and the theoreti? cal relationships among EG, ET, EV, and stochastic dominance (SD) selection criteria. He did not address either the empirical properties of EG and ET or the relationship between the empirical efficient sets of EG and ET and other portfolio selection criteria. Yitzhaki suggested that the next step in the development and application of his proposed selection criteria should be an empirical investigation of how the EG and ET criteria compare with other selection criteria. The purpose of this research is to analyze the empirical properties of the EG and ET criteria relative to EV, SD, and mean-semivariance (ES) portfolio selec? tion criteria. In particular, the major empirical properties of the selection criteria studied include:

The Impact of Stochastic Project Lives on Capital Budgeting Decisions

The concept of evaluating capital budgeting projects on the basis of a discounted cash-flow analysis is well established in both the academicians’ and the practitioners’ worlds [4, 9,13]. However, for purposes of simplification the suggested procedures often are based on a set of unrealistic assumptions. For example, a project’s life (N) often is assumed to be a constant, that is, known with certainty, whereas in reality it is stochastic. The stochastic nature of a project’s life may be due to competitive technological advances, changes in consumer tastes and preferences, and the impact of complementary projects. The consequences of incorrectly assuming that N is a constant are unknown. However, as shown in the following section, the mean and variance of the net present value (NPV) distribution are biased if N is assumed to be a constant when in fact it is stochastic.

An Evaluation of Capital Budgeting Portfolio Models Using Simulated Data

The effectiveness of six approaches to capital budgeting under uncertainty is evaluated. The standard of comparison was a second-degree stochastic dominance model. The research environment consisted of ten hypothetical capital budgeting projects and an existing asset base. Variations in project demand, competitive actions, and technological changes were simulated by changing the shapes of the cash flow distributions. The required net present value distributions were obtained through a simulation and state of the economy methodology. Results of the study indicated that the models were sensitive to the characteristics of the cash flow distributions and that financial managers must exercise considerable care in their selection of a capital budgeting decision model. The mean-semivariance model yielded the most consistent results while decisions based upon the traditional net present value model were incorrect much of the time.