Recent studies of mutual funds have all arrived at the same conclusion: mutual fund performance has been inferior to the performance of the market indices. One of the most prominent of these studies was conducted by William F. Sharpe. He showed that if his measure of mutual fund performance, the reward-to-variability ratio, is calculated net of management expenses for each fund in his sample of thirty-four, then the average value of this ratio over the thirty-four funds is significantly less than the same measure applied to the Dow Jones Industrials over the 1954–1963 period. From this evidence, Sharpe concluded that average mutual fund performance was distinctly inferior to an investment in the Dow Jones Industrial Average. It is the intent of this paper to show that if another variable, namely the third moment of the fund's annual rate of return, is introduced into the investor's decision process, Sharpe's conclusion must be altered.

In recent years we have seen significant progress in the development of models of capital asset pricing and the related models of portfolio selection and performance appraisal. The central focus, in all of these models, has been one of dealing directly with the difficult and perplexing issues of the measurement of risk and the underlying theories of investor choice-making under uncertainty.

The relationship between capital market equilibrium and firm financial policy has received extensive attention in recent years. Until recently, accepted theory was generally consistent in its view that the diversification effect of new investment on firm earnings is a necessary consideration in project selection. In arguing this position, no distinction was made between the perfect market situation exemplified by the models of Modigliani and Miller (M-M) [9, 10, 11] and those of Sharpe [19], Lintner [6, 7] and Mossin [12] (LSM model) and the traditional case in which firm value is not independent of debt policy, e.g., as might be the case if individual investors cannot lever on terms comparable to those available to firms. In a recent article, Mossin [13] examines the implications of the former case of perfect markets. Using a single period model with riskless rate borrowing and lending by individuals and firms, homogeneous expectations, mean-variance portfolio selection, and no taxes, Mossin shows that the effect on the investing firm's value of a new project is independent of the stochastic properties of the other income earned by the firm. This conclusion and the M-M [10] Proposition I follow from the statistical property of Mossin's model that any income stream has the same value regardless of how that stream is divided into the equity or debt streams of one or more firms; or, equivalently, firm value and financial structure are independent. Schall [18] presents a general proof that firm value and financial structure are independent and that firm investment diversification effects are irrelevant in perfect capital markets.

After integrating risky debt instruments into the generalized asset pricing model developed by Sharpe and Lintner, we have demonstrated that the required return-to-equity capital in this model is a linear function of the debt-to-equity ratio with a slope equal to the difference between the unlevered cost of equity and the direct cost of debt. Consequently, we take the average cost of capital to be invariant with respect to leverage. The particular nature of the debt instrument issued by the firm does not affect this result.

Our analysis supporting the net operating income valuation construct is of interest in that it takes into account not only the variance of the probability distribution of equity returns but also the covariance relationships between these returns and all other returns in the system. Further, we need not rely on assumptions of “equivalent return” classes or arbitrage possibilities to arrive at our solution. More important, our conclusion is quite general in that we demonstrate indifference toward finance with any instrument regardless of its inherent risk characteristics.

This paper presents a discrete stochastic programming model for commercial bank bond portfolio management. It differs from previous bond portfolio models in that it provides an optimization technique that explicitly takes into consideration the dynamic nature of the problem and that incorporates risk by treating future cash flows and interest rates as discrete random variables. The model's data requirements and its computational demands are sufficiently limited so that it can be implemented as a normative aid to bond portfolio management. In addition, it can be extended by the addition of other asset and liability categories to serve as a more general model for commercial bank asset and liability management.

The advent of the computer has permitted financial theorists to collect and analyze large amounts of financial data. In the field of investments some of the most important work has focused on historical rates of return in investments in common stocks. The classical study in this area is the Fisher-Lorie study [8,9] in which intern al rates of return were calculated for every security listed on the New York Stock Exchange from 1926–1965. Other studies related to the area have been complicated by Herzog [10], Fisher [6,7], Latané and Young [11], Soldofsky and Biderman [12], and Evans [3,4].

The problem considered in this paper is the optimal expansion or contraction of credit by a single bank in response to a change in its reserve account. Such a change is usually not of an exogenous nature when the entire operations of the bank are considered. Quite often, the change in the reserve position comes as a result of the bank's decision to engage in securities transactions, thereby changing its mix of reserves and securities without affecting the total asset position of the bank. We do not consider the overall portfolio selection problem faced by the bank; we assume that changes in the reserve account from such decisions are exogenous to our models.

The results of this study indicate that the individual common stocks in the Dow-Jones. Industrial Average were not consistent inflation hedges. Assuming an 8.2 percent normal required rate of return, none of the common stocks was a complete inflation hedge during all three recent inflationary periods tested. Even assuming a zero normal required rate of return á la traditional investment theory, only six (20 percent) of the thirty common stocks sampled were inflation hedges during all three inflationary periods.

This paper has discussed the importance of stationary data in statistical applications and has at the same time suggested one method for testing for stationarity. An application of the testing procedure is made to common stock prices. The results indicate that these data could be nonstationary in the usual sense of stability of the mean and mean-square values despite efforts to transform the data into a stationary form using first differences.

State universities are budgeted by their respective states based upon the number of students in attendance at each university. The university must often make a forecast of enrollment for budgetary purposes two years in advance because of various administrative and legislative procedures. When the university under-predicts enrollment, upon which state appropriations will be based, there will be a subsequent shortage of funds. On the other hand, an overprediction of enrollment entails the return of funds to the state unless the overprediction is within some limit prescribed by the state.

In a paper published in an earlier issue of this journal, Melnik and Kraus [3] reported the results of their time-series analysis of the yields on U.S. government securities. The data used by the authors are somewhat unique in that the observations were derived from a regression-fitted yield curve, and in that the trend in mean was removed by employing deviations from a fitted trend line as the time series to be analyzed. The authors applied cross-spectral methods to their derived monthly time series for ninety-day Treasury bills and ten-year Treasury bonds, encompassing the years 1954–1967. Their interpretation of the results of their analysis led the authors to conclude that a cycle of eighteen to twenty-four months is significant and that the ten-year rate leads the short rate, thus apparently lending credence to the expectations hypothesis of the term structure of interest rates. A close examination of the basis for the Melnik and Kraus conclusions leads one to believe that they are questionable on the following two counts.

In an article published earlier in this journal [4], we studied the term structure of interest fates in a dynamic context. Instead of focusing on the yield curve at a point in time, we investigated the joint movement of short and long-term interest rates through time. We compared the cyclical behavior of the ninety-day Treasury bill rate and the ten-year U.S. government bond rate by using cross-spectral analysis. The data used for the analysis were obtained from regression-fitted yield curves. These fitted yield curves enabled us to obtain the monthly yields of securities of prespecified term to maturity. The derivation was done in a precise manner which at the same time is in line with most of the previous term structure studies.

Recently, Cohen and Gujarati [2] have suggested that when multicollinearity is present there is “ …danger involved in mechanically dropping variables from multiple regression equations by t tests because t values of the regression coefficients may not be significantly different from zero when the true (population) values of these coefficients are in fact not zero…” The problem they discuss is not a new one and has been extensively treated in the existing literature. However, their approach is straightforward and will certainly aid the practitioner in his understanding of the problems associated with multicollinearity.