Knowledge that Transforms

This paper examines the validity of two widely used methods for forming conditional predicted portfolio returns. The first method relies on a one-period, mean-variance theory of equilibrium expected return, sometimes referred to as the “capital asset pricing model” (CAPM). The second method is based upon a proposal by Markowitz [14] and is called the [market model] (MM).

The shortcomings of a quadratic utility function are so serious and so widely known that by now one might assume that it would simply have been dropped from consideration. Arrow [1] and Pratt [6] have shown that such a function implies ever increasing absolute risk aversion, that is, reduced risk taking as wealth increases, which contradicts everyday experience. Moreover, the assumption of quadratic utility also implies ultimate satiation with respect to risk taking. This function has a well-defined maximum beyond which the marginal utility of money declines, and as a result the range of admissable returns must be restricted. Wippern [12] has focused attention on the second of the above two shortcomings. Using a rather ingenious device, based on the Sharpe-Lintner market model [8 and 5], Wippern has measured empirically the admissable range of returns implied by the quadratic utility function. Since his empirical findings imply that returns beyond as little as 1.3 standard deviations from the expected return provide negative marginal utility to investors, Wippern concludes that the Sharpe-Lintner market model, and/or the mean-variance portfolio theory upon which it is based, have “inconsistent and implausible properties.”

Stable distributions are suggested as being the underlying distributions for many economic variables. Capital market variables, in particular, are said to follow a member of the symmetric stable class.

The objective of this paper is to determine upper bounds of the potential savings that can be realized by the application of cash management optimization models. These upper bounds are found by simulation as the difference between the performance of a deterministic optimization model--which finds the optimal policy in hindsight--and the simulated performance of a hypothetical treasurer who uses simple heuristic cash management rules as informally practiced by many treasurers, based on prediction of random cash flows. The results of this analysis leave serious doubts as to profitability of cash management optimization models.

There have been many efforts in recent years to explain differences in the performance of commercial banks. Interest has centered on the extent to which changes in a selected group of indices of bank performance are related to the structure of banking markets and selected other factors thought to influence bank behavior. While various techniques have been used, the most common has been multiple linear regression. The measures of performance entered into the regression equations have included the price and quantity of bank services and bank profitability, while the explanatory variables have included, to name only a few, the one-, two-, or three-bank concentration ratio, the number of banks in the market, the existence of competition from nonbank financial institutions, bank costs, bank size, and proxies for the demand for banking services. Generalizations then have been made about the impact of market structure and other variables on bank performance, generalizations based upon the regression coefficients of the explanatory variables. The consensus appears to be that the demand for banking services and bank costs are significant determinants of the performance of individual commercial banks; market structure appears to be much less important. However, the conclusions are by no means unanimous.

Certainly, the concept of skewness of returns and its role in the context of portfolio analysis has gained increasing attention in recent literature. Witness the studies by Alderfer and Bierman [1], Arditti [2, 3], Jean [4], and Simonson [5]. Each of these studies has treated skewness as the third moment of a series expansion—accordingly, skewness has been measured and interpreted as a logical extension of the traditional two-dimensional return-versus-standard deviation analysis of security evaluation.

Edward I. Altman, Michel Margaine, Michel Schlosser, Pierre Vernimmen, Financial and Statistical Analysis for Commercial Loan Evaluation: A French Experience, The Journal of Financial and Quantitative Analysis, Vol. 9, No. 2 (Mar., 1974), pp. 195-211

Ronald W. Melicher, Financial Factors which Influence Beta Variations within an Homogeneous Industry Environment, The Journal of Financial and Quantitative Analysis, Vol. 9, No. 2 (Mar., 1974), pp. 231-241

For many years, the stock option has been an investment device used primarily by speculators and some “sophisticated” investors. During the past few years, much more attention has been paid to options by mutual funds, insurance companies, and conservative investors who previously showed little concern for this investment alternative. The opening of the Chicago Board of Trade's exchange for the trading of options will lead to even wider interest in the area.

A relationship between money supply and stock prices is fairly well recognized in the literature. More recently the studies of Hamburger and Kochin [7], Modigliani [12], Keran [9], and Homa and Jaffee [8] have attempted to specify the short- and long-run nature and the direct and indirect nature of these relationships. Also, these studies have focused on determining the transition variables through which the money-supply effect is transmitted to stock prices. A more pragmatic approach is that of Sprinkel [17 and 18] and Palmer [14] who have attempted to analyze the money-supply and stock-market relationships to see if the former can be a predictor of the latter. More reliable forecasts of future market movements, if available, could be extremely useful for individual and institutional investors. At one extreme, information could be used to time the investment in and out of the market portfolio. Alternatively, the investor could more profitably use the B information on market volatility of stocks available from the capital-asset pricing model, relating expected rate of return on a security, E(Ri), with that on the market portfolio, E(Rm). Accordingly, the prediction of the market would indicate when to shift the composition of the portfolio from relatively low to high or from relatively high to low β stocks and cash.