Professors Simkowitz and Logue (S-L) remind us that capital asset pricing is a simultaneous process. Their approach differs from traditional capital market models [3 and 6 ], where an investment's risk and return characteristics depend solely on a structural relationship between asset and market portfolio returns. Simkowitz and Logue argue that, within groups of homogeneous securities, investment returns are determined simultaneously and that market portfolio return as well as certain firm-related factors are exogeneous determinants of investment returns. This comment will, first, examine their basis for a simultaneous model and, then, look at presented empirical results.

Results of the analysis imply that rates of return on the property-liability portfolios included in this study are less than rates of return on either the investment company portfolios or the Standard and Poor's industrials. However, it appears that these differences are likely due to different investment objectives of the different portfolios.

In the last decade the theories of Investor behavior and capital markets have broadened to recognize the importance of the portfolio decision of investors and the simultaneity of price determination of equity values. Tobin, Sharpe, Lintner, and Lerner and Carleton have all made contributions to the theory that equity markets are essentially a system in which many investors simultaneously seek to maximize their wealth by allocating their available liquidity among various equities.

In this paper a model is presented to explain the structure of systematic risk. The starting point is the expression for the total dollar return to investors who hold the securities from period t - 1 to t assuming no new securities have been issued in the interim:

where Xt is earnings before interest, preferred dividends, and taxes; T is the corporate tax rate; Dd,t is the interest paid on debt in year t; Dp,t is the dividend paid to preferred shareholders in year t; Dc,t is the dividend (total) paid to equity shares-holders in year t; ΔPt · Nt-1 is the aggregate capital gains for the Nt-1 shares of common stock outstanding as of t-1 and ΔGt is the change in the capitalized value of future growth opportunities. The hypothesis is that the risk associated with the left-hand side variables, the market determined systematic risk, is derived from the corporate variables on the right-hand side of the equation. The market determined level of systematic risk is a linear function of the sensitivity of percent changes in revenues of the firm to percent changes in GNF (asset betas), financial leverage, and changes in growth potential.

Careful examination of the behavior of financial variables over time uncovers an important distinction: variables expressed in dollars, such as earnings per share, behave very differently from percentage changes in those same variables. Similarly, financial ratios, such as the price/earnings ratio, behave quite differently from percentage changes in financial ratios. Variables expressed in dollars and financial ratios appear comparatively stable and predictable over time. Successive values are fair approximations of one another. Percentage changes in dollar and financial ratio variables-i.e., growth variables on the other hand, tend to be erratic, volatile, and unpredictable over time. Successive values bear little relation to one another. This distinction between dollar and ratio variables, on the one hand, and percentage changes in these variables-i.e., growth variables-on the other, serves as a useful basis for a financial model.

Municipal bond credit ratings are currently being created and distributed by two main rating agencies. Controversy has urrounded these ratings and their effects on interest costs to municipalities. This paper summarizes these controversies and presents a methodology which would enable a more objective appraisal of municipal credit quality.

The classic article about liquidity premiums is Kessel [3]. Kessel regresses the liquidity premium, measured as the difference between the forward rate and the actual future spot rate, against the current spot rate and observes a positive relation between the level of the current spot rate and the size of the liquidity premium. Olsen alters Kessel's model in two respects. First, he introduces a proxy for risk or uncertainty about interest rates as an additional determinant of the liquidity premium. Second, instead of using the actual future spot rate as a measure of the expected future spot rate, he specifies the expected future spot rate as a linear combination of the current and past spot rates.

Robert J. Saunders [4] has demonstrated that, because of the high degree of linear interdependence among many of the variables commonly used in banking studies, it may be necessary to interpret explanatory variables in a cross-sectional regression equation, not as representing individual influences but as representing more general factors. He attempted to demonstrate how principal component analysis might be used to isolate and identify some of these general factors.

Marion L. Chiattello [1] has provided additional empirical support for the suggestion that, because of the high degree of linear interdependence between many of the variables commonly used in banking regression studies, it may be necessary to interpret explanatory variables in a cross-sectional regression equation, not as representing individual influences, but as representing more general factors. Further, he has provided more empirical support for the suggestion that principal component analysis might be useful in helping to isolate and identify some of these general factors.

We are grateful to Don Panton (1989) for pointing out a problem with the IMSL version 9 of the pseudo-random number simulator, GGSTA, written by Chambers, Mallows, and Stuck (1976), which we used in (1987).

As Panton (1989) points out, the correction:

(a) may or may not materially affect the simulation per se, and/or

(b) may or may not materially affect our inferences (drawn from the simulation methodology).

The control of profit-making corporations long has been the subject of investigation and discussion, and in recent years same of this interest has shifted to the control of corporations by financial institution. Very little attention has been paid to the control of mutually-owned fiduciaries. This article reports a preliminary investigation of the control of associations in the savings and loan industry.

Presence of a variable market demand function for a given product has significant implications for factor input levels and for the resulting production costs to the firm. In a recent paper, McKean argues that in order to describe the costs of producing a product subject to fluctuating demand it is necessary to take into account the entire distribution of outputs as it relates to the static total cost function. While it is evident that influences on costs and factor inputs will differ in the case of a fluctuating demand schedule compared to the conventional stable demand conditions of classical micro theory, it is not clear that the use of a static cost function in conjunction with a probability distribution of outputs is the proper framework in which to examine the problem.

Professors Lewellen and Edmister (L-E) are to be complimented on careful development and specification of the basic conceptual framework for an accounts-receivable control model. I consider the model to be a real contribution to the basic theory of financial management and will find it a useful supplement to my basic financial management classes.

An attempt was made in this paper to build time-series models with strong structural relationships with various maturity-bond, holding period yields. The relative strength of several groups of key macroeconomic variables with bond returns was specified and tested.

The most useful regressor with regard to short maturity issues was the monetary policy ratio which had a strong association with the dependent variable in the intermediate and short maturities. The rate of change of wholesale prices was also important in the explanation of capital market yields. The rate of change in the English bank rate added still to the explanatory power of the model, particularly in the longer maturities; the combination of these three variables produced the most effective model. The regression coefficients were frequently many times their respective standard errors, and the correlation for every equation for the final model was statistically significant.

The purpose of this study is to build and test a statistical model for the dynamic estimation of portfolio Betas. Of particular interest is the quality of Beta estimates obtainable from relatively small samples of daily return data. Also of particular interest is an assessment of the relationship between the quality of these estimates and the degree of portfolio diversification.

This study tests whether a “portfolio effect” exists in a given branch bank; i.e., does the addition of branches reduce the variability of demand deposits for the bank? The approach taken is narrower than that in the usual portfolio selection model. The study measures risk by the intrayear coefficient of variation of average monthly demand deposits. The term “portfolio effect” was defined operationally as a reduction in the overall coefficient of variation of demand deposits through the addition of sets of branches. Relatively few portfolio effects were realized from the chronological addition of sets of branches. The absence of portfolio effects is largely attributable to (1) generally high positive correlations between deposits of the various sets of branches and the defined deposit base and (2) the small size of the sets of branches relative to the base deposits. Further, while the correlations were generally high and positive, they were also serially unstable which suggests they would be poor predictors of future correlations. Based on the experience of this bank, it does not appear that the reduction in demand deposit variability by adding branches is general or consistent enough to facilitate improved management of reserves or selection of branch locations. However, this conclusion does not necessarily imply that the bank should not have undertaken branch expansion. For example, this study does not include an analysis of branch profitability. While the results of this study based on a single branch bank cannot be generalized, they do suggest the need for a more comprehensive analysis of the impact of branching on the variability of demand deposits.

The trading of security options is one of the fastest growing and most dynamic areas of investment concern. When the Chicago Board of Trade's proposal to develop an exchange for trading option contracts is implemented, security option trading will become an even more important aspect of the investment world.

The concept of a relationship between assumed risk and realized return is intuitively pleasing and has become widely accepted in the field of finance. Until recently this acceptance was anchored largely in what Hirschleifer [11] has called the “notorious fact” that stocks yield more in the long run than bonds and Hickman's finding [10] (since challenged by Fraine [7]) that over the years 1900–1943 the average ex post yield on publicly issued corporate debt was higher the lower the initial quality rating. However, with the advent of the capital asset pricing model of Sharpe [21], Lintner [16], and Mossin [19] the risk-return tradeoff concept has grown in importance and scope. The capital asset pricing model itself has weathered the years well, but has been theoretically and empirically revised, extended, and otherwise altered. Little remains of the original formulation except the proposition that in equilibrium more risk leads to more return--where “risk” for common stocks now means the nondiversified component as measured by the “Beta” coefficient of return volatility vis-à-vis the general market. As Modigliani and Pogue [18] observe after a review of the “more important” empirical tests of the capital asset pricing model: “Obviously, we cannot claim that the CAPM is absolutely right. On the other hand, the empirical tests do support the view that beta is a useful risk measure and that high beta stocks tend to be priced so as to yield correspondingly high rates of return.”

We are grateful for the opportunity which Professors Gressis and Remaley's Comment [1] has afforded us to clarify our analysis of the relationship between Roy's Safety First principle and the Mean-Variance expected utility rule, which unfortunately they find misleading. In our original presentation we did not explicitly analyze situations in which Roy's criterion leads to an extreme corner solution; and G-R are perfectly correct in noting that if a riskless asset exists, a “Safety-Firster” will not invest in risky securities at all if the risk-free interest rate, r, exceeds the disaster level d. The reason for this is straightforward: such an investment strategy minimizes the risk of disaster. In fact in this particular instance the investor can reduce the probability of disaster to zero.

If all urban renewal projects were perfectly divisible and completely independent of one another, and if the urban renewal authority had perfect foresight as well as unlimited funds, the investment decision would be ideally simplified. There would be no need to choose between competing projects, and the urban renewal authority could evaluate each project on its own merits, without reference to any other project. Its decisions would be merely decisions to accept or reject single projects, uncomplicated by portfolio considerations.