From an historical perspective, the Federal funds market has evolved from primarily a receptacle for residual funds into a leading money market. During the market's formative years, banks borrowed Federal funds strictly as a reserve adjustment measure to supplement their discount window borrowings. Gradually, money market pressures have created a situation in which banking institutions find it profitable to finance some of their loan expansions and to expedite various types of transactions with the temporary use of these funds. Consequently, banks have reversed their earlier views and are presently treating discounting as being subservient to the purchasing of Federal funds. Initially, banks lent only their idle reserves in the funds market. In due succession, the practice of selling Federal funds replaced call loans as the predominant secondary reserve and has presently grown into a leading liquid asset in many banks' portfolios. Paralleling this rapid expansion has been the diversification of the funds market. Presently, one-third of the total volume of funds in this market originate from institutions other than commercial banks, i.e. U. S. Government Security Dealers and large corporations. Not only have the number and types of participants enlarged, but they tend to participate continuously rather than on an occasional basis.

Financial or capital market theory is intimately concerned with the concept of a general equilibrium. But most of the empirical work in finance has been concerned with the estimation of single-equation ordinary least squares cross-sectional models. One way of capturing some of the flavor of a general equilibrium is to use a simultaneous equation valuation model. Thus the value or the return on a security can be determined simultaneously in relationship to and in competition with the other securities in the system. Simkowitz and Jones [4] have recently described how the methodology could be used. Simkowitz and Logue [5] have recently performed a study using this methodology.

State-preference theory has developed as a choice-theoretic framework through which many problems of finance and economics dealing with time and uncertainty can be analyzed. Hirshleifer [3], [4], [5], [6] has used the approach to provide significant insights to areas such as production and exchange, investment decisions, and speculative behavior. However, the theory has not made progress in attempting to incorporate state-labeled utility functions into the body of the theory. This paper will develop a method of graphically and analytically allowing for differing utility functions across states. As a further step, the impact of belief-deviation upon the tangency optimum will be discussed. Finally, the significance of these findings upon the consideration of risk will be discussed.

The first major study of industry effects in market returns was performed by King [2]. He used principal components analysis and clustering techniques on a sample of 63 companies chosen from six 2-digit industries based on Security and Exchange Commission codes. SEC codes are similar to the Standard Industrial Classification codes defined by the U.S. Bureau of the Budget [4]. SIC codes are 4-digit codes based on the principal end product of the firm. They are chosen so that, as the lowest order digit is removed, the companies are aggregated into broader but still similar groups.

Since the appearance in 1969 of Kadar and Russell's paper [1] and in 1970 of Whitmore's paper [4] extending stochastic dominance to the second and third degrees, a considerable interest has developed in stochastic dominance methods as an alternative to moment methods in investment ranking models. The particular attraction of stochastic dominance is that its results are consistent with the expected utility hypothesis without depending on a particular mathematical form of utility function or on a specific type of distribution of investment returns. Although both stochastic dominance ranking models and moment ranking models are based on probability distributions of investment returns, it has been difficult to relate the two types of models mathematically for a complete comparison of results. In this paper the common moments are expressed in terms of successive integrals of a probability density function to allow a systematic comparison of the two methods.

In Gonedes [5], the results of an empirical analysis of accounting-based and market-based estimates of systematic risk were presented. These results suggested that there is, in general, a “statistically significant” relationship between accounting-based and market-based estimates of systematic risk at the level of individual securities, if the accounting-based estimates are conditional upon first-differences or scaled first-differences of the accounting numbers. The differencing transformation seemed to induce relatively better specified models for the accounting numbers.

The purpose of this study is to investigate the determinants of savings and loan profitability. Although considerable discussion has been devoted to studying the cyclical behavior of thrift institution profitability, there is a void with respect to the determinants of individual association profitability. This paper investigates whether performance variations are more determined by differences in the individual association's financial characteristics than by the external factors of the market in which they operate, such as state usury laws, market structure, and other economic characteristics of market areas.

The purpose of this note is to present a simple computational algorithm to approximate the E, S portfolio selection model. The essential feature of the model is the utilization of the familiar linear programming framework by representing risks as a series of linear constraints. Suppose we have m states and n securities, and we assume the investor is able to specify the contingent returns for all securities in each state. Following [7], we define risk as being the downside deviation from the investor's target rate of return.

It has been suggested by many [1, 2, 5, 6, 7, 10 and more] and denied by few that, ceteris paribus, a well-informed risk-averse investor should prefer investments which have positively skewed distributions of rates of return. Passing over the models which underlie such assertions, the question is addressed empirically here. Do (as opposed to “should”) investors prefer investments that are positively skewed, ceteris paribus?

More than twenty years ago the portfolio selection problem was stated as a parametric quadratic programming problem [3]. Since that time there has been an ongoing search for methods that would allow reductions in both the data and the computational effort required to implement the Markowitz formulation. Markowitz himself developed a special algorithm for the problem [4] Sharpe followed with his famous diagonal model [6], a linear programming approximation for the special case of mutual funds [7], and a linear programming approximation for the general problem [0]. And during this period there were substantial advances in quadratic programming computer codes. A very fast code is now widely available [1], but the size of the code itself (a listing of the annotated program runs to more than 3,000 lines) makes its everyday use for portfolio selection somewhat unattractive.

The comment by Linke and Kim correctly observes that the assumption regarding the maintenance of constant proportional use of capital sources is not appropriate for our argument and should be deleted. As our analysis did not make use of this assumption, the two conclusions hold.

1. The weighted average cost of capital calculated with the usual weights (original capital structure proportions) is not in general equal to the discount rate which equates the current value of the firm to the present value of future cash flows.

2. The above conclusion holds for any weights which can be constructed from the cash flows.

The growing sensitivity of savings deposits to flow in response to changes in interest rate differentials has become so commonplace in the past decade that the term “disintermediation” has become a part of the economists' vocabulary. It is a major conclusion of this paper that the volatility of savings deposits began to increase as early as 1950 for savings and loan associations and credit unions and as early as 1945 for mutual savings banks. As an indication of this, we proxy changes in the competitive environment for savings deposits by making yearly estimates of the elasticity of savings deposits with respect to deposit rates at savings and loan associations, mutual savings banks, and credit unions.

The financial experiences of the last two years impel a careful and wide-ranging review of the stability of our major types of financial institutions. That review ought to be followed by actions to redress weaknesses or proclivities that, upon analysis, are judged to contribute an undesirable degree of instability within the financial system.

Although they did not affect the individual rates of return per inflationary period that were the main concern of our study previously published in the Journal, it has recently come to our attention that there were computational errors in the summary weighted average real returns. Thus, we are taking this opportunity to correct the original errors and also to update the study results for the period from December 31, 1968, through December 31, 1973. In the analysis and discussion which follows it is assumed that the reader is familiar with the previous study and the methodology employed. Therefore, Table 1 is simply a correction of Table 4 in the 1971 article. This is followed by a discussion of the revised results and changes in conclusions prompted by the adjusted results. Table 2 contains nominal and real rates of return for the period from December 31, 1968, to December 31, 1973, nominal and real rates of returns for the full period from December 31, 1965, to December 31, 1973, and finally, a new set of weighted average returns using the figures updated through 1973.

Professor Jennings, in his recent article [2], developed a model to estimate convertible bond premiums. The model incorporates the capital asset pricing model to evaluate convertible bonds. The purpose of this comment is not to criticize the general development of the model but to point out flaws in its implementation which influence Jennings' empirical results.

Jack Clack Francis' paper is a most interesting and provocative one, because it is the first to present empirical evidence questioning the importance of a distribution's skewness parameter in the investor's decision process. In particular, Francis claims his evidence demonstrates that stock market investors do not consider skewness in choosing among alternative investments.

In their recent paper Porter, Wart, and Ferguson (PWF) [6] discuss the factors which they allege to be responsible for “the time-consuming nature of empirical tests of” stochastic dominance (SD) efficiency relative to Markowitz (EV) efficiency. Based upon their analysis, a number of algorithms for improving computational efficiency are offered. The “tricks” upon which applications would be based are of no concern here. Our concern is with the logical inconsistency of any attempt to compare and contrast SD and EV efficiency criteria on empirical grounds.

In his comment [1] Professor Frankle raises four potential problems that may have had an influence on the empirical results that I obtained when testing my model describing convertible bond prices [2]. Two of the points are interrelated and do much to explain the problems with the original empirical work. The other two points probably did not have an influence on the final outcome.