The purpose of this study is to develop and estimate sectoral demand for money functions and an aggregate supply function for money within the framework of a simultaneous-equations model of major United States financial markets. The study is exploratory in nature in several respects. The final form of the behavioral equations is, of course, open to question. Also, the data used in the estimation of the behavioral equations have recently been revised by the Flow of Funds Section of the Federal Reserve Board.

One of the most important concepts in economic theory is the “invisible hand,” introduced by Adam Smith in the following terms:

Every individual intends only his own gain, and he is in this, as in many other cases, led by an invisible hand to promote an end, which was no part of his intention. Nor is it always the worse for the society that it was no part of it. By pursuing his own interest, he frequently promotes that of the society more effectually than when he really intends to promote it.

The vector of equilibrium aggregate market values (or per share prices) of a given set of risk assets trading in purely competitive markets of individually risk-averse investors has been derived in earlier work under certain simplifying assumptions, including the absence of taxes and transactions costs and a single (uniform) holding period for the assessment of uncertain outcomes (See [8], [9], [10], [15], and [12]). The other critical assumptions in these studies were (a) the existence of a riskless asset available for holding or borrowing at a fixed, exogenously determined interest rate, (b) an assumption that all investors act in terms of identical joint probability distributions over end-of-period outcomes, and (c) the acceptance of a mean-variance criterion for portfolio decisions.

Until very recently, in most work on normative models for capital investment planning, it has been assumed that availability of capital is unconstrained; i.e., that money may be freely borrowed or lent at a single market rate of interest, and that no other constraints affect the proper choice of available productive investment projects to be undertaken. Since practical situations almost universally do involve such constraints, the traditional theories have, for the most part, been an unsatisfactory guide to achievement of optimal capital investment behavior in the real world.

This paper examines the operating efficiency of three types of financial intermediaries in the United States: (1) credit unions, (2) savings and loan associations, and (3) mutual savings banks during the past three decades. In particular, it examines by how much, if any, the operating efficiency of these intermediaries has been enhanced.

The Sharpe model of capital asset pricing under conditions of risk has received wide theoretical acclaim and empirical support. This paper presents an econometric study of the model with the following objectives: (a) to show the effect of measuring the model's independent variables incorrectly; (b) to derive and use a new procedure for empirically testing the adequacy of the model as it is currently formulated.

Many students of business finance subsume the risks associated with a firm's income stream under two general cognomens, namely, “business risk” and “financial risk.”1 The degree of business risk associated with a firm's income stream is considered to be a function of all determinants of risk except those that relate to the means by which a firm's operations are financed (i.e., the nature of a firm's capital structure). In general, business risk is determined by a firm's asset structure, the purposes for which a firm's assets are used, and the efficiency and effectiveness with which a firm's assets are utilized. The determinants of business risk include the competitive position of a firm, the nature of a firm's operating expenses, the intensity of demand for a firm's products, and a firm's managerial resources, inter alia. A measurement of the variability of net operating income (i.e., earnings before interest expenses and income taxes) is usually employed as a surrogate of business risk.

One of the problems which has plagued microeconomic theory is the difficulty in achieving close correspondence between formal models and practical market situations. The most widely accepted models envisage the firm set in a static mold with the implication that profits will be maximized in every period. The model is more generally acceptable in the case of perfect competition, for then the market results are most likely to be “as if” the firms acted marginally to maximize profits. Only the profit maximizers will survive. While such a scheme may lead to realistic results in the case of perfect competition, this condition does not bulk large in the United States economy.

This article examines some aspects of the portfolio selection problem when the “no-easy-money-condition” holds and the investor is constrained to stay solvent. The possible presence of a non-capital income is also taken into consideration.

Current conditions in the money and credit markets, along with the memory of the “crunch” of late 1966, have caused both the managers of financial institutions and their regulators to reconsider their concepts of “liquidity.” Both Minsky and Ritter have argued forcefully that traditional attention paid to balance sheet proportions should be abandoned in favor of an intertemporal analysis of cash flows [6], [7], and [8]. Ritter states that: “With a multidimensional cash flow forecast extending several years into the future, probability estimates can be made regarding potential liquidity needs over time” [8]. The purpose of this paper is to suggest a forecasting method which explicitly deals with the problem of uncertainty. The method is specifically designed for a mutual savings bank, but it could easily be adapted for a savings and loan association and, with perhaps greater effort, a commercial bank.2 In Section I, components of cash inflows and outflows are examined with the general objective of classification according to: (1) degree of uncertainty and (2) degree of management control. A risk analysis simulation model which permits explicit consideration of uncertainty is presented in Section II. Problems of implementing the model are discussed in Section III, while the implications of the analysis for future research are considered in Section IV.

Presently, foreign banks may enter the United States, depending on state law, in one of four forms:

(1) Branches,

(2) Agencies,

(3) Affiliates, and

(4) Representative offices.

Not all racetrack bettors want to handicap. Some prefer to buy expert opinion on the winners of each race and on the day's best bet. Investors are not very different. Many like to avoid the analysis required to choose among investment funds and so seek some summary measure that will rank funds relative to one another and indicate which one is the best bet. Two measures suggested in work by John Lintner [2], William Sharpe [6], Jack Treynor[7], and Henry Latané and Donald Tuttle [1] rank investment funds considering both return and risk. These measures not only can be taken to provide an indication of the best bet among investment fund managements but also offer a guide to building a portfolio.

It has been observed that when the level of interest rates rises all rates increase, but short-term rates rise systematically more than longterm rates. Over time, therefore, short-term rates experience wider fluctuations than long-term rates. This behavior, however, does not provide any clue as to which interest rate leads the other over the cycle. Most research on term structure of interest rates has focused on the yield curve at a point in time; little has been done to investigate the joint movement of short- and long-term interest rates through time. In this study, we compare the cyclical behavior of short-term and long-term interest rates in the United States during the period 1954–1967. The relationship between the 90-day Treasury bill rate and the 10-year U. S. Government bond rate is analyzed by the cross-spectral method.

The objectives of this paper are to derive the relationship of the geometric mean of a distribution of positive values to the conventional first four moments — arithmetic mean, variance, absolute skewness, and absolute kurtosis — and to empirically evaluate certain approximations involving these four moments for estimating the geometric means of monthly and annual holding period returns for individual stocks and for portfolios. The geometric mean is shown to be positively related to the arithmetic mean and absolute skewness and negatively related to variance and absolute kurtosis. In the case of a normal distribution a very good approximation to the geometric mean is revealed to be a function of just the arithmetic mean and variance. Additionally, empirical evidence indicates that even though a number of the monthly and annual distributions deviate significantly from normality, the approximation involving only the mean and variance produces quite accurate estimates of the geometric means of these distributions.

A number of studies have recently attempted to explain cross sectional variations in selected characteristics of commercial bank operations through least squares regression analysis.1 Generally, whether the particular study objective was to explain inter bank differences in costs, revenues, profits, or loan rates, several explanatory variables were isolated which varied systematically among banks, and which at least partially explained differences in the dependent variable. The explanatory variables which were shown to be theoretically relevant, and statistically significant, were then interpreted literally and designated as having causal characteristics.

Many important contributions to the body of theoretical literature in business finance are built upon the pioneering efforts of others. In a recent article in this Journal, Vaughn and Bennett attempt to build a risk adjusted capital budgeting framework upon foundations laid by Gordon, Miller and Modigliani, Sharpe, Solomon, and others. Unfortunately, they are building a house of straw. Their knowledge of the contributions of their predecessors is superficial, and their approach is inconsistent with some of the basic tenets of business finance.

The question “What's in a bond rating?” has been asked at least since 1909 when such ratings were started in the United States. Informed persons who answer this question typically admit that ratings depend in part on readily available statistics on a firm's operations and financial condition. Examples of such statistics are earnings coverage, leverage ratios, and profit rates.

The determination of the term structure of interest rates, a subject of much controversy in recent years, has emphasized two principal viewpoints: the expectations hypothesis stresses the importance of expectations of future yields as determining the present term structure of interest rates; and the liquidity hypothesis emphasizes the greater “moneyness” of short-term debt as opposed to long-term debt. These main theories provide certain unique insights into the term structure; the acceptance of one need not require the rejection of the other. And the factors they stress at least hold the possibility of simultaneously influencing the term structure of interest rates.

Risk continues to be a widely discussed topic within the field of finance. Academicians add risk variables to their quantitative models, while financial practitioners include risk considerations in their qualitative deliberations. In both contexts, risk — together with some measure of profit or return — generally comprise a dual or composite criteria for investment decision-making purposes. Whereas the decisionmaking situation can be described as ex ante, this article deals with risk in an ex post context. In particular, it reports an investigation of alternative risk-return measures which are designed to rank and evaluate the ex post performance of investment portfolios. Section I reviews three composite measures of performance and examines their interrelationships. A fourth alternative measure is also suggested. In Section II, the measures are used to rank the portfolio performance of a sample of mutual funds. Some difficulties in making performance comparisons of these funds against the market are discussed in Section III. The final section briefly explores the implications of the study and suggests areas for subsequent research.

In testing hypotheses, researchers are almost always faced with the problem of isolating the effect of certain variables. This is a particularly acute problem in the social sciences, where the absence of an experimental environment means that researchers must resort to statistical methods of adjustment.