Is there a “military-industrial complex” in the United States? What is the relationship between business, government, and the military with its needs for vast quantities of goods and services? How has organization for war and defense changed since the demands of World War I first made such questions important? How much do we know about what actually happened between World War I and Vietnam to change the relationship between private and public organizations? Professor Cuff discusses the complexities involved in trying to answer such historical questions, and prescribes a professional historian's regimen for future work on this subject.

Stochastic Dominance rules are playing an increasingly prominent role in the literature on choice under uncertainty. Their foundation is the mainstream VonNeumann-Morgenstern expected utility paradigm. Their essence is to provide an admissible set of choices under restrictions on the utility functions that follow from prevalent and appealing modes of economic behavior: The admissible sets generated are useful for a large group of individual decision makers and the optimal choice for an individual can then be obtained from among the smaller set of admissible choices.

In the applications of mathematical programming to the “pure capital rationing” problem, much of the attention has been focused on the search for an appropriate discount rate to account for the time value of money. The essential difficulty was first observed by Hirshleifer [10] in the classical economics context: “The discount rate to be used for calculating present values…cannot be discovered until the solution is attained, and so is of no assistance in reaching the solution.” Baumol and Quandt [1] showed that this problem persists in the Lorie and Savage [11] and Weingartner [15, Chap. 3] mathematical programming formulation and concluded that: “If there is capital rationing and external rates of interest are irrelevant, we cannot simultaneously insist on a present value formulation of the objective function and have the relevant discount rates determined internally by our program.” They then went on to propose an alternative utility formulation of the objective function.

As the multinational corporation (MNC) becomes the norm rather than the exception, the need to internationalize the tools of domestic financial analysis is apparent. A key question is: What cost-of-capital figure should be used in appraising the profitability of foreign investments? This paper seeks to provide a comprehensive approach to analyze the cost-of-capital question. It begins by extending the weighted cost-of-capital concept to the multinational firm. It then builds on previous research to address the following related topics: national or multinational financial structure norms; the role of parent company guarantees; the costing of various fund sources particularly when exchange risk is present; the impact of tax and regulatory factors; risk and diversification; and joint ventures.

The purpose of this paper is to show that the internal rate of return (IRR) even when unique and real may nevertheless be an incorrect measure of the return on investment, and to prove that all projects characterized by negative flows occurring only at the beginning and end will be mixed investments for which the IRR, whether unique and real or not, is not a correct measure of investment return.

When a unit of a multinational corporation requires short-term funding, it normally has the opportunity to obtain funds from a number of different sources, both internal and external to the country where these funds will be deployed. The possibility exists for borrowing in a currency other than the local currency of the borrowing unit. The purpose of this paper is to find the optimal currency source or sources for such a loan, when the borrowing unit can enter the forward exchange market for a term equivalent to the time to maturity of the loan.

In recent years, several papers [Mossin [13], Hamada [7], Rubinstein [14]] have addressed the normative implications of the CAPM (developed by Sharpe [15], Lintner [9] and Mossin [12]) for the capital budgeting and capital structure decisions of a value maximizing firm. The model has been extended by Chen and Boness [3] to analyze the effects of uncertain inflation and by Adler and Dumas [1] to study optimal international acquisitions.

In this paper, we examine the effects of errors in measurement of the two independent variables, return on market (Rm) and return on risk-free assets (Rf), in the traditional one-factor capital asset pricing model (CAPM). After discussing Sharpe-Lintner's CAPM and both Jensen and Fama's specifications thereof, we review briefly the recent results of Friend and Blume [6], hereafter FB; Black, Jensen and Scholes [1], hereafter BJS; and Miller and Scholes (11], hereafter MS. In Section II, we first explore possible sources of measurement errors for both Rm and Rf; then we specify these errors mathematically and derive analytically their effects on estimates of systematic risk of a security or portfolio, , and the Jensen's measure of performance, . In Section III, we derive an analytical expression for the regression coefficient of estimated b's where we estimate the equation . The result is then examined to find the conditions under which errors in measurement of Rm and Rf can cause b to have a positive or negative value even if the true b is zero. The conditions are then used to examine FB's results and their interpretation. In Section IV, an alternative hypothesis testing procedure for the CAPM is examined. We show that the empirical results so derived are also affected by the measurement errors and the sample variation of the systematic risk. The relative advantage between the two different testing hypothesis procedures is then explored. Finally, we comment on the relevance of the result to the popular zero-beta model and indicate areas for further research.

Empirical research has played an important role in recent theoretical developments in the theory of finance, particularly in the formulation and testing of various theories of capital asset pricing. A common procedure in much of that empirical research is to use historical price and dividend data to estimate the parameters of a characteristic line which relates the return on an asset or portfolio to the return on the market. While several possible limitations of such procedures have been explored, one recurring question is the appropriate length of each interval used in the estimation. The purpose of this study is to investigate intervaling in greater detail so as to better understand its impact on the results of empirical research and hence of further developments in the field of finance. This is accomplished by examining the effect of different intervals on the return distributions and estimated characteristic lines of 200 common stocks over the two decades 1950–1969. Section II reviews the relevant literature and attempts to place the intervaling effect in perspective. Research design for the investigation is described in Section III, and findings are presented in Section IV. A brief conclusion appears as Section V.

In many circles the Mean Variance Capital Asset Pricing Model (MV CAPM) is synonymous with the theory of capital asset pricing. But in a single-period discrete-time model which explicitly recognizes the existence of limited liability the derivation of the MV CAPM, if it is to be consistent with the von Neumann-Morgenstern postulates of rational behavior, must be based on the assumption that all investors have quadratic utility functions. This assumption in turn implies that risky assets are inferior goods. However, if we turn to the broader class of linear risk tolerance (LRT) utility functions, for which the separation property holds, other simple two-mutual-fund CAPMs can be derived. The power utility LRT CAPMs are of particular interest as they are consistent with risky assets being normal goods.

Capital budgeting can be described as the problem of allocating scarce capital among a number of investment opportunities in such a manner that the outcome most preferred by a decision maker will result. When a single, mathematically explicit criterion is assumed, mathematical programming techniques can be applied. However, a single criterion, such as maximizing the return on investment or minimizing the risk of losing a sizable fraction of the original investment, is not appropriate for a significant number of real-world decision makers for whom two or more criteria, e.g., a judicious combination of return on investment and risk, are important. It has been argued [1] that it is usually not possible to obtain an explicit utility function for the decision maker and, consequently, that it is usually not possible to apply conventional (optimizing) mathematical programming techniques to find the most preferred outcome.

Notwithstanding the importance of maintaining soundness, commercial banks in early ante-bellum America still strove to maximize profits, according to Professor Adams, who cites Stephen Girard's conservative private bank as an example. Using internal data from Girard's bank, of the kind that is seldom available for banks, he shows that a flexible policy of shifting from government and quasi-government securities, as they became scarcer, to business debt, both long- and short-term, kept bank profits from declining. So long as Girard lived, his bank remained fully competitive with the growing number of chartered institutions.