Three important methods exist for the treatment of risk in capital budgeting problems: the certainty equivalent method (CE), the risk-adjusted discount method (RAD), and the probability distribution or Hillier-Hertz approach (PD, based on [4]). Each one of these methods evaluates the multiperiod stream of risky returns generated by an investment for given distributions of the returns in each period. A common assumption for all three methods is the certainty of the occurrence of a given risky cash inflow (defined by its distribution) in a given time period. This assumption is probably derived from accounting practices. In references [8] and [9] the PD approach was generalized by removing the certain timing assumption. This paper examines the implications of random timing of cash returns within the framework of the better known CE method.

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.

The need for a corporate marginal cost of capital to be used for internal accept-reject decisions (either as a rate of discount for net-present-value (NPV) computations or as a “cut-off” rate with the internal rate of return (IRR) criterion) has led numerous textbook writers to advocate some variant of a weighted average cost of capital. These authors agree substantially on how costs of individual sources of capital are to be assessed but are uncertain of how the weights should be determined, whether they should reflect the firm's existing capital structure, a target structure, or the mix, however determined, in the firm's forthcoming capital budget, and whether they should be based on book or market values. Moreover, it is not obvious how book or even market values should be measured. These writers have not proven that their intuitively held definitions do in general, for capital budgeting, imply maximizing shareholder wealth.

A market is commonly called thin if a large change in price is associated with a small change in supply or demand. The concept of thinness can refer to the markets for stocks, bonds, any category of financial instrument, or even any type of good. Most frequently, thinness has been casually discussed with regard to bond markets and stock markets.

The investment managers of financial institutions face a multiplicity of factors that influence the performance of fixed coupon securities in their investment portfolios. These factors can be classified under three headings:

1) Condition of the economy–changes in interest rate levels and spreads;

2) Nature of the financial institution–institution's tax rate, economic factors such as deposit variability and loan demand for banks, and premium inflow and benefit payments for insurance companies;

3) Characteristics of individual securities–(a) their attractiveness in terms of risk level and marketability; and (b) their contractual factors (coupon level and maturity) and tax status.

Linear programming models of specialized financial decision problems such as working capital management [21], short-term financing [22], or capital bug-geting [24] are deficient in that they may lead to decisions which are suboptimal with respect to the firm as a whole. Each model attacks a single decision problem and neglects its interaction with the other activities of the firm. On the other hand, a model which reflects these interdependences and interactions by including the various financing, investment, and operating decisions in a single model tends to become excessively large and inefficient to use. What is needed is a model that incorporates the efficiencies inherent in smaller, more specialized models which can be utilized on a decentralized basis and which can simultaneously lead to decisions that are optimal for the firm as a whole.

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.

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?

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.

The problem of allocating a fixed set of time-phased investment allowances among competing investment projects and proposals has received great attention in the literature since Weingartner [11] established a linear/integer programming formulation. Reviews of the literature can be found in Weingartner [12], Hunter [6], Hodges [5], and Bernhard [2].

At any point in time a firm must decide both the level of working capital consistent with its productive assets and how to finance these assets. Academic theorists in business administration have traditionally approached decision making of the firm on a segmented rather than on a global basis and have been satisfied with developing suboptinizing decision rules. Thus there has been concern about managing working capital and concern about choosing the optimum capital structure, but traditionally the two decisions have not been made jointly. And even if they were made jointly, decisions would still remain in the working capital area involving inventories, credit granting, and marketable securities. This paper is an attempt to interrelate working capital and capital structure decisions with working capital used not only as a buffer to avoid ruin but also to affect sales via changing inventory levels and credit policies. The possibility of ruin introduces a discontinuity that precludes perfect elimination of leverage effects via a market.

In their comment on a paper by Porter, Wart, and Ferguson [7], Professors Frankfurter and Phillips [2] have raised two serious objections to “any attempt to compare and contrast SD and EV efficiency criteria on empirical grounds.” Essentially, they have argued that empirical comparisons of SD and EV selection rules are invalid because:

a. The EV portfolio building algorithms are not allowed to operate in such tests, and

b. While the estimators of true E and V are derived from and supported by elementary sampling theory, there exists no comparable sampling theory for the estimation of total probability functions.

Models developed to explain variations in cash balances of firms have generally postulated forms of rational choice for the decision maker. Two examples of these kinds of models are (1) Baumol's inventory-type model where the choice of the initial balance is made in terms of a planning period in which outflows of cash, but no inflows, are considered; (2) Miller and Orr's model wherein inflows and outflows occur randomly and a decision is triggered to increase or reduce cash balances when an upper or lower threshold is passed. Statistical tests of the inventory-type model have had limited success, particularly in attempts to identify the increasing efficiency in the use of cash balances as a function of the size of the firm. The apparent linear double logarithmic relationship between cash balances of firms and their sales volumes has cast doubt upon the increased efficiency proposition that derives from the Baumol model. Meltzer has modified this model to demonstrate that it implies linearity. The Meltzer tests will be challenged in this paper, and we shall establish that his results, as well as Baumol's conclusions, are particular outcomes that can be better explained in another type of model.

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.