A financial decision model of the firm, in which most prior deterministic decision models' assumptions were relaxed, was developed and solved for its policy and state variables' time-optimal trajectories. In particular, the three alternative modes of corporate financing, with their respective explicit and implicit costs, were treated as distinct, time-variant decision variables. In addition, their dynamic interdependent relationship with the firm's investment-possibilities schedule was clearly delineated. Besides eliminating the usual constant returns assumption, our model further introduced a dividends discount factor which was an explicit function of the firm's debt-equity ratio.

Furthermore, despite the generality of the model solutions, valuable economic implications were determined; namely, (1) conditions for the existence of a steady-state equilibrium were established with the critical role of nonproportional external equity flotation costs being observed; (2) the firm's dynamic equilibrium path was locally unstable in the initial, high-growth phase of its life cycle and was locally stable in its declining-growth stage–a result consistent with the growth literature in security valu ation theory; and (3) the usual assumptions of the balanced-growth path models are sufficient for the optimality of their decision policies.

Most textbooks in finance are apparently embarrassed by the Modigliani-Miller (MM) theorem on capital structure. The intense controversy it has provoked in academic circles over the past sixteen years makes it hard to ignore, and yet many textbook writers seem to be unable to distill anything from it that might be of interest to their readers. A typical example might begin by laying out the economist's conventional perfect market assumptions and showing that the theorem may be derived deductively from these assumptions. It is then observed that markets are not perfect and it is implied that perfect market theorems, while perhaps interesting to ivory-tower academics, are of no use to a businessman who has to act in imperfect real-world markets. (For example, Weston and Brigham [16], in their appendix to Chapter 11, on p. 339 state: “Given their assumptions, their theoretical arguments were quite correct. However, their assumptions have been questioned extensively, and very few authorities today accept the MM position.”) We are then returned rather uneasily to the traditional world of U-shaped cost of capital curves, in which managers are required to examine such holy relics as financial break-even charts (Van Home [15, p. 231]) or to exercise judgment about the stockholders' utility preferences (Weston and Brigham [16, p. 258]) in order to make their debt/equity decision.

The Stable (or Pareto-Lévy) distribution has been of considerable interest in describing the behavior of security prices ever since the important work by Mandelbrot [6], [7] and Fama [1]. The aforementioned contributions focused on the empirical hypothesis that security price data are better fitted by theoretical distributions with infinite variance rather than finite variance. Specifically, in the case of Stable distributions, the “characteristic exponent” is less than two, and the data are not adequately fitted by a normal distribution. Remarkably, however, although almost the entire body of literature addressing empirical questions with respect to the distribution of security prices investigages the behavior of the (natural) logarithm of security price relatives, to this author's knowledge no paper exists which analyzes the portfolio choice implications of the assumption that the logarithm of the asset returns has a symmetric Stable distribution with infinite variance. Thus, in Fama [2], Samuelson [10], and Ziemba [12], where the problem of selecting an optimal portfolio in Stable markets is the object of concern, one finds that it is assumed that the price-relatives (returns) have a Stable distribution; this rather than the logarithm of the price relatives. And it should be noted that none of these authors suggests that the untransformed price-relatives are better-fitted by a symmetric Stable distribution as compared with the logarithm of the price-relatives.

Portfolio analysis has generally been restricted to problems in, at most, two dimensions, expected return and risk, the latter usually measured by standard deviation. In two papers Jean [2, 4] has attempted to extend the analysis to three and many dimensions by deriving risk premiums as functions of higher order moments. This paper corrects several errors in his work and derives a normative, individual pricing model for risky securities analogous to the capital market line within the framework of a perfect market.

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.

The normative theory of portfolio selection has, since Markowitz, proceeded for the most part on the assumption that there are no costs of transacting in securities markets. Exceptions to this generalization are the work of Pogue who proposes a quadratic programming solution to the portfolio selection problem with variable transactions costs, and, in a multiperiod context, the ad hoc portfolio revision models of Smith, and the more rigorous, though computationally burdensome dynamic programming models of Chen et al. All of these models focus exclusively on the variable costs of transacting. Mao deals implicitly with the fixed costs of purchasing securities and the limited diversification which these will imply. However, his model both lacks an explicit optimization criterion for determining the number of securities to include in the portfolio and assumes a homogeneous security universe; this latter assumption is relaxed when he later considers the problem of which securities should be included in the portfolio. Clearly an ideal solution to the problem must consider simultaneously both how many and which securities to include. More recently, Jacob has developed some simplified models for selecting optimal portfolios, given a constraint on the number of securities which may be included in the portfolio. While her models are superior to Mao's in taking account of the residual risk of the securities as well as their systematic risk, they do not contain any explicit procedure for determining the optimal number of securities to include in the portfolio.

Two prominent views pertaining to measures of risk aversion can be found in the literature. First, Arrow [2] and Pratt [3]developed risk aversion measures based on the curvature characteristics of the individual investor's utility for wealth function. If the investor's utility for wealth function is given by V(W), then

are the Arrow-Pratt measures of absolute and relative risk aversion, respectively. The investor is risk averse or a risk lover as r(W) and r* (W) are positive or negative. The investor exhibits increasing, constant, or decreasing absolute risk aversion as while he exhibits increasing, constant, or decreasing relative risk aversion as .

In recent years the expected-utility approach to decision making under risk has gained increasing acceptance among portfolio theorists. On the other hand, the mean-variance (MV) approach of Markowitz [13], which has dominated portfolio theory in the past, continues to enjoy great popularity. In MV theory, the investor is assumed to rank his preferences for risky returns solely in terms of their means and variances, with higher means and lower variances, being preferred. Tobin [20] showed that MV theory is consistent with expected utility theory in the special case of joint-normally distributed asset returns. The MV approach enjoys a ready acceptance among practitioners, and requires only modest informational and computational inputs. Perhaps its most attractive feature is its ability to decompose the overall portfolio problem into a sequence of much simpler problems: first, the “efficient” set of portfolios (which minimize variance for any given mean return) is calculated, and then the investor chooses one of the efficient portfolios in a manner consistent with his personal preferences. This efficient set is the same for all investors having the same mean-variance-covariance estimates of risky-asset returns, and can, in principle, be determined once and for all using parametric quadratic programming [12, 22] Despite these real advantages, the MV theory embodies certain problems of principle in the case of nonnormally distributed asset returns, and this fact has led to increasing emphasis on the presumably more rational expected-utility theory.

We develop a household portfolio selection model that (1) generalizes some aspects of the conventional theory of the portfolio selection process; (2) integrates the investment and financing decisions, and (3) reflects the liability structure of the household. We incorporate the above influences by placing restrictions on the portfolio maximand in the form of probabilistic and deterministic constraints. The maximand is a tradeoff between the expected net value and the volatility of the portfolio as measured by Sharpe's beta coefficient. The maximization of the objective function is restricted by (1) the Cash Flow, (2) the Credit Limit, (3) the Adding-Up, and (4) the Non-Negativity constraints.

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.

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.

New stock financing is assuming increasing significance as a source of funds for private firms. The problem of management of external financing has grown as well. As a practical matter, financial managers must depend on the assistance of underwriters with respect to pricing and distribution of new corporate stock. But recent changes, some set in the context of the capital asset pricing model, imply systematic underpricing of new securities. If these charges are true, the financial manager is faced with the dilemma of paying monopsony profits, or accepting the cost and risk involved in taking the issue to market without the investment banker, or seeking an alternative source of funds. In any event, the process of marketing new equity depends on the relationship among the many characteristics unique to the firm and that firm's cost of equity capital. This paper discussed these interrelated issues.

Choice under uncertainty may be viewed as choice between alternative probability distributions of returns. Under Von Neumann and Morgenstern's assumptions, an individual's optimal choice is a distribution that maximizes the expected utility of returns. In the theoretical analysis, the distribution functions are assumed to be known However, in most realistic cases, the distributions of returns are unknown and are estimated using available economic data. The traditional mode of analysis is to neglect the estimation risk and use the estimated distribution (in lieu of the true distribution) in determining the optimal choice under uncertainty. In this paper, we consider the portfolio choice problem and determine the effect of estimation risk on an individual's optimal choice under uncertainty.

Preference orderings of uncertain prospects have progressed from the two-moment EV model first developed by Markowitz [1952] to the more general efficiency analysis that is based on the entire probability function. This general efficiency approach, referred to as the Stochastic Dominance (SD) approach, does not depend on specific assumptions about the investor's utility function and has been shown to be theoretically superior to the “moment methods” [1].

The literature on security selection and evaluation is quite extensive-Aside from the chart readers, however, there is little or no theoretical framework on the optimal trading price. The theories of technical analysts have been almost completely debunked by the evidence on the random walk nature of price performance. This state of affairs leaves the investor, who has decided to buy or sell a particular security, with very little insight as to the optimum price to trade. The random nature of price performance suggests that both higher and lower prices are likely to be obtainable in the near future. Thus, trading at the current market price may not be the best strategy. On the other hand, waiting for the stock to move decisively in the desired direction exposes one to the risk of an equally large movement in the opposite direction.

In the process of managing a financial institution there are decisions that require special considerations beyond those in other firms. In this paper dividend disbursal practices in the banking firms will be analyzed. The central issue is what part of profits should be distributed and what part should be retained within firms as an addition to the banks' net worth.

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.

It is clear from several previous empirical studies that diversification reduces portfolio return variability and that the reduction of variability appears very quickly. The purpose of this paper is to reexamine the question of how diversification affects the distribution of portfolio returns. There is no doubt that increasing the number of securities in a portfolio will reduce the nonmarket related return variability. However, there are several areas of controversy. For example, it should be recognized that the term “variability” can have different definitions. Furthermore, the ability of diversication to eliminate independent elements of the variability of return is important only if this reduction can be incorporated into predictions of future risk. In addition, the method of portfolio formation may determine the extent to which diversification enables portfolio managers to more accurately assist future risk.

Numerous empirical studies have appeared in recent years concerning the behavior of stock market prices. Cootner's book [2] presents an excellent summary of pre-1964 efforts, while Fama's paper [5] discusses some of the more recent work. While a few writers believe that certain price trends and patterns exist which enable the investor to make better predictions of the expected value of future stock price changes, the majority of these studies conclude that past price data alone cannot form the basis for the prediction of the expected value of price movements in the stock market.

The dramatic surge of the commodity option market over the past few years may well be illustrated by the growth (before its collapse) of one leading option writer, Goldstein, Samuelson, Inc., whose sales rose from $1 million in 1971 to over $45 million by the end of 1972. The commodity option market is closely related to the commodity futures market, except that options are available only on the so-called “international” commodities.