Knowledge that Transforms

An abstract is not available for this content so a preview has been provided. As you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

Presence of a variable market demand function for a given product has significant implications for factor input levels and for the resulting production costs to the firm. In a recent paper, McKean argues that in order to describe the costs of producing a product subject to fluctuating demand it is necessary to take into account the entire distribution of outputs as it relates to the static total cost function. While it is evident that influences on costs and factor inputs will differ in the case of a fluctuating demand schedule compared to the conventional stable demand conditions of classical micro theory, it is not clear that the use of a static cost function in conjunction with a probability distribution of outputs is the proper framework in which to examine the problem.

This paper analyzes risk-return relationships in regional and national securities markets. Specifically, it presents methods and results of an investigation of simulated portfolios of common stocks traded on the Minnesota over-the-counter (OTC) market and the New York Stock Exchange (NYSE).

In our earlier note, we drew attention to the problem of interdependency between the opportunity cost used as a discount rate in determining the net present values of the objective function and the optimal solution of a linear program. In expanding on our article, Lockett and Tompkins (L and T) rightly point to the need for an appropriate definition of opportunity costs.

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.

Recently, Lusztig and Schwab [5] drew attention to a problem which occurs when applying linear programming methods to portfolio selection where capital is rationed over several investment periods. The problem is to determine the relevant discount rate with which to calculate the net present values of the objective function when this same rate is dependent upon the optimal solution to the linear program. Lusztig and Schwab suggested a neat procedure by which it was claimed that one could overcome this difficulty and which also does not rely upon the measurement of subjective utility as did the Baumol and Quandt approach [2]. It was decided to test the Lusztig and Schwab model (afterwards called the L-S procedure for brevity) on a hypothetical problem, and doing so resulted in two conclusions:(a) The L–S procedure, as described, is incomplete but with small modification may be useful; however, (b) concentration upon the discount rate problem in isolation from other capital budgeting problems may well be a pointless exercise.

This paper proposes a model that a dealer or investor may employ in determining• The potential investment value of a debt instrument, • The potential gains in net after-tax yield which result from swaps, and• The trade-off, effective, after-tax yield on a municipal vs a taxable corporate bond of the same quality or rating.

In this paper, a simple model of individual financial decision making is used (1) to construct the market demand curve for common stock, and (2) to question the appropriateness of market value maximization as the objective of corporate financial management.

Neil B. Murphy, Harry Weintrob, Evaluating Liquidity Under Conditions of Uncertainty in Mutual Savings Banks, The Journal of Financial and Quantitative Analysis, Vol. 4, No. 5 (Jan., 1970), pp. 559-568

This paper discusses the use of simulation as a means of studying the operations of securities markets. To place simulation's role in the proper context, Section I begins with a review of public policy, research, and teaching considerations that have combined in recent years to create a growing need to improve our understanding of the operations of these markets. Following this is a brief discussion of the limitations of traditional price theory models to meet this need. Section II demonstrates the significant, yet largely untapped, potential of simulation in this regard.