THE EFFECTS OF VARIABLE AND FIXED TRANSACTION COSTS ON OPTIMAL INVESTMENT DECISIONS

This paper studies the impact of variable and fixed transaction costs on investment decisions under conditions of risk. The decision model is first formulated as a mixed-integer nonlinear program. The following subjects are then examined: the structure of the investment frontier facing the investor and the effects of transaction costs on this frontier, the impact of transaction costs on the investor's optimal investment strategy, and the conditions for the equilibrium structure of risky asset prices and risk-return relationships. The main finding is that the relaxation of the assumption of the absence of transaction costs eliminates some of the most unattractive implications of the classic capital asset pricing model (CAPM) while preserving the more attractive implications of this model. Also, our model provides explanations for some discrepancies between the theoretical CAPM and empirical findings and, therefore, is a step toward narrowing the gap between theory and practice.     

OBTAINING AND PARAMETERIZING MULTIPERIOD PORTFOLIOS WITH DESIRABLE CHARACTERISTICS UNDER LOGNORMAL RETURNS

The expected geometric mean has been shown to be a valuable criterion in ranking portfolios of assets. For example, under certain conditions the maximum expected geometric-mean portfolio minimizes the expected time to amass a fixed level of wealth as wealth becomes “large.” This paper develops a simple algorithm for obtaining such portfolios when portfolio returns are assumed to be lognormally distributed. The risk-return characteristics of portfolios are derived and illustrated using data from a subset of stocks found on the New York Stock Exchange.     

Concepts,Theory, and Techniques ROBUST METHODS: AN ALTERNATIVE APPROACH FOR ANALYZING DATA SETS CONTAINING INFLUENTIAL DATA POINTS

Recent advances in statistical estimation theory have resulted in the development of new procedures, called robust methods, that can be used to estimate the coefficients of a regression model. Because such methods take into account the impact of discrepant data points during the initial estimation process, they offer a number of advantages over ordinary least squares and other analytical procedures (such as the analysis of outliers or regression diagnostics). This paper describes the robust method of analysis and illustrates its potential usefulness by applying the technique to two data sets. The first application uses artificial data; the second uses a data set analyzed previously by Tufte [15] and, more recently, by Chatterjee and Wiseman [6].     

A Test of the Cross-Sectional Robustness of the Arbitrage Pricing Model Using Foreign Exchange Rates*

This paper tests the cross-sectional robustness of the arbitrage pricing theory (APT) model using foreign exchange rate data to determine if the model is robust with respect to the various random samples and various factor analytic techniques. Factor scores are developed using various samples and factor analytic techniques to explain the returns for other samples and groupings. The APT model is found to be robust across samples and techniques.     

ON THE IMPLICATIONS OF SPECIFICATION UNCERTAINTY IN FORECASTING*

Forecasters typically select a statistical forecasting model from among a set of alternative models. Subsequently, forecasts are generated with the chosen model and reported to management (forecast consumers) as if specification uncertainty did not exist (i.e., as if the chosen model were the “true” model of the forecast variable). In this note, a well-known Bayesian model-comparison procedure is used to illustrate some of the ambiguities and distortions of forecasts that do not reflect specification uncertainty. It is shown that a single selected forecasting model (however chosen) will generally misstate measures of forecast risk and lead to point and interval forecasts that are misplaced from a decision-theoretic point of view.     

USING L2 ESTIMATION FOR L1 ESTIMATORS: AN APPLICATION TO THE SINGLE-INDEX MODEL

The bootstrap method is used to compute the standard error of regression parameters when the data are non-Gaussian distributed. Simulation results with L₁ and L₂ norms for various degrees of “non-Gaussianess” are provided. The computationally efficient L₂ norm, based on the bootstrap method, provides a good approximation to the L₁ norm. The methodology is illustrated with daily security return data. The results show that decisions can be reversed when the ordinary least-squares estimate of standard errors is used with non-Gaussian data.     

A NOTE ON THE USE OF SELECTED NONFINANCIAL RATIO VARIABLES TO PREDICT SMALL-BUSINESS LOAN PERFORMANCE*

Small business loan applications have not been evaluated successfully by traditional methods. This paper explores the possibility of using three types of nonfinancial ratio variables (owner, firm, and loan characteristics) to predict whether a small business will pay off or default its loan. The owner and loan variables were better predictors of loan success than the firm variables.     

SOME STATISTICAL ISSUES IN THE ESTIMATION OF A SIMPLE COST-VOLUME-PROFIT MODEL*

This paper presents point and interval estimators of both long-run and single-period target quantities in a simple cost-volume-profit (C-V-P) model. This model is a stochastic version of the “accountant's break-even chart” where the major component is a semivariable cost function. Although these features suggest obvious possibilities for practical application, a major purpose of this paper is to examine the statistical properties of target quantity estimators in C-V-P analysis. It is shown that point estimators of target quantity are biased and possess no moments of positive order, but are consistent. These properties are also shared by previous break-even models, even when all parameters are assumed known with certainty. After a test for positive variable margins, Fieller's [6] method is used to obtain interval estimators of relevant target quantities. This procedure therefore minimizes possible ambiguities in stochastic break-even analysis (noted by Ekern [3]).     

THE CAPITAL MARKET VALUE OF A MULTIPERIOD INVESTMENT WITH THE OPTION OF PREMATURE ABANDONMENT

Conventional approaches to determining optimal abandonment of a project under uncertainty either assume risk-neutrality or impose a mean-variance criterion. Risk-neutrality is unrealistic while the mean-variance criterion precludes determination of the optimal strategy without consideration of covariances of returns among projects. Further, the use of variance of present value as a risk measure may result in the “optimality” of a time 0 strategy that involves maintaining a position at time t that will be “suboptimal” and would not be maintained. The use of the multiperiod capital asset pricing model (CAPM) as a decision criterion is consistent with contemporary theory of market behavior and remedies the deficiencies of the mean-variance approach noted above. Computationally, the optimal strategy for abandonment, when the commitment must be made at time 0 (a lease, say), can be determined with little difficulty beyond that of mean-variance models. When time of abandonment can remain unspecified, the value of the prospect that abandonment will occur at the optimal time can be determined, though the technique necessary is considerably more complicated. In both cases, the marginal costs of commitments that limit discretion over abandonment can be determined and attributed to those commitments.     

Concepts,Theory, and Techniques PRINCIPLES OF MULTIOBJECTIVE OPTIMIZATION*

This paper attempts to isolate and analyze the principal ideas of multiobjective optimization. We do this without casting aspersions on single-objective optimization or championing any one multiobjective technique. We examine each fundamental idea for strengths and weaknesses and subject two—efficiency and utility—to extended consideration. Some general recommendations are made in light of this analysis. Besides the simple advice to retain single-objective optimization as a possible approach, we suggest that three broad classes of multiobjective techniques are very promising in terms of reliably, and believably, achieving a most preferred solution. These are: (1) partial generation of the efficient set, a rubric we use to unify a wide spectrum of both interactive and analytic methods; (2) explicit utility maximization, a much-overlooked approach combining multiattribute decision theory and mathematical programming; and (3) interactive implicit utility maximization, the popular class of methods introduced by Geoffrion, Dyer, and Feinberg [24] and extended significantly by others.     

A NOTE ON PROCEDURES FOR TESTING THE QUALITY OF A CLUSTERING OF A SET OF OBJECTS

Despite the increased application of cluster analysis in decision sciences, few attempts have been made to derive hypothesis-testing procedures for the evaluation of clustering solutions. In fact, the present paper shows that at least one such attempt failed to specify a meaningful sampling distribution for the test procedure. An alternative index based on the concept of point-biserial correlation is proposed as a possible recovery measure. The index is subsequently used to form the basis of a valid statistical test for the existence of cluster structure.     

ESTIMATION OF MISCLASSIFICATION PROBABILITIES IN THE LINEAR PROGRAMMING APPROACHES TO THE TWO-GROUP DISCRIMINANT PROBLEM

Expressions for misclassification probabilities are derived under a contaminated multivariate normal model for the linear-programming approaches to the two-group discriminant problem.     

THE COEFFICIENT OF VARIATION AS A FACTOR IN MRP RESEARCH

This paper investigates the use of the coefficient of variation (CV) as a measure of requirements lumpiness (the amount of variation in requirements from period to period) in material requirements planning (MRP) research. CV is used as a factor in MRP research even though it is limited as a measure. Any sequence of requirements will have a unique CV, but any CV can represent a variety of requirements sequences. This limitation raises questions concerning the robustness of CV as a measure. In this paper, two other aspects of the requirements sequence for a given CV are investigated: the procedure used to generate the requirements and the manner in which the requirements are grouped. A simulation comparison of selected lot-sizing techniques is used to conduct the investigation. With respect to these two (new) aspects of the requirements sequence, CV appears to be a robust measure.     

Concepts,Theory, and Techniques: FACTORS AFFECTING THE PERCEIVED UTILIZATION OF COMPUTER-BASED DECISION SUPPORT SYSTEMS IN THE OIL INDUSTRY

This study investigates factors that may affect the use of decision support systems. The independent variables investigated included: characteristics of the decision makers such as age, educational level, experience, and cognitive style; characteristics of the decision support system such as response time, accuracy, timeliness, relevancy of output, format, and the distance traveled to interact with the system; and characteristics of the implementation process such as user involvement, user training, and top management support. The study included 8 systems and 64 subjects from the oil industry. The results indicated that the most important variables affecting decision support system usage were accuracy of output, user training, relevancy of output, and the decision maker's experience. Subject Areas: Decision Support Systems, Information Management, and Organizational Behavior.     

An Empirical Comparison of Direct Questioning,Scenario, and Randomized Response Methods for Obtaining Sensitive Business Information*

Certain business practices include legal but ethically questionable activities. Surveys intended to determine the nature and extent of such activities must employ questioning methods which mitigate the inherent threat of sensitive questions and account for social desirability effects. This study uses a national mail survey of chief executive officers (CEOs) of manufacturing firms to compare the performance of direct questioning, scenario, and randomized response methods for estimating the prevalence of several sensitive business practices. The direct questioning and scenario versions used self-reporting (individual-based) questions, as well as the CEO's perceptions of the extent to which others engage in questionable activities (other-based). In general, the estimates of the prevalence of selected questionable activities were lowest when the individual-based direct questioning was used and highest when other-based (either direct questioning or scenario) methods were used. The individual- based scenario and randomized response estimates represented intermediate estimates. Suggested guidelines for using the three methods for eliciting sensitive information are discussed.     

Concepts,Theory, and Techniques INFLATION,MAINTENANCE OF CAPITAL,AND THE IRR MODEL OF CAPITAL BUDGETING*

Businesses operating under inflationary conditions need capital-budgeting models that help them judge the adequacy of returns on their investments and also allow them to keep capital intact by considering the erosive effects of inflation. The model proposed in this paper computes a modified internal rate of return (IRR); if cash inflows from a project are divided between earnings and recovery of capital, total recovery equals that amount which the capital-budgeting concept adopted by the business specifies (such as the original investment in constant dollars or its replacement cost). Under this model, a project should be accepted only if this computed rate equals or exceeds a hurdle rate that consists of the inflation-free rate of return plus the effect of inflation on such a return. Other modifications to the IRR model suggested in the literature do not completely satisfy the objective of capital budgeting under inflationary conditions.     

Notes and Communications RESOLVING CERTAIN DIFFICULTIES AND IMPROVING THE CLASSIFICATION POWER OF LP DISCRIMINANT ANALYSIS FORMULATIONS

In certain settings, difficulties arise that limit the effectiveness of LP formulations for the discriminant problem. Explanations and possible remedies have been offered, but these have had only limited success. We provide a simple way to overcome these problems based on an appropriate use and interpretation of normalizations. In addition, we demonstrate a normalization that is invariant under all translations of the problem data, providing a stability property not shared by previous approaches. Finally, we discuss the possibility of using more general models to improve discrimination.     

THE IMPACT OF TAX POLICY ON INVESTMENT BEHAVIOR IN THE CHEMICAL INDUSTRY, 1951-65

The purpose of this study was to estimate the impact of changes in Federal tax policy on investment behavior in the chemicals and allied products industry and to examine the possibility of a differential impact of changes in tax policy on investment behavior in manufacturing industries. The impact of changes in tax policy on investment behavior in the chemical and allied products industry was estimated and compared with the impact of tax policy on investment behavior in total manufacturing found in earlier studies. The model used to estimate the impact of changes in tax policy on investment behavior in the chemicals and allied products industry was the neo-classical model of capital accumulation as formulated initially by Dale Jorgenson. The conclusions reached were that changes in tax policy have had a measurable impact on investment behavior in the chemical and allied products industry which was greater as a percentage of gross investment than that found on total manufacturing in earlier studies.     

Concepts,Theory, and Techniques THE LOT-SIZE REORDER-POINT MODEL WITH UPSTREAM-DOWNSTREAM UNCERTAINTY*

One assumption in the classical lot-size problem is certainty in the amount requisitioned. In many practical situations, however, the amount received may be a random variable. In this paper, we discuss a lot-size inventory problem in which the quantity received does not necessarily match the quantity requisitioned. We develop explicit and approximate solutions for the back-orders case by assuming that the standard deviation of the amount received is linearly related to the quantity requisitioned.     

ON THE USE OF VERBAL PROTOCOLS IN CONJOINT ANALYSIS STUDIES

A recent study by Olshavsky and Acito helped gain insight into (a) the decision process used by respondents when asked to do a conjoint task, (b) the predictive validity of conjoint models compared to models derived from verbal protocols, and (c) the reasons for prediction errors. In this article, the author argues that the Olshavsky and Acito results do not lead one to believe that the compensatory conjoint models can be potentially more misleading than models that try to represent more accurately the underlying evaluation process with noncompensatory and compensatory rules. Hence, it does not seem necessary to try to identify noncompensatory rules in a conjoint study.