Roy L. Crum

Implementation of Large-Scale Financial Planning Models: Solution Efficient Transformations

It has long been recognized in the literature of finance that the robustness and analytical potential of mathematical programming procedures can be utilized to structure highly complex decision environments and to ascertain quickly and efficiently the dominant set(s) of actions for achieving an explicit objective(s). Although some formulations involve nonlinear relationships (for instance [13] [15]), the vast majority of the models appearing in the finance literature are variants of linear programming, including such identifiable methodologies as linear programming, goal programming, networks, integer programming, mixed integer programming, and chance-constrained programming. The decision processes for capital budgeting ([25] [1] [2] [4] [14] [16] [24]), working capital management ([20] [18] [21] [6]), cash management ([17] [23]), and portfolio selection ([22] [24]), have been structured as linear programs and have contributed significantly to understanding the dynamics of financial systems. Given the potential of these mathematical approaches, the limited industrial use of financial optimization models is disturbing.

Pitfalls in using portfolio techniques—Assessing risk and potential

Abstract Since share/growth portfolio techniques are often the main tool for strategy formulation in multiproduct corporations, reflection on the possibilities and the limitations in applying such techniques merits attention. It is demonstrated that these techniques cannot distinguish between sometimes subtle but nevertheless strategically important situational characteristics, and therefore they can lead to recommendations that will exacerbate the real problems. Thus, share/growth techniques are at best incomplete for planning, since they are too limited. They should be placed in a broader context using a financial-strategic method. In this article such a method for developing proper directional guidance is dealt with.

An operational approach to integrated working capital planning

Abstract Working capital management involves the balancing of activities from three functional areas that are generally considered to be somewhat separable—marketing, production, and finance. Proper integration of the various parts of the working capital process requires simultaneous consideration of both cross-sectional interrelationships among the functional activities and intertemporal dynamics of the linkages between elements of the problem that are either out of phase with each other or have differing short-and long-run ramifications. This suggests that a formal modeling approach can be advantageously employed. Two modeling methodologies, simulation and optimization, are usually offered as mutually exclusive alternatives for this application, but both have serious shortcomings when used in this kind of planning context. In real-world applications, simulation trends to be employed for more often than optimization because model comprehensibility and computational feasibility difficulties have severely restricted the ability of optimization models to address large, complex, but realistic problems. However, many of the shortcomings of optimization approaches are caused by technical problems, while simulation models suffer from several conceptual inadequacies. In this article we describe an optimization modeling procedure that is capable of eliminating many of the technical difficulties and suggest how it can be employed alongside a simulation model in such a way that the two approaches are mutually reinforcing and overcome most of the conceptual problems associated with either technique when used alone.

Risk Preference: Empirical Evidence and Its Implications for Capital Budgeting

Finance is a subject that must inevitably deal with decisions that involve choices from risky alternatives in a wide variety of settings—for example, choices of investment portfolios or selections of capital assets. In order to develop normative and predictive models for choice problems such as these, assumptions about the risk preference of individual decision makers have been necessary. The traditional assumption made about risk preference is that individuals are uniformly risk averse. In its strongest form, the assumption of risk aversion has been translated into the proposition that individuals choose between risky alternatives on the basis of mean and variance (or semivariance) and that individuals are averse to risk as measured by variance (or semivariance). In a weaker form, the assumption of risk aversion implies that the utility function of individuals is concave in terminal wealth.

The Development and Empirical Validation of Strategic Decision Models

Strategic management emerged in the last decade as a leading factor in advancing managerial capabilities. A major reason for its having achieved this status is that it focuses attention on making decisions that (i) are consistent with whatever reliable information is available at the time a particular decision has to be made, and (2) position the company most effectively, offensively as well as defensively, to meet future challenges of the environment. Significant contributions to study of strategic management have come from numerous researchers employing a multiplicity of analytical methods. Harrigan [1] presents an excellent survey of these contributions and their relationships with one another. Much of this research has been directed toward explaining the process by which strategy is formulated. To shift the orientation and focus more to the content of strategy, Harrigan advocates what she refers to as a hybrid methodology that combines the great contextual detail of case studies with the statistical rigor

Risk attitudes in the telecommunications industry

It has been suggested in the economics literature that strongly risk-averse individuals may tend to gravitate toward large monopolistic firms rather than work for more competitive companies. In this article we provide results of an experiment designed to test this hypothesis. Operating managers from both regulated telecommunications firms and a broad cross section of unregulated industrial and service corporations were placed in the same controlled decision context to examine whether there are any systematic differences between telecommunications managers and their counterparts in nonregulated companies in their risk attitude toward losses.

Risk, Capital Costs, and Project Financing Decisions

I Risk Specification and Defensive Behavior.- 1 Behavioral Risk Constraints in Investment Analysis.- 2 Risk Preference: Empirical Evidence and Its Implications for Capital Budgeting.- 3 The Factor of Urgency in Corporate Capital Budgeting Decisions.- 4 Geographic Perspectives of Risk: A Financial Strategic Approach.- II Estimating the Cost of Capital.- 5 Estimating the Market Risk Premium.- 6 Growth Opportunities and Real Investment Decisions.- 7 A Multiperiod Cost of Capital Concept and Its Impact on the Formulation of Financial Policy.- 8 Inefficient Capital Markets and Their Implications.- 9 An Investment Decision Model for Small Business.- III Financing the Investment of Capital.- 10 On Working Capital as an Investment by the Firm.- 11 Capital Budgeting Proposals with Negative Salvage Values.- 12 The Cost of Financing to the Firm in Foreign Exchange: Some Empirical Results and Their Implications.- 13 Leasing: The Gulf between Theory and Practice.- 14 A Pragmatic Approach to the Estimation Problems Encountered in Lease-Purchase Analysis.- Epilogue.

Multicriteria Approaches to Decision Modeling

As described in most finance and economics textbooks, the process of asset selection by business organizations and models intended for identifying appropriate allocations of scarce resources are based upon a number of usually implicit assumptions. These involve such things as the availability and quality of information, the capabilities of decision makers to process this information properly, the motivations under which management operates, and the dynamic or intertemporal characteristics of corporate operations. Conforming to the maxim of the positivist philosophy, criticisms of and objections to these models of asset selection, when based on deviations of their underlying assumptions from observable conduct by operating managers, are traditionally considered to be largely irrelevant; the positivists believe that the ability to derive economically “correct” resource allocations is the sole criterion upon which the validity and usefulness of a model can and should be judged. Nevertheless, in the past few years the frequency and intensity of such objections have increased. Greater attention is now being directed by researchers to the question of what constitutes a “correct” allocation of scare corporate resources in the business environment of the 1980s [18, 11].

Comparison of Typological Implications

The recommendations for the example firm derived from the analysis in chapter 6 represent both effective and efficient guidance for action to improve the strategic posture of the company. However, they are very situation specific and cannot safely be generalized to apply to other situations. The reason is that the recommendations are sensitive to the PARE class into which the firm is placed. Different PARE classifications will lead to divergent, sometimes even contrary, suggestions for action. This notion was used to support the argument that the less-comprehensive corporate planning techniques are unable to differentiate adequately between the problem situations represented by the PARE classes and thus can lead to unjustified generalizations in their recommendations. The argument is proved in this chapter.

Follow-up Situational Typification

The overall typification method should not be too elaborate in order to prevent unnecessary complexity. A careful selection of fourteen steps has been made to characterize the most important dimensions of a company’s departure situation. This implies that the introductory typification, though rather comprehensive, is sometimes still too general to give recommendations for all relevant dimensions, considering the various problems that a firm could encounter. Especially for determining detailed actions for strategic improvement and the associated implementation issues in case of a particular problem, a follow-up is needed. This follow-up cannot be given here in an encyclopedic way since each peculiar problem requires additional information and has to be considered on its own merits. Therefore the discussion is confined to an extended treatment of a distinctive problem—internationalization—to illustrate how such issues can be approached effectively. The typification process is demonstrated with the help of two example cases, both dealing with companies considering further international expansion but for different reasons.