A Simulation Model Of The Investment Decision Of The Firm

Provides finance specialists in industry and students of management with a comprehensive set of practical procedures for evaluating the total risk in the major capital investment decisions facing a business. It discusses in detail how companies can make effective use of sensitivity analyses, risk simulations and other techniques, and deals in depth with important issues, such as: How should the results of a sensitivity analysis be interpreted?; How can adequate subjective probability distributions be obtained? How can dependencies between variables be dealt with in a practical way?; The emphasis throughout is on 'how to do it' and the reader needs only a slight knowledge of statistics. A particularly important feature of the book is the FORTRAN subroutines in Appendices A and B which the author prepared for calculating risk evaluations

As societies face the challenge of securing an efficient and environmentally sound energy supply, researchers have strived to determine the future development of energy consumption, infrastructure and technology endowment. This book proposes a new agent-based approach to studying the development of urban energy systems. Decisions of private and commercial investors and operators are modeled using a bounded rational decision model which are parameterized by socio-economic data.

A properly structured financial model can provide decision makers with a powerful planning tool that helps them identify the consequences of their decisions before they are put into practice. Introduction to Financial Models for Management and Planning enables professionals and students to learn how to develop and use computer-based models for financial planning. Providing critical tools for the financial toolbox, this volume shows how to use these tools to build successful models. Placing a strong emphasis on the structure of models, the book focuses on developing models that are consistent with the theory of finance and, at the same time, are practical and usable. The authors introduce powerful tools that are imperative to the financial management of the operating business. These include interactive cash budgets and pro forma financial statements that balance even under the most extreme assumptions, valuation techniques, forecasting techniques that range from simple averages to time series methods, Monte Carlo simulation, linear programming, and optimization. The tools of financial modeling can be used to solve the problems of planning the firm’s investment and financing decisions. These include evaluating capital projects, planning the financing mix for new investments, capital budgeting under capital constraints, optimal capital structure, cash budgeting, working capital management, mergers and acquisitions, and constructing efficient security portfolios. While the primary emphasis is on models related to corporate financial management, the book also introduces readers to a variety of models related to security markets, stock and bond investments, portfolio management, and options. This authoritative book supplies broad-based coverage and free access to @Risk software for Monte Carlo simulation, making it an indispensible text for professionals and students in financial management. Please contact customer service for access to the software if your copy of the book does not contain this information.

Microscopic Simulation (MS) uses a computer to represent and keep track of individual ("microscopic") elements in order to investigate complex systems which are analytically intractable. A methodology that was developed to solve physics problems, MS has been used to study the relation between microscopic behavior and macroscopic phenomena in systems ranging from those of atomic particles, to cars, animals, and even humans. In finance, MS can help explain, among other things, the effects of various elements of investor behavior on market dynamics and asset pricing. It is these issues in particular, and the value of an MS approach to finance in general, that are the subjects of this book. The authors not only put their work in perspective by surveying traditional economic analyses of investor behavior, but they also briefly examine the use of MS in fields other than finance. Most models in economics and finance assume that investors are rational. However, experimental studies reveal systematic deviations from rational behavior. How can we determine the effect of investors' deviations from rational behavior on asset prices and market dynamics? By using Microscopic Simulation, a methodology originally developed by physicists for the investigation of complex systems, the authors are able to relax classical assumptions about investor behavior and to model it as empirically and experimentally observed. This rounded and judicious introduction to the application of MS in finance and economics reveals that many of the empirically-observed "puzzles" in finance can be explained by investors' quasi-rationality. Researchers use the book because it models heterogeneous investors, a group that has proven difficult to model. Being able to predict how people will invest and setting asset prices accordingly is inherently appealing, and the combination of computing power and statistical mechanics in this book makes such modeling possible. Because many finance researchers have backgrounds in physics, the material here is accessible. Emphasizes investor behavior in determining asset prices and market dynamics Introduces Microscopic Simulation within a simplified framework Offers ways to model deviations from rational decision-making