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Microsimulation modelling of the corporate firm
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International Journal of Forecasting 13 (1997) 143-147
Book reviews Reviews that appear in IJF describe and evaluate books about new developments in research on forecasting. They cover theory, practical applications, and methodology. New books that deal with any of the social and behavioral sciences are reviewed if they contribute to the advancement of forecasting. Suggestions of books for one review are welcomed: please send them to one of the editors listed below.
North and South America: Professor Herman O. Stekler and Professor Frederick Jourtz (Professor of Economics) Department of Economics George Washington University 2201 G. St. NW Washington DC 20052 USA
Rest of the world: Dr Nigel Meade Department of Management Science Imperial College 53 Prince's Gate Exhibition Road London SW7 2BX UK
Microsimulation Modelling of the Corporate Firm, F.W. van Tongeren, 1995, Springer-Verlag, Berlin, 275 pp., DM 82.00, ISBN 3-540-59443-4 Microsimulation methods became popular in
the 1960s as a result of the work of Guy Orcutt and his associates (e.g. Orcutt et al. (1961)) on the microsimulation of US households. The idea is to staff with a simple computer model of a household or firm and to use it to simulate behaviour under various alternative assumptions. By aggregating the behaviour of a large number of such micro units, macro behaviour can be determined, which can then feedback to the micro level. In this book the focus is on firms which operate in uncertain environments, learning from past errors. The activities of firms generate revenue streams giving profits and leading to short-run decisions on output and longer-run decisions on investment. These are affected by the market structure, where the interactions across sectors and inter-industry links determine the market shares of individual firms. The production process generates labour and non-labour income which goes to households for consumption and savings. The circular flow of incomes and expenditures has an aggregate representation in the social accounting matrix. There are three interdependent layers of analysis-the individual firm, the sector and the economy-wide level-which allow examination of the impact of the individual firm on aggregate outcomes, the responses of firms to government policy, and the effects at the sector level of the entry of firms on competition. The results are interesting. For example the effects of the government policy of subsidising investment are found to depend crucially on the structure of the industry and the timing of the subsidy. It is doubtful whether this evidence could be found by other-methods.
144
Book Reviews / Journal of Forecasting 13 (1997) 143-147
Throughout the focus is on the economics of the situation rather than on computer programmes. While this is understandable, some references to the algorithms used in solving the models would have been appreciated. The author admits the high use of resources in developing micro-simulation models and argues that the richness of the results justify the costs. This book supports this view and will stimulate future research on the microsimulation of the economy. Ken Holden Liverpool John Moores University Liverpool, U K
Reference Orcutt, G., M. Greenberger, J. Korbel and A.M. Rivlin, 1961. Microanalysisof SocioeconomicSystems: a/Simulation Study (Harper, New York). Pll s0169-2070(96)00721-2
Neural Networks in Finance and Investing. Using Artificial Intelligence to improve Realworld Performance. Robert R. Trippi and Efraim Turban (eds.) 1996, Irwin Professional Publishing Co., Burr Ridge IL, 1996, $70.00 821 pp., ISBN 1-55738-919-5.
The past decade has seen a vast amount of research on neural networks and their application to areas of financial economics. A total of 39 papers representing a balanced sample of this research are brought together in this volume. Rather than focusing on the statistical foundations of neural networks, the book is an attempt to evaluate the usefulness of neural nets through their ability to represent complex data-generating processes. This is achieved mainly through a systematic emphasis on the forecasting performance of neural nets in a variety of experimental settings covering firms' financial situation and
returns on assets such as bonds, exchange rates, stocks, and derivatives. The book's emphasis on assessment of neural nets through their (comparative) performance in prediction experiments is a natural choice: neural nets, contrary to more traditional expert systems, typically do not assist the researcher with insights into the structure of the data generating process, and their justification in most applications is therefore solely based on whether they outperform other available forecasting methods. Three survey papers introducing concepts and terminology from the neural net literature comprise the first section of the book. This section also provides detailed advice on how researchers should decide on the structure of a neural net, organize input data, and split the data into training and test samples, and it gives useful rules of thumb for optimization and training of the networks. Reflecting its emphasis on the practical use of neural nets, the remaining part of the book is organized according to the objects being analyzed. Section II report analyses of financial conditions of firms, while the closely related third section of the book analyzes the extent to which business failures can be predicted. Sections IV and V present applications to the debt and stock markets, while some applications to commodity prices, futures, and options are provided in Section VI. Further topics in network modelling are analyzed in Section VII. Finally the book comes with a software program enabling readers to experiment with a variety of algorithms in neural net estimation. Bringing together so many applied papers in one volume creates the risk of overwhelming the reader by the sheer amount of detail. Against this goes the benefit the reader gets in the form of a wealth of stimulating new ideas on how to design neural nets, train complex nonlinear models, report predictive performance and graphically present complex nonlinear relations. Many of the papers included in the volume were originally prepared for conference volumes or practioner journals and pursue a very short, straight-to-the point exposition. In the vast majority of cases this editing style greatly facilitates reading the papers. There are exceptions, how-
International Journal of Forecasting 13 (1997) 143-147
Book reviews Reviews that appear in IJF describe and evaluate books about new developments in research on forecasting. They cover theory, practical applications, and methodology. New books that deal with any of the social and behavioral sciences are reviewed if they contribute to the advancement of forecasting. Suggestions of books for one review are welcomed: please send them to one of the editors listed below.
North and South America: Professor Herman O. Stekler and Professor Frederick Jourtz (Professor of Economics) Department of Economics George Washington University 2201 G. St. NW Washington DC 20052 USA
Rest of the world: Dr Nigel Meade Department of Management Science Imperial College 53 Prince's Gate Exhibition Road London SW7 2BX UK
Microsimulation Modelling of the Corporate Firm, F.W. van Tongeren, 1995, Springer-Verlag, Berlin, 275 pp., DM 82.00, ISBN 3-540-59443-4 Microsimulation methods became popular in
the 1960s as a result of the work of Guy Orcutt and his associates (e.g. Orcutt et al. (1961)) on the microsimulation of US households. The idea is to staff with a simple computer model of a household or firm and to use it to simulate behaviour under various alternative assumptions. By aggregating the behaviour of a large number of such micro units, macro behaviour can be determined, which can then feedback to the micro level. In this book the focus is on firms which operate in uncertain environments, learning from past errors. The activities of firms generate revenue streams giving profits and leading to short-run decisions on output and longer-run decisions on investment. These are affected by the market structure, where the interactions across sectors and inter-industry links determine the market shares of individual firms. The production process generates labour and non-labour income which goes to households for consumption and savings. The circular flow of incomes and expenditures has an aggregate representation in the social accounting matrix. There are three interdependent layers of analysis-the individual firm, the sector and the economy-wide level-which allow examination of the impact of the individual firm on aggregate outcomes, the responses of firms to government policy, and the effects at the sector level of the entry of firms on competition. The results are interesting. For example the effects of the government policy of subsidising investment are found to depend crucially on the structure of the industry and the timing of the subsidy. It is doubtful whether this evidence could be found by other-methods.
144
Book Reviews / Journal of Forecasting 13 (1997) 143-147
Throughout the focus is on the economics of the situation rather than on computer programmes. While this is understandable, some references to the algorithms used in solving the models would have been appreciated. The author admits the high use of resources in developing micro-simulation models and argues that the richness of the results justify the costs. This book supports this view and will stimulate future research on the microsimulation of the economy. Ken Holden Liverpool John Moores University Liverpool, U K
Reference Orcutt, G., M. Greenberger, J. Korbel and A.M. Rivlin, 1961. Microanalysisof SocioeconomicSystems: a/Simulation Study (Harper, New York). Pll s0169-2070(96)00721-2
Neural Networks in Finance and Investing. Using Artificial Intelligence to improve Realworld Performance. Robert R. Trippi and Efraim Turban (eds.) 1996, Irwin Professional Publishing Co., Burr Ridge IL, 1996, $70.00 821 pp., ISBN 1-55738-919-5.
The past decade has seen a vast amount of research on neural networks and their application to areas of financial economics. A total of 39 papers representing a balanced sample of this research are brought together in this volume. Rather than focusing on the statistical foundations of neural networks, the book is an attempt to evaluate the usefulness of neural nets through their ability to represent complex data-generating processes. This is achieved mainly through a systematic emphasis on the forecasting performance of neural nets in a variety of experimental settings covering firms' financial situation and
returns on assets such as bonds, exchange rates, stocks, and derivatives. The book's emphasis on assessment of neural nets through their (comparative) performance in prediction experiments is a natural choice: neural nets, contrary to more traditional expert systems, typically do not assist the researcher with insights into the structure of the data generating process, and their justification in most applications is therefore solely based on whether they outperform other available forecasting methods. Three survey papers introducing concepts and terminology from the neural net literature comprise the first section of the book. This section also provides detailed advice on how researchers should decide on the structure of a neural net, organize input data, and split the data into training and test samples, and it gives useful rules of thumb for optimization and training of the networks. Reflecting its emphasis on the practical use of neural nets, the remaining part of the book is organized according to the objects being analyzed. Section II report analyses of financial conditions of firms, while the closely related third section of the book analyzes the extent to which business failures can be predicted. Sections IV and V present applications to the debt and stock markets, while some applications to commodity prices, futures, and options are provided in Section VI. Further topics in network modelling are analyzed in Section VII. Finally the book comes with a software program enabling readers to experiment with a variety of algorithms in neural net estimation. Bringing together so many applied papers in one volume creates the risk of overwhelming the reader by the sheer amount of detail. Against this goes the benefit the reader gets in the form of a wealth of stimulating new ideas on how to design neural nets, train complex nonlinear models, report predictive performance and graphically present complex nonlinear relations. Many of the papers included in the volume were originally prepared for conference volumes or practioner journals and pursue a very short, straight-to-the point exposition. In the vast majority of cases this editing style greatly facilitates reading the papers. There are exceptions, how-