Skip to main content
SpringerLink
Log in

Neural Networks in Economics

Background, Applications and New Developments

  • Chapter
Computational Techniques for Modelling Learning in Economics

Part of the book series: Advances in Computational Economics ((AICE,volume 11))

Abstract

Neural Networks – originally inspired from Neuroscience – provide powerful models for statistical data analysis. Their most prominent feature is their ability to “learn” dependencies based on a finite number of observations. In the context of Neural Networks the term “learning” means that the knowledge acquired from the samples can be generalized to as yet unseen observations. In this sense, a Neural Network is often called a Learning Machine. As such, Neural Networks might be considered as a metaphor for an agent who learns dependencies of his environment and thus infers strategies of behavior based on a limited number of observations. In this contribution, however, we want to abstract from the biological origins of Neural Networks and rather present them as a purely mathematical model.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  • Altman, E. L.: 1968, ‘Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy’. Journal of Finance 23, 589–609.

    Article  Google Scholar 

  • Altman, E. L., G. Marco, and E. Varetto: 1994, ‘Corporate Distress Diagnosis: Comparisons using Linear Discriminant Analysis and Neural Networks’. Journal of Banking and Finance 18, 505–529.

    Article  Google Scholar 

  • Anders, U. and O. Korn: 1997, ‘Model Selection in Neural Networks’. Technical Report 96–21, ZEW. http://www.zew.de/pub_dp/2196.html.

    Google Scholar 

  • Arthur, W. B.: 1993, ‘On Designing Economic Agents that Act like Human Agents’. Journal of Evolutionary Economics 3, 1–22.

    Article  Google Scholar 

  • Bartlett, P. L.: 1998, ‘The sample complexity of pattern classification with neural networks: The size of the weights is more important than the size of the network’. IEEE Transactions on Information Theory 44(2), 525–536.

    Article  Google Scholar 

  • Baum, E.: 1988, ‘On the capabilites of multilayer perceptrons’. Journal of Complexity 3, 331–342.

    Google Scholar 

  • Beltratti, N., S. Margarita, and P. Terna: 1996, Neural Networks for Economic and Financial Modelling. Intl. Thomson Computer Press.

    Google Scholar 

  • Bishop, C. M.: 1995, Neural Networks for Pattern Recognition. Oxford: Clarendon Press.

    Google Scholar 

  • Blien, U. and H.-G. Lindner: 1993, ‘Neuronale Netze – Werkzeuge für Empirische Analysen ökonomischer Fragestellungen’. Jahrbücher für Nationalökonomie und Statistik 212, 497–521.

    Google Scholar 

  • Bosarge, W. E.: 1993, ‘Adaptive Processes to Exploit the Nonlinear Structure of Financial Market’. In: R. R. Trippi and E. Turban (eds.): Neural Networks in Finance and Investing. Probus Publishing, pp. 371–402.

    Google Scholar 

  • Boser, B., I. Guyon, and V. N. Vapnik: 1992, ‘A training algorithm for optimal margin classifiers’. In: Proceedings of the Fifth Annual Workshop on Computational Learning Theory. pp. 144–152.

    Google Scholar 

  • Brock, W. A., D. H. Hsieh, and B. LeBaron: 1991, Nonlinear Dynamics, Chaos and Instability: Statistical Theory and Economic Evidence. MIT Press.

    Google Scholar 

  • Brockett, P. W., W. W. Cooper, L. L. Golden, and U. Pitaktong: 1994, ‘A Neural Network Method for Obtaining an Early Warning of Insurer Insolvency’. The Journal of Risk and Insurance 6, 402–424.

    Google Scholar 

  • Burges, C. J.: 1998, ‘A Tutorial on Support Vector Machines for Pattern Recognition’. Data Mining and Knowldge Discovery 2(2).

    Google Scholar 

  • Chatfield, C.: 1993, ‘Neural Networks: Forecasting Breakthrough of Passing Fad?’. International Journal of Forecasting 9, 1–3.

    Article  Google Scholar 

  • Cho, I. K.: 1994, ‘Bounded Rationality, Neural Network and Folk Theorem in Repeated Games with Discounting’. Economic Theory 4, 935–957.

    Article  Google Scholar 

  • Cho, I. K. and T. J. Sargent: 1996, ‘Neural Networks for Econding and Adapting in Dynamic Economies’. In: H. M. Amman, D. A. Kendrick, and J. Rust (eds.): Handbook of Computational Economics, Vol. 1. Elsevier, pp. 441–470.

    Google Scholar 

  • Church, K. B. and S. P. Curram: 1996, ‘Forecasting Consumer’s Expenditure: A comparison between Econometric and Neural Network Models’. International Journal of Forecasting 12, 255–267.

    Article  Google Scholar 

  • Coleman, K. G., T. J. Graettinger, and W. F. Lawrence: 1991, ‘Neural Networks for Bankruptcy Prediction: The Power to Solve Financial Problems’. Al Review July/August, 48–50.

    Google Scholar 

  • Cortes, C.: 1995, ‘Prediction of Generalization Ability in Learning Machines’. Ph.D. thesis, University of Rochester, Rochester, USA.

    Google Scholar 

  • Cortes, C. and V. Vapnik: 1995, ‘Support Vector Networks’. Machine Learning 20, 273–297.

    Google Scholar 

  • Courant, R. and D. Hilbert: 1953, Methods of Mathematical Physics. New York: Jon Wiley.

    Google Scholar 

  • Engle, R. F.: 1982, ‘Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of U.K. Inflations’. Econometrica 50, 987–1007.

    Article  Google Scholar 

  • Erxleben, K., J. Baetge, M. Feidicker, H. Koch, C. Krause, and P. Mertens: 1992, ‘Klassifikation von Unternehmen’. Zeitschrift für Betriebswirtschaft 62, 1237–1262.

    Google Scholar 

  • Fahlman, S.: 1989, ‘Faster Learning Variations on Backpropagation: An Empirical Study’. In: Proccedings of the 1988 Connectionist Models Summer School. pp. 38–51.

    Google Scholar 

  • Fama, E.: 1970, ‘Efficient Capital markets: A review of Theory and Empirical Work’. Journal of Finance 25, 383–417.

    Article  Google Scholar 

  • Feng, C. and D. Michie: 1994, ‘Machine Learning of Rules and Trees’. In: Machine Learning, Neural and Statistical Classification. pp. 50–83.

    Google Scholar 

  • Franses, P. H. and G. Draisma: 1997, ‘Regcognizing changing Seasonal Patterns using Artificial Neural Networks’. Journal of Econometrics 81, 273–280.

    Article  Google Scholar 

  • Gallant, A. R. and H. White: 1992, ‘On Learning the Derivatives of an Unknown Mapping with Multilayer Feedforward Networks’. Neural Networks 5, 129–138.

    Article  Google Scholar 

  • Granger, C. W. J.: 1991, ‘Developements in the Nonlinear Analysis of Economic Series’. Scandinavian Journal of Economics 93, 263–281.

    Article  Google Scholar 

  • Grudnitzki, G.: 1997, ‘Valuations of Residential Properties using a Neural Network’. Handbook of Neural Computation 1, G6.4:1-G6.4:5.

    Google Scholar 

  • Hadley, G.: 1964, Nonlinear and Dynamic Programming. London: Addison-Wesley.

    Google Scholar 

  • Haefke, C. and C. Helmenstein: 1996, ‘Neural Networks in the Capital Markets: An Application to Index Forecasting’. Computational Economics 9, 37–50.

    Article  Google Scholar 

  • Haussier, D.: 1988, ‘Quantifying Inductive Bias: AI Learning Algorithms and Valiant’s Learning Framework’. Artifical Intelligence 38, 177–221.

    Article  Google Scholar 

  • Haykin, S.: 1994, Neural Networks: A Comprehensive Foundation. Macmillan College Publishing Company Inc.

    Google Scholar 

  • Herbrich, R., T. Graepel, P. Bollmann-Sdorra, and K. Obermayer: 1998, ‘Learning a preference relation for information retrieval’. In: Proceedings of the AAAI Workshop Text Categorization and Machine Learning.

    Google Scholar 

  • Hestenes, M. and E. Stiefel: 1952, ‘Methods of conjugate gradients for solving linear systems’. Journal of Research of the National Bureau of Standards 49(6), 409–436.

    Article  Google Scholar 

  • Hiemstra, Y.: 1996, ‘Linear Regression versus Backpropagation Networks to Predict Quarterly Stock market Excess Returns’. Computational Economics 9, 67–76.

    Article  Google Scholar 

  • Hill, T., L. Marquez, M. O’Connor, and W. Remus: 1994, ‘Artificial Neural Network Models for Forecasting and Decision Making’. International Journal of Forecasting 10, 5–15.

    Article  Google Scholar 

  • Hinton, G.: 1987, ‘Learning translation invariant recognition in massively parallel networks’. In: Proceedings Conference on Parallel Architectures and Laguages Europe. pp. 1–13.

    Google Scholar 

  • Hopfield, J. and D. Tank: 1986, ‘Computing with Neural curcuits’. Science 233, 625–633.

    Article  Google Scholar 

  • Hornik, K., M. Stinchcombe, and H. White: 1989, ‘Mulytilayer Feedforward Networks are Universal Approximators’. Neural Networks 2, 359–366.

    Article  Google Scholar 

  • Hornik, K., M. Stinchcombe, and H. White: 1990, ‘Universal Approximation of an Unknown Mapping and its Derivatives using Multilayer Feedforward Networks’. Neural Networks 3, 551–560.

    Article  Google Scholar 

  • Jagielska, I. and J. Jaworski: 1996, ‘Neural Network for Predicting the Performance of Credit Card Accounts’. Computational Economics 9, 77–82.

    Article  Google Scholar 

  • Joachims, T.: 1997, ‘Text categorization with Support Vector Machines: Learning with Many Relevant Features’. Technical report, University Dortmund, Department of Artifical Intelligence. LS-8 Report 23.

    Google Scholar 

  • Johansson, E., F. Dowla, and D. Goodmann: 1992, ‘Backpropagation learning for multilayer feed-forward neural networks using the conjugate gradient method’. International Journal of Neural Systems 2(4), 291–301.

    Article  Google Scholar 

  • Kaastra, I., B. S. Kermanshahi, and D. Scuse: 1995, ‘Neural networks for forecasting: an introduction’. Canadian Journal of Agricultural Economics 43, 463–474.

    Article  Google Scholar 

  • Kirchkamp, O.: 1996, ‘Simultaneous Evolution of Learning Rules and Strategies’. Technical Report B-379, Universität Bonn, SFB 303. Can be downloaded from http://www.sfb504.uni-mannheim.de/~oliver/EndogLea.html

    Google Scholar 

  • Kuan, C. and H. White: 1994, ‘Artificial Neural Networks: An Econometric Perspective’. Econometric Reviews 13, 1–91.

    Article  Google Scholar 

  • Kuan, C. M. and T. Liu: 1995, ‘Forecasting Exchange Rates using Feedforward and Recurrent Neural Networks’. Journal of Applied Econometrics 10, 347–364.

    Article  Google Scholar 

  • Lee, T H., H. White, and C. W. J. Granger: 1993, ‘Testing for Neglected Nonlinearity in Time Series Models’. Journal of Econometrics 56, 269–290.

    Article  Google Scholar 

  • Luna, F.: 1996, ‘Computable Learning, Neural Networks and Institutions’. University of Venice (IT),http://helios.unive.it/fluna/english/luna.html.

    Google Scholar 

  • Malkiel, B.: 1992, ‘Efficient Markets Hypotheses’. In: J. Eatwell (ed.): New Palgrave Dictionary of Money and Finance. MacMillan.

    Google Scholar 

  • Marks, R. E. and H. Schnabl: 1999, ‘Genetic Algorithms and Neural Networks: A Comparison based on the Repeated Prisoner’s Dilemma’. In: This Book. Kluwer, pp. 197–219.

    Google Scholar 

  • Marose, R. A.: 1990, ‘A Financial Neural Network Application’. Al Expert May, 50–53.

    Google Scholar 

  • Martin-del Brio, B. and C. Serrano-Cinca: 1995, ‘Self-organizing Neural networks: The Financial State of Spanisch Companies’. In: A. P. Refenes (ed.): Neural Networks in the Capital Markets. Wiley, pp. 341–357.

    Google Scholar 

  • Meese, R. A. and A. K. Rogoff: 1983, ‘Empirical exchage Rate Models of the Seventies: Do They fit out of Sample?’. Journal of International Economics 13, 3–24.

    Article  Google Scholar 

  • Mercer, T.: 1909, ‘Functions of positive and negative type and their connection with the theory of integral equations’. Transaction of London Philosophy Society (A) 209, 415–446.

    Article  Google Scholar 

  • Odom, M. D. and R. Sharda: 1990, ‘A Neural Network Model for Bankruptcy Prediction’. Proceedings of the IEEE International Conference on Neural Networks,San Diego II, 163–168.

    Google Scholar 

  • Orsini, R.: 1996, ‘Esternalita locali, aspettative, comportamenti erratici: Un modello di consumo con razionalita limitata’. Rivista Internazionale di Scienze Economiche e Commerciali 43, 981–1012.

    Google Scholar 

  • Osuna, E., R. Freund, and F. Girosi: 1997a, ‘An Improved Training Algorithm for Support Vector Machines’. In: Proccedings of the IEEE NNSP.

    Google Scholar 

  • Osuna, E. E., R. Freund, and F. Girosi: 1997b, ‘Support Vector Machines: Training and Applications’. Technical report, Massachusetts Institute of Technology, Artifical Intelligence Laboratory. AI Memo No. 1602.

    Google Scholar 

  • Packalén, M.: 1998, ‘Adaptive Learning of Rational Expectations: A Neural Network Approach’. Paper presented at the 3rd SIEC workshop, May 29–30, Ancona. Can be downloaded from http://www.econ.unian.it/dipartimento/siec/HIA98/papers/Packa.zip.

    Google Scholar 

  • Poddig, T.: 1995, ‘Bankruptcy Prediction: A Comparison with Discriminant Analysis’. In: A. R. Refenes (ed.): Neural Networks in the Capital Markets. Wiley, pp. 311–323.

    Google Scholar 

  • Poggio, T. and F. Girosi: 1990, ‘Regularization algorithms for learning that are equivalent to multilayer networks’. Science 247, 978–982.

    Article  Google Scholar 

  • Pollard, D.: 1984, Convergence of Stochastic Processess. New York: Springer-Verlag.

    Book  Google Scholar 

  • Powell, M.: 1992, ‘The theory of radial basis functions approximation in 1990’. In: Advances in Numerical Analysis Volume II: Wavelets, Subdivision algorithms and radial basis functions. pp. 105–210.

    Google Scholar 

  • Raghupati, W., L. L. Schkade, and B. S. Raju: 1993, ‘A Neural network Approach to Bankruptcy Prediction’. In: R. R. Trippi and E. Turban (eds.): Neural Networks in Finance and Investing. Probus Publishing, pp. 141–158.

    Google Scholar 

  • Rahimian, E., S. Singh, T. Thammachote, and R. Virmani: 1993, ‘Bankruptcy Prediction by Neural Network’. In: R. R. Trippi and E. Turban (eds.): Neural Networks in Finance and Investing. Probus Publishing, pp. 159–171.

    Google Scholar 

  • Refenes, A. P.: 1995, Neural networks in the Capital Markets. Wiley.

    Google Scholar 

  • Refenes, A. P., A. D. Zapranis, and G. Francis: 1995, ‘Modelling Stock Returns in the Framework of APT: C Comparative Study with Regression Models’. In: A. P. Refenes (ed.): Neural Networks in the Capital Markets. Wiley, pp. 101–125.

    Google Scholar 

  • Ripley, B. D.: 1994, ‘Neural Networks and Related Methods for Classification’. Journal of the Royal Statistical Society 56, 409–456.

    Google Scholar 

  • Rosenblatt, M.: 1962, Principles of neurodynamics: Perceptron and Theory of Brain Mechanisms. Washington D.C.: Spartan-Books.

    Google Scholar 

  • Rumelhart, D. E., G. E. Hinton, and R. J. Williams: 1986, ‘Learning Representations by backpropagating Errors’. Nature 323, 533–536.

    Article  Google Scholar 

  • Salchenberger, L., E. Cinar, and N. Lash: 1992, ‘Neural Networks: A New Tool for Predicting Bank Failures’. Decision Sciences 23, 899–916.

    Article  Google Scholar 

  • Sargent, T. S.: 1993, Bounded Rationality in Macroeconomics. Clarendon Press.

    Google Scholar 

  • Saunders, C., M. O. Stitson, J. Weston, L. Bottou, B. Schölkopf, and A. Smola: 1998, ‘Support Vector Machine Reference Manual’. Technical report, Royal Holloway, University of London. CSD-TR-98–03.

    Google Scholar 

  • Schölkopf, B.: 1997, ‘Support Vector Learning’. Ph.D. thesis, Technische Universitä Berlin, Berlin, Germany.

    Google Scholar 

  • Shawe-Taylor, J., P. L. Bartlett, R. C. Williamson, and M. Anthony: 1996, ‘Structural Risk Minimization over Data-Dependent Hierarchies’. Technical report, Royal Holloway, University of London. NC-TR-1996–053.

    Google Scholar 

  • Swanson, N. R. and H. White: 1997, ‘A Model Selection Approach to Real-Time Macroecoomic Forecasting Using Linear Models and Artificial Neural Networks’. The Review of Economics and Statistics LXXIX, 540–550.

    Article  Google Scholar 

  • Tam, K. Y.: 1991, ‘Neural Networks and the Prediction of Bank Bankruptcy’. OMEGA 19, 429–445.

    Article  Google Scholar 

  • Tam, K. Y. and Y. M. Kiang: 1992, ‘Managerial Applicationf of Neural Networks: The Case of Bank Failure Predictions’. Management Science 38, 926–947.

    Article  Google Scholar 

  • Tangian, A. and J. Gruber: 1995, ‘Constructing Quadratic and Polynomial Objective Functions’. In: Proceedings of the 3rd International Conference on Econometric Decision Models. Schwerte, Germany, pp. 166–194.

    Google Scholar 

  • Trippi, R. R. and E. Turban: 1990, ‘Auto Learning Approaches for Building Expert Systems’. Computers and Operations Research 17, 553–560.

    Article  Google Scholar 

  • Tsibouris, G. and M. Zeidenberg: 1995, ‘Testing the Efficient Markets Hypotheses with Gradient Descent Algorithms’. In: A. P. Refenes (ed.): Neural Networks in the Capital Markets. Wiley, pp. 127–136.

    Google Scholar 

  • Vapnik, V.: 1982, Estimation of Dependences Based on Empirical Data. New York: Springer-Verlag.

    Google Scholar 

  • Vapnik, V.: 1995, The Nature of Statistical Learning Theory. New York: Springer-Verlag.

    Google Scholar 

  • Vapnik, V.: 1998, Statistical Learning Theory. New York: John Wiley and Sons.

    Google Scholar 

  • Vapnik, V. and A. Chervonenkis: 1971, ‘On the uniform Convergence of Relative Frequencies of Events to their Probabilities’. Theory of Probability and its Application 16(2), 264–281.

    Article  Google Scholar 

  • Verkooijen, W.: 1996, ‘A Neural Network Approach to Long-Run Exchange Rate Prediction.’. Computational Economics 9, 51–65.

    Article  Google Scholar 

  • Weigend, A. S., B. A. Huberman, and D. E. Rumelhart: 1992, ‘Predicting Sunspots and Exchange Rates with Connectionist Networks’. In: M. Casdagli and S. Eubank (eds.): Nonlinear Modeling and Forecasting. SFI Studies in the Science of Complexity, Proc. Vol. XII, pp. 395–432.

    Google Scholar 

  • White, H.: 1988, ‘Economic Prediction using Neural Networks: The Case of IBM Daily Stock Returns’. Proceeding of the IEEE International Conference on Neural Networks II, 451–458.

    Article  Google Scholar 

  • Wolpert, D. H.: 1995, The Mathematics of Generalization, Chapt. 3,The Relationship between PAC, the Statistical Physics Framework, the Bayesian Framework, and the VC framework, pp. 117–215. Addison Wesley.

    Google Scholar 

  • Wong, F. S.: 1990, ‘Time Series Forecasting using Backpropagation neural Networks’. Neurocomputing 2, 147–159.

    Article  Google Scholar 

  • Wong, S. K. M., Y. Y. Yao, and P. Bollmann: 1988, ‘Linear Structure in Information Retrieval’. In: Proceedings of the 11th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. pp. 219–232.

    Google Scholar 

Download references

Authors
  1. Ralf Herbrich
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Max Keilbach
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Thore Graepel
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Peter Bollmann-Sdorra
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Klaus Obermayer
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Max-Planck-Institute for Research into Economic Systems, Germany

    Thomas Brenner

Rights and permissions

Reprints and permissions

Copyright information

© 1999 Springer Science+Business Media New York

About this chapter

Cite this chapter

Herbrich, R., Keilbach, M., Graepel, T., Bollmann-Sdorra, P., Obermayer, K. (1999). Neural Networks in Economics. In: Brenner, T. (eds) Computational Techniques for Modelling Learning in Economics. Advances in Computational Economics, vol 11. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-5029-7_7

Download citation

  • DOI: https://doi.org/10.1007/978-1-4615-5029-7_7

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4613-7285-1

  • Online ISBN: 978-1-4615-5029-7

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation