Abstract
We present an approach of multi-agent market modeling on the basis of cognitive systems with three functionality features. These features are perception, internal processing and acting. A cognitive system is structurally represented by an error correction neural network. On the mirco-level we describe agents decisions behavior by combining cognitive systems with a framework of multi-agent market modeling. By aggregating agents decisions we are able to capture the underlying market dynamics on the macro-level. As an application, we apply our approach to the DEM / USD FX-Market. Fitting real-world data, our approach is superior to more conventional forecasting techniques.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Beltratti, A., Margarita, S. and Terna, P.: Neural Networks for economic and financial modeling, International Thomson Computer Press, London, UK. 1996.
Chalmers, D.: Facing up the problem of consciousness, in: Explaining Consciousness: The Hard Problem, ed. J. Shear, MIT Press, 1997.
De Grauwe, P., Dewachter, H. and Embrechts, M.: Exchange Rate Theory: Chaotic Models of Foreign Exchange Markets, Blackwell, Oxford. 1993.
Epstein, J.M. and Axtell, R.: Growing Artificial Societies, MIT Press, 1997.
Grossberg, S.: Neural Networks and Natural Intelligence. MIT Press, 1988.
Haykin S.: Neural Networks. A Comprehensive Foundation., Macmillan College Publishing, New York, 1994, 2nd edition 1998.
LeBaron, B.: Agent-based computational finance: Suggested readings and early research, Journal of Economic Dynamics and Control, Vol. 24, 2000, pp. 679–702.
MacPhail, E.: The Evolution of Consciousness, Oxford Uni. Pr., New York, 1998.
Metzinger, Th.: Neural Correlates of Consciousness, Empirical and Conceptual Questions, MIT Press, Cambridge, Massachusetts, London 2000.
Murphy, J.J.: Intermarket Technical Analysis, New York.
Neuneier, R. and Zimmermann, H.G.: How to Train Neural Networks, in: Tricks of the Trade: How to make algorithms really to work, Springer, Berlin 1998.
Pearlmatter, B.:Gradient Calculations for Dynamic Recurrent Neural Networks: A survey, in: IEEE Transactions on Neural Networks, Vol. 6, 1995.
Perlovsky, L.I.: Neural Networks and intellect: using model-based concepts. Oxford Uni. Pr., New York, 2001.
Tesfatsion, L.:Agent-Based Computational Economics: A Brief Guide to the Literature, Reader’s Guide to the Social Sciences, Fitzroy-Dearborn, London, 2000.
Zimmermann, H.G. and Neuneier, R.:Neural Network Architectures for the Modeling of Dynamical Systems, in: A Field Guide to Dynamical Recurrent Networks, Eds. Kolen, J.F.; Kremer, St.; IEEE Press 2001.
Zimmermann, H.G., Neuneier, R., Grothmann, R.: Modeling of Dynamical Systems by Error Correction Neural Networks, in: Modeling and Forecasting Financial Data, Techniques of Nonlinear Dynamics, Eds. Soofi, A. and Cao, L., Kluwer 2001.
Zimmermann, H.G., Neuneier, R., Grothmann, R.: Multi-Agent Market Modeling of Multiple FX-Markets by Neural Networks, IEEE Trans. on Neural Networks, special issue, 2001 forthcoming.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2001 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zimmermann, G., Neuneier, R., Grothmann, R. (2001). Multi-agent FX-Market Modeling Based on Cognitive Systems. In: Dorffner, G., Bischof, H., Hornik, K. (eds) Artificial Neural Networks — ICANN 2001. ICANN 2001. Lecture Notes in Computer Science, vol 2130. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44668-0_107
Download citation
DOI: https://doi.org/10.1007/3-540-44668-0_107
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-42486-4
Online ISBN: 978-3-540-44668-2
eBook Packages: Springer Book Archive