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Multi-agent modeling of multiple FX-markets by neural networks

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3 Author(s)
H. G. Zimmermann ; Corp. Technol., Siemens AG, Munich, Germany ; R. Neuneier ; R. Grothmann

We introduce an explanatory multi-agent approach of multiple FX-market modeling based on neural networks. We consider the explicit and implicit dynamics of the market price. This paper extends previous work of modeling a single FX-market to an integrated approach, which allows one to treat several FX-markets simultaneously. Our approach is based on feedforward neural networks. Neural networks allow the fitting of high-dimensional nonlinear models, which is often utilized in econometrics. Merging the economic theory of multi-agents with neural networks, our model concerns semantic specifications instead of being limited to ad hoc functional relationships. As an advantage, our multi-agent model allows one to fit the behavior of real-world financial data. We exemplify the USD/DEM and YEN/DEM FX-Market simultaneously. Fitting real-world data, our approach is superior to more conventional forecasting techniques

Published in:

IEEE Transactions on Neural Networks  (Volume:12 ,  Issue: 4 )