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

Area of Science:

• Econometrics
• Computational Finance
• Artificial Intelligence

Background:

• Existing models often focus on single foreign exchange (FX) markets.
• There is a need for integrated approaches to model multiple FX markets concurrently.
• Traditional econometric models may struggle with the high-dimensional, nonlinear dynamics of financial markets.

Purpose of the Study:

• To introduce an explanatory multi-agent approach for modeling multiple FX markets simultaneously.
• To integrate economic theory of multi-agents with neural networks for FX market analysis.
• To develop a model capable of fitting real-world financial data and outperforming conventional methods.

Main Methods:

• Utilized feedforward neural networks for high-dimensional nonlinear modeling.
• Developed a multi-agent framework incorporating semantic specifications.
• Extended single FX-market modeling to an integrated multi-market approach.
• Applied the model to simultaneously analyze the USD/DEM and YEN/DEM FX markets.

Main Results:

• The multi-agent neural network model successfully captured explicit and implicit market price dynamics.
• The approach demonstrated superior performance in fitting real-world financial data compared to conventional techniques.
• Simultaneous modeling of multiple FX markets was effectively achieved.

Conclusions:

• The proposed multi-agent neural network approach offers a powerful tool for integrated FX market modeling.
• This method provides a more robust and accurate way to forecast FX market behavior.
• The integration of economic theory with neural networks enhances the semantic understanding of market dynamics.