Forecasting financial asset processes: stochastic dynamics via learning neural networks.

Area of Science:

• Quantitative Finance
• Machine Learning
• Econometrics

Background:

• Financial asset dynamics are modeled using stochastic processes with parameters calibrated to historical data.
• Traditional calibration methods (e.g., maximum likelihood, regression) have limitations due to static or no weighting of historical time series, impacting predictive power.
• The inherent unpredictability of financial markets necessitates advanced modeling techniques.

Purpose of the Study:

• To propose a novel "intelligent" calibration method for stochastic financial models.
• To dynamically adapt model parameters using learning neural networks.
• To enhance the predictive accuracy of financial asset forecasting.

Main Methods:

• Development of a novel calibration technique employing neural networks.
• Implementation of a stochastic process with time-dependent parameters.
• Continuous learning of parameter dynamics by a neural network with backpropagation limited to a defined memory length (e.g., 10 business days).

Main Results:

• Demonstrated learning efficiency of the proposed intelligent calibration algorithm.
• Successful tracking of next-day forecasts for EURTRY and EUR-HUF exchange rates.
• Validation of the dynamic adaptation of stochastic model parameters.

Conclusions:

• The intelligent calibration method offers a significant improvement over traditional approaches.
• Neural network-driven dynamic parameter adaptation enhances the accuracy of financial forecasting models.
• The method shows promise for real-time financial market analysis and prediction.