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Related Experiment Videos

Higherorder neural network group models for financial simulation.

M Zhang1, J C Zhang, J Fulcher

  • 1Department of Computing & Information Systems, University of Western Sydney, Macarthur, Campbelltown, NSW, Australia. M.Zhang@uws.edu.au

International Journal of Neural Systems
|August 12, 2000
PubMed
Summary
This summary is machine-generated.

Higher Order Neural network Group (HONG) models, including PHONG and THONG, offer superior financial data simulation. These models provide significantly improved accuracy for discontinuous and non-smooth real-world financial data compared to standard neural networks.

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Area of Science:

  • Computational finance
  • Artificial intelligence
  • Financial modeling

Background:

  • Real-world financial data frequently exhibits discontinuities and non-smoothness, posing challenges for traditional neural network simulations.
  • Standard neural networks struggle with accuracy when modeling complex, discontinuous financial functions.
  • Higher Order Neural network Group (HONG) models offer a potential solution for these data complexities.

Purpose of the Study:

  • To develop and evaluate Higher Order Neural network Group (HONG) models for simulating discontinuous financial data.
  • To compare the performance of HONG models against traditional neural networks in financial applications.
  • To demonstrate the capability of HONG models in handling high-frequency, non-linear, and discontinuous financial datasets.

Main Methods:

  • Development of Polynomial Higher Order Neural network Group (PHONG) and Trigonometric polynomial Higher Order Neural network Group (THONG) models.
  • Implementation of HONG models within a financial simulator.
  • Comparative analysis of HONG models against standard neural networks for prediction and simulation tasks.

Main Results:

  • HONG group models demonstrate convergence without difficulty.
  • HONG models achieve significantly higher accuracy in financial data simulation and prediction.
  • Specifically, HONG models show approximately twice the accuracy in prediction and four times the improvement in simulation compared to standard neural networks.

Conclusions:

  • HONG models, including PHONG and THONG, are effective open-box, convergent models for approximating piecewise continuous functions with high accuracy.
  • These models excel at handling higher frequency, higher order non-linear, and discontinuous financial data.
  • HONG models represent a substantial advancement over traditional neural networks for financial data modeling.