A comparison of the effectiveness of neural and wavelet networks for insurer credit rating based on publicly available financial data

Authors:
Martyn Prigmore;J. Allen Long
Affiliations:
South Bank University, Computing, Information Systems and Mathematics, London, UK;South Bank University, Computing, Information Systems and Mathematics, London, UK
Venue:
IEA/AIE'2003 Proceedings of the 16th international conference on Developments in applied artificial intelligence
Year:
2003

Citing 8
Cited 0

Ten lectures on wavelets

Ten lectures on wavelets
Neural network fundamentals with graphs, algorithms, and applications

Neural network fundamentals with graphs, algorithms, and applications
Neural Networks: A Comprehensive Foundation

Neural Networks: A Comprehensive Foundation
Neuro-Wavelet Parametric Modeling

IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 4 - Volume 4
Tree structured classifiers, interconnected data, and predictive accuracy

Intelligent Data Analysis
Using wavelet network in nonparametric estimation

IEEE Transactions on Neural Networks
Bayesian wavelet networks for nonparametric regression

IEEE Transactions on Neural Networks
Multiresolution forecasting for futures trading using wavelet decompositions

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

We apply neural and wavelet network architectures to publicly available financial data to match the credit ratings of insurance companies. The main aim is to assess whether wavelet networks are likely to provide sufficiently improved results to justify further work. We consider three aspects when comparing the networks: complexity, predictive accuracy and prediction confidence.