Real-world financial data is often nonlinear, comprises high-frequency multipolynomial components, and is discontinuous (piecewise continuous). Not surprisingly, it is hard to model such data. Classical neural networks are unable to automatically determine the optimum model and appropriate order for financial data approximation. We address this problem by developing neuron-adaptive higher order neural-network (NAHONN) models. After introducing one-dimensional (1-D), two-dimensional (2-D), and n-dimensional NAHONN models, we present an appropriate learning algorithm. Network convergence and the universal approximation capability of NAHONNs are also established. NAHONN Group models (NAHONGs) are also introduced. Both NAHONNs and NAHONGs are shown to be "open box" and as such are more acceptable to financial experts than classical (closed box) neural networks. These models are further shown to be capable of automatically finding not only the optimum model, but also the appropriate order for specific financial data.

Item Type:	Refereed Article
Research Division:	Information and Computing Sciences
Research Group:	Machine learning
Research Field:	Neural networks
Objective Division:	Information and Communication Services
Objective Group:	Information systems, technologies and services
Objective Field:	Information systems, technologies and services not elsewhere classified
UTAS Author:	Xu, S (Dr Shuxiang Xu)
ID Code:	22913
Year Published:	2002
Web of Science® Times Cited:	60
Deposited By:	Computing
Deposited On:	2002-08-01
Last Modified:	2011-06-09
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