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Proceedings Paper

New theorem for the definition of the optimal neural structure for financial forecasting: applications to stochastic time series
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Paper Abstract

Aim of this work is to demonstrate theoretically and experimentally how straightforwardly simple neural structures can obtain satisfying results in financial forecasting that can be easily used by market operators. The simplicity of the structures can allow indeed very flexible and user friendly implementations also for real-time forecasting. Such structure simplicity however has to be rightly understood. In fact, it is the result of a wide experimental research and a consequent theoretical demonstration devoted to outline a mathematical theorem for the definition of the optimal minimal neural structure for particular and very diffused typologies of financial data. The discussion of these theoretical and experimental results will be developed in this paper according to the following scheme: Deep theoretical discussion of the precedent points in terms of the 'generalization-learning theorem' for classical neural architectures. Recalling of the main principles underlying our 'dynamic perceptron' architecture presented and discussed elsewhere, also in precedent Orlando's SPIE Conferences. Partial neural implementation of these ideas by modification in a 'dynamic' sense of a classical back-propagation architecture. Application of the theoretical results discussed above to the time series of monetary cross-rates.

Paper Details

Date Published: 22 March 1999
PDF: 13 pages
Proc. SPIE 3722, Applications and Science of Computational Intelligence II, (22 March 1999); doi: 10.1117/12.342910
Show Author Affiliations
Antonio Luigi Perrone, Pontifical Lateran Univ. (Italy)
Gianfranco Basti, Pontifical Lateran Univ. (Italy)


Published in SPIE Proceedings Vol. 3722:
Applications and Science of Computational Intelligence II
Kevin L. Priddy; Paul E. Keller; David B. Fogel; James C. Bezdek, Editor(s)

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