22 March 1999 New theorem for the definition of the optimal neural structure for financial forecasting: applications to stochastic time series
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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.
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Antonio Luigi Perrone, Antonio Luigi Perrone, Gianfranco Basti, Gianfranco Basti, } "New theorem for the definition of the optimal neural structure for financial forecasting: applications to stochastic time series", Proc. SPIE 3722, Applications and Science of Computational Intelligence II, (22 March 1999); doi: 10.1117/12.342910; https://doi.org/10.1117/12.342910
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