We propose a new methodology to aid in designing a portjolio of investment over multiple stock markets. It is our hypothesis that financial stock market trends may be predicted better over a set of markets instead of any one single market. A selection criteria is proposed in this paper to make this choice effectively. This criteria is based upon the observed backpropagation and recurrent neural networks prediction accuracy, and the overall change recorded in the previous year. The results obtained when using data for four consecutive years over five international stock markets supports our claim. Backpropagation nehvorks use gradient descent to learn spatial relationships. On the other hand, recurrent networks are capable of capturing spatiotemporal information from training data. This paper analyzes application of recurrent networks to the stock market return prediction problem in contrast with backpropagation networks. On the basis of the results observed during these experiments it follows that the effect of learning temporal information was not substantial on the prediction accuracy for the stock market returns.
Resumen: El planteamiento de implementar sistemas multiagente de forma que puedan reutilizarse con distintos objetivos, dota a los desarrolladores de una metodología más eficaz en sus fines. Este artículo presenta SCODA, una arquitectura basada en cinco principios: estandaridad, especialización, facilidad de implementación, reutilización y computación distribuida. SCODA pretende facilitar el desarrollo de sistemas multiagente, basándose en el concepto de pequeños grupos de agentes denominados Comunidades Inteligentes Especializadas (CIE). Las CIE cuentan con funcionalidades específicas que permiten llevar a cabo la implementación de sistemas multiagente de forma escalable, dentro del marco de SCODA, pudiendo ser reutilizadas en diversos desarrollos.Palabras clave: Agentes, Sistemas, Comunidades, Especialización, Reutilización.
Abstract:The approach to develop multi-agent systems that they can be reused for different purposes, provides to developers an effective methodology in their aims. In this paper is presented SCODA, an architecture based on five principles: standarization, specialization, facility of implementation, reuse and distributed computing. SCODA has intended to facilitate the development of multi-agent systems relying on the concept of small groups of agents, known as Intelligent Specialized Communities (CIE). The CIE have specific features that allow to carry out the scalable implementation of multi-agent systems, within the framework of SCODA and can be reused in several developments.
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