Abstract

ARRIETA BECHARA, Jaime Enrique; TORRES CRUZ, Juan Camilo  and  VELASQUEZ CEBALLOS, Hermilson. Econometric and neural network model forecasts: the case of SURAMINV stock. Semest. Econ. [online]. 2009, vol.12, n.25, pp.95-109. ISSN 0120-6346.

The purpose of this paper is to construct statistical, econometric and artificial intelligence models that permit market behavior forecasts of the SURAMINV stock. Evidence was found in favor of using econometric and artificial intelligence models constructed form main components that enable the daily behavior of the SURAMINV stock contrasting with the market's weak efficiency theory. The paper goes further than others that relate to the same topic in the sense that instead of looking for a good in sample forecast, it looks for out of sample results, controlling this way snooping data and therefore providing information that can be used in negotiation strategies.

Keywords : Artificial neural network (ANN); artificial intelligence; econometric models; main component analysis (MCA); market efficiency.

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