Modelización y predicción de series de tiempo financieras utilizando redes neuronales
AbstractThe purpose of this work is to model and predict Financials Time Series by using neural networks. In order to achieve this aim, a recurrent total neural network with two hidden layers has been chosen; one layer for the linear threshold function and the other for the arctangent function. The series used in this research paper are the MERVAL index (Argentina) and the DOW JONES (USA). These results are based on information obtained over a period that goes from 1995 to 2006. The presentation will deal with the comparison of alternative techniques and the results obtained by other research workers.
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Bibliographic InfoArticle provided by Departamento de Economía, Facultad de Ciencias Económicas, Universidad Nacional de La Plata in its journal Económica.
Volume (Year): LVII (2011)
Issue (Month): (January-December)
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Web page: http://www.depeco.econo.unlp.edu.ar/economica/ing/
More information through EDIRC
Neural Network; Forecast; Architecture Types; Transfer Functions; Mean Absolute Error.;
Find related papers by JEL classification:
- C40 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - General
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Leung, Mark T. & Daouk, Hazem & Chen, An-Sing, 2000. "Forecasting stock indices: a comparison of classification and level estimation models," International Journal of Forecasting, Elsevier, vol. 16(2), pages 173-190.
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