FINANCIAL FORECASTING USING NEURAL NETWORKS
DOI:
https://doi.org/10.17770/etr2003vol1.2027Keywords:
neural networks, backpropagation, Kohonen network, financial forecastingAbstract
This paper presents an application of neural networks to financial time-series forecasting. No additional indicators, but only the information contained in the sales time series was used to model and forecast stock exchange index. The forecasting is carried out by two different neural network learning algorithms – error backpropagation and Kohonen self-organising maps. The results are presented and their comparative analysis is performed in this article.Downloads
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References
Anderson O. D. (1976). Time Series Analysis and forecasting. Butterworths, Londod and Boston, 182 p.
Baestaens D. E., Van den Bergh W. M. (1995). Tracking the Amsterdam Stock Index Using NeuralNetworks. Neural Networks in Capital Markets, Vol. 5. P. 149-161.
Fausett L. (1994). Fundamentals of Neural Networks. Architectures, algorithms and applications. Prentice Hall, New Jersey, 560 p.
Refenes A. N., Azema-Barac M., Chen L., Karoussos S. A., (1993). Currency Exchange Rate Prediction and Neural Network Design Strategies. Springer-Verlag, London Limited. P. 46 – 58.
Zurada J. M. (1992). Introduction to Artificial Neural Systems. St. Paul: West Publishing Company, 684 p.
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Published
2006-06-26
Issue
Section
IT and Mathematical Methods in Environmental Sciences
How to Cite
[1]
A. Zorins, “FINANCIAL FORECASTING USING NEURAL NETWORKS”, ETR, vol. 1, pp. 392–396, Jun. 2006, doi: 10.17770/etr2003vol1.2027.
