Abstract
In this paper we evaluate the performance of backpropagation neural networks applied to the problem of predicting stock market prices. The neural networks are trained to approximate the mathematical function generating the semi-chaotic timeseries which represents the history of stock market prices in order to predict the values for the future. In contrast to previous investigations, the training data used in our experiments is not exclusively based on stock market prices, but also incorporates a variety of other economical factors. The prediction quality obtained is illustrated by presenting several simulation results.
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© 1992 Springer-Verlag Berlin Heidelberg
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Freisleben, B. (1992). Stock market prediction with backpropagation networks. In: Belli, F., Radermacher, F.J. (eds) Industrial and Engineering Applications of Artificial Intelligence and Expert Systems. IEA/AIE 1992. Lecture Notes in Computer Science, vol 604. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0024997
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DOI: https://doi.org/10.1007/BFb0024997
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