Index Forecasting with Neural Networks
In a joint research project Schröder Münchmeyer Hengst Research GmbH, Frankfurt/Main,
and the University of Trier, Germany, developed a software which uses latest numerical techniques to achieve
an efficient design of neural networks for forecasting stock and indices in the financial market.
The financial markets are based on a complex combination of mutually interacting factors. Since the markets
behave neither in a uniform nor in an ordered way, small changes of a few input factors can result in large
reactions of the market. Neural networks are used to improve the modelling of these nonlinear dependencies.
The number of the parameters to be determined in a neural network can lead very quickly to exceeding the
computing time resources even for the most powerful computers. The use of modern numerical techniques for the
training of neural networks reduces the computing time substantially compared to known methods. This allows to
train networks with more substantial data input and improves the accuracy of the forecasting.
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FB 4 - Department of Mathematics |
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University of Trier |
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WAP-Homepage: http://www.mathematik.uni-trier.de/wap/ |
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