Zusammenfassung
Results are reported on applying neural networks to tasks of predicting economic data. These are, on the one hand, stock prices or foreign exchange rates and, on the other hand, the daily numbers of sales of a German newspaper.
Starting from our network “George”, which very successfully traded at New York stock exchange for nearly two years, our methods of evolving neural networks for forecasting tasks have been Bayesian optimization, input compression, and multitasking. The networks obtained have been applied to several markets.
Experiences with predicting sales numbers of a specific product are relatively new. The situation differs essentially from that one concerning prices or exchange rates, but the results are equally convincing.
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Menzel, W. (2000). Neuronale Netze zur Prognose von Finanzzeitreihen und Absatzzahlen. In: Bol, G., Nakhaeizadeh, G., Vollmer, KH. (eds) Datamining und Computational Finance. Wirtschaftswissenschaftliche Beiträge, vol 174. Physica, Heidelberg. https://doi.org/10.1007/978-3-642-57656-0_7
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DOI: https://doi.org/10.1007/978-3-642-57656-0_7
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