Advertisement

Wirtschaftsinformatik

, Volume 45, Issue 3, pp 325–333 | Cite as

MoneyBee: Aktienkursprognose mit künstlicher intelligenz bei hoher rechenleistung

  • Andreas Bohn
  • Thomas Güting
  • Till Mansmann
  • Stefan Selle
WI-Innovatives Produkt
  • 142 Downloads

MoneyBee: A new product to predict stock market developments using artificial intelligence and increased calculation capacitiy

Abstract

The company i42 GmbH, Mannheim, developed MoneyBee: a system to predict stock market values, basing on artificial intelligence (neural networks), distributed computing and different applications to optimize the input- and output-data (e.g. genetic algorithms, statistical methods). More than 200 values (especially from German stock market) are observed by this system continuously, with daily updated predictions. The information technology product is an innovation — not by its basic technology, but by its cooperation of different program groups on high level.

Keywords

stock market predictions artificial intelligence neural networks genetic algorithms 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Literatur

  1. [Bagl67]
    Bagley, J. D.: The behavior of adaptive systems which employ genetic and correlation algorithms. Dissertation, University of Michigan, 1967.Google Scholar
  2. [Brau97]
    Braun, Heinrich: Neuronale Netze: Optimierung durch Lernen und Evolution. Springer, Berlin et. al. 1997.Google Scholar
  3. [BvBV96]
    Baestaens, D. J. E.; van den Bergh, W. M.; Vaudrey, H.: Market Inefficiencies, Technical Trading and Neural Networks. In: Dunis, C. (Hrsg.): Forecasting Financial Markets, John Wiley & Sons, Chichester 1996, S. 245–260.Google Scholar
  4. [ChUn93]
    Chichocki, A.; Unbehauen, R.: Neural Networks for Optimization and Signal Processing. John Wiley & Sons Ltd., Chichester 1993.Google Scholar
  5. [Fues95]
    Fü ser, K.: Neuronale Netze in der Finanzwirtschaft. Gabler, Wiesbaden 1995.Google Scholar
  6. [GuTh01]
    Güting, Thomas: Verbesserung der Prognosequalität mit Hilfe evolutionärer Algorithmen oder eines anderen Verfahrens, Diplomarbeit, Fachhochschule Mannheim, 2001.Google Scholar
  7. [Hell00]
    Hellström, Thomas: Prediction a Rank Measure for Stock Returns. Theory of Stochastic Processes, Band 6, 22 9, Nr. 3-4 (2000), S. 64–83.Google Scholar
  8. [Kinn94]
    Kinnebrock, W.: Optimierung mit genetischen und selektiven Algorithmen. Oldenbourg Verlag, München 1994.Google Scholar
  9. [KrVe95]
    Krogh, Andres; Vedelsby, Jesper: Neural Network Ensembles, Cross Validation, and Active Learning. In: Tesauro, G.; Touretzky, D.; Leen, T. (Hrsg.): Advances in Neural Information Processing Systems, MIT Press, Band 7, 1995, S. 231–238.Google Scholar
  10. [LeCu86]
    Le Cun, Y.: Learning Processes in an Asymmetric Threshold Network. In: Bienestock, E.; Fogelman Souli, F.; Weisbuch, G. (Hrsg.): Disordered Systems and Biological Organization. Springer, Berlin et al. 1986.Google Scholar
  11. [MaSe02]
    Mansmann, Till; Selle, Sefan: MoneyBee — Vernetzung künstlicher Intelligenz. In: Schoder, Detlef; Fischbach, Kai; Teichmann, René (Hrsg.): Peer-to-Peer. Springer, Berlin et al. 2002, S. 41–58.CrossRefGoogle Scholar
  12. [McPi43]
    McCulloch, W. S.; Pitts, W.: A Logical Calculus of the Ideas Immanent in Neurons Activity. In: Bulletin of Mathematical Biophysics 5 (1943), S. 115–133.Google Scholar
  13. [PJWe74]
    Werbos, P. J.: Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences. Dissertation, Harvard University, 1974.Google Scholar
  14. [RaTh00]
    Ragg, Thomas: Problemlösung durch Komitees neuronaler Netze. Dissertation, Fakultät für Informatik der Universität Karlsruhe, Karlsruhe 2002.Google Scholar
  15. [Rech94]
    Rechenberg, I.: Evolutionsstrategie. Frommann-Holzboog, Stuttgart 1994.Google Scholar
  16. [NaKK94]
    Nauck, Detlef; Klawonn, Frank; Kruse, Rudolf: Neuronale Netze und Fuzzy-Systeme. Vieweg 1994.CrossRefGoogle Scholar
  17. [RHWi86]
    Rummelhart, D. E.; Hinton, G. E.; Williams, R. J.: Learning Internal Representations by Error Propagation. In: Rummelhart, D. E.; McCelland, J. L. (Hrsg.): Parallel Distributed Processing: Explorations in the Microstructures of Cogniton. MIT Press, Cambridge (MA) 1986.Google Scholar
  18. [RuHW86]
    Rummelhart, D. E.; Hinton, G. E.; Williams, R. J.: Learning Representations by Back-Propagating Errors. In: Nature (1986) 323, S. 533–536.Google Scholar
  19. [SeSt98]
    Selle, Stefan: Einsatz kü nstlicher neuronaler Netze auf dem Aktienmarkt. Diplomarbeit, Universität Heidelberg, 1998.Google Scholar
  20. [TsZe95]
    Tsibouris, G.; Zeidenberg, M.: Testing the Efficient Markets Hypothesis with Gradient Descent. In: Refenes, A. P. (Hrsg.): Neuronal Networks in the Capital Markets, John Wiley & Sons, Chichester 1995, S. 127–136.Google Scholar

Copyright information

© Springer Fachmedien Wiesbaden GmbH 2003

Authors and Affiliations

  • Andreas Bohn
    • 1
  • Thomas Güting
    • 1
  • Till Mansmann
    • 1
  • Stefan Selle
    • 1
  1. 1.i42 Informationsmanagement GmbHMannheimDeutschland

Personalised recommendations