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L'utilisation des réseaux de neurones artificiels en finance

Author

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  • Philippe Paquet

    (Laboratoire Orléanais de Gestion)

Abstract

Depuis le début de la décennie 1990, les réseaux de neurones artificiels habituellement utilisés en physique appliquée font leur entrée dans les sciences de gestion en tant que méthode quantitative de prévision, à côté des méthodes statistiques classiques. Ils sont en particulier utilisés en finance, mais d’autres champs de la gestion sont aussi concernés. L’objet du présent article est d’abord de présenter succinctement l’architecture et le mode de fonctionnement de la classe de réseaux les plus couramment utilisés en finances : les réseaux à couches. Il est ensuite de montrer l’intérêt de cet outil pour les applications de finance, face aux méthodes statistiques classiques, à travers un balayage des champs d’application déjà explorés. Enfin, la dernière partie de l’article s’attache à recenser les imperfections dont souffre encore cet outil, aujourd’hui en plein développement.

Suggested Citation

  • Philippe Paquet, 1997. "L'utilisation des réseaux de neurones artificiels en finance," Working Papers 1997-1, Laboratoire Orléanais de Gestion - université d'Orléans.
    • Handle: RePEc:log:wpaper:1997-1
    as

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    File URL: http://www.univ-orleans.fr/log/Doc-Rech/Textes-PDF/1997-1.pdf
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    References listed on IDEAS

    as
    1. Hutchinson, James M & Lo, Andrew W & Poggio, Tomaso, 1994. "A Nonparametric Approach to Pricing and Hedging Derivative Securities via Learning Networks," Journal of Finance, American Finance Association, vol. 49(3), pages 851-889, July.
    2. Altman, Edward I. & Marco, Giancarlo & Varetto, Franco, 1994. "Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience)," Journal of Banking & Finance, Elsevier, vol. 18(3), pages 505-529, May.
    3. Kar Yan Tam & Melody Y. Kiang, 1992. "Managerial Applications of Neural Networks: The Case of Bank Failure Predictions," Management Science, INFORMS, vol. 38(7), pages 926-947, July.
    4. Black, Fischer & Scholes, Myron S, 1973. "The Pricing of Options and Corporate Liabilities," Journal of Political Economy, University of Chicago Press, vol. 81(3), pages 637-654, May-June.
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