IDEAS home Printed from https://ideas.repec.org/p/hal/journl/hal-01697639.html
   My bibliography  Save this paper

D’un indice de détection d’anomalies à l’usage des investisseurs, An index of detection of anomalies for investors

Author

Listed:
  • Philippe Bernard

    (LEDa - Laboratoire d'Economie de Dauphine - Université Paris-Dauphine)

  • Najat El Mekkaoui de Freitas

    (LEDa - Laboratoire d'Economie de Dauphine - Université Paris-Dauphine)

  • Bertrand Maillet

    (emlyon business school, CEMOI - Centre d'Économie et de Management de l'Océan Indien - UR - Université de La Réunion)

  • Alejandro Modesto

Abstract

La détection de fraudes est un enjeu essentiel pour les investisseurs et les autorités financières. Le système de Ponzi mis en place par Bernard Madoff est une illustration emblématique d'une fraude de grande envergure, toujours possible lorsqu'elle est bien orchestrée. Les méthodes traditionnelles pour détecter les fraudes exigent de longues et coûteuses enquêtes, nécessitant des connaissances financières et juridiques pointues, et des professionnels hautement qualifiés. Nous poursuivons et étendons ici l'intuition de Billio et al. [2015], qui suggèrent l'utilisation d'une mesure de performance – dénommée GUN – pour construire un indice de détection de fraude. Afin d'illustrer la méthodologie et d'en montrer son utilité, nous analysons d'abord le cas Madoff, puis, sur plusieurs marchés d'OPCVM internationaux d'actions commercialisables en France, le nombre de fonds potentiellement susceptibles de fraude (ou de sous-performance avérée). Le système d'alerte proposé permet de détecter des anomalies sur plusieurs dizaines de fonds, qui devraient ainsi faire l'objet d'une attention particulière. Fraud detection is a key issue for investors and financial authorities. The Ponzi scheme organized by Bernard Madoff is a magnified illustration of a fraud, always possible when well-orchestrated. Traditional methods to detect fraud require costly and lengthy investigations that involve complex financial and legal knowledge and high skilled analysts. We pursue and generalize here the intuition of Billio et al. [2015], who suggest the use of a performance measure—called GUN—to construct a fraud detection index. To illustrate the methodology and to demonstrate its usefulness, first, we analyze the case of Madoff. Then, in a universe of equity mutual funds marketable in France from several international markets, we secondly highlight the number of funds with an apparent anomalous behavior. The proposed alert system reveals dozens of funds that would be interesting to investigate.

Suggested Citation

  • Philippe Bernard & Najat El Mekkaoui de Freitas & Bertrand Maillet & Alejandro Modesto, 2016. "D’un indice de détection d’anomalies à l’usage des investisseurs, An index of detection of anomalies for investors," Post-Print hal-01697639, HAL.
  • Handle: RePEc:hal:journl:hal-01697639
    Note: View the original document on HAL open archive server: http://hal.univ-reunion.fr/hal-01697639
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hal:journl:hal-01697639. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (CCSD). General contact details of provider: https://hal.archives-ouvertes.fr/ .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.