Advanced Search
MyIDEAS: Login

Ýlk Halka Arzlarda Uzun Dönem Getirilerinin Yapay Sinir Aðlarý ile ÝMKB Ýçin Ampirik Bir Çalýþma

Contents:

Author Info

  • Ulas UNLU

    ()
    (Nevsehir University)

  • Birol YILDIZ

    ()
    (Eskisehir Osmangazi University)

  • Abdullah YALAMA

    ()
    (Eskisehir Osmangazi University)

Registered author(s):

    Abstract

    The purpose of this study is to estimate the long run IPO (Initial Public Offerings) returns using artificial neural network (ANN). In wide-ranging literature OLS (Ordinary Least Squares) is commonly preferred to estimate long run IPO returns. This study applies artificial neural network addition to OLS. As a result of comparing the performance of ANN and OLS, ANN has better estimation than OLS for long run IPO returns in Turkey.

    Download Info

    If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
    File URL: http://eidergisi.istanbul.edu.tr/sayi10/iueis10m3.pdf
    Download Restriction: no

    Bibliographic Info

    Article provided by Department of Econometrics, Faculty of Economics, Istanbul University in its journal Istanbul University Econometrics and Statistics e-Journal.

    Volume (Year): 10 (2009)
    Issue (Month): 1 (December)
    Pages: 29-47

    as in new window
    Handle: RePEc:ist:ancoec:v:10:y:2009:i:1:p:29-47

    Contact details of provider:
    Web page: http://eidergisi.istanbul.edu.tr
    More information through EDIRC

    Related research

    Keywords: Initial public offerings; long-run performance; ANN; OLS; ISE;

    Find related papers by JEL classification:

    References

    No references listed on IDEAS
    You can help add them by filling out this form.

    Citations

    Lists

    This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

    Statistics

    Access and download statistics

    Corrections

    When requesting a correction, please mention this item's handle: RePEc:ist:ancoec:v:10:y:2009:i:1:p:29-47. 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: (Kutluk Kagan Sumer).

    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.

    If references are entirely missing, you can add them using this form.

    If the full references list an item that is present in RePEc, but the system did not link to it, you can help with 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 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.