• Login
    View Item 
    •   Vlerick Repository Home
    • Research Output
    • Research Communication
    • View Item
    •   Vlerick Repository Home
    • Research Output
    • Research Communication
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of Vlerick RepositoryCommunities & CollectionsPublication DateAuthorsTitlesSubjectsKnowledge Domain/IndustryThis CollectionPublication DateAuthorsTitlesSubjectsKnowledge Domain/Industry

    My Account

    LoginRegister

    Contact & Info

    ContactVlerick Journal ListOpen AccessVlerick Business School

    Statistics

    Display statistics

    Business failure prediction: simple-intuitive models versus statistical models

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Thumbnail
    Name:
    Ooghe_H_WP_BusinessFailurePred ...
    Size:
    237.9Kb
    Format:
    PDF
    Download
    Publication type
    Working paper
    Author
    Ooghe, Hubert
    Spaenjers, Christophe
    Vandermoere, Pieter
    Publication Year
    2005
    Publication Issue
    22
    Publication Number of pages
    55
    
    Metadata
    Show full item record
    Abstract
    We give an overview of the shortcomings of the most frequently used statistical techniques in failure prediction modelling. The statistical procedures that underpin the selection of variables and the determination of coefficients often lead to ‘overfitting'. We also see that the ‘expected signs' of variables are sometimes neglected and that an underlying theoretical framework mostly does not exist. Based on the current knowledge of failing firms, we construct a new type of failure prediction models, namely ‘simple-intuitive models'. In these models, eight variables are first logit-transformed and then equally weighted. These models are tested on two broad validation samples (1 year prior to failure and 3 years prior to failure) of Belgian companies. The performance results of the best simple-intuitive model are comparable to those of less transparent and more complex statistical models.
    Keyword
    Corporate Finance
    Knowledge Domain/Industry
    Accounting & Finance
    URI
    http://hdl.handle.net/20.500.12127/1872
    Other links
    http://public.vlerick.com/Publications/16522f07-6aa9-e011-8a89-005056a635ed.pdf
    Collections
    Research Communication

    entitlement

     
    DSpace software (copyright © 2002 - 2022)  DuraSpace
    Quick Guide | Contact Us
    Open Repository is a service operated by 
    Atmire NV
     

    Export search results

    The export option will allow you to export the current search results of the entered query to a file. Different formats are available for download. To export the items, click on the button corresponding with the preferred download format.

    By default, clicking on the export buttons will result in a download of the allowed maximum amount of items.

    To select a subset of the search results, click "Selective Export" button and make a selection of the items you want to export. The amount of items that can be exported at once is similarly restricted as the full export.

    After making a selection, click one of the export format buttons. The amount of items that will be exported is indicated in the bubble next to export format.