BRADFORD SCHOLARS

    • Sign in
    View Item 
    •   Bradford Scholars
    • Management and Law
    • Management and Law Publications
    • View Item
    •   Bradford Scholars
    • Management and Law
    • Management and Law Publications
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of Bradford ScholarsCommunitiesAuthorsTitlesSubjectsPublication DateThis CollectionAuthorsTitlesSubjectsPublication Date

    My Account

    Sign in

    HELP

    Bradford Scholars FAQsCopyright Fact SheetPolicies Fact SheetDeposit Terms and ConditionsDigital Preservation Policy

    Statistics

    Most Popular ItemsStatistics by CountryMost Popular Authors

    Multi-criteria ranking of corporate distress prediction models: empirical evaluation and methodological contributions

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Thumbnail
    View/Open
    Mousavi_AOR.pdf (1.173Mb)
    Download
    Publication date
    2018-12
    Author
    Mousavi, Mohammad M.
    Quenniche, J.
    Keyword
    Corporate distress prediction
    Performance criteria
    Performance measures
    Context-dependent data envelopment analysis
    Slacks-based measure
    Rights
    © Springer Science+Business Media, LLC, part of Springer Nature 2018. Reproduced in accordance with the publisher's self-archiving policy. The final publication is available at Springer via http://dx.doi.org/10.1007/s10479-018-2814-2
    Peer-Reviewed
    Yes
    
    Metadata
    Show full item record
    Abstract
    Although many modelling and prediction frameworks for corporate bankruptcy and distress have been proposed, the relative performance evaluation of prediction models is criticised due to the assessment exercise using a single measure of one criterion at a time, which leads to reporting conflicting results. Mousavi et al. (Int Rev Financ Anal 42:64–75, 2015) proposed an orientation-free super-efficiency DEA-based framework to overcome this methodological issue. However, within a super-efficiency DEA framework, the reference benchmark changes from one prediction model evaluation to another, which in some contexts might be viewed as “unfair” benchmarking. In this paper, we overcome this issue by proposing a slacks-based context-dependent DEA (SBM-CDEA) framework to evaluate competing distress prediction models. In addition, we propose a hybrid crossbenchmarking- cross-efficiency framework as an alternative methodology for ranking DMUs that are heterogeneous. Furthermore, using data on UK firms listed on London Stock Exchange, we perform a comprehensive comparative analysis of the most popular corporate distress prediction models; namely, statistical models, under both mono criterion and multiple criteria frameworks considering several performance measures. Also, we propose new statistical models using macroeconomic indicators as drivers of distress.
    URI
    http://hdl.handle.net/10454/16704
    Version
    Accepted Manuscript
    Citation
    Mousavi MM and Quenniche J (2018) Multi-criteria ranking of corporate distress prediction models: empirical evaluation and methodological contributions. Annals of Operations Research. 271(2): 853-886.
    Link to publisher’s version
    https://doi.org/10.1007/s10479-018-2814-2
    Type
    Article
    Collections
    Management and Law Publications

    entitlement

     
    DSpace software (copyright © 2002 - 2023)  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.