You are being redirected because this document is part of your ASTM Compass® subscription.
    This document is part of your ASTM Compass® subscription.

    Volume 40, Issue 6 (November 2012)

    An Integral Predictive Model of Financial Distress

    (Received 6 December 2011; accepted 24 April 2012)

    Published Online: 2012


      Format Pages Price  
    PDF (184K) 8 $25   ADD TO CART

    Cite this document

    X Add email address send
      .RIS For RefWorks, EndNote, ProCite, Reference Manager, Zoteo, and many others.   .DOCX For Microsoft Word


    Traditional statistic models for financial distress are subject to constraints which may lead to imprecise prediction. To contribute to the issue, we construct a two-staged integral model by applying a stepwise regression analysis and a data-mining approach. Specifically, we employ stepwise regression and rough set analysis in feature selection to sieve out variables, and perform decision tree, neural network, and logistic regression analysis to classify firms with financial distress. The findings show that the rates of accuracy for the combinations in descending order are stepwise regression-logistic, stepwise regression-neutral network, stepwise regression-decision tree, rough set theory-neutral network, rough set theory-decision tree, and rough set theory-logistic.

    Author Information:

    Lee, Mushang
    Dept. of Accounting, Chinese Culture Univ., Taipei,

    Wu, Tsui-Chih
    Dept. of Accounting, Shih Chien Univ., Taipei,

    Stock #: JTE104584

    ISSN: 0090-3973

    DOI: 10.1520/JTE104584

    ASTM International is a member of CrossRef.

    Title An Integral Predictive Model of Financial Distress
    Symposium , 0000-00-00
    Committee E53