The Application of Statistical Classification to Business Failure Prediction Metadata

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  • Main Title The Application of Statistical Classification to Business Failure Prediction


  • Author: Haensly, Paul J.
    Creator Type: Personal


  • Chair: McDonald, James L.
    Contributor Type: Personal
    Contributor Info: Major Professor
  • Committee Member: Pavur, Robert J.
    Contributor Type: Personal
  • Committee Member: Cole, C. Steven
    Contributor Type: Personal
  • Committee Member: Nieswiadomy, Michael L.
    Contributor Type: Personal


  • Name: University of North Texas
    Place of Publication: Denton, Texas


  • Creation: 1994-12


  • English


  • Content Description: Bankruptcy is a costly event. Holders of publicly traded securities can rely on security prices to reflect their risk. Other stakeholders have no such mechanism. Hence, methods for accurately forecasting bankruptcy would be valuable to them. A large body of literature has arisen on bankruptcy forecasting with statistical classification since Beaver (1967) and Altman (1968). Reported total error rates typically are 10%-20%, suggesting that these models reveal information which otherwise is unavailable and has value after financial data is released. This conflicts with evidence on market efficiency which indicates that securities markets adjust rapidly and actually anticipate announcements of financial data. Efforts to resolve this conflict with event study methodology have run afoul of market model specification difficulties. A different approach is taken here. Most extant criticism of research design in this literature concerns inferential techniques but not sampling design. This paper attempts to resolve major sampling design issues. The most important conclusion concerns the usual choice of the individual firm as the sampling unit. While this choice is logically inconsistent with how a forecaster observes financial data over time, no evidence of bias could be found. In this paper, prediction performance is evaluated in terms of expected loss. Most authors calculate total error rates, which fail to reflect documented asymmetries in misclassification costs and prior probabilities. Expected loss overcomes this weakness and also offers a formal means to evaluate forecasts from the perspective of stakeholders other than investors. This study shows that cost of misclassifying bankruptcy must be at least an order of magnitude greater than cost of misclassifying nonbankruptcy before discriminant analysis methods have value. This conclusion follows from both sampling experiments on historical financial data and Monte Carlo experiments on simulated data. However, the Monte Carlo experiments reveal that as the cost ratio increases, robustness of linear discriminant rules improves; performance appears to depend more on the cost ratio than form of the distributions.
  • Physical Description: xiv, 455 leaves : ill.


  • Keyword: bankruptcy
  • Keyword: businesses
  • Keyword: forecasting
  • Library of Congress Subject Headings: Business failures -- Forecasting.
  • Library of Congress Subject Headings: Bankruptcy -- Forecasting.


  • Name: UNT Theses and Dissertations
    Code: UNTETD


  • Name: UNT Libraries
    Code: UNT


  • Rights Access: public
  • Rights License: copyright
  • Rights Statement: Copyright is held by the author, unless otherwise noted. All rights reserved.
  • Rights Holder: Haensly, Paul J.

Resource Type

  • Thesis or Dissertation


  • Text


  • Call Number: 379 N81d no.4024
  • UNT Catalog No.: b1837123
  • Accession or Local Control No: 1002720735-haensly
  • Archival Resource Key: ark:/67531/metadc278187


  • Degree Level: Doctoral
  • Degree Grantor: University of North Texas
  • Degree Name: Doctor of Philosophy
  • Academic Department: Department of Finance, Insurance, Real Estate, and Law
  • Degree Discipline: Finance