The Application of Statistical Classification to Business Failure Prediction

PDF Version Also Available for Download.

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 ... continued below

Physical Description

xiv, 455 leaves : ill.

Creation Information

Haensly, Paul J. December 1994.

Context

This dissertation is part of the collection entitled: UNT Theses and Dissertations and was provided by UNT Libraries to Digital Library, a digital repository hosted by the UNT Libraries. It has been viewed 25 times . More information about this dissertation can be viewed below.

Who

People and organizations associated with either the creation of this dissertation or its content.

Publisher

Rights Holder

For guidance see Citations, Rights, Re-Use.

  • Haensly, Paul J.

Provided By

UNT Libraries

With locations on the Denton campus of the University of North Texas and one in Dallas, UNT Libraries serves the school and the community by providing access to physical and online collections; The Portal to Texas History and UNT Digital Libraries; academic research, and much, much more.

Contact Us

What

Descriptive information to help identify this dissertation. Follow the links below to find similar items on the Digital Library.

Degree Information

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.

Subjects

Language

Identifier

Unique identifying numbers for this dissertation in the Digital Library or other systems.

Collections

This dissertation is part of the following collection of related materials.

UNT Theses and Dissertations

Theses and dissertations represent a wealth of scholarly and artistic content created by masters and doctoral students in the degree-seeking process. Some ETDs in this collection are restricted to use by the UNT community.

What responsibilities do I have when using this dissertation?

When

Dates and time periods associated with this dissertation.

Creation Date

  • December 1994

Added to The UNT Digital Library

  • March 24, 2014, 8:07 p.m.

Description Last Updated

  • Nov. 14, 2014, 9:42 a.m.

Usage Statistics

When was this dissertation last used?

Yesterday: 0
Past 30 days: 0
Total Uses: 25

Interact With This Dissertation

Here are some suggestions for what to do next.

Start Reading

PDF Version Also Available for Download.

Citations, Rights, Re-Use

Haensly, Paul J. The Application of Statistical Classification to Business Failure Prediction, dissertation, December 1994; Denton, Texas. (digital.library.unt.edu/ark:/67531/metadc278187/: accessed August 17, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; .