The ability to predict corporate financial distress can be strengthened using models that account for serial correlation in the data, incorporate information from more than one period and include stationary explanatory variables. This paper develops a stationary financial distress model for AMEX and NYSE manufacturing and retailing firms based on the statistical methodology of time-series Cumulative Sums (CUSUM). The model has the ability to distinguish between changes in the financial variables of a firm that are the result of serial correlation and changes that are the result of permanent shifts in the mean structure of the variables due to financial distress. Tests performed show that the model is robust over time and outperforms similar models based on the popular statistical methods of Linear Discriminant Analysis and Logit. Copyright 1999 by Kluwer Academic Publishers
Download Info
To download:
If you experience problems downloading a file, check if you have the
proper application to
view it first. Information about this may be contained
in the File-Format links below. In case of further problems read
the IDEAS help
page. Note that these files are not on the IDEAS
site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
Volume (Year): 13 (1999) Issue (Month): 4 (December) Pages: 323-45 Download reference. The following formats are available: HTML
(with abstract),
plain text
(with abstract),
BibTeX,
RIS (EndNote, RefMan, ProCite),
ReDIF
For technical questions regarding this item, or to correct its listing, contact: (Christopher F. Baum).
Related research
Keywords:
Cited by: (explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)