Twenty‐five years of the Taffler z‐score model: Does it really have predictive ability?

Vineet Agarwal and Richard Taffler

Accounting and Business Research, 2007, vol. 37, issue 4, 285-300

Abstract: Although copious statistical failure prediction models are described in the literature, appropriate tests of whether such methodologies really work in practice are lacking. Validation exercises typically use small samples of non‐failed firms and are not true tests of ex ante predictive ability, the key issue of relevance to model users. This paper provides the operating characteristics of the well‐known Taffler (1983) UK‐based z‐score model for the first time and evaluates its performance over the 25‐year period since it was originally developed. The model is shown to have clear predictive ability over this extended time period and dominates more naïve prediction approaches. This study also illustrates the economic value to a bank of using such methodologies for default risk assessment purposes. Prima facie, such results also demonstrate the predictive ability of the published accounting numbers and associated financial ratios used in the z‐score model calculation.

Date: 2007
References: View complete reference list from CitEc
Citations: View citations in EconPapers (18)

Downloads: (external link)
http://hdl.handle.net/10.1080/00014788.2007.9663313 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:acctbr:v:37:y:2007:i:4:p:285-300

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/RABR20

DOI: 10.1080/00014788.2007.9663313

Access Statistics for this article

Accounting and Business Research is currently edited by Vivien Beattie

More articles in Accounting and Business Research from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().