An empirical evaluation of four financial distress prediction models for Greek firms: is there a 'most appropriate' model?
Dimitrios P. Charalambidis and
Dimitrios L. Papadopoulos
International Journal of Managerial and Financial Accounting, 2010, vol. 2, issue 1, 95-112
Abstract:
In this paper, four financial distress prediction models for Greek firms are tested. Relevant analysis is based on a sample of 37 financially distressed (18 listed and 19 non-listed) and 226 non-distressed (48 listed and 178 non-listed) firms. The superiority of a particular model relates to its predictive accuracy and expected loss of misclassification errors in a range of likely values for the prior probability of financial distress and the cost ratio of Types 1 and 2 errors. We find that: a) rates of correct predictions are unstable when models are used to predict financial distress in periods following the one that was considered to estimate them; b) if a model is found to be the most superior, it does so for almost all likely values of cost and prior probabilities ratios; c) no single model can be considered absolutely appropriate to predict the financial distress of Greek firms as superiority of models differs between non-listed and listed firms.
Keywords: financial distress prediction; predictive ability; misclassification cost ratio; expected loss; Greek firms; empirical evaluation; Greece. (search for similar items in EconPapers)
Date: 2010
References: Add references at CitEc
Citations:
Downloads: (external link)
http://www.inderscience.com/link.php?id=32491 (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:ids:injmfa:v:2:y:2010:i:1:p:95-112
Access Statistics for this article
More articles in International Journal of Managerial and Financial Accounting from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().