Economics at your fingertips  

Prediction of company failure: Past, present and promising directions for the future

Ranadeva Jayasekera

International Review of Financial Analysis, 2018, vol. 55, issue C, 196-208

Abstract: The quest for a reliable tool that can predict company failure has been pursued with keen interest both by academics and practitioners for a more than a century. This paper presents a survey of the key papers published on bankruptcy prediction from a critical perspective and discusses models published in the academic literature, are frequently cited and considered to have made a significant contribution to the literature on business failure. This study provides an overview of the significant models, conducts a critical discussion of these models highlighting their virtues and drawbacks. The paper concludes with the author outlining the basic concept of a "Value Erosion Model", where ‘value’ is defined as the future potential of a firm and argues that such a model can address some of the significant drawbacks associated with existing models, offering interesting avenues for future research in the development of an academically robust practically applicable bankruptcy prediction model.

Keywords: Bankruptcy prediction; Financial distress; Accounting based statistical models; Market based models; Value erosion model (search for similar items in EconPapers)
JEL-codes: G33 G17 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1) Track citations by RSS feed

Downloads: (external link)
Full text for ScienceDirect subscribers only

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:

Access Statistics for this article

International Review of Financial Analysis is currently edited by B.M. Lucey

More articles in International Review of Financial Analysis from Elsevier
Bibliographic data for series maintained by Dana Niculescu ().

Page updated 2019-06-22
Handle: RePEc:eee:finana:v:55:y:2018:i:c:p:196-208