Do Traditional Financial Distress Prediction Models Predict the Early Warning Signs of Financial Distress?
Sumaira Ashraf (),
Elisabete Félix and
Zelia Serrasqueiro ()
Additional contact information
Sumaira Ashraf: Management Department, University of Évora, Largo dos Colegiais, nº 2, 7000-803 Évora, Portugal
Journal of Risk and Financial Management, 2019, vol. 12, issue 2, 1-1
Purpose: This study aims to compare the prediction accuracy of traditional distress prediction models for the firms which are at an early and advanced stage of distress in an emerging market, Pakistan, during 2001–2015. Design/methodology/approach: The methodology involves constructing model scores for financially distressed and stable firms and then comparing the prediction accuracy of the models with the original position. In addition to the testing for the whole sample period, comparison of the accuracy of the distress prediction models before, during, and after the financial crisis was also done. Findings: The results indicate that the three-variable probit model has the highest overall prediction accuracy for our sample, while the Z-score model more accurately predicts insolvency for both types of firms, i.e., those that are at an early stage as well as those that are at an advanced stage of financial distress. Furthermore, the study concludes that the predictive ability of all the traditional financial distress prediction models declines during the period of the financial crisis. Originality/value: An important contribution is the widening of the definition of financially distressed firms to consider the early warning signs related to failure in dividend/bonus declaration, quotation of face value, annual general meeting, and listing fee. Further, the results suggest that there is a need to develop a model by identifying variables which will have a higher impact on the financial distress of firms operating in both developed and developing markets.
Keywords: financial distress; emerging market; prediction models; Z-score; logit analysis; probit model (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4) Track citations by RSS feed
Downloads: (external link)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:gam:jjrfmx:v:12:y:2019:i:2:p:55-:d:219945
Access Statistics for this article
Journal of Risk and Financial Management is currently edited by Prof. Dr. Michael McAleer
More articles in Journal of Risk and Financial Management from MDPI, Open Access Journal
Bibliographic data for series maintained by XML Conversion Team ().