The effectiveness of discriminant models based on the example of the manufacturing sector

Sebastian Tomczak and Edward Radosiński ()

Operations Research and Decisions, 2017, vol. 27, issue 3, 81-97

Abstract: The best models of bankruptcy prediction have been selected that can indicate the deteriorating situation of a company several years before bankruptcy occurs. There are a lot of methods for evaluating the financial statements of enterprises, but only a few can assess a company as a whole and recognise sufficiently early the deteriorating financial standing of a business. The matrix method was used to classify companies in order to assess the models. The correctness of the classification made by the models was tested based on data covering a period of five years before the bankruptcy of enterprises. To analyse the effectiveness of these discriminant models, the financial reports of manufacturing companies were used. Analysis of 33 models of bankruptcy prediction shows that only 5 models were characterized by sufficient predictive ability in the five years before the bankruptcy of enterprises. The results obtained show that so far a unique, accurate, optimal model, by which companies could be assessed with very high efficiency, has not been identified. That is why it is vital to continue research related to the construction of models enabling accurate evaluation of the financial condition of businesses.

Keywords: insolvency; models of bankruptcy prediction; manufacturing sector (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

Downloads: (external link)
https://ord.pwr.edu.pl/assets/papers_archive/1310%20-%20published.pdf (application/pdf)

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:wut:journl:v:3:y:2017:p:81-97:id:1310

DOI: 10.5277/ord170306

Access Statistics for this article

More articles in Operations Research and Decisions from Wroclaw University of Science and Technology, Faculty of Management Contact information at EDIRC.
Bibliographic data for series maintained by Adam Kasperski ().