Applicability of Selected Predictive Models in the Slovak Companies

Ivana Weissová ()
Additional contact information
Ivana Weissová: University of Žilina

Chapter Chapter 64 in New Trends in Finance and Accounting, 2017, pp 699-708 from Springer

Abstract: Abstract Nowadays, the issue of companies’ bankruptcy is very actual topic not only in Slovakia but also in abroad. Many companies have problem with the question of their probability of default or bankruptcy and also with their financial health as a whole. In this paper, we will try to show the importance of predictive models in the area of possibility to predict company’s default. It means in this paper we will choose several predictive models and we will calculate these models in the selected companies from Slovak Republic. Furthermore, we will choose one method for the determination of the value of company and we will calculate this method in all of the selected companies. Thanks to the comparison between the results of predictive models and the results of method for the determination of the value of company we will try to show the applicability and successfulness of the selected predictive models in the Slovak companies. This will be the main aim of this paper.

Keywords: Predictive models; Economic value added; Default of company; Bankrupt of company; Financial health (search for similar items in EconPapers)
Date: 2017
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

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:spr:prbchp:978-3-319-49559-0_64

Ordering information: This item can be ordered from
http://www.springer.com/9783319495590

DOI: 10.1007/978-3-319-49559-0_64

Access Statistics for this chapter

More chapters in Springer Proceedings in Business and Economics from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().