A Comparative Study on Credit Risk Assesment of Enterprises In Turkey

Olcay Erdogan, Zafer Konakli and Adnan Hodzic

International Journal of Academic Research in Business and Social Sciences, 2016, vol. 6, issue 11, 542-555

Abstract: Credit risk prediction models attempt to predict whether a business will experience to be in a level of investment, speculative or below investment. The purpose of this paper is to propose an alternative model for predicting failure. The constructed credit rating model was on a sample data that consists of financial ratios from 356 enterprises that are listed on the Istanbul Stock Exchange. The data covers observations running from the first quarter of 2014 to the end of year. We have classified 356 enterprises into three levels using 18 parameters for each. The applied methods are discriminant analysis and Adaptive Neuro Fuzzy Inference Systems (ANFIS). The study supports building a balanced financial environment and help to determine the firms which are appropriate for credit loan.

Keywords: ANFIS; credit risk assessment; discriminant analysis; financial ratios (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://hrmars.com/hrmars_papers/A_Comparative_Stud ... prises_In_Turkey.pdf (application/pdf)
http://hrmars.com/hrmars_papers/A_Comparative_Stud ... prises_In_Turkey.pdf (text/html)

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:hur:ijarbs:v:6:y:2016:i:11:p:542-555

Access Statistics for this article

More articles in International Journal of Academic Research in Business and Social Sciences from Human Resource Management Academic Research Society, International Journal of Academic Research in Business and Social Sciences
Bibliographic data for series maintained by Hassan Danial Aslam ().