A Theoretical Approach To Financial Distress Prediction Modeling
Loading...
Files
Date
2017
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Emerald Group Publishing Ltd
Open Access Color
Green Open Access
No
OpenAIRE Downloads
OpenAIRE Views
Publicly Funded
No
Abstract
Purpose - The purpose of this paper is to examine a theoretical base for the financial distress prediction modeling over eight countries for a sample of 2,500 publicly listed non-financial firms for the period from 2000 to 2014. Design/methodology/approach - The prediction model derived through the theory has the potential to produce prediction results that are generalizable over distinct industry and country samples. For this reason, the prediction model is on the earnings components, and it uses two different estimation methods and four sub-samples to examine the validity of the results. Findings - The findings suggest that the theoretical model provides high-level prediction accuracy through its earnings components. The use of a large sample from different industries in distinct countries increases the validity of the prediction results, and contributes to the generalizability of the predictionmodel in distinct sectors. Originality/value - The results of the study fulfill the gap and extend the literature through a distress model, which has the theoretical origin enabling the generalization of the prediction results over different samples and estimation methods.
Description
ORCID
Keywords
Accounting theory, Modelling, Financial distress, Generalizability, Bankruptcy Prediction, Discriminant-Analysis, Neural-Networks, Credit Risk, Cash Flows, Failure, Ratios, Information, Accruals, Firms
Fields of Science
0502 economics and business, 05 social sciences
Citation
WoS Q
Q2
Scopus Q
Q2

OpenCitations Citation Count
11
Source
Managerıal Fınance
Volume
43
Issue
2
Start Page
212
End Page
230
PlumX Metrics
Citations
CrossRef : 11
Scopus : 18
Captures
Mendeley Readers : 206
SCOPUS™ Citations
18
checked on Mar 17, 2026
Web of Science™ Citations
11
checked on Mar 17, 2026
Page Views
3
checked on Mar 17, 2026
Google Scholar™


