A Theoretical Approach To Financial Distress Prediction Modeling

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Date

2017

Journal Title

Journal ISSN

Volume Title

Publisher

Emerald Group Publishing Ltd

Open Access Color

Green Open Access

No

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Publicly Funded

No
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Average
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Average
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Top 10%

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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

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 Logo
OpenCitations Citation Count
11

Source

Managerıal Fınance

Volume

43

Issue

2

Start Page

212

End Page

230
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Citations

CrossRef : 11

Scopus : 18

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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

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5.973

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