A theoretical approach to financial distress prediction modeling

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 prediction model 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.

Citation

Oz, I.O. and Yelkenci, T. (2017), "A theoretical approach to financial distress prediction modeling", Managerial Finance, Vol. 43 No. 2, pp. 212-230. https://doi.org/10.1108/MF-03-2016-0084