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.

Predicting Bankruptcy through Neural Network: Case of PSX Listed Companies

“…Similarly, many recent researchers (Bapat & Nagale, 2014;Ciampi & Gordini, 2013;Kasgari, Salehnezhad, & Ebadi, 2013;S. Y. Kim, 2011;Mansouri et al, 2016;S.-S. Park & Hancer, 2012) and recently, Oz and Yelkenci (2017) concluded that ANN has dominance over LR in classifying companies to become bankrupt.…”

“…This is because financial firm works under more diverse operating environment. Therefore, except from financial firms all industries sectors of non-financial firms are part of the population to introduce larger dataset and surely it will enhance the generalizability capacity of the estimating models (Oz & Yelkenci, 2017). The financial data of companies is computed from SBP-BSA 2010-2015.…”

A proposed multidimensional model for predicting financial distress: an empirical study on Egyptian listed firms

“…In addition, Ong et al [18], Alifiah [19], and Ahmad [20] defined a firm as financially distressed in terms of restructuring and arrangement, debt restructuring, and deteriorating financial conditions in accordance with the Malaysian laws. Oz and Yelkenci [21] employed negative stock return, restructuring, and low credit score classifications for identifying financial distress.…”

Analisis Financial Distress Saat Krisis Keuangan Global: Studi Empiris Pada Bumn Non-Keuangan

“…Kondisi riil perusahaan diproksikan dengan Laba Sebelum Pajak. Financial distress untuk perusahaan dalam sampel penelitian didefinisikan memiliki laba sebelum pajak yang negatif selama dua tahun berturut-turut (Oz & Yelkenci, 2017). Pada penelitian Miswanto & Aslan (2019) menyatakan bahwa laba setelah pajak tidak mencerminkan kinerja keuangan yang sesungguhnya, dan disarankan menggunakan laba sebelum pajak yang mencerminkan kegiatan operasional perusahaan serta mencegah peluang adanya penghindaran pajak.…”