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Forecasting Bankruptcy for organizational sustainability in Pakistan

Forecasting Bankruptcy for organizational sustainability in Pakistan Considering the economic dimension of sustainability, the purpose of this paper is to analyze the risk of bankruptcy in the Pakistani firms of the non-financial sector from years 1995 to 2017.Design/methodology/approachThree techniques were used which include multivariate discriminant analysis (MDA), logit regression and multilayer perceptron artificial neural networks. The accounting data of firms were selected one year before the bankruptcy.FindingsFindings were obtained by comparing and analyzing the methods which show that neural networks model outperforms in the prediction of bankruptcy. They further conclude that profitability and leverage indicators have the power of discrimination in bankruptcy prediction and the best variables to predict financial distress are also found and indicated.Practical implicationsPractically, this study may help the firms to better anticipate the risks of getting bankrupt by choosing the right method and to make effective decision making for organizational sustainability.Originality/valueThree different techniques were used in this research to predict the bankruptcy of non-financial sector in Pakistan to make an effective prediction. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Economic and Administrative Sciences Emerald Publishing

Forecasting Bankruptcy for organizational sustainability in Pakistan

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Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
1026-4116
DOI
10.1108/jeas-05-2018-0063
Publisher site
See Article on Publisher Site

Abstract

Considering the economic dimension of sustainability, the purpose of this paper is to analyze the risk of bankruptcy in the Pakistani firms of the non-financial sector from years 1995 to 2017.Design/methodology/approachThree techniques were used which include multivariate discriminant analysis (MDA), logit regression and multilayer perceptron artificial neural networks. The accounting data of firms were selected one year before the bankruptcy.FindingsFindings were obtained by comparing and analyzing the methods which show that neural networks model outperforms in the prediction of bankruptcy. They further conclude that profitability and leverage indicators have the power of discrimination in bankruptcy prediction and the best variables to predict financial distress are also found and indicated.Practical implicationsPractically, this study may help the firms to better anticipate the risks of getting bankrupt by choosing the right method and to make effective decision making for organizational sustainability.Originality/valueThree different techniques were used in this research to predict the bankruptcy of non-financial sector in Pakistan to make an effective prediction.

Journal

Journal of Economic and Administrative SciencesEmerald Publishing

Published: Sep 11, 2019

Keywords: Bankruptcy; Forecasting; Neural networks; Logistic regression; Prediction; Discriminant analysis

References