Forecasting Bankruptcy for organizational sustainability in Pakistan: Using artificial neural networks, logit regression, and discriminant analysis

Abstract

Purpose: 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/approach: Three 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. Findings: Findings 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 implications: Practically, 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/value: Three different techniques were used in this research to predict the bankruptcy of non-financial sector in Pakistan to make an effective prediction.