Predicting Bankruptcy through Neural Network:Case of PSX Listed Companies

Javed Iqbal (), Furrukh Bashir (), Rashid Ahmad () and Hina Arshad ()
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Javed Iqbal: Assistant Professor, Institute of Management Sciences, Bahauddin Zakariya University Multan, Pakistan
Furrukh Bashir: Assistant Professor, School of Economics, Bahauddin Zakariya University, Multan, Pakistan.
Rashid Ahmad: Assistant Professor, School of Economics, Bahauddin Zakariya University, Multan, Pakistan
Hina Arshad: MS Student,Institute of Management Science, Bahauddin Zakariya University, Multan, Pakistan.

iRASD Journal of Management, 2022, vol. 4, issue 2, 299-315

Abstract: The paper reconnoiters if logistic regression (LR) and neural network (NN) can estimate bankruptcy for PSX non-financial companies a year aheadof bankruptcy occurrence; particularly it endeavors to explore how exact LR and NN models are? Financialratios were utilized forecast the bankruptcy in firms. Empirical results demonstrated that both models have capability to predict the event of bankruptcy with NN outperforming LR model. Although both models possess capability to predict bankruptcy, current research demonstrated that use of neural networks (NN) enhances the precision of prediction by being a superior approach over logistic regression method (this is based on accuracy level achieved earlier by NN over LR). These results will cover the literature gapexistent in bankruptcy researchinPakistan especially about NN estimation model,proposing anadvanced forecasting with precision as proven through figure 4.1.

Keywords: Bankruptcy Prediction; Artificial Neural Network; Logistic Regression; ROAUC; PSX; Non-financial companies (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:ani:irdjom:v:4:y:2022:i:2:p:297-313

DOI: 10.52131/jom.2022.0402.0080

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