Comparative Analysis of Machine Learning Models for Bankruptcy Prediction in the Context of Pakistani Companies

Domicián Máté, Hassan Raza and Ishtiaq Ahmad
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Hassan Raza: Department of Management Sciences, Shaheed Zulfikar Ali Bhutto Institute of Science & Technology University, Islamabad 44000, Pakistan
Ishtiaq Ahmad: Department of Management Sciences, National University of Modern Languages University, Islamabad 44000, Pakistan

Risks, 2023, vol. 11, issue 10, 1-17

Abstract: This article presents a comparative analysis of machine learning models for business failure prediction. Bankruptcy prediction is crucial in assessing financial risks and making informed decisions for investors and regulatory bodies. Since machine learning techniques have advanced, there has been much interest in predicting bankruptcy due to their capacity to handle complex data patterns and boost prediction accuracy. In this study, we evaluated the performance of various machine learning algorithms. We collect comprehensive data comprising financial indicators and company-specific attributes relevant to the Pakistani business landscape from 2016 through 2021. The analysis includes AdaBoost, decision trees, gradient boosting, logistic regressions, naive Bayes, random forests, and support vector machines. This comparative analysis provides insights into the most suitable model for accurate bankruptcy prediction in Pakistani companies. The results contribute to the financial literature by comparing machine learning models tailored to anticipate Pakistani stock market insolvency. These findings can assist financial institutions, regulatory bodies, and investors in making more informed decisions and effectively mitigating financial risks.

Keywords: bankruptcy prediction; machine learning models; comparative analysis; Pakistani companies; financial risk assessment (search for similar items in EconPapers)
JEL-codes: C G0 G1 G2 G3 K2 M2 M4 (search for similar items in EconPapers)
Date: 2023
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