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

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  • Domicián Máté

    (Department of Engineering Management and Enterprise, Faculty of Engineering, University of Debrecen, 4028 Debrecen, Hungary
    Department of Higher Education and Training—National Research Foundation, South African Research Chairs Initiative in Entrepreneurship Education, Department of Business Management, University of Johannesburg, Johannesburg 2006, South Africa)

  • 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)

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.

Suggested Citation

  • Domicián Máté & Hassan Raza & Ishtiaq Ahmad, 2023. "Comparative Analysis of Machine Learning Models for Bankruptcy Prediction in the Context of Pakistani Companies," Risks, MDPI, vol. 11(10), pages 1-17, October.
  • Handle: RePEc:gam:jrisks:v:11:y:2023:i:10:p:176-:d:1256713
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    References listed on IDEAS

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    1. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
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