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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. [ABSTRACT FROM AUTHOR]

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.

The excellence of corporate governance in companies lies in their ability to adopt the corporate social responsibility (CSR), which enhances their growth. This study examines the effect of agency cost, firm size, and CSR disclosure on the firms’ growth. Specifically, the study analyzed the agency cost and firms’ size as the moderators that influence the firms’ performance asymmetrically. In its approach, the study compiled data of 300 Pakistani listed companies, which have a significant concern with CSR for the period 2010–2018. Using the 2SLS and GMM instrumental panel regressions, our empirical results show that the agency cost is detrimental to the firms’ growth. In contrast, the firms’ size boosts the firms’ growth. Moreover, the growth of firms with leverage declines and the presence of independent directors improves the firms’ growth.

The excellence of corporate governance in companies lies in their ability to adopt the corporate social responsibility (CSR), which enhances their growth. This study examines the effect of agency cost, firm size, and CSR disclosure on the firms' growth. Specifically, the study analyzed the agency cost and firms' size as the moderators that influence the firms' performance asymmetrically. In its approach, the study compiled data of 300 Pakistani listed companies, which have a significant concern with CSR for the period 2010–2018. Using the 2SLS and GMM instrumental panel regressions, our empirical results show that the agency cost is detrimental to the firms' growth. In contrast, the firms' size boosts the firms' growth. Moreover, the growth of firms with leverage declines and the presence of independent directors improves the firms' growth. [ABSTRACT FROM AUTHOR]