Bankruptcy Prediction of Manufacturing Companies of India Post-IBC : A Comparative Study Between Various Predictive Techniques - Kaur, Simrat; Arora, Adarsh; Goyal, Anil Kumar; Munde, Anjali (Indian Journal of Finance, 2024)
Purpose : The primary goal was to forecast insolvency by comparing different bankruptcy prediction methods among Indian industrial enterprises. Methodology : This study assessed the use of machine learning models in the financial industry. A comparison of random forests (RFs), decision trees (DTs), artificial neural networks (ANNs), and logistic regression was done. The consideration period for companies that were declared bankrupt under the 2016 Insolvency and Bankruptcy Code was April 1, 2017, until March 31, 2020. Data from 48 companies, 24 of which were bankrupt and 24 of which were not, was gathered two years ago. Findings : In the comparative examination, the RF predictive technique outperformed the other predictive strategies in terms of accuracy. Practical Implications : A company’s financial characteristics provide valuable insights into its overall financial well-being. Examining a field of information that would be fascinating to regulators and investors will be facilitated by this study. Originality : There has been little research on bankrupt companies after the IBC 2016 took effect. This study concentrated on predicting a company’s bankruptcy following the implementation of the 2016 Insolvency and Bankruptcy Code.