| Pub. Date | : Dec, 2023 |
|---|---|
| Product Name | : The IUP Journal of Financial Risk Management |
| Product Type | : Article |
| Product Code | : IJFRM011223 |
| Author Name | : Uma Maheswari B, Kavitha D, Sujatha R, Abinaya R and Abhirami B |
| Availability | : YES |
| Subject/Domain | : Finance Management |
| Download Format | : PDF Format |
| No. of Pages | : 18 |
According to the Insolvency and Bankruptcy Board of India, the number of companies filing for insolvency witnessed a 30.29% jump to 3,312 in the fourth quarter of 2019. The increasing rate of company failures has prompted efforts to provide better measures to predict bankruptcy well in advance. The objective of the current study is to predict the bankruptcy of firms listed on the National Stock Exchange (NSE) using the data for three years prior to bankruptcy. The data consists of financial variables (categorized into profitability, solvency, liquidity, and activity ratios) and non-financial macroeconomic variables. To achieve this objective, the study uses predictive models such as Altman Z-score, logistic regression, support vector machine (SVM), ensemble methods, artificial neural networks, etc. It also compares the accuracy with performance measures to find the best model for the prediction of financial distress in Indian firms. The findings suggest that logistic regression model has relatively higher (96%) bankruptcy prediction ability, and SVM has the highest model accuracy of 88.54% and demonstrates great ability in predicting healthy companies.
Bankruptcy is defined as "the inability of a firm to pay its financial obligations as they mature" (Beaver, 1996), and bankruptcy prediction models are considered to be early warning systems developed based on the analysis of certain factors that can help identify the threats dampening the financial health of a company. Bankruptcy of a firm has serious negative effects on its creditworthiness, and therefore awareness about the financial wellbeing of an organization is important to all stakeholders, including creditors, investors, suppliers, and retailers. Several models based on traditional statistical methods and machine learning (ML) algorithms have been developed and implemented in previous studies to predict bankruptcy of firms.