| The institute organised a research seminar on October 29, 2024, featuring a presentation based on a study by Debdatta Saha and Jessica Thacker from South Asian University. The study examines the financial performance and distress of firms within the context of bankruptcy, specifically defined as a potential exit, employing an inductive data analysis approach. The methodology combines the principles of continuity and monotonicity with the machine learning technique XGBoost (Extreme Gradient Boosting) to evaluate the comparative performance and distress levels of firms facing bankruptcy against other firms. Although some evidence supporting continuity and monotonicity is found, the study highlights the limitations of relying solely on an inductive approach to link financial performance and distress in the bankruptcy context. Distinguished panellists included Prof Pulak Mishra from IIT Kharagpur, Dennis Rajkumar, Director at EPW Research Foundation, and Dr Santosh Das, Assistant Professor at ISID, with Dr Satyaki Roy, Associate Professor at ISID, serving as the moderator. |
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Meeting ID: 822 1343 0695 | Passcode: 20241029 |
| Abstract: The study examines the financial performance and financial distress of the firms in the context of bankruptcy (interpreted as narrow definition of potential exit) using an inductive approach to data. The method uses the concepts of continuity and monotonicity coupled with the machine learning of XGBoost (Extreme Gradient Boosting) to examine the extent to which the performance and distress of firms facing bankruptcy stand out relatively to all other firms. While we do find some evidence of continuity and monotonicity, the paper points out the limitations of using a purely inductive approach to build bridges across the financial performance and financial distress, in the particular context of bankruptcy. |