Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/304087 
Year of Publication: 
2023
Citation: 
[Journal:] Cogent Economics & Finance [ISSN:] 2332-2039 [Volume:] 11 [Issue:] 1 [Article No.:] 2210916 [Year:] 2023 [Pages:] 1-13
Publisher: 
Taylor & Francis, Abingdon
Abstract: 
The study's objective is to check whether the predictive power of Machine Learning Techniques is better than Logistic Regression in predicting the bankruptcy of firms and that the same predictive power of ascertaining bankruptcy improves when a proxy for uncertainty is added to the model as a default driver. We considered the covid pandemic a black swan event that had caused ambiguity. A significant factor that has increased the probability of bankruptcy in recent times has been the large-scale supply chain disruptions and crippling lockdowns. Firms are trying to get back to pre-Covid utilization of plant capacity or pivot their business models differently to seize newer opportunities amidst the crisis. We considered the change in operating expenditure (primarily decrease) as our proxy for uncertainty as firms were forced to cut down majorly on their operations and thus incurred lesser variable costs. In an economy showing inflationary trends, the operating expenses will generally increase. But we found that the operational costs had shown a dip in the case of many of the firms during FY 20-21, and we attributed it to Covid disruptions. Results show that Machine Learning Techniques are better than Logistic Regression in predicting the bankruptcy of firms and that the same predictive power of ascertaining bankruptcy improves when a proxy for uncertainty is added to the model.
Subjects: 
artificial intelligence
bankruptcy
Business, Management and Accounting
Econometrics
Finance
financial distress
machine learning
neural network
predictive analytics
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article

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