Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/4743
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dc.contributor.authorSethi, Soumya Ranjan-
dc.contributor.authorMahadik, Dushyant Ashok-
dc.date.accessioned2024-11-08T10:34:00Z-
dc.date.available2024-11-08T10:34:00Z-
dc.date.issued2024-09-
dc.identifier.citationInternational Finance Conference (IFC), XLRI, Jamshedpur, 13-14 September 2024en_US
dc.identifier.urihttp://hdl.handle.net/2080/4743-
dc.descriptionCopyright belongs to proceeding publisheren_US
dc.description.abstractPredicting corporate financial distress remains crucial for the growth and stability of the global economic and financial landscape. Accurately forecasting a company's financial health is essential for business leaders, policymakers, shareholders, and regulatory bodies to make timely and informed decisions that promote sustainable growth. This study evaluates the likelihood of insolvency among Indian non-financial service sector firms from 2012 to 2022, examining the predictive accuracy of artificial neural networks (ANN), logistic regression (LR), and linear discriminant analysis (LDA) in forecasting bankruptcy. Using a panel dataset spanning eleven years, the study applies all three models to assess their effectiveness. Results show that the Logit model achieved the highest accuracy at 87.28%, outperforming ANN's training accuracy of 85.39%, testing accuracy of 86.39%, and LDA's 72.02% accuracy. Findings from this investigation are expected to benefit managers, depositors, regulatory bodies, shareholders, and other stakeholders in the service sector as they strive to manage their interests effectively.en_US
dc.subjectLogistic Regressionen_US
dc.subjectANNen_US
dc.subjectLDAen_US
dc.subjectForecastingen_US
dc.titlePredicting Financial Distress for Organizational Sustainability in India: A Comparative Study of Logistic Regression, LDA, and ANN Approachesen_US
dc.typePresentationen_US
Appears in Collections:Conference Papers

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