Forecasting financial distress for organizational sustainability: An empirical analysis
Predicting corporate financial distress has always been a key theme in the world's economic and financial development. The technology to predict a company's financial distress is critical for business and policy decision-makers, shareholders, and policymakers to take the necessary measures...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-06-01
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| Series: | Sustainable Futures |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666188824002776 |
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| author | Soumya Ranjan Sethi Dushyant Ashok Mahadik |
| author_facet | Soumya Ranjan Sethi Dushyant Ashok Mahadik |
| author_sort | Soumya Ranjan Sethi |
| collection | DOAJ |
| description | Predicting corporate financial distress has always been a key theme in the world's economic and financial development. The technology to predict a company's financial distress is critical for business and policy decision-makers, shareholders, and policymakers to take the necessary measures to adopt the appropriate decisions and policies for sustainable growth. This study touches the sustainability of the economic view to analyse the probability of insolvency of Indian non – financial service sector companies throughout 2012- 2013 to 2021–2022. This study aims to assess the predictive capabilities of Artificial Neural Network (ANN), Logistic Regression (LR), and Linear Discriminant Analysis (LDA) in predicting a company's bankruptcy. A panel dataset encompassing ten years was subjected to applying all three models. The Logit model obtained an accuracy of 87.28%, which was superior to the ANN's 85.39% in training, 86.39% in testing, and 72.02% in LDA. Managers, depositors, regulatory agencies, shareholders, and all other stakeholders in the service sector economy may anticipate that our investigation's conclusions will prove advantageous in their pursuance of interest management. |
| format | Article |
| id | doaj-art-ce4617dd40394a818e87fb981b048198 |
| institution | Kabale University |
| issn | 2666-1888 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Sustainable Futures |
| spelling | doaj-art-ce4617dd40394a818e87fb981b0481982025-01-08T04:53:31ZengElsevierSustainable Futures2666-18882025-06-019100429Forecasting financial distress for organizational sustainability: An empirical analysisSoumya Ranjan Sethi0Dushyant Ashok Mahadik1Corresponding author.; School of Management (SOM), National Institute of Technology Rourkela, Odisha, IndiaSchool of Management (SOM), National Institute of Technology Rourkela, Odisha, IndiaPredicting corporate financial distress has always been a key theme in the world's economic and financial development. The technology to predict a company's financial distress is critical for business and policy decision-makers, shareholders, and policymakers to take the necessary measures to adopt the appropriate decisions and policies for sustainable growth. This study touches the sustainability of the economic view to analyse the probability of insolvency of Indian non – financial service sector companies throughout 2012- 2013 to 2021–2022. This study aims to assess the predictive capabilities of Artificial Neural Network (ANN), Logistic Regression (LR), and Linear Discriminant Analysis (LDA) in predicting a company's bankruptcy. A panel dataset encompassing ten years was subjected to applying all three models. The Logit model obtained an accuracy of 87.28%, which was superior to the ANN's 85.39% in training, 86.39% in testing, and 72.02% in LDA. Managers, depositors, regulatory agencies, shareholders, and all other stakeholders in the service sector economy may anticipate that our investigation's conclusions will prove advantageous in their pursuance of interest management.http://www.sciencedirect.com/science/article/pii/S2666188824002776G01G00G33 |
| spellingShingle | Soumya Ranjan Sethi Dushyant Ashok Mahadik Forecasting financial distress for organizational sustainability: An empirical analysis Sustainable Futures G01 G00 G33 |
| title | Forecasting financial distress for organizational sustainability: An empirical analysis |
| title_full | Forecasting financial distress for organizational sustainability: An empirical analysis |
| title_fullStr | Forecasting financial distress for organizational sustainability: An empirical analysis |
| title_full_unstemmed | Forecasting financial distress for organizational sustainability: An empirical analysis |
| title_short | Forecasting financial distress for organizational sustainability: An empirical analysis |
| title_sort | forecasting financial distress for organizational sustainability an empirical analysis |
| topic | G01 G00 G33 |
| url | http://www.sciencedirect.com/science/article/pii/S2666188824002776 |
| work_keys_str_mv | AT soumyaranjansethi forecastingfinancialdistressfororganizationalsustainabilityanempiricalanalysis AT dushyantashokmahadik forecastingfinancialdistressfororganizationalsustainabilityanempiricalanalysis |