Forecasting financial distress for organizational sustainability

An empirical analysis

Bibliographic Data

ID6455343
AuthorsSoumya Ranjan Sethi (0000-0002-7533-684X), Dushyant Ashok Mahadik (0000-0003-4828-5503)
Year2024
Volume9
Pages100429-100429
Publication date2024-12-30
Open AccessYes
TypeARTICLE
VenueSustainable Futures (JOURNAL)
Journal identifiersISSN: 2666-1888
PublisherElsevier BV (PUBLISHER)
DOI10.1016/j.sftr.2024.100429
OpenAlexW4405893314
LanguageEN
Citations received3
References cited69

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

Business · Distress · Empirical research · Financial distress · Financial system · Statistics · Sustainability · Clinical Psychology · Corporate Finance and Governance · Credit Risk and Financial Regulations · Financial Distress and Bankruptcy Prediction · Mathematics · Psychology

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