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A systematic review of models for the prediction of corporate insolvency

By
N. Sathyanarayana ,
N. Sathyanarayana

Dayananda Sagar University, Bangalore, India

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Raja Narayanan ,
Raja Narayanan

SCMS, Dayananda Sagar University, Bangalore, India

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Abstract

A thorough evaluation of recent developments in bankruptcy prediction models developed specifically for listed firms in India is presented in this research. Beginning with influential contributions from the evolution of bankruptcy prediction methodologies is traced through various statistical techniques, including logistic regression, neural networks and discriminant analysis. Recent innovations, such as duration models, partial least squares with support vector machines, and efficiency-driven distress prediction, are discussed in the context of their applicability to the Indian market. The paper highlights the significance of early warning systems in the wake of bankruptcy reforms in India and examines the regulatory framework's impact on bankruptcy prediction modeling. And it goes further into how macroeconomic variables and industry-specific variables might make bankruptcy models better predictors. Limitations such as small sample sizes, short time periods for samples, and the incorporation of qualitative data into predictive models are highlighted in the study as areas that require further investigation in future studies. Overall, this paper provides valuable insights for academics, practitioners, and policymakers involved in bankruptcy prediction and risk management within the Indian corporate sector.

How to Cite

1.
Sathyanarayana N, Narayanan R. A systematic review of models for the prediction of corporate insolvency. Salud, Ciencia y Tecnología - Serie de Conferencias [Internet]. 2024 Jun. 17 [cited 2024 Jul. 19];3:952. Available from: https://conferencias.saludcyt.ar/index.php/sctconf/article/view/952

The article is distributed under the Creative Commons Attribution 4.0 License. Unless otherwise stated, associated published material is distributed under the same licence.

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