Academy of Accounting and Financial Studies Journal (Print ISSN: 1096-3685; Online ISSN: 1528-2635)


Predicting Bankruptcy of Selected Indian Steel Companies - A Multinomial Regression Approach

Author(s): Sheeba Kapil, Aniruddha Ghosh

The Indian steel industry is facing a mammoth liability from its outstanding loans amounting to Rs three lakh crore in various banks. To explore this anomaly, a study has been conducted to analyse the bankruptcy risk of 150 steel companies in India. The first objective was to identify the number of steel companies lying in the distress zone. The second objective was to find whether the distress condition has deteriorated more in large companies or in small and medium companies. The third objective was to develop a bankruptcy prediction model using the Multinomial Logistic Regression to assess the authenticity of the bankruptcy risks these steel companies face. Emphasis was also made to explore the major reasons behind the distressed condition of the various types of steel companies. Altman Z-score model was used as a proxy to determine the level of distress among the various selected Indian steel companies. Thereafter, Multinomial Logistic Regression Model is used for predicting the accuracy of the bankruptcy among the steel companies. Results include that bankruptcy risk has a significant relationship with debt-equity level and level of working capital of the company. The overall model explanation was 81.07%. Other outcomes include lower operating margin level, and lower level of net profits as well. This study again proves that excessive usage of debt is hazardous for the survival of the company. The study also shows that the prediction capability of the model is significantly strong and is better for predicting bankruptcy risks among steel companies. It also provides the provision for further research regarding the introduction of additional explanatory variables and qualitative variables to improve the model.

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